bluuebunny/crossref_metadata_2025 huggingface dataset:
Foliar H2O2 Application Improve the Photochemical and Osmotic Adjustment of Tomato Plants Subjected to Drought
Authors: Barzotto Gustavo Ribeiro , Cardoso Caroline Pardine , Jorge Letícia Galhardo , Campos Felipe Girotto , Boaro Carmen Sílvia Fernandes
Published: September 2024

Water limits may have a disastrous impact on agricultural productivity, and the current climate change scenario presents additional problems for crops that rely on regular rainfall. Reactive oxygen species, such as hydrogen peroxide (H2O2), are a recognized stress-sensing mechanism in plants, and may be investigated as an approach for reducing stress impact via systemic acquired acclimation. Here, we looked at how H2O2 foliar application impacts tomato plants’ photosynthetic activity, antioxidant system, sugar chemical profile, and osmotic adjustment during drought and recovery. The experiment was in randomized blocks, 3 × 2 factorial design, with no, one, or two foliar application of 1 mM H2O2, on plants that were either continually watered or subjected to drought. The plants were tested both during the drought period and after they had resumed irrigation (recovered). Leaf water potential, chlorophyll a fluorescence, gas exchange, lipid peroxidation, H2O2 concentrations, phenols, proline, antioxidant enzyme activity, and sugar chemical profile were all measured. Our findings showed that H2O2 application generated metabolic alterations in tomato plants independent of water status, and that two applications in drought plants resulted in a 30% decrease in oxidative stress during drought and faster recovery following irrigation return, with greater production of defence-related molecules such as the APX enzyme, phenols, arabinose, and mannose. Continually watered plants also benefited from H2O2 application, which increased carbon assimilation by 35%.
Hyperspectral Imaging and Machine Learning: A Promising Tool for the Early Detection of Tetranychus urticae Koch Infestation in Cotton
Authors: Yamada Mariana , Thiesen Leonardo Vinicius , Iost Filho Fernando Henrique , Yamamoto Pedro Takao
Published: September 2024

Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (>30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods.
Design of a 2R Open-Chain Plug Seedling-Picking Mechanism and Control System Constrained by a Differential Non-Circular Planetary Gear Train
Authors: Zhou Maile , Xu Tingbo , Wang Guibin , Dong Herui , Yang Shiyu , Wang Zeliang
Published: September 2024

With a focus on the problems of complex structure and accumulated lateral clearance in the single degree of freedom non-circular wheel system seedling-picking mechanism, which leads to poor motion accuracy, trajectory, and attitude, this study developed a 2R open-chain chili plug seedling-picking mechanism (SPM) constrained by a differential non-circular wheel system. The picking arm was driven by a single-stage non-uniform speed transmission mechanism to reproduce the seedling-picking trajectory and attitude. A protruding seedling-picking device, SPM control system, and test bench were designed. A kinematic model of a differential non-circular gear system was established, and an optimization design software for the SPM was developed based on kinematic analysis. The kinematic characteristics of the SPM were analyzed under optimal parameters. This study completed the seedling-picking performance test of the SPM on the control panel. The test showed that the designed chili SPM can sequentially complete the processes of seedling picking, conveying, retracting, pushing, and returning under the automatic control of the test bench without damaging the main root. The lateral root damage rate was 15.7%, effectively ensuring the integrity of the seedling bowl substrate.
Alterations in Suitable Cultivation Area for Scutellaria baicalensis under Future Climatic Scenarios in China: Geodetector-Based Prediction
Authors: Yin Yuanyuan , Wang Jing’ai , Zhang Wensheng , Yin Benfeng , Huang Jixia , Zhang Zijing
Published: September 2024

The dried roots of Scutellaria baicalensis (S. baicalensis) have been widely used as a traditional medicine. Recently, climate change and human activities have caused the degeneration of its wildlife habitat. However, there is rare knowledge on the effect and interactive effect of different variables on the spatial heterogeneity of S. baicalensis and how the pattern of suitable cultivation area in China would shift in response to climate change. Based on the Geodetector and Habitat Suitability Index (HSI) method, we proposed an assessment model to identify the critical environmental variable(s) affecting the distribution of suitable cultivation area for S. baicalensis in China and to project its shift under climate change. The results showed that soil and mean annual temperature are two determining variables in its spatial heterogeneity in China. Compared to 1981–2010, future climate change may result in a decrease in its suitable area, and yet may result in an increase in the extremely suitable area (about 1.00–1.35 million km2). S. baicalensis in the southern and northwestern part of its current distribution and the southwestern part and small area of northern China may experience expansion during the 21st century, while S. baicalensis in southern China, the Huang-Huai-Hai plain, and the midwest of northwestern China may experience contractions. Meanwhile, climate warming is expected to shift its distribution northwest through an expansion at the northern (at least 84 km) and western (at least 62 km) boundary and contraction at the southern (at least 529 km) boundary, respectively. These results could provide valuable information to policy-makers for the conservation and scientific introduction of S. baicalensis.
The Propagation Characteristics of Meteorological Drought to Vegetation Drought Based on Three-Dimensional Clustering Algorithm in China
Authors: Gao Shikai , Wang Fei , Men Ruyi , Yan Shaofeng , Wang Zipeng , Lai Hexin , Li Yanbin , Guo Wenxian
Published: September 2024

The spatiotemporal continuity characteristics of drought are the basis for analyzing its spatial migration and evolution, which is significant for mitigation and early warning of drought. The aim of this paper is to identify meteorological and vegetation drought events in China from 1982 to 2022, reveal the dynamic changes of typical drought events, and elucidate the propagation characteristics of meteorological and vegetation drought. The results showed that (1) based on a three-dimensional spatiotemporal clustering algorithm, China experienced 138 meteorological drought events and 76 vegetation drought events; (2) the severity of the meteorological drought event No. M138 (2022.03–2022.11) reached 667.58 × 104 km2·month, and the severity of the vegetation drought event No. V68 (2019.06–2020.04) reached 572.89 × 104 km2·month; (3) a total of 40 meteorological-vegetation drought event pairs had been identified, which was divided into three main types: “single”, “simple”, and “complexity”; and (4) in the typical drought event pair No. P-34, the area difference was 16.45 × 104 km2, and the severity difference was 3.89 × 104 km2. The research results can provide a new perspective for identifying the dynamic changes and propagation characteristics of drought events from a three-dimensional perspective, which is of great significance for predicting vegetation drought and protecting the ecological environment.
Integrated Crop–Livestock Systems as a Strategy for the Sustainable Production of Corn and Soybean Grain in Tropical Sandy Soils
Authors: Soares Deyvison de Asevedo , Lupatini Gelci Carlos , Sekiya Bianca Midori Souza , Mateus Gustavo Pavan , Andrighetto Cristiana , Modesto Viviane Cristina , da Silva Junio Reina , da Luz João Henrique Silva , Galindo Fernando Shintate , Crusciol Carlos Alexandre Costa , Pavinato Paulo Sergio , Andreotti Marcelo
Published: September 2024

Integrated crop–livestock systems (ICLS) have sustainably intensified modern agricultural practices worldwide. This research assessed how production systems and crop types impact the chemical properties of an Oxisol in the Brazilian Cerrado, the grain yield of corn intercropped with palisade grass (Urochloa) in the off-season in an ICLS, and the grain yield (GY) of soybean in succession. Intercropped and monocropped systems were assessed in a three-year field experiment: corn + Urochloa ruziziensis–soybean; corn + U. brizantha cv. Piatã–soybean; corn + U. brizantha cv. Paiaguás–soybean (ICL–Paiaguás); corn–soybean under a no-tillage system (NTS); corn–soybean under a conventional tillage system (CTS); Piatã grass–continuous grazing (Perennial Piatã); and Paiaguás grass–continuous grazing (Perennial Paiaguás). The residual impact of phosphate fertilization was more pronounced in the ICLS treatments. In the soil layer from 0.0 to 0.2 m depth, ICLS–Paiaguás and Perennial Piatã had the most positive effects on soil chemical quality. In the last year, grain yield was highest in corn monoculture under the NTS and soybean in succession under the ICLS. ICL–Paiaguás improved soil chemical properties for soybean in succession. These results confirm that an intermittent pasture system for legume crops in sequence is an alternative that can maintain or improve soil chemical composition, and that CTS should be avoided in tropical sandy soils.
Preparation of Polymeric Aluminum Chloride-Loaded Porous Carbon and Evaluation of Its Pb2+ Immobilization Mechanisms in Soil
Authors: Cheng Huanquan , Peng Longgui , Zheng Bin , Wang Rong , Huang Jiushuang , Yang Jianye
Published: September 2024

In recent years, the remediation of heavy metal-contaminated soils has attracted great attention worldwide. Previous research on the removal of toxic heavy metals from wastewater effluents through adsorption by typical solid wastes (e.g., fly ash and coal gangue) has mainly focused on the control of wastewater pollutants. In this study, a coal gangue (CG) by-product from Hancheng City was used as a raw material to prepare polymeric aluminum chloride-loaded coal gangue-based porous carbon (PAC-CGPC) by hydrothermal synthesis. This material was subsequently employed to assess its performance in mitigating Pb2+ in soils. In addition, the effects of the pore structure of the prepared material on the adsorption rates, adsorption mechanisms, and plant root uptakes of soil Pb2+ were investigated in this study. The raw CG and prepared PAC-CGPC materials exhibited specific surface areas of 1.8997 and 152.7892 m2/g, respectively. The results of adsorption kinetics and isotherms indicate that the adsorption of Pb2+ based on PAC-CGPC mainly follows a pseudo-second-order kinetic model, suggesting that chemisorption may be the dominant process. In addition, the adsorption isotherm results showed that the Freundlich model explained better the adsorption process of Pb2+, suggests that the adsorption sites of lead ions on APC-CGPC are not uniformly distributed and tend to be enriched in APC, and also shows the ion exchange between aluminum and lead ions. The thermodynamic model fitting results demonstrated the occurrence of spontaneous and exothermic PAC-CGPC-based adsorption of Pb2+, involving ion exchange and surface complexation. The effects of the PAC-CGPC addition on soybean plants were further explored through pot experiments. The results revealed substantial decreases in the Pb2+ contents in the soybean organs (roots, stems, and leaves) following the addition of the PAC-CGPC material at a dose of 3% compared with the control and raw CG groups. Furthermore, the addition of the PAC-CGPC material at a dose of 3% effectively reduced the bioavailable Pb2+ content in the soil by 82.11 and enhanced soybean growth by 15.3%. These findings demonstrated the inhibition effect of the PAC-CGPC material on the translocation of Pb2+ in the soybean seedlings. The modified CG adsorbent has highly pore structure and good hydrophilicity, making it prone to migration in unsaturated soils and, consequently, enhancing Pb2+ immobilization. This research provides theoretical support for the development of CG-based materials capable of immobilizing soil pollutants.
Effects of Ecotypes and Reduced N Fertilization on Root Growth and Aboveground Development of Ratooning Sorghum × Sudangrass Hybrids
Authors: Choi Nayoung , Choi Miri , Lee Sora , Jo Chaelin , Kim Gamgon , Jeong Yonghyun , Lee Jihyeon , Na Chaein
Published: September 2024

Reduced N input while maintaining biomass production of sorghum × sudangrass hybrids (Sorghum bicolor L. × Sorghum sudanense; SSG) is essential; however, its effects on root sustainability and photosynthetic capacity during the ratooning period are not well defined in a multiple harvests system. The physiological response and root morphology of SSG were investigated under different N application levels during the ratooning period in a two-year field experiment. Treatments were all combinations of two ecotypes (late-flowering, Greenstar; early-flowering, Honeychew) and four N levels (0, 50, 100, 150 kg N ha−1). The total root length, surface area, volume, tips, and dry matter (DM) were significantly influenced by both ecotype and N level, with Greenstar outperforming Honeychew. Specifically, Greenstar’s root length increased by up to three times with reduced N application (50 kg N ha−1), while Honeychew showed significant root length increases only at higher N levels (100 and 150 kg N ha−1). Our data support the conclusion that a low level of N (50–100 kg N ha−1) was the optimal rate for ratooning root sustainability. The findings highlight the critical role of root development in sustaining biomass production and suggest that the late-flowering ecotype, Greenstar, is more suitable for a multiple harvests system with a robust root system.
Effect of Presence of Uni- or Bilateral Thyroid Adenoma on Recovery of Pituitary–Thyroid Axis and Creatinine Concentration in Radioiodine-Treated Cats
Authors: Menzel Anna Paulina , Lin Joanna , Güssow Arne , Patzelt Ve , Bauer Natali , Hazuchova Katarina
Published: September 2024

Radioiodine therapy (RAIT) is the gold standard for treatment of hyperthyroidism in cats. The aim of this study was to evaluate the effect of the presence of uni- or bilateral thyroid adenoma on changes in total thyroxine (TT4), thyroid-stimulating hormone (TSH), and creatinine concentration over a period of 6 to 12 months following RAIT. Fifty-one hyperthyroid cats presented for RAIT between April 2021 and April 2022 were prospectively enrolled. Cats with an increased creatinine concentration (creatinine ≥ 140 µmol/L), renal morphology abnormalities, and suspected thyroid carcinoma were excluded. TT4, TSH, and creatinine were determined before and one week and one, three, six, and twelve months following RAIT. The effects of the re-examination timepoint following RAIT and the presence of uni- or bilateral thyroid adenoma based on technetium-99m scintigraphy on TT4, TSH, and creatinine were analysed by mixed effects modelling. Cats with bilateral adenoma had significantly higher TSH concentrations after RAIT compared to those with unilateral adenoma. TT4 concentration significantly decreased one week (p < 0.001) and again one month following RAIT (p < 0.001). TSH and creatinine concentration significantly increased one month post RAIT (both p < 0.001). As indicated by an increase in TSH concentration, the pituitary–thyroid axis needs a minimum of one month post RAIT to recover from hyperthyroidism-induced suppression, but hypothyroidism necessitating levothyroxine supplementation might not be diagnosed before 6 or even 12 months post RAIT. Although creatinine did not increase significantly after one month post RAIT in this cohort, an increased creatinine concentration was detected at later timepoints in individual cats.
Prevalence of Hypertrophic Cardiomyopathy and ALMS1 Variant in Sphynx Cats in New Zealand
Authors: Seo Joonbum , Loh Yvonne , Connolly David J. , Luis Fuentes Virginia , Dutton Emily , Hunt Hayley , Munday John S.
Published: September 2024

Recently, hypertrophic cardiomyopathy (HCM) in Sphynx cats has been associated with a variant in the gene encoding Alström syndrome protein 1 (ALMS1). The primary aims of this study were to describe the prevalence of HCM in Sphynx cats in New Zealand, and to assess the association between HCM and the ALMS1 variant in this population. In this prospective study, 55 apparently healthy Sphynx cats from registered Sphynx breeders and pet owners in New Zealand were screened by a cardiologist. A total of 42 of these cats had a repeat cardiac examination after median 1.8 years (range: 1.6–2.2). The frequency of the ALMS1 variant was 70.9% (11 homozygous and 28 heterozygous). At the median age of 5.8 years (range: 2.4–13.1), the prevalence of HCM was 40% (20 out of 55 cats). Three cats with HCM died during the study with congestive heart failure. All three cats had focal but extensive myocardial ischemia or infarction at necropsy. The ALMS1 variant was not associated with the HCM diagnosis. In summary, HCM was common in the studied cohort, suggesting Sphynx cats are predisposed to this disease. While the ALMS1 variant was also frequently detected, it was not associated with HCM in this population.
Genomic Analysis of Enterobacter Species Isolated from Patients in United States Hospitals
Authors: Tenover Fred C. , Tickler Isabella A.
Published: September 2024

We analyzed the whole genome sequences (WGS) and antibiograms of 35 Enterobacter isolates, including E. hormaechei and E. asburiae, and the recently described E. bugandensis, E. kobei, E. ludwigii, and E. roggenkampii species. Isolates were obtained from human blood and urinary tract infections in patients in the United States. Our goal was to understand the genetic diversity of antimicrobial resistance genes and virulence factors among the various species. Thirty-four of 35 isolates contained an AmpC class blaACT allele; however, the E. roggenkampii isolate contained blaMIR-5. Of the six Enterobacter isolates resistant to ertapenem, imipenem, and meropenem, four harbored a carbapenemase gene, including blaKPC or blaNDM. All four isolates were mCIM-positive. The remaining two isolates had alterations in ompC genes that may have contributed to the resistance phenotype. Interpretations of cefepime test results were variable when disk diffusion and automated broth microdilution results were compared due to the Clinical Laboratory and Standards Institute use of the “susceptible dose-dependent” classification. The diversity of the blaACT alleles paralleled species identifications, as did the presence of various virulence genes. The classification of recently described Enterobacter species is consistent with their resistance gene and virulence gene profiles.
Applications of Antioxidant Nanoparticles
Authors: Cortesi Rita , Sguizzato Maddalena
Published: September 2024

The regulation of numerous physiological processes is strictly dependent on the production of reactive oxygen species (ROS) [...]
Digital-Focused Approaches in Cancer Patients’ Management in the Post-COVID Era: Challenges and Solutions
Authors: Georgescu Ilona , Dricu Anica , Artene Stefan-Alexandru , Vrăjitoru Nicolae-Răzvan , Barcan Edmond , Tache Daniela Elise , Giubelan Lucian-Ion , Staicu Georgiana-Adeline , Manea (Carneluti) Elena-Victoria , Pană Cristina , Popescu (Purcaru) Stefana Oana
Published: September 2024

The COVID-19 pandemic has significantly accelerated the adoption of telemedicine and digital health technologies, revealing their immense potential in managing cancer patients effectively. This article explores the impact of recent technological developments and widened consumer perspectives on personalised healthcare and patient awareness, particularly in oncology. Smartphones and wearable devices have become integral to daily life, promoting healthy lifestyles and supporting cancer patients through remote monitoring and health management. The widespread use of these devices presents an unprecedented opportunity to transform clinical trials and patient care by offering convenient and accessible means of collecting health data continuously and non-invasively. However, to fully harness their potential, it is crucial to establish standardised methods for measuring patient metrics to ensure data reliability and validity. This article also addresses the challenges of integrating these technologies into clinical practice, such as cost, patient and professional reluctance, and technological oversaturation. It emphasises the need for continuous innovation, the development of robust digital infrastructures, and the importance of fostering a supportive environment to integrate these advancements permanently. Ultimately, the convergence of technological innovation and personalised healthcare promises to enhance patient outcomes, improve quality of life, and revolutionise cancer management in the post-COVID era.
Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions
Authors: Shao Qianqian , Miao Jiawei , Liao Penghui , Liu Tao
Published: September 2024

As an important part of urban terminal delivery, automated guided vehicles (AGVs) have been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding a stochastic programming model into a rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, to handle real-time orders under dynamic demand, an optimization mechanism based on a rolling scheduling framework is proposed, which allows the AGV’s route to be continuously updated. Unlike most VRP models, an open chain structure is used to describe the dynamic delivery path of AGVs. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expected order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on a NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm.
Exploring Facial Somatosensory Distortion in Chronic Migraine: The Role of Laterality and Emotion Recognition—A Cross-Sectional Study
Authors: Taxer Bernhard , von Piekartz Harry , Lauth Wanda , Christova Monica , Leis Stefan
Published: September 2024

Background: In addition to being highly distressing, chronic migraine headaches are a major socioeconomic challenge. Like other pain syndromes, migraine headaches are associated with psychological and physiological impairments, including sensorimotor and somatosensory deficits. Aim: This study aims to explore whether patients with high-frequency or chronic migraine differ from a healthy population in the areas of laterality recognition (LAT) and facial emotion recognition (FER) and whether there are correlations between these areas and central sensitization of pain and psychological components like stress, depression, anxiety, and alexithymia. Methods: Using a cross-sectional design, individuals with high-frequency or chronic (ICHD classification) migraine (migraine group MG = 45) and healthy individuals (control group CG = 25) were studied using LAT testing (hand, neck, and face); FER testing; and questionnaires, including the Central Sensitization Inventory (CSI) and the Toronto Alexithymia Scale (TAS-20). Results: Data from 70 participants were collected for analysis. Statistically significant differences were found only in the assessment of central sensitization (p < 0.001). Weak to moderate monotonic correlations were found for the MG, especially between alexithymia detection (TAS-20) and facial emotion recognition (FER test). Discussion: The methodological procedure and its accompanying challenges can be seen as limitations of this study. The lack of significant effects must be mentioned, but the selection of the collected questionnaires, the uniform diagnostics, and the statistical processing of a large amount of data represent methodological strengths. Conclusion: The CSI and the TAS-20 could be used in combination with FER to assess chronic migraine. Implementing the described sensorimotor parameters as a therapeutic intervention requires further investigation.
The Impact of Job Insecurity on Miner Safety Behavior—A Study Based on SEM and fsQCA
Authors: Lei Ting , Li Jizu , Yan Yong , Guo Yanyu
Published: September 2024

The intelligent transformation of coal mines is one of the current trends in developing China’s coal mining industry. To explore the impact of miners’ insecurity on their safety behavior under this trend, miners’ psychological resilience was introduced as the mediating variable, and team safety climate was used as the moderating variable to conduct a questionnaire survey of frontline miners. The data analysis was carried out using descriptive statistics, correlation analysis, structural equation modeling (SEM), and the fsQCA method to explore the impact of job insecurity on miners’ risk behavior through psychological resilience from the dimensions of job loss insecurity, job performance insecurity, and interpersonal insecurity. The results show that the sense of insecurity of the miners has a significant negative correlation with security behavior and a significant negative correlation with psychological toughness; miners’ psychological resilience plays an intermediary role in the correlation between job loss insecurity and miners’ risk behavior. Meanwhile, team safety climate has a significant moderating effect on the relationship between job insecurity and psychological resilience, as well as the relationship between psychological resilience and safety behavior; that is, a good team safety climate can effectively reduce the negative impact of job insecurity brought about by the transformation and upgrading of coal mines.
The Use of a Laser Diffractometer to Analyze the Particle Size Distribution of Selected Organic Soils
Authors: Straż Grzegorz , Szostek Małgorzata
Published: September 2024

This study was conducted to verify the usefulness of the laser diffractometer method for determining the particle size distribution of selected organic soils from the Podkarpacie region in Poland. The soil selected for this research represented three main classification groups, namely, low-organic, medium-organic and high-organic soil, in accordance with the standard criterion. Particle size distribution was determined using two types of laser diffractometers: the Helos laser diffractometer manufactured by Sympatec GmbH (Clausthal-Zellerfeld, Germany) and the laser particle size analyzer Analysette 22 MicroTech plus manufactured by Fritsch GmbH (Idar-Oberstein, Germany). The standard mechanical and sedimentation methods, which are perfect for testing mineral soils, are not applicable to organic soils; therefore, a serious problem was found and examined. A reference method that could verify the test results obtained using the laser diffractometer methods was required. After analyzing the literature, the hydrometric (sedimentation) method was adopted as the reference method. Currently, there are no reliable and fully verified methods for testing soils with such a complex skeleton structure, and the resources, standards and guidelines concerning the issues discussed are extremely limited; therefore, new research methods are being sought to fill this gap, and this work is a step in this direction. The results of the conducted studies and analyses have shown that laser diffractometry methods can be useful for determining the particle size distribution of organic soils, but to a limited extent, depending mainly on the quantity of organic substances. The highest agreement was obtained by comparing the results of the sedimentation method with those obtained using the diffractometer analyzer Analysette 22 in the group of highly organic soils.
Long-Term Amendment with Sewage Sludge: Effects on Nutrient Value and Trace-Metal Content in Different Parts of Maize Plants
Authors: Camps-Sagué Francesc , Bosch-Serra Àngela Dolores , Cifuentes-Almeida Alicia Daniela , Boixadera-Bosch Montserrat Maria , Domingo-Olivé Francesc
Published: September 2024

Agricultural soils play a key role in the achievement of a circular nutrient economy. The use of sewage sludges as fertilizers is important for such an achievement, assisting in the maintenance of soil health and nutritional crop value. This study was established, after 23 years of a fertilization experiment, in calcareous soil under a maize monoculture. The treatments included mineral fertilization as a control (MIN, 225 kg N ha−1) and two sludge treatments, where doses followed a threshold sludge nitrogen criterion (SNC, 170 kg org-N ha−1) or a threshold soil phosphorus criterion (SPC; when the soil Olsen-P value exceeded 40–60 kg P ha−1, the sludge application was stopped). A detailed study was performed on Cd, Cu, F, Mn, Pb, and Zn soil extractable with diethylenetriaminepentaacetic acid (DTPA), as well as the nutrient and heavy metal concentration of different fractions of the maize plant (grain, cob, and the rest of the plant). Extractions were also quantified. No biomass-yield differences were observed in the different parts of the maize plant in the year of sampling. Sludges increased the soil DTPA extraction of Cd, Cu, Fe, and Zn and diminished Mn extraction, without differences in extractable Pb. The SNC, when compared with MIN, showed increased P cob concentrations, and in grain, it showed increased Fe, Cr, and Co concentrations. The SPC figures of the studied parameters were, in general, between both treatments (MIN and SNC), although Cr extractions in grain diminished vs. SNC. Based on the results, the SPC can be recommended as it also avoids excessive available-P build up.
A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework
Authors: Fera Fation , Spandonidis Christos
Published: September 2024

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions.
Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review
Authors: Wyatt Lucie S. , van Karnenbeek Lennard M. , Wijkhuizen Mark , Geldof Freija , Dashtbozorg Behdad
Published: September 2024

This review provides an overview of explainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between 25 March and 14 April 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.
A Literature Review of Adhesive Systems in Dentistry: Key Components and Their Clinical Applications
Authors: Bourgi Rim , Kharouf Naji , Cuevas-Suárez Carlos Enrique , Lukomska-Szymanska Monika , Haikel Youssef , Hardan Louis
Published: September 2024

The central aim of adhesive dentistry is to improve the compatibility between current adhesives and a range of substrates by employing diverse application techniques. Therefore, the overarching objective of this review is to offer a comprehensive analysis of dentin bonding systems, starting with an introduction to adhesion and a detailed overview of enamel and dentin structures, their histology, and the impact of dentin structure on resin–dentin bonding. It covers the mechanisms of resin–dentin bonding, including resin adhesive application, bonding mechanisms, and factors influencing efficacy. Further, this review explores the composition of resin adhesive systems, including acidic components, cross-linking monomers, solvents, and other critical elements. It also examines various adhesive strategies—etch-and-rinse, self-etch, and universal adhesives—highlighting their applications and advantages. The review extends to clinical applications of dental adhesion, including direct restorations, indirect restorations, and immediate dentin sealing (IDS), demonstrating the practical implications of adhesive systems in enhancing restoration longevity and performance. In conclusion, despite significant advancements, no gold-standard method for optimal adhesion exists. Each adhesive system has distinct strengths and limitations. The review emphasizes the importance of evaluating methods for achieving durable adhesion and staying current with technological advancements in adhesive systems. Summary: This review provides a thorough analysis of dentin bonding systems, delving into the structures and bonding mechanisms of both enamel and dentin. By exploring various adhesive systems and their components, it highlights the ongoing challenges in achieving optimal resin–dentin adhesion. The review also addresses the clinical applications of dental adhesion, including direct restorations, indirect restorations, and immediate dentin sealing (IDS), illustrating how different adhesive techniques impact clinical outcomes. It underscores the necessity for continuous innovation and assessment of adhesive systems to enhance long-term bonding effectiveness in clinical practice.
Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
Authors: Feng Jianyu , Zhou Minghao , Chen Chuan , Wang Qidong , Cao Liqiang
Published: September 2024

With the development of artificial intelligence (AI) and high-performance computing (HPC), the microelectronic industry is challenged with increased device integration density. Chiplet architecture can break through a variety of limitations on the system-on-chip (SoC) to create a high-computility system. However, chiplet heterogeneous integration suffers from high heat flux and serious thermal interaction problems. The factors affecting thermal interaction are not clear. In this paper, a collective parameter model and a distribution parameter model are developed to clarify the optimization method to mitigate thermal interaction. The trends predicted by the parameter model are consistent with the finite element method (FEM) simulation results. Furthermore, to cool the chiplets, a thermal test vehicle is designed and fabricated, and the cooling performance of the test vehicle with different flow rates, different TIMs (Thermal Interfacial Materials) (DOW5888 vs. liquid metal), and different heat modes is experimentally investigated. Compared with DOW5888, the utilization of liquid metal TIM can mitigate thermal interaction by 56% and 76% at flow rates of 0.2 L/min and 0.8 L/min, respectively. Consequently, at a temperature rise of 60 °C and a flow rate of 0.8 L/min, using liquid metal TIM can achieve heat fluxes of 330 W/cm2 and 520 W/cm2 for two chiplets, respectively.
Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network
Authors: Chang Yuchen , Zong Mengya , Dang Yutian , Wang Kaiping
Published: September 2024

Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, previous research primarily focuses on local spatial dependencies, struggling to capture implicit global information. We propose a spatial modeling module that leverages a dynamic global attention network (DGAN) to capture dynamic global information from all-pair interactions, intricately fusing prior knowledge from the input graph with a graph convolutional network. In the temporal dimension, we design a temporal modeling module tailored to navigate the challenges of both long-term and recent-term temporal passenger flow patterns. This module consists of series decomposition blocks and locality-aware sparse attention (LSA) blocks to incorporate multiple local contexts and reduce computational complexities in long sequence modeling. Experiments conducted on both simulated and real-world datasets validate the exceptional predictive performance of our proposed model.
Structure and Principles of Operation of a Quaternion VLSI Multiplier
Authors: Cariow Aleksandr , Naumowicz Mariusz , Handkiewicz Andrzej
Published: September 2024

The paper presents the original structure of a processing unit for multiplying quaternions. The idea of organizing the device is based on the use of fast Hadamard transform blocks. The operation principles of such a device are described. Compared to direct quaternion multiplication, the developed algorithm significantly reduces the number of multiplication and addition operations. Hardware implementations of the developed structure, in FPGA and ASIC, are presented. The FPGA blocks were implemented in the Vivado environment. The ASICs were designed using 130nm technology. The developed scripts in VHDL are available in the GitHub repository.
The Potential of Indole Alkaloids in Bone Health and Osteoporosis Management
Authors: Caruso Anna , Caira Virginia , El-Kashef Hussein , Saturnino Carmela
Published: September 2024

Indole alkaloids, a class of plant-derived nitrogen-containing compounds, have emerged as promising candidates for osteoporosis treatment. Their favorable biocompatibility profile demonstrated efficacy in preclinical models, and low reported toxicity make them attractive alternatives to existing therapies. This review focuses on the therapeutic potential of specific indole alkaloids, including vindoline, rutaecarpine, harmine, and its derivatives, in promoting bone health and managing osteoporosis.
Data Augmentation in Histopathological Classification: An Analysis Exploring GANs with XAI and Vision Transformers
Authors: Rozendo Guilherme Botazzo , Garcia Bianca Lançoni de Oliveira , Borgue Vinicius Augusto Toreli , Lumini Alessandra , Tosta Thaína Aparecida Azevedo , Nascimento Marcelo Zanchetta do , Neves Leandro Alves
Published: September 2024

Generative adversarial networks (GANs) create images by pitting a generator (G) against a discriminator (D) network, aiming to find a balance between the networks. However, achieving this balance is difficult because G is trained based on just one value representing D’s prediction, and only D can access image features. We introduce a novel approach for training GANs using explainable artificial intelligence (XAI) to enhance the quality and diversity of generated images in histopathological datasets. We leverage XAI to extract feature information from D and incorporate it into G via the loss function, a unique strategy not previously explored in this context. We demonstrate that this approach enriches the training with relevant information and promotes improved quality and more variability in the artificial images, decreasing the FID by up to 32.7% compared to traditional methods. In the data augmentation task, these images improve the classification accuracy of Transformer models by up to 3.81% compared to models without data augmentation and up to 3.01% compared to traditional GAN data augmentation. The Saliency method provides G with the most informative feature information. Overall, our work highlights the potential of XAI for enhancing GAN training and suggests avenues for further exploration in this field.
Speed Control for PMSM with Fast-Terminal Super-Twisting Sliding Mode Controller via Extended State Disturbance Observer
Authors: Wang Hengqiang , Zhang Guangming , Liu Xiaojun
Published: September 2024

Since robustness is only maintained on the sliding-mode surface, sliding mode control is inherently non-globally stable. Therefore, reducing the time to reach the sliding mode is crucial for enhancing sliding mode robustness. To improve the performance of conventional super-twisting reaching law (CSTRL) further, we propose a fast-terminal super-twisting reaching law (FTSTRL) designed to improve the quality of sliding mode control. This approach incorporates a terminal term and an exponential term with the CSTRL to ensure rapid convergence. The effectiveness of the designed FTSTRL is validated by comparing it to the CSTRL and a new sliding mode reaching law, demonstrating its superior performance. Finally, integrated square error (ISE), integrated time square error (ITSE), integrated absolute error (IAE), and integrated time absolute error (ITAE) are employed for detailed comparative and quantitative analyses of the simulation results.
Structural Characteristics of the Pine Stands on Degraded Lands in the South-East of Romania, in the Context of Climate Changes
Authors: Cristinel Constandache , Ciprian Tudor , Popovici Laurențiu , Radu Vlad , Crișan Vlad , Dincă Lucian Constantin
Published: September 2024

The present research was carried out in stands of Scots pine and black pine, pure or mixed with deciduous trees, installed on degraded lands from the Curvature Subcarpathian area, Romania, in a representative network of permanent research plots and followed the analysis of the structural diversity and stability indicators of these stands at different ages and in different conditions of degraded lands. The relationships between the quantitative variables with reference to the structure were established by analyzing the significance of the Pearson correlation coefficient (r) and also including datasets of slenderness indexes, which were classed into three domains of vulnerability to abiotic factors (like wind and snow). The compositional diversity of pine stands (pure or mixed with deciduous ones) is different in relation to age and is correlated with the structural diversity. The obtained correlation coefficients (r Pearson) express very strong and significant relationships between biometric parameters (h x Dbh, h x Lc%, Dc x Dbh, and Lc% x Dbh) of the structural diversity (r = 0.800–0.930), which is important for the analysis of the stability and vulnerability of pine forests. The strong correlation between the analyzed variables expresses a weak vulnerability to the action of harmful abiotic factors and the increase in the stability and resilience of the studied stands, especially of over 50 years old. In the old pine stands, the low-vulnerability domain (I < 0.80) is the best represented one, with an average of 64.01% from the total number of trees. At this age, trees with DBH > 22 cm fall into the low-vulnerability category. The explanation is that the stands were affected in their youth by the action of snow and wind, which, combined with the silvotechnical works performed, led to their compositional and structural diversification and increased stability. The young (<45 years) and pure-pine stands with higher consistency (>0.8) and even-aged structure are the most vulnerable to abiotic factors due to the fact that a large number of trees are passing gradually into the higher cenotic classes.