bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Chitosan–Riboflavin Bioconjugate on Green Mold Caused by Penicillium digitatum in Lemon Fruit
Authors: Ipinza-Concha Brenda M. , Dibona-Villanueva Luciano , Fuentealba Denis , Pinilla-Quispe Alexander , Schwantes Daniel , Garzón-Nivia María A. , Herrera-Défaz Mario A. , Valdés-Gómez Héctor A.
Published: March 2024

Penicillium digitatum is the causal agent of green mold, a primary postharvest disease of citrus fruits. This study evaluated the efficacy of a novel photoactive chitosan–riboflavin bioconjugate (CH-RF) to control green mold in vitro and in lemon fruit. The results showed total inhibition of P. digitatum growth on APDA supplemented with CH-RF at 0.5% (w/v) and a significant reduction of 84.8% at 0.25% (w/v). Lemons treated with CH-RF and kept under controlled conditions (20 °C and 90–95% relative humidity) exhibited a noteworthy reduction in green mold incidence four days post-inoculation. Notably, these effects persisted, with all treatments remaining significantly distinct from the control group until day 14. Furthermore, CH-RF showed high control of green mold in lemons after 20 days of cold storage (5 ± 1 °C). The disease incidence five days after cold storage indicated significant differences from the values observed in the control. Most CH-RF treatments showed enhanced control of green mold when riboflavin was activated by white-light exposure. These findings suggest that this novel fungicide could be a viable alternative to conventional synthetic fungicides, allowing more sustainable management of lemon fruit diseases.
Eco-Friendly Cork–Polyurethane Biocomposites for Enhanced Impact Performance: Experimental and Numerical Analysis
Authors: Dymek Mateusz , Ptak Mariusz , Kaczyński Paweł , Fernandes Fábio A. O. , Alves de Sousa Ricardo J. , Serra Gabriel F. , Kurańska Maria
Published: March 2024

Cork composites are byproducts from wine stopper production, resulting from the agglomeration of cork granules with a thermoset resin. The resulting compound is a versatile and durable material with numerous industrial applications. Due to its unique properties, such as low-density, high-strength, excellent energy absorption, and good thermal and acoustic insulators, cork composites find room for application in demanding industries such as automotive, construction, and aerospace. However, agglomerated cork typically has a polyurethane matrix derived from petrochemical sources. This study focuses on developing eco-friendly porous polyurethane biocomposites manufactured with the used cooking oil polyol modified with cork. Since cork and polyurethane foam are typically used for impact shock absorption, the manufactured samples were subjected to impact loading. The assessment of crashworthiness is performed through 100 J impact tests. A finite element numerical model was developed to simulate the compression of these new composites under impact, and the model validation was performed. The highest specific absorbed energy was obtained for petrochemical polyol composites with the 3% addition of natural or modified cork. The research conducted in this study showcased the feasibility of substituting certain petrochemical components used for the synthesis of the polyurethane matrix with ecological waste vegetable oil components.
A Systematic Investigation of the Kinetic Models Applied to the Transport Behaviors of Aromatic Solvents in Unfilled Hydrogenated Nitrile Rubber/Ethylene Propylene Diene Monomer Composites
Authors: Liu Susu , Jing Yiran , Liu Guangyong
Published: March 2024

Kinetic models of solvent transport behaviors are widely used in rubber–solvent systems, and some key points are still worthy of attention. In this work, the Korsmeyer–Peppas and Peppas–Sahlin models were chosen to fit the transport behaviors of three aromatic solvents, benzene, toluene and p-xylene, in the hydrogenated nitrile rubber (HNBR)/ethylene propylene diene monomer (EPDM)-based vulcanizates. The different effects of the various selected transport times (ti) used for fitting on the results of the mathematical models were compared. Moreover, a method to obtain the n parameter for the Korsmeyer–Peppas model and the m parameter for the Peppas–Sahlin model at ti = 0 was discussed. It was found that the differences in values of ti greatly influenced the impact on the fitting results of all the parameters for the two models. In addition, the n parameter for the Korsmeyer–Peppas model along with the m parameter for the Peppas–Sahlin model, which can characterize the transport mechanism, showed differing applicability. But the n and m parameters at ti = 0 obtained by linear fitting showed similar rules with some differences in values. These discussions give important guidance for the application of kinetic transport models in rubber–solvent systems.
Biobased Composites of Poly(Lactic Acid) Melt Compounded with Bacterial and Vegetal Nanocelluloses Incorporated through Different Strategies
Authors: Bovi Jimena , Delgado Juan Francisco , de la Osa Orlando , Peltzer Mercedes Ana , Bernal Celina Raquel , Foresti María Laura
Published: March 2024

In the current contribution, bacterial nanocellulose obtained from a by-product of Kombucha tea production and vegetal nanocellulose isolated from milled rice husks were employed as fillers of PLA-based composites prepared by intensive mixing followed by compression molding. Given the challenges associated with the incorporation of nanocelluloses—initially obtained as aqueous suspensions—into melt compounding processes, and also with achieving a proper dispersion of the hydrophilic nanofillers within PLA, three different nanofibrils incorporation strategies were studied: i.e., direct mixing of dried milled nanocelluloses and PLA; masterbatching by solvent casting of native nanocelluloses followed by melt compounding; and masterbatching by solvent casting of acetylated nanocelluloses followed by melt compounding. Composites with varying filler content (from 0.5 wt.% to 7 wt.%) were characterized in terms of morphology, optical properties, and mechanical performance. Results revealed the relative suitability of each strategy employed to promote nanocelluloses dispersion within the PLA matrix. PLA/nanocellulose masterbatches prepared by solvent casting proved to be particularly useful for feeding the nanocelluloses into the processing equipment in a dry state with limited hornification. Acetylation also contributed to a better dispersion of both nanocelluloses within the PLA matrix, although no clear positive impact on the mechanical properties of the films was observed. Finally, filler loading played an important role in the films’ properties by increasing their stiffness while reducing their translucency.
Polyimide Films Based on β-Cyclodextrin Polyrotaxane with Low Dielectric and Excellent Comprehensive Performance
Authors: Zhang Xuexin , Dou Yao , Liu Liqun , Song Meixuan , Xi Zhenhao , Xu Yisheng , Shen Weihua , Wang Jie
Published: March 2024

In order to prepare polyimide (PI) films with a low dielectric constant and excellent comprehensive performance, a two-step method was employed in this study to integrate β-cyclodextrin into a semi-aromatic fluorine-containing polyimide ternary system. By introducing trifluoromethyl groups to reduce the dielectric constant, the dielectric constant was further reduced to 2.55 at 10 MHz. Simultaneously, the film exhibited noteworthy thermal stability (a glass transition temperature exceeding 300 °C) and a high coefficient of thermal expansion. The material also demonstrated outstanding mechanical properties, boasting a strength of 122 MPa and a modulus of 2.2 GPa, along with high optical transparency (transmittance reaching up to 89% at 450 nm). Moreover, the inherent high transparency of colorless polyimide (CPI) combined with good stretchability contributed to the attainment of a low dielectric constant. This strategic approach not only opens up new opportunities for novel electroactive polymers but also holds potential applications in flexible displays, circuit printing, and chip packaging.
Enhancing Accuracy in Numerical Simulations for High-Speed Flows: Integrating High-Order Corrections with Weighted Essentially Non-Oscillatory Flux
Authors: Yan Yonghua , Yang Yong , Yuan Shiming , Chen Caixia
Published: March 2024

This study introduces a novel method to enhance numerical simulation accuracy for high-speed flows by refining the weighted essentially non-oscillatory (WENO) flux with higher-order corrections like the modified weighted compact scheme (MWCS). Numerical experiments demonstrate improved sharpness in capturing shock waves and stability in complex conditions like two interacting blast waves. Key highlights include simultaneous capture of small-scale smooth fluctuations and shock waves with precision surpassing the original WENO and MWCS methods. Despite the significantly improved accuracy, the extra computational cost brought by the new method is only marginally increased compared to the original WENO, and it outperforms MWCS in both accuracy and efficiency. Overall, this method enhances simulation fidelity and effectively balances accuracy and computational efficiency across various problems.
Research on the Process of Laser Cladding Ni60 Coating on High-Nickel Cast Iron Surfaces
Authors: Chen Shunhu , Feng Aixin , Chen Chunlun , Song Xiangyu
Published: March 2024

In order to achieve high-performance coatings on the surface of electric submersible pump impellers, it is crucial to optimize the laser cladding process parameters. Using Ansys 2021 R1 commercial software, a numerical simulation of laser cladding Ni60 powder on high nickel cast iron was conducted. The simulation utilized a 3D Gaussian heat source, parametric language, and life–death unit technology to replicate the characteristics of synchronous powder delivery laser cladding. The study focused on analyzing the temperature field cloud map and molten pool size under different laser power and scanning speeds, narrowing down the process parameter window, selecting optimized laser power and scanning speed, and assessing the changes in surface morphology, melting height and width, dilution rate, microhardness, and microstructure of the laser cladding coating. Results indicate that the coating width and thickness increase with higher laser power and lower scanning speeds. The microstructure consists primarily of dendritic, block, short rod, and long strip formations, and exhibits a tightly distributed and uniform grain structure. Furthermore, the microhardness of the coating shows a negative correlation with laser power and scanning speed. The optimal process parameters were determined to be a laser power of 1100 W and a scanning speed of 6 mm/s. A comparison with the simulation confirmed the effectiveness of the simulation in effectively guiding actual production.
The Influence of Slim Tube Length on the Minimum Miscibility Pressure of CO2 Gas–Crude Oil
Authors: Su Yanchun , Yang Renfeng , Zhang Lijun , Tian Xiaofeng , Yang Xugang , Shu Xiaohan , Guo Qinyuan , Zhao Fajun
Published: March 2024

This study focuses on the Bozhong 25-1 oilfield formation oil as the experimental subject, systematically investigating the influence of different slim tube lengths (1 m, 12.5 m, 20 m, and 25 m) on the minimum miscibility pressure (MMP) of the CO2 and formation oil mixture system. Through slim tube experiments, the interaction process of CO2 with formation oil in slim tubes of different lengths was simulated, with a particular focus on analyzing how changes in slim tube length affect the MMP. The experiments revealed an important phenomenon: as the slim tube length gradually increased from shorter dimensions, the MMP showed a decreasing trend; when the slim tube length reached 12.5 m, this trend stabilized, meaning that further increasing the slim tube length no longer led to significant changes in the MMP, with its stable value determined to be 27.86 MPa. This phenomenon can be explained within the theoretical framework of fluid dynamics and interfacial science, where several key factors play a significant role. Firstly, the flow characteristics of the fluid inside the slim tube significantly influence it; secondly, the interfacial tension between phases is also a decisive factor; lastly, the impact of the internal microstructure of the slim tube cannot be overlooked. These aspects together form the basis for understanding the impact of slim tube length on MMP and reveal the underlying mechanisms. This research is significant for deeply understanding and quantifying this effect, providing a solid theoretical basis for optimizing CO2 flooding technology and guiding more precise operational strategies in oilfield development practices to enhance oil displacement efficiency and economic benefits.
Fluorine-Free and Transparent Superhydrophobic Coating with Enhanced Anti-Icing and Anti-Frosting Performance by Using D26 and KH560 as Coupling Agents
Authors: Xiao Ting , Wang Yudian , Lang Xia , Chen Siyu , Jiang Lihua , Tao Fujun , Xiao Yequan , Li Xinyi , Tan Xinyu
Published: March 2024

Superhydrophobic surfaces with non-wetting characteristics have been considered to be potential candidates for ice/frost prevention. In this study, a transparent superhydrophobic coating was created by using a simple method that employed (3-glycidoxypropyl) trimethoxysilane (KH560) and 1,2-Bis (trimethoxysilyl) ethane (D26) as coupling agents and epoxy resin (E51) as an adhesive. The synergy between KH560 and D26 significantly improves the long-term outdoor durability, anti-icing, and anti-frosting performance of the superhydrophobic coating. The coating also has good acid and alkali resistance, UV resistance, and durability. The obtained SiO2@E51@KH560@D26 can delay the freezing time of water by 1974 s, much longer than bare glass (345 s) and also longer than the coatings with only D26 (932 s) or with only KH560 (1087 s). Moreover, the SiO2@E51@KH560@D26 showed an improved anti-frosting capability compared with the other three samples and better maintained its superhydrophobic properties at low temperatures. Our study proposes a potential method to fabricate a superhydrophobic coating with both anti-icing and anti-frosting properties.
Exploring Stresses in Mandibular Jawbone during Implant Insertion: A Three-Dimensional Explicit Dynamic Analysis
Authors: K N Chethan , Eram Afiya , Shetty Nisha , Shetty Divya D. , Futane Mohan , Keni Laxmikant G.
Published: March 2024

In dental implant insertion, an artificial foundation is prepared for the prosthetic device, which involves the surgical positioning of the implant in the jaw bone. The success of dental implants relies on the osseointegration process. The biomechanical factors, such as stress and strain, developed during the insertion affect the jawbone and its surroundings. In this current study, the stresses during the implant insertion in the mandibular jawbone bone are analyzed using three-dimensional explicit dynamic analysis, and the Cowper–Symonds model is implemented with the damage model. The implant’s design has a substantial impact on stress distribution within the cancellous bone during the insertion procedure. The stress variation takes place as the implant moves into the pre-drilled hole. This is because of the contact between the bone and the fixture on the implant. The upper edge of the predrilled site shows that the stresses are more at the crestal region of the implant due to surface area. There is a gradual increase in the stress level as the implant reaches the lower edge from the top edge. This is because of the concept of mechanical interlocking. Clinicians can use this information to anticipate and address potential stress-related challenges during implant placement.
Producing Altered States of Consciousness, Reducing Substance Misuse: A Review of Psychedelic-Assisted Psychotherapy, Transcendental Meditation and Hypnotherapy
Authors: Sekula Agnieszka D. , Puspanathan Prashanth , Downey Luke , Liknaitzky Paul
Published: March 2024

A set of interventions that can produce altered states of consciousness (ASC) have shown utility in the treatment of substance misuse. In this review, we examine addiction-related outcomes associated with three common interventions that produce ASCs: psychedelic-assisted psychotherapy (PP), Transcendental Meditation (TM) and hypnotherapy (HT). While procedurally distinct, all three interventions are associated with some common phenomenological, psychological, and neurobiological features, indicating some possible convergent mechanisms of action. Along with addiction and mental health outcomes, these common features are reviewed, and their impact on substance misuse is discussed. While our review highlights some mixed findings and methodological issues, results indicate that PP and TM are associated with significant improvements in substance misuse, alongside improvements in emotional, cognitive and social functioning, behavior-change motivation, sense of self-identity, and meaning. In contrast, and despite its broader acceptance, HT has been associated with mixed and minimal results with respect to substance misuse treatment. Authors identify key research gaps in the role of ASC interventions in addiction and outline a set of promising future research directions.
A Cryptotephra Layer in Sediments of an Infilled Maar Lake from the Eifel (Germany): First Evidence of Campanian Ignimbrite Ash Airfall in Central Europe
Authors: Schenk Fiona , Hambach Ulrich , Britzius Sarah , Veres Daniel , Sirocko Frank
Published: March 2024

We analyzed mineralogical characteristics, and major as well as rare earth element concentrations, from a cryptotephra layer in sediments of the infilled maar of Auel (Eifel, Germany). The results of detailed geochemical analyses of clinopyroxenes and their glassy rims from the Auel cryptotephra layer showed that they are similar to those from the thick Campanian Ignimbrite tephra occurrence in a loess section at Urluia (Romania). Both tephras show idiomorphic green clinopyroxenes and formation of distorted grains up to millimeter scale. The cryptotephra in the Auel core has a modelled age of around 39,940 yr b2k in the ELSA-20 chronology, almost identical to the latest 40Ar/39Ar dates for the Campanian Ignimbrite/Y-5 (CI/Y-5) eruption. These observations suggest that parts of the CI/Y-5 ash cloud were transported also northwestward into Central Europe, whereas the main branch of the CI/Y-5 ash plume was transported from southern Italy towards the NE, E, and SE. Based on pollen analyses, we conclude there was no direct effect on vegetation from the CI/Y-5 fallout in the Eifel area. Trees, shrubs, and grasses remained at pre-tephra-airfall levels for roughly 240 years, but changed around 39,700 yr b2k when thermophilic woody plants (e.g., Alnus and Carpinus) disappeared and Artemisia spread. This change in vegetation was well after the Laschamp geomagnetic excursion and also after the GI9 interstadial and quite probably represents the onset of the Heinrich Event 4 (H4) cold spell, when climatic conditions over the North Atlantic, and apparently also in Central Europe, deteriorated sharply.
LOVE Is in the R—Two R Tools for Local Vegetation Reconstruction
Authors: Theuerkauf Martin , Couwenberg John
Published: March 2024

Pollen deposition in small lakes and peatlands is composed of local pollen deposition arriving from the nearby vegetation and of regional pollen deposition arriving from farther away. The LOVE model aims to reconstruct past vegetation on a small local scale by extracting only the local pollen signal. To this end, pollen deposition from small sites is related to pollen deposition from large lakes with predominantly regional pollen deposition. We here present a new implementation of the LOVE model in the R environment for statistical computing that is more user friendly than existing implementations. It allows more readily application with fossil pollen data and facilitates extensive testing of the approach in simulated landscapes. The LOVE model derives from critical mathematical assumptions that strongly limit its application. We additionally present LOVEoptim as an adjusted approach to local-scale reconstructions. Other than in LOVE, past local plant abundances are approximated using numerical optimization. Tests in a simulated landscape, which is based on true landscape patterns from digital maps, show that both approaches are valid. Due to fewer mathematical assumptions, LOVEoptim is more widely applicable. The modeling results help to better interpret the spatial scale of vegetation reflected in LOVE/LOVEoptim reconstructions.
Preaching Wholeness: Attending to Mental Health in Preaching Ministries
Authors: Mumford Debra J.
Published: March 2024

“Wilt thou be made whole?” This is a question posed by Jesus in the Gospels. Wholeness for Jesus means being in right relationship with God and in right relationship with other human beings. Wholeness can also be defined as completeness or well-being. This essay argues that preaching ministries committed to addressing the total well-being of all of God’s people must include sermons about mental health. As evidence of the need, the author cites statistics from the National Institutes of Health (NIH) that at least one in four people in the United States is affected by mental illness directly or indirectly during their lifetime. Then, to equip preachers to address mental health concerns, the author: addresses the causes of mental health stigma both in and beyond the pulpit; shares theological and hermeneutical approaches and concerns of disability proposed by the theologians Nancy Eiesland and John Swinton and homiletician Kathy Black; and provides resources for preachers to use in their preaching.
Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images
Authors: Wang Yongjun , Jin Shuanggen , Dardanelli Gino
Published: March 2024

The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands’ health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification.
Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map
Authors: Zhu Changda , Zhu Fubin , Li Cheng , Yan Yunxin , Lu Wenhao , Fang Zihan , Li Zhaofu , Pan Jianjun
Published: March 2024

Soil surveying and mapping provide important support for environmental science research on soil and other resources. Due to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy. Therefore, there is an urgent need for accurate and up-to-date soil maps. Soil has a high correlation with its corresponding environmental factors in space, and typical samples contain an appropriate soil–environment relationship of soil types. Understanding how to extract typical samples according to environmental factors and determine the implied soil–environment relationship is the key to updating soil maps. In this study, a hierarchical typical sample extraction method based on land use type and environmental factors was designed. According to the corresponding relationship between the soil type and the land use type (ST-LU), the outdate soil map patches caused by changes in land use were excluded, follow by typical samples being extracted according to the peak intervals of the soil–environmental factor histograms. Additionally, feature selection was performed through variance analysis and mutual information, and four machine learning models were used to predict soil types. In addition, the influence of environmental factors on soil prediction was discussed, in terms of variable importance analysis. Using an overall common validation set, the results show that the prediction accuracy using typical samples for learning in the modeling set is above 0.8, while the prediction accuracy when using random samples is only about 0.4. Compared with the original soil map, the accuracy and resolution of the predicted soil maps based on typical samples are greatly improved. In general, typical samples can effectively explore the actual soil–environment knowledge implied in the soil type map. By extracting typical samples from historical soil type map and combining them with high-resolution remote sensing data, we can generate new soil type maps with high accuracy and short update cycle. This can provide some references for typical sampling design and soil type prediction.
An Improved NLCS Algorithm Based on Series Reversion and Elliptical Model Using Geosynchronous Spaceborne–Airborne UHF UWB Bistatic SAR for Oceanic Scene Imaging
Authors: Hu Xiao , Xie Hongtu , Yi Shiliang , Zhang Lin , Lu Zheng
Published: March 2024

Geosynchronous spaceborne–airborne (GEO-SA) ultra-high-frequency ultra-wideband bistatic synthetic aperture radar (UHF UWB BiSAR) provides high-precision images for marine and polar environments, which are pivotal in glacier monitoring and sea ice thickness measurement for polar ocean mapping and navigation. Contrasting with traditional high-frequency BiSAR, it faces unique challenges, such as the considerable spatial variability, significant range–azimuth coupling, and vast volumes of echo data, which impede high-resolution image reconstruction. This paper presents an improved bistatic nonlinear chirp scaling (NLCS) algorithm for imaging oceanic scenes with GEO-SA UHF UWB BiSAR. This methodology extends the two-dimensional (2-D) spectrum up to the sixth order via the method of series reversion (MSR) to meet accuracy demands and then employs an elliptical model to elucidate the alterations in the azimuth frequency modulation (FM) rate mismatch. Initially, the imaging geometry and signal model are introduced, and then a separation of bistatic slant ranges based on the configuration is proposed. In addition, during range processing, after eliminating linear range cell migration (RCM), the derivation process for the sixth-order 2-D spectrum is detailed and an improved filter is applied to correct the high-order RCM. Finally, during azimuth processing, the causes of the FM rate mismatch are analyzed, a cubic perturbation function derived from the elliptical model is used for FM rate equalization, and a unified sixth-order filter is applied to complete the azimuth compression. Experimental results with point targets and natural oceanic scenes validate the outstanding efficacy of the proposed NLCS algorithm, particularly in imaging quality enhancements for GEO-SA UHF UWB BiSAR.
A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements
Authors: Logothetis Stavros-Andreas , Salamalikis Vasileios , Kazantzidis Andreas
Published: March 2024

Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD under cloud-free conditions based on the synergy of machine learning algorithms (MLAs) and ground-based solar irradiance data. The performance of the proposed methodology was investigated by applying different components of solar irradiance. In particular, the use of direct instead of global irradiance as a model feature led to better performance. The MLA-based AODs were compared to reference AERONET retrievals, which encompassed RMSE values between 0.01 and 0.15, regardless of the underlying climate and aerosol environments. Among the MLAs, artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of MLA-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), MAE of 0.01, and RMSE of 0.02. Compared to satellite (MODIS) and reanalysis (MERRA-2 and CAMSRA) data, the MLA-AOD retrievals revealed the highest accuracy at all stations. The ML-AOD retrievals have the potential to expand and complement the AOD information in non-existing timeframes when solar irradiances are available.
Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm
Authors: Sui Ao , Fan Wenyi
Published: March 2024

The inversion of forest height using the RVoG (Random Volume over Ground) model is susceptible to overestimation or underestimation due to three error sources, propagating inaccuracies to the estimated forest height. Furthermore, variations in the height and density of the scenario could impact how well the RVoG three-stage inversion performs. This work utilizes the L-band single-baseline full polarization interferometric dataset as its basis. It optimally applies the CRITIC (Criteria Importance Through Intercriteria Correlation) method to the first stage of a three-stage process. This approach aims to overcome the issues mentioned above and enhance the accuracy of forest parameter estimation. A CRITIC weighted least squares temporal decoherence iterative algorithm is also proposed for the characteristics of the spaceborne data, in combination with the temporal decoherence algorithm of previous research. The proposed approach is tested and applied to both simulated and actual data. The optimization approach is first assessed using four simulated datasets that simulate coniferous forests with different densities and heights. The preliminary findings suggest that optimizing the complex coherence fitting process through the weighted least squares method enhances the accuracy of ground phase estimation and, consequently, improves the accuracy of the three-stage approach for inverting forest height. The ground phase estimation results for low forest height consistently remained within 0.02 rad, with a root mean square error (RMSE) below 0.05 rad, and no saturation occurred with increasing forest density. The enhanced algorithm outperforms the traditional technique in terms of accuracy in ground phase estimation. Subsequently, the optimized approach is applied to ALOS-2 spaceborne data, proving more successful than the conventional algorithm in reducing the RMSE of forest height. The findings illustrate the method’s superior inversion performance, obtaining an accuracy exceeding 80% in both the test and validation sets. The validation set’s RMSE is approximately 2.5 m, and the mean absolute error (MAE) is within 2 m. Moreover, it is observed that to counteract the uncertainty in temporal decoherence induced by climate change, a larger temporal baseline necessitates a larger random motion compensation term and phase offset term.
Ecosystem Integrity Remote Sensing—Modelling and Service Tool—ESIS/Imalys
Authors: Selsam Peter , Bumberger Jan , Wellmann Thilo , Pause Marion , Gey Ronny , Borg Erik , Lausch Angela
Published: March 2024

One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a “compact tool”. Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale.
Tracking of Bacteriophage Predation on Pseudomonas aeruginosa Using a New Radiofrequency Biofilm Sensor
Authors: Longo Matthieu , Lelchat Florian , Le Baut Violette , Rioual Stéphane , Faÿ Fabienne , Lescop Benoit , Hellio Claire
Published: March 2024

Confronting the challenge of biofilm resistance and widespread antimicrobial resistance (AMR), this study emphasizes the need for innovative monitoring methods and explores the potential of bacteriophages against bacterial biofilms. Traditional methods, like optical density (OD) measurements and confocal microscopy, crucial in studying biofilm–virus interactions, often lack real-time monitoring and early detection capabilities, especially for biofilm formation and low bacterial concentrations. Addressing these gaps, we developed a new real-time, label-free radiofrequency sensor for monitoring bacteria and biofilm growth. The sensor, an open-ended coaxial probe, offers enhanced monitoring of bacterial development stages. Tested on a biological model of bacteria and bacteriophages, our results indicate the limitations of traditional OD measurements, influenced by factors like sedimented cell fragments and biofilm formation on well walls. While confocal microscopy provides detailed 3D biofilm architecture, its real-time monitoring application is limited. Our novel approach using radio frequency measurements (300 MHz) overcomes these shortcomings. It facilitates a finer analysis of the dynamic interaction between bacterial populations and phages, detecting real-time subtle changes. This method reveals distinct phases and breakpoints in biofilm formation and virion interaction not captured by conventional techniques. This study underscores the sensor’s potential in detecting irregular viral activity and assessing the efficacy of anti-biofilm treatments, contributing significantly to the understanding of biofilm dynamics. This research is vital in developing effective monitoring tools, guiding therapeutic strategies, and combating AMR.
A Non-Disposable Electrochemical Sensor Based on Laser-Synthesized Pd/LIG Nanocomposite-Modified Screen-Printed Electrodes for the Detection of H2O2
Authors: Song Ruijie , Zhang Jianwei , Yang Ge , Wu Yu , Yu Jun , Zhu Huichao
Published: March 2024

There have been many studies on the significant correlation between the hydrogen peroxide content of different tissues or cells in the human body and the risk of disease, so the preparation of biosensors for detecting hydrogen peroxide concentration has been a hot topic for researchers. In this paper, palladium nanoparticles (PdNPs) and laser-induced graphene (LIG) were prepared by liquid-phase pulsed laser ablation and laser-induced technology, respectively. The complexes were prepared by stirring and used for the modification of screen-printed electrodes to develop a non-enzymatic hydrogen peroxide biosensor that is low cost and mass preparable. The PdNPs prepared with anhydrous ethanol as a solvent have a uniform particle size distribution. The LIG prepared by laser direct writing has good electrical conductivity, and its loose porous structure provides more adsorption sites. The electrochemical properties of the modified electrode were characterized by cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy. Compared with bare screen-printed electrodes, the modified electrodes are more sensitive for the detection of hydrogen peroxide. The sensor has a linear response range of 5 µM–0.9 mM and 0.9 mM–5 mM. The limit of detection is 0.37 µM. The above conclusions indicate that the hydrogen peroxide electrochemical biosensor prepared in this paper has great advantages and potential in electrochemical catalysis.
Reliability Evaluation for Continuous-Wave Functional Near-Infrared Spectroscopy Systems: Comprehensive Testing from Bench Characterization to Human Test
Authors: Gao Chenyang , Xiu Jia , Huang Chong , Ma Kaixue , Li Ting
Published: March 2024

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.
Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation
Authors: Priebe Drew , Ghani Burooj , Stowell Dan
Published: March 2024

The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analyzing human disturbance and for privacy filtering. Despite significant strides in deep learning in recent years, the deployment of large neural networks on compact devices poses challenges due to memory and latency constraints. Our approach focuses on leveraging knowledge distillation techniques to design efficient, lightweight student models for speech detection in bioacoustics. In particular, we employed the MobileNetV3-Small-Pi model to create compact yet effective student architectures to compare against the larger EcoVAD teacher model, a well-regarded voice detection architecture in eco-acoustic monitoring. The comparative analysis included examining various configurations of the MobileNetV3-Small-Pi-derived student models to identify optimal performance. Additionally, a thorough evaluation of different distillation techniques was conducted to ascertain the most effective method for model selection. Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring.
Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter?
Authors: Burma Joel S. , Griffiths James K. , Lapointe Andrew P. , Oni Ibukunoluwa K. , Soroush Ateyeh , Carere Joseph , Smirl Jonathan D. , Dunn Jeff F.
Published: March 2024

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20–100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland–Altman plots and coefficient of variation (CoV). A sampling frequency of ~100–200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40–50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1–8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.
Distortion Correction and Denoising of Light Sheet Fluorescence Images
Authors: Julia Adrien , Iguernaissi Rabah , Michel François J. , Matarazzo Valéry , Merad Djamal
Published: March 2024

Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson–Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data.
Cross-and-Diagonal Networks: An Indirect Self-Attention Mechanism for Image Classification
Authors: Lyu Jiahang , Zou Rongxin , Wan Qin , Xi Wang , Yang Qinglin , Kodagoda Sarath , Wang Shifeng
Published: March 2024

In recent years, computer vision has witnessed remarkable advancements in image classification, specifically in the domains of fully convolutional neural networks (FCNs) and self-attention mechanisms. Nevertheless, both approaches exhibit certain limitations. FCNs tend to prioritize local information, potentially overlooking crucial global contexts, whereas self-attention mechanisms are computationally intensive despite their adaptability. In order to surmount these challenges, this paper proposes cross-and-diagonal networks (CDNet), innovative network architecture that adeptly captures global information in images while preserving local details in a more computationally efficient manner. CDNet achieves this by establishing long-range relationships between pixels within an image, enabling the indirect acquisition of contextual information. This inventive indirect self-attention mechanism significantly enhances the network’s capacity. In CDNet, a new attention mechanism named “cross and diagonal attention” is proposed. This mechanism adopts an indirect approach by integrating two distinct components, cross attention and diagonal attention. By computing attention in different directions, specifically vertical and diagonal, CDNet effectively establishes remote dependencies among pixels, resulting in improved performance in image classification tasks. Experimental results highlight several advantages of CDNet. Firstly, it introduces an indirect self-attention mechanism that can be effortlessly integrated as a module into any convolutional neural network (CNN). Additionally, the computational cost of the self-attention mechanism has been effectively reduced, resulting in improved overall computational efficiency. Lastly, CDNet attains state-of-the-art performance on three benchmark datasets for similar types of image classification networks. In essence, CDNet addresses the constraints of conventional approaches and provides an efficient and effective solution for capturing global context in image classification tasks.
An Investigation of Efficiency Issues in a Low-Pressure Steam Turbine Using Neural Modelling
Authors: Bělohoubek Marek , Liška Karel , Kubín Zdeněk , Polcar Petr , Smolík Luboš , Polach Pavel
Published: March 2024

This study utilizes neural networks to detect and locate thermal anomalies in low-pressure steam turbines, some of which experienced a drop in efficiency. Standard approaches relying on expert knowledge or statistical methods struggled to identify the anomalous steam line due to difficulty in capturing nonlinear and weak relations in the presence of linear and strong ones. In this research, some inputs that linearly relate to outputs have been intentionally neglected. The remaining inputs have been used to train shallow feedforward or long short-term memory neural networks using measured data. The resulting models have been analyzed by Shapley additive explanations, which can determine the impact of individual inputs or model features on outputs. This analysis identified unexpected relations between lines that should not be connected. Subsequently, during periodic plant shutdown, a leak was discovered in the indicated line.