bluuebunny/crossref_metadata_2025 huggingface dataset:
A Test of Activated Carbon and Soil Seed Enhancements for Improved Sub-Shrub and Grass Seedling Survival With and Without Herbicide Application
Authors: Svejcar Lauren N. , Martyn Trace E. , Edlund Hayley R. , Davies Kirk W.
Published: November 2024

Re-establishing native plants while controlling invasive species is a challenge for many dryland restoration efforts globally. Invasive plants often create highly competitive environments so controlling them is necessary for effective establishment of native species. In the sagebrush steppe of the United States, invasive annual grasses are commonly controlled with herbicide treatments. However, the same herbicides that control invasive annual grasses also impact the native species being planted. As such, carbon-based seed technologies to protect native seeds from herbicide applications are being trialed. In addition to controlling invasive species, ensuring good seed-to-soil contact is important for effective establishment of native species. In this grow room study, we explored the impact of different seed ameliorations when no herbicide was applied and when herbicide was applied. We selected two native species that are important to the sagebrush steppe for this study—the sub-shrub Krascheninnikovia lanata and the perennial bunchgrass Pseudoroegneria spicata—and used three different seed ameliorations—seed pelleting with local soil alone, local soil plus activated carbon and activated carbon alone—to ensure both greater seed-to-soil contact and protection against herbicides. Shoot and root biomass data were collected eight weeks after planting. We found that when herbicide was not applied, K. lanata had the strongest response to the soil alone amelioration, while P. spicata had the strongest response to the activated carbon alone amelioration. However, when herbicide was applied, K. lanata performed best with the soil plus activated carbon treatments, with an average 1500% increase in biomass, while P. spicata performed best with the activated carbon alone treatments, with an over 4000% increase in biomass, relative to bare seed. The results from our study indicate that there is a positive effect of local soils and activated carbon as seed ameliorations, and further testing in the field is needed to understand how these ameliorations might perform in actual restoration scenarios.
Antiviral Potential and Chemical Composition of Wild Baccharis crispa Spreng. Populations (Asteraceae) from Córdoba, Argentina: Perspective on Population Variability
Authors: Lingua Giuliana , Chaves Ana Guadalupe , Aguilar Juan Javier , Martinez Florencia , Gomez Tomás Isaac , Rucci Kevin Alen , Torres Lorena E. , Ancín-Azpilicueta Carmen , Esparza Irene , Jiménez-Moreno Nerea , Contigiani Marta , Nuñez Montoya Susana , Konigheim Brenda S.
Published: November 2024

Medicinal plants have been explored worldwide as potential alternatives for the prevention and treatment of different diseases, including viral infections. Baccharis crispa Spreng. (Asteraceae) is a native medicinal species widely used in South America. Given the influence of genetic and environmental factors on secondary metabolites biosynthesis and accumulation, this study aimed to evaluate the in vitro antiviral activity of four wild populations of B. crispa from Córdoba, Argentina, and assess the variability in their bioactivity and chemical composition. The cytotoxicity of chloroform, ethanol, and aqueous extracts from aerial parts was evaluated by the neutral red uptake method. Antiviral and virucidal activity against herpes simplex virus type 1 (HSV-1) and chikungunya virus (CHIKV) were assessed via plaque-forming unit (PFU) reduction assay. Phytochemical analyses of the extracts were conducted using HPLC-ESI- MS/MS. The Puesto Pedernera population showed the strongest antiviral activity, with inhibition rates of 82% for CHIKV and 79% against HSV-1, as well as potent virucidal effects, reducing PFU formation by up to 5 logarithms for both viruses. Remarkably, ethanol extract exhibited the least toxicity and strongest inhibitory activity. Villa del Parque population was inactive. We identified 38 secondary metabolites, predominantly phenolic acids (12) and flavonoids (18), in varying proportions. Delphinidin and delphinidin-3-glucoside are described for the first time in the species. Differences in phytochemical profiles were observed among extract types and populations. Key phenolic compounds showed moderate positive correlations with the evaluated bioactivities, emphasizing the complexity of phytochemical properties and interactions. These results highlight the therapeutic potential of B. crispa extracts against viral infections and underscore the importance of considering the geographical source of plant material in bioactivity evaluations.
Comparison of In Vitro Hair Growth Promotion and Anti-Hair Loss Potential of Thai Rice By-Product from Oryza sativa L. cv. Buebang 3 CMU and Sanpatong
Authors: Muangsanguan Anurak , Ruksiriwanich Warintorn , Arjin Chaiwat , Jamjod Sansanee , Prom-u-Thai Chanakan , Jantrawut Pensak , Rachtanapun Pornchai , Hnorkaew Patipan , Satsook Apinya , Sainakham Mathukorn , Castagnini Juan Manuel , Sringarm Korawan
Published: November 2024

The bioactive compounds in herbal extracts may provide effective hair loss treatments with fewer side effects compared to synthetic medicines. This study evaluated the effects of Buebang 3 CMU and Sanpatong rice bran extracts, macerated with dichloromethane or 95% ethanol, on hair growth promotion and hair loss prevention. Overall, Buebang 3 CMU extracts contained significantly higher levels of bioactive compounds, including γ-oryzanol, tocopherols, and various polyphenols such as phytic acid, ferulic acid, and chlorogenic acid, compared to Sanpatong extracts. Additionally, ethanolic extracts demonstrated greater bioactive content and antioxidant activities than those extracted with dichloromethane. These compounds enhanced the proliferation of human hair follicle dermal papilla cells (HFDPCs) by 124.28 ± 1.08% (p < 0.05) and modulated anti-inflammatory pathways by reducing nitrite production to 3.20 ± 0.36 µM (p < 0.05). Key hair growth signaling pathways, including Wnt/β-catenin (CTNNB1), Sonic Hedgehog (SHH, SMO, GLI1), and vascular endothelial growth factor (VEGF), were activated by approximately 1.5-fold to 2.5-fold compared to minoxidil. Also, in both human prostate cancer (DU-145) and HFDPC cells, the ethanolic Buebang 3 CMU extract (Et-BB3-CMU) suppressed SRD5A1, SRD5A2, and SRD5A3 expression—key pathways in hair loss—by 2-fold and 1.5-fold more than minoxidil and finasteride, respectively. These findings suggest that Et-BB3-CMU holds promise for promoting hair growth and preventing hair loss.
Image-Based Quantitative Analysis of Epidermal Morphology in Wild Potato Leaves
Authors: Zubairova Ulyana S. , Fomin Ivan N. , Koloshina Kristina A. , Barchuk Alisa I. , Erst Tatyana V. , Chalaya Nadezhda A. , Gerasimova Sophia V. , Doroshkov Alexey V.
Published: November 2024

The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.
Rapeseed—An Important Oleaginous Plant in the Oil Industry and the Resulting Meal a Valuable Source of Bioactive Compounds
Authors: Petraru Ancuţa , Amariei Sonia
Published: November 2024

Rapeseeds (Brassica napus), cultivated widely as a source of oil, generate substantial by-products after oil extraction. Unfortunately, rapeseed meal is considered a waste product and as such is discharged into environment as compost or used as animal feed. However, this meal is rich in bioactive compounds (proteins, minerals, fibers and polyphenols), indicating its potential for the development of value-added products. The meal shows a higher content of minerals, total dietary fibers and proteins. Rapeseed meal contains a proportion of oil rich in polyunsaturated fatty acids, predominately linoleic and α-linolenic acid. The amino acid proportion in the meal is higher than that in the seeds and contains essential amino acids, predominately valine. The analyses show the presence of valuable components in the cake, which makes it suitable for use in obtaining value-added products.
Dual-Template Molecularly Imprinted Polymers for Dispersive Solid-Phase Extraction Combined with High Performance Liquid Chromatography for the Determination of Sulfonamide Antibiotics in Environmental Water Samples
Authors: Wen Yuhao , Hou Mingyang , Hao Xingkai , Sun Dani , Zhang Hao , Saqib Farooq , Lu Wenhui , Liu Huitao , Chen Lingxin , Li Jinhua
Published: November 2024

In this study, we designed a molecularly imprinted polymers-dispersive solid-phase extraction-high-performance liquid chromatography (MIPs-DSPE-HPLC) method, as a simple and efficient platform for the sensitive detection of two sulfonamide antibiotics (SAs) of sulfamethoxine (SMM) and sulfamethoxazole (SMZ) in environmental water samples. Using SMM and SMZ as templates, methacrylic acid as the functional monomer, ethylene glycol dimethacrylate as the crosslinking agent, and azodiisobutyronitrile as the catalyst, the dual-template molecularly imprinted polymers (dt-MIPs) were successfully synthesized via surface imprinting technology and multi-template imprinting strategy. The adsorption properties of the prepared MIPs were characterized, and the adsorption capacities of MIPs towards SMZ and SMM were 27.35 mg/g and 30.92 mg/g, respectively. The detection limits of the method in three environmental water samples were in the range of 0.23–1.74 μg/L, and the recoveries were between 82.7 and 110.3%, with relative standard deviations less than 5.93%. The construction process of this MIPs-DSPE-HPLC method is straightforward, exhibits high sensitivity and selectivity, and thus provides a versatile method for the quantification of SAs in complex matrices.
Advancing Extractive Metallurgy: Computational Approaches for a Sustainable Future
Authors: Navarra Alessandro
Published: November 2024

Extractive metallurgy, combined with mineral processing, is at the heart of modern industry, facilitating the transformation of raw mineral resources into valuable metals essential for infrastructure, technology, and energy systems [...]
In Situ CALB Immobilization in Xerogel and Sonogel Employing TMOS as Silica Precursor and Polyethylene Glycol as Additive
Authors: Antunes Angela , Oro Carolina E. Demaman , Denti Andressa Franco , da Silva Leonardo M. , Ficanha Aline M. M. , Mulinari Jéssica , Venquiaruto Luciana D. , Zeni Jamile , Mignoni Marcelo L. , Dallago Rogério M.
Published: November 2024

The immobilization of enzymes, especially lipases, presents a significant challenge in contemporary biotechnology due to their wide-ranging application in industrial processes. Given the array of available techniques for enzyme immobilization, this study aimed to immobilize Candida antarctica B (CALB) lipase within silica xerogel and sonogel matrices obtained through the sol–gel technique. Polyethylene glycol (PEG) was incorporated as an additive, with tetramethylorthosilicate (TMOS) serving as the silica precursor. This study assessed the operational stability, storage stability, and thermal properties of the resulting supports. Results revealed that both sonogel and xerogel supports, supplemented with PEG, maintained storage stability above 50% throughout a 365-day period. Moreover, operational stability tests demonstrated that the xerogel support could be reused up to 21 times, while the sonogel support exhibited 10 reuses. Thermal analysis further highlighted a reduction in the deactivation constant and an elongation of the half-life time for both supports. These observations suggest that the supports effectively shield the enzyme from thermal inactivation. Overall, these findings underscore the potential utility of PEG-enhanced sonogel and xerogel supports in various industrial enzyme applications, providing valuable insights into their operational, storage, and thermal stability.
Fouling Prediction of a Heat Exchanger Based on Wavelet Neural Network Optimized by Improved Particle Swarm Optimization Algorithm
Authors: Liang Yandong , Zhu Lipeng , Wang Yang , Wu Hao , Zhang Junwei , Guan Jing , Wang Jianguo
Published: November 2024

The relevant experimental data of the fouling formation process of a heat exchanger were obtained through the fouling monitoring experimental platform. Whereafter, with regard to the conventional particle swarm optimization (PSO) algorithm, this study commenced from the iteration formula and innovatively presented an optimization approach for improving the inertia weight, thereby obtaining the improved particle swarm optimization (IPSO) algorithm. The wavelet neural network (WNN) was optimized through the application of the IPSO–WNN algorithm, resulting in the development of the IPSO–WNN model. Utilizing this model, a predictive model for fouling thermal resistance was constructed, incorporating input variables such as conductivity, pH, dissolved oxygen, average wall temperature, and bulk temperature, while the output variable represented fouling thermal resistance. Comparative analyses demonstrated that the IPSO–WNN model exhibited superior prediction accuracy and robust generalization capabilities to that of the conventional WNN and PSO–WNN models, as evidenced by significantly lower values across all indicators, including MAPE, MAE, and RMSE. The IPSO algorithm effectively optimized the initial parameters of the WNN, addressing the challenge of local minimum and enhancing the model’s overall capacity to identify optimal solutions. This model effectively captures the dynamic trends of fouling thermal resistance during its growth stage and approaches the asymptotic value in the stable stage. Precise prediction models for heat exchanger fouling contribute valuable insights for its prediction in practical industrial applications.
The Effect of Ethylene Diamine Tetra(Methylene Phosphonic Acid) Sodium on the Hydration of Tricalcium Silicate at 80 °C
Authors: Zou Yiwei , Song Weikai , Sun Chao , Yu Tianshuai , Zhu Sijia , Wang Chunyu , Yao Xiao
Published: November 2024

In the field of oil well cementing, controlling the hydration process of cement slurries is essential to ensure successful placement and long-term well integrity, especially under challenging high-temperature and high-pressure conditions. Organic phosphonates such as ethylene diamine tetra(methylenephosphonic acid) sodium (EDTMPS) have been identified as effective retarders that can delay the hydration of tricalcium silicate (C3S), the primary phase responsible for early strength development in cement. This research explores the effect of EDTMPS on C3S hydration, using a combination of analytical techniques including isothermal calorimetry, TGA, XRD, and SEM. The results demonstrate that 0.2% EDTMPS extended the induction period of C3S hydration by up to 4.27 h, reduced the heat flow during the acceleration period by 65%, and lowered the cumulative heat of hydration at 1 d by approximately 14%. As a result, EDTMPS significantly delayed the development of C-S-H gel and CH, thereby extending the setting time of the cement slurry and causing a reduction in the early compressive strength of the cement pastes. Therefore, EDTMPS had a slight effect on the compressive strength of cement pastes at 28 d. The results offer important insights into the retarding mechanism of organic phosphonates and their potential applications in enhancing the performance of oil well cement under high temperatures.
Pressmud Compost for Improved Nitrogen and Phosphorus Content Employing Bacillus Strains
Authors: Sajid Uzma , Aslam Sumaira , Hussain Ali , Mumtaz Tamseela , Kousar Safina
Published: November 2024

Pressmud, a by-product of sugarcane processing, is typically disposed of through incineration or landfilling, though it has considerable potential in organic agriculture. This study explored the composting of pressmud through bioaugmentation using specific bacterial strains. Two experimental setups were created: E2 with a cellulolytic and phosphorus-solubilizing strain, Bacillus amyloliquefaciens-ASK11, and E3 with a nitrogen-fixing strain, Bacillus megaterium-ASNF3. A control setup (E1) was also maintained without bacterial augmentation. Results indicated that the Bacillus-enhanced composts in E2 and E3 showed significant increases of 129% and 83% in nitrogen and of 49% and 91% in phosphorus contents, respectively, after 60 days. Additionally, organic matter decomposition improved by 49–50% in the bioaugmented setups after 60 days. FTIR analysis revealed organic phosphate peaks and P-O-C stretching bands at 1025 cm−1 in the E2 compost, while a nitrogen vibration band at 3849 cm−1 in E3 indicated significantly higher nitrogen content compared to the control. The Bacillus-enriched pressmud compost not only accelerated the composting process but also enhanced nutrient levels, positioning it as a promising biofertilizer for rehabilitating barren lands.
“I Learnt Much About…” the Impact of Cooperative Interreligious Education
Authors: Danner Sonja , Akpinar Halid
Published: November 2024

Population growth in Austria means that school classes—including those in apprenticeship training—are no longer homogeneous. Apprentices, too, often come from different cultural backgrounds and therefore belong to different religions and worldviews, which makes the classes “multi” in every respect. This can lead to encounters in their everyday working life that are not always smooth because they are accompanied by prejudices and misunderstandings. Can cooperative religious education based on didactics of facilitation and TCI help to gain new insights into religions/worldviews and thus reinforce social cohesion? It has been shown that cooperative religious education gives pupils an insight into religious communities to which they themselves do not belong but does not give much concrete knowledge. It creates an awareness of being different without devaluation and the importance of dialogue that leads to a better mutual understanding and consequently to an awareness of one’s own prejudices and judgements towards others. It cannot be said that the attitudes towards religions/denominations/worldviews that were foreign to the pupils have changed with KORU but effects on social interaction are recognisable. The evaluation according to Philipp Mayring (content analysis) was carried out using a triangulation of data: Observation of religious education lessons, lesson preparations and interviews with the participating teachers and two group interviews with students and their written reflections.
Another Fateful Triangle: Jews, Muslims, Europe
Authors: Becker Elisabeth Jane
Published: November 2024

This paper argues that Jews–Muslims–Europe is a fateful triangle, in which identities and identifications both inform and form one another. It draws on interview-based research at the Jewish Museum Berlin to showcase how Jewish and Muslim positionalities have become entwined in the context of contemporary Berlin, Germany, and Europe. At the same time, it showcases how the positionality of strangerhood can provide a critical perspective for understanding and articulating Europe as a place of pluralism, both present and past. What emerges is a sociocultural terrain in which Muslims, Jews, and Europe are made by and with one another, rather than simply against one another.
Risk Factors for Acute Kidney Injury in Patients Undergoing Total Joint Arthroplasty
Authors: Kılıc Hazal , Cakar Turhan K. , Karadag Erkoc Suheyla , Hayriye Kocaoglu Merve
Published: November 2024

Objective: The present study investigates the incidence of postoperative acute kidney injury (AKI) and related risk factors in patients undergoing total joint arthroplasty. Methods: Included in the study were patients undergoing joint arthroplasty in 2015–2020. The patients with acute or chronic renal failure were excluded. The participants’ demographical data, American Society of Anesthesiologist (ASA) score, Charlson Comorbidity Index (CCI), type of operation, duration of surgery, presence of comorbidities, preoperative anemia, preoperative albumin levels, use of nephrotoxic agents, number of transfusions during perioperative period, presence of AKI according to Kidney Disease Improving Global Outcome (KDIGO) scores, and length of hospital and intensive care unit stay were evaluated. Results: The study was initiated with 1780 patients: 113 patients were excluded due to chronic kidney failure, 108 patients were excluded due to acute kidney failure, 648 patients were excluded because their data could not be reached, and finally, 911 patients were included in the study. AKI was detected in 134 patients (14.7%), and the number of patients in the KDIGO1 and KDIGO2 groups were 120 and 14, respectively. When evaluated according to the variable significance test result and clinical significance, the model consists of variables such as ASA, CCI, hypertension, nonsteroidal anti-inflammatory drugs (NSAIDs), vancomycin, beta lactam, contrast material and preoperative anemia, operation type, and anesthesia management. Machine learning analyses were performed using 11 variables (10 independent and 1 dependent variable). Logistic regression, naive Bayes, multilayer perceptron, bagging, and random forrest approaches were used for evaluation of the predictive performance. In an evaluation of the true classification ratio, the best result was obtained with the logistic regression method at 85.2%. Conclusions: The study revealed advanced age, high ASA and CCI, presence of diabetes and hypertension, NSAID, vancomycin and contrast material, and the presence of preoperative anemia to be independent risk factors for AKI.
Remote Sensing Image Change Captioning Using Multi-Attentive Network with Diffusion Model
Authors: Yang Yue , Liu Tie , Pu Ying , Liu Liangchen , Zhao Qijun , Wan Qun
Published: November 2024

Remote sensing image change captioning (RSICC) has received considerable research interest due to its ability of automatically providing meaningful sentences describing the changes in remote sensing (RS) images. Existing RSICC methods mainly utilize pre-trained networks on natural image datasets to extract feature representations. This degrades performance since aerial images possess distinctive characteristics compared to natural images. In addition, it is challenging to capture the data distribution and perceive contextual information between samples, resulting in limited robustness and generalization of the feature representations. Furthermore, their focus on inherent most change-aware discriminative information is insufficient by directly aggregating all features. To deal with these problems, a novel framework entitled Multi-Attentive network with Diffusion model for RSICC (MADiffCC) is proposed in this work. Specifically, we introduce a diffusion feature extractor based on RS image dataset pre-trained diffusion model to capture the multi-level and multi-time-step feature representations of bitemporal RS images. The diffusion model is able to learn the training data distribution and contextual information of RS objects from which more robust and generalized representations could be extracted for the downstream application of change captioning. Furthermore, a time-channel-spatial attention (TCSA) mechanism based difference encoder is designed to utilize the extracted diffusion features to obtain the discriminative information. A gated multi-head cross-attention (GMCA)-guided change captioning decoder is then proposed to select and fuse crucial hierarchical features for more precise change description generation. Experimental results on the publicly available LEVIR-CC, LEVIRCCD, and DUBAI-CC datasets verify that the developed approach could realize state-of-the-art (SOTA) performance.
Deep Ensemble Remote Sensing Scene Classification via Category Distribution Association
Authors: He Zhenxin , Li Guoxu , Wang Zheng , He Guanxiong , Yan Hao , Wang Rong
Published: November 2024

Recently, deep learning models have been successfully and widely applied in the field of remote sensing scene classification. But, the existing deep models largely overlook the distinct learning difficulties associated with discriminating different pairs of scenes. Consequently, leveraging the relationships within category distributions and employing ensemble learning algorithms hold considerable potential in addressing these issues. In this paper, we propose a category-distribution-associated deep ensemble learning model that pays more attention to instances that are difficult to identify between similar scenes. The core idea is to utilize the degree of difficulty between categories to guide model learning, which is primarily divided into two modules: category distribution information extraction and scene classification. This method employs an autoencoder to capture distinct scene distributions within the samples and constructs a similarity matrix based on the discrepancies between distributions. Subsequently, the scene classification module adopts a stacking ensemble framework, where the base layer utilizes various neural networks to capture sample representations from shallow to deep levels. The meta layer incorporates a novel multiclass boosting algorithm that integrates sample distribution and representations of information to discriminate scenes. Exhaustive empirical evaluations on remote sensing scene benchmarks demonstrate the effectiveness and superiority of our proposed method over the state-of-the-art approaches.
A New Method for Extracting Three-Dimensional Surface Deformation in Underground Mining Areas Based on the Differentiability of D-InSAR Line-of-Sight Displacements
Authors: Chen Junjie , Zhao Chunsu , Yan Weitao , Chen Zhiyu
Published: November 2024

Monitoring three-dimensional (3D) deformation in underground mining areas is crucial for the prevention and control of mining-induced disasters. Differential interferometric synthetic aperture radar (D-InSAR) is limited to detecting one-dimensional (1D) deformation along the line of sight (LOS). This paper proposes a new method for extracting 3D mining-induced deformation based on the differentiability of D-InSAR LOS deformation fields. The method approximates the D-InSAR LOS deformation field in underground mining areas as a differentiable function and constructs a 3D deformation extraction model utilizing directional derivatives of this function. The least squares method is used for estimating and evaluating the 3D deformation. Simulation and real data experiments have been used to verify the feasibility of the method in extracting mining-induced 3D deformation. The simulation results show relative root mean square errors (RRMSES) of 1.24%, 6.05%, 0.97%, and 11.47% for vertical and horizontal displacements along the east–west and south–north directions, respectively. The real data experiments using Sentinel-1 images show that the root mean square errors (RMSES) of the up–down, south–north, and east–west directions are 14.06 mm, 7.37 mm, and 11.56 mm, respectively. Experimental results show that the method can provide a certain basis for 3D surface deformation monitoring of mining subsidence.
Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy
Authors: Zhu Changda , Zhu Fubin , Li Cheng , Lu Wenhao , Fang Zihan , Li Zhaofu , Pan Jianjun
Published: November 2024

Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping.
A New Type of Dynamic Vibration Fiber Sensor
Authors: Chang I-Nan , Chiu Chih-Chuan , Liu Wen-Fung
Published: November 2024

A new-type vibration sensor based on a fiber Bragg grating combined with a special structure-packaged design is proposed for monitoring the mechanical vibration signals. Three different sensing structures, including the film squeeze type, new film squeeze type, and elastic tape squeeze type are proposed for measuring the vibration signals with the frequency range from tens to thousands of Hz. In the comparison to experimental results, the new film squeeze structure has a nice sensing performance in the range from 100 to 1000 Hz with a sensitivity of 0.302 mV/g. For the elastic tape squeeze structure, the elastic tape is designed to encapsulate the optical fiber with a good frequency response from 1100 to 3100 Hz. In addition, by using the new film squeeze structure to measure the steady-state and non-steady-state vibration signals, the spectral components of sensing signals are analyzed by using the wavelet transformation for confirming the testing signals. These vibration fiber sensors can be applied in the measurement of high-end manufacture-facility vibration or earthquake vibrations etc.
Short-Peptide-Modified Copper Nanoclusters as a Fluorescent Probe for the Specific Detection of Ascorbic Acid
Authors: Li Jiataiqi , Lan Xin , Liu Xingcen
Published: November 2024

Metal nanoclusters assembled using short peptides as templates exhibit significant potential for development and application in the fields of catalysis and biomedicine, owing to their distinctive electronic structure, favorable optical properties, and biocompatibility. Among them, tripeptides exhibit a simpler structure and greater flexibility, enabling them to readily co-assemble with other functional components to create novel materials with significant application value. They can be assembled with copper ions to synthesize highly efficient luminescent nanoclusters, which can serve as an effective fluorescent probe. Here, we report a method for the synthesis of copper nanoclusters (Cu NCs) using tripeptides as templates, which also act as stabilizers and reducing agents. The synthesis conditions and properties were explored and optimized. Under optimal conditions, the Cu NCs exhibit excellent stability and are strongly fluorescent. The Cu NCs can detect 0.1–1.0 μmol/L of ascorbic acid with a low detection limit of 0.075 μmol/L, demonstrating high sensitivity and offering significant application potential for the trace of ascorbic acid in various substances. It also provides new ideas for the assembly of metal nanoclusters and the construction of fluorescent probe sensing platforms.
Real-Time and Long-Term Monitoring of Coastal Water Turbidity Using an Ocean Buoy Equipped with an ADCP
Authors: Bian Jia-Wei , Huang Ching-Jer
Published: November 2024

In this study, an acoustic Doppler current profiler (ADCP) operating at 600 kHz was installed on an ocean data buoy in the Qigu waters, Taiwan, to gather real-time sound echo intensity data. These data were then correlated with turbidity measurements obtained by a turbidimeter mounted on the buoy’s mooring line at a water depth of 13 m. The data buoy operated from 6 June to 16 August 2017. During this period, turbidity measurements were recorded from 6 to 21 June 2017. This study established a calibration between the sound echo intensity measured by the ADCP and the turbidity measured using the turbidimeter; a strong linear correlation was discovered between these two variables. This correlation enabled the conversion of echo intensity data into a continuous time series of turbidity measurements, facilitating real-time and long-term monitoring of coastal water turbidity through the deployment of a buoy equipped with an ADCP. The relationships between turbidity and environmental factors such as rainfall, tides, current speeds, and wave activity over an extended period were then investigated. The results revealed that stronger tides and currents in the Qigu waters often lead to higher turbidity, suggesting that these two factors are the primary driving forces for sediment transport in the Qigu waters. Additionally, sampling of water in the Qigu area revealed sediment particles of size ranging from 2 to 120 μm.
Radiofrequency Enhancer to Recover Signal Dropouts in 7 Tesla Diffusion MRI
Authors: Subramaniam Varun , Frankini Andrew , Al Qadi Ameen , Herb Mackenzie T. , Verma Gaurav , Delman Bradley N. , Balchandani Priti , Alipour Akbar
Published: November 2024

Diffusion magnetic resonance imaging (dMRI) allows for a non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost the signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, wavelength effects at 7T cause reduced transmit magnetic field efficiency in the human brain, mainly in the posterior fossa, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. In this study, we demonstrate the feasibility and effectiveness of the RF enhancer in improving in vivo dMRI at 7T. The electromagnetic simulation results demonstrated a 2.1-fold increase in transmit efficiency with the use of the RF enhancer. The experimental results similarly showed a 1.9-fold improvement in transmit efficiency and a 1.4-fold increase in normalized SNR. These improvements effectively mitigated signal dropouts in regions with inherently lower SNR, such as the cerebellum, resulting in a better depiction of principal fiber orientations and an enhanced visualization of extended tracts.
Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments
Authors: Sravanthi Mangali , Gunturi Sravan Kumar , Chinnaiah Mangali Chinna , Lam Siew-Kei , Vani G. Divya , Basha Mudasar , Janardhan Narambhatla , Krishna Dodde Hari , Dubey Sanjay
Published: November 2024

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot’s intention to serve based on the human’s location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.
Velocity Estimation of Passive Target Based on Sparse Bayesian Learning Cross-Spectrum
Authors: Li Xionghui , Liang Guolong , Shen Tongsheng , Luo Zailei
Published: November 2024

To solve the poor performance or even failure of the cross-spectrum (CS) method in hydroacoustic weak-target passive velocimetry, a sparse Bayesian learning cross-spectrum method (SBL-CS), combining phase compensation with sparse Bayesian learning (SBL) is proposed in this paper. Firstly, the cross-correlation sound intensity is taken as the observation quantity and compensates for each frequency point of the cross-spectrum, which enables the alignment of cross-spectrum results at different frequencies. Then, the inter-correlation sound intensity of all frequencies is fused in the iterative estimation of the target velocity, verifying the proposed method’s ability to suppress the background noise when performing multi-frequency processing. The simulation results show that the proposed method is still effective in estimating the target velocity when the CS method fails and that the performance of the proposed method is better than the CS method with a decrease in SNR. As verified using the SWellEx-96 sea trial dataset, the RMSE of the proposed method for surface vessel speed measurement is 0.3545 m/s, which is 46.1% less than the traditional CS method, proving the feasibility and effectiveness of the proposed SBL-CS method for the estimation of the radial speed of a passive target.
Auto Aligning, Error-Compensated Broadband Collimated Transmission Spectroscopy
Authors: Pink Karsten , Kienle Alwin , Foschum Florian
Published: November 2024

Broadband spectral measurements of the ballistic transmission of scattering samples are challenging. The presented work shows an approach that includes a broadband system and an automated adjustment unit for compensation of angular distortions caused by non-plane-parallel samples. The limits of the system in terms of optimal transmission and detected forward scattering influenced by the scattering phase function are investigated. We built and validated a setup that measures the collimated transmission signal in a spectral range from 300 nm to 2150 nm. The system was validated using polystyrene spheres and Mie calculations. The limits of the system in terms of optimal transmission and detected forward scattering were researched. The optimal working parameters of the system, analyzed by simulations using the Monte Carlo method, show that the transmission should be larger than 10% and less than 90% to allow for a reliable measurement with acceptable errors caused by noise and systematic errors of the system. The optimal transmission range is between 25% and 50%. We show that the phase function is important when considering the accuracy of the measurement. For strongly forward-scattering samples, errors of up to 80% can be observed, even for a very small numerical aperture of 6.6·10−4, as used in our experimental system. We also show that errors increase with optical thickness as the ballistic transmission decreases and the multiscattered fraction increases. In addition, errors caused by multiple reflections in the sample layer were analyzed and also classified as relevant for classical absorption spectroscopy.
Influence of Training Load on Muscle Contractile Properties in Semi-Professional Female Soccer Players Across a Competitive Microcycle: A Pilot Study
Authors: Rey Ezequiel , Lois-Abal María , Padrón-Cabo Alexis , Lorenzo-Martínez Miguel , Costa Pablo B.
Published: November 2024

This study aimed to evaluate changes in muscle contractile properties during a training microcycle in semi-professional female football players and explore their relationship with training load variables. Nineteen players (age: 23.9 ± 3.9 years; body mass: 60.6 ± 6.9 kg; height: 164.5 ± 6.7 cm) underwent myotonometric assessments of the biceps femoris (BF) and rectus femoris (RF) before and after the following training sessions: MD1 (i.e., 1 day after the match), MD3, MD4, and MD5. Training loads were quantified for each session, revealing significant variations, with MD4 exhibiting the highest values for high-speed running distance, number of sprints, and accelerations. Notably, MD3 showed the highest perceived exertion (RPE), while MD5 recorded the lowest total distance run. Myotonometric assessments indicated significant differences in stiffness of the RF in MD3 and BF in MD5, as well as RF tone in MD5. The findings underscore a notable relationship between training load and myotometric variables, particularly in muscle stiffness and tone. These results emphasize the need for further research to clarify how training loads affect muscle properties in female athletes.
VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network
Authors: Zhang Hu , Liao Yujia , Zhu Chang , Meng Wei , Liu Quan , Xie Sheng Q.
Published: November 2024

Traditional rehabilitation training for stroke patients with ankle joint issues typically relies on the expertise of physicians. However, when confronted with complex challenges, such as online decision-making or assessing rehabilitation progress, even seasoned experts may not anticipate all potential hurdles. A novel approach is necessary—one that effectively addresses these complexities without solely leaning on expert experience. Previous studies have introduced a rehabilitation assessment method based on fuzzy neural networks. This paper proposes a novel approach, which is a VR-aided ankle rehabilitation decision-making model based on a convolutional gated recurrent neural network. This model takes various inputs, including ankle dorsiflexion range of motion, angular velocity, jerk, and motion performance scores, gathered from wearable motion inertial sensors during virtual reality rehabilitation. To overcome the challenge of limited data, data augmentation techniques are employed. This allows for the simulation of five stages of rehabilitation based on the Brunnstrom staging scale, providing tailored control parameters for virtual training scenarios suited to patients at different stages of recovery. Experiments comparing the classification performance of convolutional neural networks and long short-term memory networks were conducted. The results were compelling: the optimized convolutional gated recurrent neural network outperformed both alternatives, boasting an average accuracy of 99.16% and a Macro-F1 score of 0.9786. Importantly, it demonstrated a strong correlation (correlation coefficient r > 0.9) with the assessments made by clinical rehabilitation experts, showing its effectiveness in real-world applications.
A Novel Robust Position Integration Optimization-Based Alignment Method for In-Flight Coarse Alignment
Authors: Ning Xiaoge , Huang Jixun , Li Jianxun
Published: November 2024

In-flight alignment is a critical milestone for inertial navigation system/global navigation satellite system (INS/GNSS) applications in unmanned aerial vehicles (UAVs). The traditional position integration formula for in-flight coarse alignment requires the GNSS velocity data to be valid throughout the alignment period, which greatly limits the engineering applicability of the method. In this paper, a new robust position integration optimization-based alignment (OBA) method for in-flight coarse alignment is presented to solve the problem of in-flight alignment under a prolonged ineffective GNSS. In this methodology, to achieve a higher alignment accuracy in case the GNSS is not effective throughout the alignment period, the integration of GNSS velocity into the local-level navigation frame is replaced by the GNSS position in the Earth-centered, Earth-fixed frame, which avoids the need for complete GNSS velocity data. The simulation and flight test results show that the new robust position integration method proposed in this paper achieves higher stability and robustness than the conventional position integration OBA method and can achieve an alignment accuracy of 0.2° even when the GNSS is partially time-invalidated. Thus, this greatly extends the application of the OBA method for in-flight alignment.
DeployFusion: A Deployable Monocular 3D Object Detection with Multi-Sensor Information Fusion in BEV for Edge Devices
Authors: Huang Fei , Liu Shengshu , Zhang Guangqian , Hao Bingsen , Xiang Yangkai , Yuan Kun
Published: November 2024

To address the challenges of suboptimal remote detection and significant computational burden in existing multi-sensor information fusion 3D object detection methods, a novel approach based on Bird’s-Eye View (BEV) is proposed. This method utilizes an enhanced lightweight EdgeNeXt feature extraction network, incorporating residual branches to address network degradation caused by the excessive depth of STDA encoding blocks. Meantime, deformable convolution is used to expand the receptive field and reduce computational complexity. The feature fusion module constructs a two-stage fusion network to optimize the fusion and alignment of multi-sensor features. This network aligns image features to supplement environmental information with point cloud features, thereby obtaining the final BEV features. Additionally, a Transformer decoder that emphasizes global spatial cues is employed to process the BEV feature sequence, enabling precise detection of distant small objects. Experimental results demonstrate that this method surpasses the baseline network, with improvements of 4.5% in the NuScenes detection score and 5.5% in average precision for detection objects. Finally, the model is converted and accelerated using TensorRT tools for deployment on mobile devices, achieving an inference time of 138 ms per frame on the Jetson Orin NX embedded platform, thus enabling real-time 3D object detection.