bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the Dynamic Change and Driving Force of Isolated Marsh Wetland in Sanjiang Plain, Northeast China
Authors: Zhang Shuangwei , Liu Jiping , Chen Yanhui , Pei Wenhan , Xuan Lihui , Wang Yingpu
Published: November 2024

Isolated marsh wetlands are crucial for maintaining regional hydrological connectivity and biological contiguity. The Sanjiang Plain is the most typical area of marsh wetland change in China. A large number of isolated marshy wetlands have been formed here due to natural and anthropogenic influences. However, there have been few quantitative studies of the dynamics of isolated marsh wetlands and their drivers at the regional scale. This study used Landsat series image data provided by the Google Earth Engine. Through field surveys, combined with visual interpretation and the Random Forest Algorithm, the distributional changes in isolated marsh wetlands, non-isolated marsh wetlands, and natural marsh wetlands in the Sanjiang Plain from 1975 to 2020 were identified and extracted. The dynamic change characteristics as well as the patch importance values (dIIC) of isolated and non-isolated marsh wetlands were analyzed using the dynamic degree, standard deviation ellipse model, and the integral index of connectivity (IIC). Finally, the driving factors and interactions affecting the distribution of isolated marsh wetlands were analyzed by the Geodetector model. The results show that (1) the temporal dynamics of the three types of marsh wetlands are less than 0 from 1975 to 2020, and the temporal dynamics of isolated marsh wetlands are the largest. The lost wetlands were concentrated in the northeastern and east–central regions of the Sanjiang Plain. The center of mass of the standard deviation ellipse moved from northeast to southwest, and the isolated marsh wetlands moved the most. (2) The IIC of non-isolated marsh wetlands and natural marsh wetlands decreased and then increased, and the non-isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern and east–central regions. On the other hand, the IIC of isolated marsh wetlands increased and then decreased, and the isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern region. (3) The elevation is the most important driving factor affecting the distribution of isolated marsh wetlands in the Sanjiang Plain. The interaction between the driving factors had a significantly higher effect on the distribution of isolated marsh wetlands than that of a single driving factor, with the strongest interaction between aspect and elevation in 1975, 1986, 2000, and 2010, and between aspect and slope in 2020.
Facilitated Forest Restoration Using Pioneer Seed Dispersers in Madagascar: The Example of Microcebus spp.
Authors: Ganzhorn Jörg , Andriambeloson Jean-Basile , Atsalis Sylvia , Behrendt Lis , Blanco Marina , Bollen An , Carrière Stéphanie , Chikhi Lounès , Dammhahn Melanie , Donati Giuseppe , Eppley Timothy , Ernest Refaly , Giertz Peggy , Goodman Steven , Hending Daniel , Holst Friederike , Hyde Roberts Sam , Irwin Mitchell , Lahann Petra , Louis Edward , Radespiel Ute , Rakotondranary S. , Ramanamanjato Jean-Baptiste , Ramananjato Veronarindra , Randriatafika Faly , Ratovonamana Yedidya , Razafindratsima Onja , Salmona Jordi , Schwab Dorothea , Tsagnangara Cedric
Published: November 2024

The concept of “facilitated restoration” aims at native biodiversity reinstatement with the help of animal seed dispersers attracted by fruiting trees. Yet, large-crowned trees will have to develop in the early stages of restoration; therefore, seed dispersal provided by small generalist mammals and birds that use rapidly growing herbs, shrubs, and small trees at early stages of forest succession would accelerate biodiversity restoration. Due to the elusive lifestyle of these small animals, it is unclear what species can contribute to the early stages of this process. Using the primate genus Microcebus (adult body mass about 60 g) as an example, we illustrate that these small generalists are possible seed dispersers in the early stages of forest restoration, not yet used by larger frugivores. We show that Microcebus spp. dispersed more seeds from herbs, shrubs, and small trees than large frugivorous primate species. These plants tend to have smaller seeds than large tree species and are often pioneer species not considered in forest restoration projects. Facilitating the colonization of restoration plots by generalist small seed dispersers that use shrubby habitats may improve plant diversity by adding a more natural sequence of successional stages towards mature forests in Madagascar and elsewhere in the tropics.
Study on the Interaction Effects of Landscape Pattern on the Synergistic Trade-Offs of Ecosystem Services Based on Multi-Model Fusion: A Case Study of Chengdu-Chongqing Economic Circle
Authors: Jin Yuhao , Li Yuanhang , Shen Weiping , Zhu Hengkang
Published: November 2024

A deep understanding of the spatiotemporal changes in ecosystem services (ESs) under the influence of urbanisation, as well as clarifying the trade-offs and synergies between different services and their driving factors, is crucial for sustainable regional development and the formulation of rational urban expansion policies. Dramatic changes in landscape patterns, driven by the interplay of human activities and natural processes, can have profound effects on regional ESs. Existing research primarily discusses the synergistic trade-offs between ESs, with less focus on the interactions among different landscape patterns and the synergies among ESs. In the present study, we established a multi-model fusion method for ES analysis (PLUS-InVEST-Trade-offs/Synergies-Geographical Detectors (GDs)) to explore the synergistic trade-offs of ESs and their driving factors in the Chengdu-Chongqing Economic Circle from an urban agglomeration perspective. Our findings indicated the following. (1) The distribution of synergistic/trade-offs relationships among ESs exhibited spatial variability. The varying responses of different urban clusters to these policies, along with their unique topography and landforms, are the reasons behind the differences in the synergistic/trade-offs relationships of ESs among these urban clusters. (2) In the Chengdu-Chongqing Economic Circle, the driving factors of the synergistic/trade-offs effects among ESs displayed spatial differentiation. In a certain range, the degree of landscape agglomeration interacts with the degree of landscape fragmentation to promote synergistic/trade-offs effects of ESs. Our findings will provide a new analytical perspective for policymakers in the region and serve as a valuable reference for formulating targeted policies in different sub-regions.
Patterns of C1-Inhibitor Plasma Levels and Kinin–Kallikrein System Activation in Relation to COVID-19 Severity
Authors: Berra Silvia , Parolin Debora , Suffritti Chiara , Folcia Andrea , Zanichelli Andrea , Gusso Luca , Cogliati Chiara , Riva Agostino , Gidaro Antonio , Caccia Sonia
Published: November 2024

Background: Although more than four years have passed since the pandemic began, SARS-CoV-2 continues to be of concern. Therefore, research into the underlying mechanisms that contribute to the development of the disease, especially in more severe forms, remains a priority. Sustained activation of the complement (CS), contact (CAS), and fibrinolytic and kinin–kallikrein systems (KKS) has been shown to play a central role in the pathogenesis of the disease. Since the C1 esterase inhibitor (C1-INH) is a potent inhibitor of all these systems, its role in the disease has been investigated, but some issues remained unresolved. Methods: We evaluated the impact of C1-INH and KKS on disease progression in a cohort of 45 COVID-19 patients divided into groups according to disease severity. We measured plasma levels of total and functional C1-INH and its complexes with kallikrein (PKa), reflecting KKS activation and kallikrein spontaneous activity. Results: We observed increased total and functional plasma concentrations of C1-INH in COVID-19 patients. A direct correlation (positive Spearman’s r) was observed between C1-INH levels, especially functional C1-INH, and the severity of the disease. Moreover, a significant reduction in the ratio of functional over total C1-INH was evident in patients exhibiting mild to intermediate clinical severity but not in critically ill patients. Accordingly, activation of the KKS, assessed as an increase in PKa:C1-INH complexes, was explicitly observed in the mild categories. Conclusions: Our study’s findings on the consumption of C1-INH and the activation of the KKS in the less severe stages of COVID-19 but not in the critical stage suggest a potential role for C1-INH in containing disease severity. These results underscore the importance of C1-INH in the early phases of the disease and its potential implications in COVID-19 progression and/or long-term effects.
Advancements and Strategies in Robotic Planning for Knee Arthroplasty in Patients with Minor Deformities
Authors: Capece Giacomo , Andriollo Luca , Sangaletti Rudy , Righini Roberta , Benazzo Francesco , Rossi Stefano Marco Paolo
Published: November 2024

Knee arthroplasty, commonly performed to treat osteoarthritis, necessitates precise surgical techniques for optimal outcomes. The introduction of systems such as the Persona Knee System (Zimmer Biomet, Warsaw, IN, USA) has revolutionized knee arthroplasty, promising enhanced precision and better patient outcomes. This study investigates the application of robotic planning specifically in knee prosthetic surgeries, with a focus on Persona Knee System prostheses. We conducted a retrospective analysis of 300 patients who underwent knee arthroplasty using the Persona Knee System between January 2020 and November 2023, including demographic data, surgical parameters, and preoperative imaging. Robotic planning was employed to simulate surgical procedures. The planning process integrated preoperative imaging data from a specific program adopted for conducting digital preoperative planning, and statistical analyses were conducted to assess correlations between patient characteristics and surgical outcomes. Out of 300 patients, 85% presented with minor deformities, validating the feasibility of robotic planning. Robotic planning demonstrated precise prediction of optimal arthroplasty sizes and alignment, closely aligning with preoperative imaging data. This study highlights the potential benefits of robotic planning in knee arthroplasty surgeries, particularly in cases with minor deformities. By leveraging preoperative imaging data and integrating advanced robotic technologies, surgeons can improve precision and efficacy in knee arthroplasty. Moreover, robotic technology allows for a reduced level of constraint in the intraoperative choice between Posterior-Stabilized and Constrained Posterior-Stabilized liners compared with an imageless navigated procedure.
Anatomical Analysis of Bicipital Groove and Its Spur Formation Using 3D-CT: A Retrospective Observational Study
Authors: Song Hyun Seok , Kim Hyungsuk
Published: November 2024

Concomitant long head of biceps (LHB) pathologies commonly occur with rotator cuff tears, but LHB problems are often underestimated. There is a lack of studies on the correlation between bicipital groove morphology and biceps pathology, as well as the significance of bony spurs around the groove. This study analyzed the anatomical parameters of the bicipital groove and spur formation using 3D-CT in 111 patients. Biceps spurs were evaluated using 3D and 2D-CT images, and patients were grouped by age (below and above 55 years). The detection rate of biceps spur was higher with 3D-CT (54.0%) than with 2D-CT (42.3%). Spur incidence was significantly greater in older patients (60.3% vs. 45.8%). The average groove width was narrower in older adults (p = 0.006) and larger in men compared to women (p = 0.000). The average spur height was also greater in men (p = 0.039). Lateral spurs were the most common type that was observed. This study highlights the differences in bicipital groove morphology and spur formation by age and gender, with 3D-CT being more effective in detecting biceps spurs.
Investigation of Tribological Performance of Ti:WS2/PFPE Composite Lubricating System Under Proton Radiation
Authors: Liu Jian , Yan Zhen , Hao Junying , Liu Weimin
Published: November 2024

The tribological performance of PFPE oil and the Ti:WS2/PFPE composite lubricating system with different oil amounts was investigated under a proton radiation (PR) irradiation environment. After PR irradiation, PFPE molecules occurred during cross-linking and a polymerization reaction and formed a volatile small molecular compound, which deteriorates the tribological performance of the Ti:WS2/PFPE system. The tribological properties of the Ti:WS2/PFPE system rely strongly on oil amount. For an unirradiated Ti:WS2/PFPE system, the amorphous layer of transfer film near the sliding contact area was converted into a well-defined crystalline WS2 layer with a (002) plane induced by the friction process. After PR irradiation, the transfer film became thicker and showed a wholly amorphous structure due to the difficulty in preventing the entrance of O and showed no reorientation with induced friction.
6,8-Dibromo-11H-indeno[1,2-b]quinolin-11-one
Authors: Kovrizhina Anastasia R. , Tolegen Dauren , Pavlov Dmitry I. , Potapov Andrei S. , Khlebnikov Andrei I.
Published: November 2024

We report the synthesis of the new compound 6,8-dibromo-11H-indeno[1,2-b]quinolin-11-one, which presents an important type of nitrogen-containing heterocycles and is a useful intermediate product in organic synthesis. The structure of the compound was confirmed by the single crystal X-ray diffraction. Molecular docking analysis revealed that 6,8-dibromo-11H-indeno[1,2-b]quinolin-11-one may effectively intercalate with DNA. The synthesized indenoquinoline derivative thus represents a promising lead compound for developing targeted anticancer and anti-inflammatory drugs.
Unveiling the Significance of Graphene Nanoplatelet (GNP) Localization in Tuning the Performance of PP/HDPE Blends
Authors: Salehiyan Reza , El-Samak Ali A. , Kamkar Milad , Erfanian Elnaz , Hodge Stephen A. , Sundararaj Uttandaraman , McNally Tony
Published: November 2024

High-density polyethylene (HDPE) and polypropylene (PP) blends are widely used in industries requiring mechanically durable materials, yet the impact of processing parameters on blend performance remains underexplored. This study investigates the influence of blending sequence and screw speed on the properties of blends of HDPE and PP filled with 1.25 wt.% graphene nanoplatelets (GNPs). Changes in crystallization behaviour, tensile strength, and viscoelastic responses with blending sequence are studied. The addition of GNP increases the crystallization temperature (Tc) of PP in the PE/PP blend by 4 °C when GNP is pre-mixed with PE to form (PE+GNP)/PP blends. In contrast, when GNP is pre-mixed with PP to create (PP+GNP)/PE blends, the Tc of PP rises by approximately 11 °C, from 124 °C for the neat PE/PP blend to 135 °C. On the other hand, the Tc of PE remains unchanged regardless of the blending sequence. XRD patterns reveal the impact of blending regime on crystallinity, with GNP alignment affecting peak intensities confirming the more efficient interaction of GNPs with PP when premixed before blending with PE, (PP+GNP)/PE. Tensile moduli are less sensitive to the changes in processing, e.g., screw speed and blending sequence. In contrast, elongation at break and tensile toughness show distinct variations. The elongation at the break of the (PP+GNP)/PE blend decreases by 30% on increasing screw speed from 50 to 200 rpm. Moreover, the elongation at the break of (PE+GNP)/PP prepared at 100 rpm is ~40% higher than that of the (PP+GNP)/PE. (PE+GNP)/PP displays a ‘quasi-co-continuous’ morphology linked to its higher elastic modulus G′ compared to that of the (PP+GNP)/PE blend. This study highlights the importance and correlation between processing and blend properties, offering insights into fine-tuning polymer composite formulation for optimal performance.
Utilization of Low-Rank Coal and Zn-Bearing Dusts for Preparation of K, Na-Embedded Porous Carbon Material and Metallized Pellets by Synergistic Activation and Reduction Process
Authors: Wang Dingzheng , Zhu Deqing , Yang Jinlin , Ma Shaojian
Published: November 2024

A technology was developed for managing Zn-bearing dust, facilitating the recycling of hazardous solid waste and the production of porous carbon materials. In the one-step process, Zn-bearing dusts were employed not only as raw materials to prepare reduced Zn-bearing dust pellets but also as activators to prepare K, Na-embedded activated carbon. In the process, the Fe, C, Zn, K, and Na in the dusts were rationally utilized. Under optimal conditions, the reduced pellets and porous carbon materials were simultaneously produced and characterized using XRD, SEM/EDS, FTIR, and adsorption of nitrogen techniques. The results indicated that the reduced pellets, with low levels of harmful elements and high iron grade and strength, could be directly used as burden for enhancing blast furnace operation without additional agglomeration. Meanwhile, the K and Na-embedded porous carbon material demonstrated superior SO2 and NO adsorption capacities compared to the commercial activated carbon, making it suitable for purifying SO2 and NO-bearing flue gas. The hazardous solid wastes were effectively used to treat flue gases through this technology. The mechanism in the synergistic reduction and activation process was elucidated. The coupling effect between the reduction reactions of Fe2O3, Fe3O4, FeO, MgFe2O4, CaFe2O4, ZnFe2O4, KFeO2, and NaFeO2 in the dusts and activation reaction of C in the coal promoted the synchronous reduction and activation process.
An Analysis of the Influence of DSC Parameters on the Measurement of the Thermal Properties of Phase-Change Material
Authors: Gao Maotiao , Zhao Shiqi , Yang Hongjun , Wu Xuehong , Xiao Yingjie
Published: November 2024

A differential scanning calorimeter (DSC) is widely used for measuring the thermal properties of phase-change materials (PCMs). Optimizing test conditions based on material characteristics is essential for accurate results. This study investigates the effects of experimental parameters, including sample mass, heating rate, measurement modes, and atmosphere flow rate, on the phase-change enthalpy and phase-change temperature results. The findings indicate that variations in sample mass and heating rate lead to significant changes in phase-change temperatures, while an increase in purge gas flow rate reduces the phase-change enthalpy of the PCM. Based on the measurements, this study optimizes the DSC parameters and provides a reference for the accurate measurement of paraffin-based phase-change materials.
Effect of Natural Fiber and Biomass on Acoustic Performance of 3D Hybrid Fabric-Reinforced Composite Panels
Authors: Nazari Shabnam , Ivanova Tatiana Alexiou , Mishra Rajesh Kumar , Müller Miroslav , Akhbari Mehdi , Hashjin Zohreh Esfahani
Published: November 2024

This research investigated the sound insulation performance of 3D woven hybrid fabric-reinforced composites using natural fibers, such as jute, along with E-glass and biomass derived from agro-waste, e.g., coffee husk and waste palm fiber. The composites made from pure E-glass, pure jute, and hybrid glass–jute configurations were tested for sound absorbance at frequencies of 1000 Hz and 10,000 Hz. A sound insulation chamber was used for measuring the sound reduction levels. Results show that the sound insulation performance of the panels was remarkably enhanced with composites containing natural fiber reinforcements. The jute-based composites provided the maximum insulation of sound, with waste palm fiber fillers in particular. At a frequency of 10,000 Hz, a noise reduction reaching 44.9 dB was observed. The highest sound absorption was observed in the 3D woven jute composites with the additive of waste palm fiber, which outperformed the other samples. When comparing the effect of coffee husk and palm fiber as biomass fillers, both exhibited notable improvements in sound insulation, but the palm fiber generally performed better across different samples. Although panels containing palm fiber additives appeared to reduce sound more than those containing coffee husk, statistical analysis revealed no significant difference between the two, indicating that both are efficient and eco-friendly fillers for soundproofing applications. One-way analysis of variance (ANOVA) confirmed the significance of the effect of reinforcing structures and biofillers on acoustic performance. This study demonstrated the possibility of using sustainable green materials for soundproofing applications within various industries.
Development and Characterization of Sustainable Cement-Free Controlled Low Strength Material Using Titanium Gypsum and Construction Waste Soil
Authors: Wu Yunfei , Geng Jian , Zhu Haoze , Jin Chen , Kang Nengneng
Published: November 2024

This study investigates the utilization of titanium gypsum (TG) and construction waste soil (CWS) for the development of sustainable, cement-free Controlled Low Strength Material (CLSM). TG, combined with ground granulated blast furnace slag, fly ash, and quicklime, serves as the binder, while CWS replaces natural sand. Testing thirteen mixtures revealed that a CWS replacement rate of over 40% controls bleeding below 5%, with a water-to-solid ratio between 0.40 and 0.46, ensuring flowability. Higher TG content reduces flowability but is crucial for strength due to its role in forming a crystalline network. Compressive strength decreases with higher TG and water-to-solid ratio, while 3–5% quicklime provides a 56 day strength below 2.1 MPa. Higher CWS reduces expansion, and TG content between 60% and 70% minimizes volume changes. XRD and SEM analyses underscore the importance of controlling TG and quicklime content to optimize CLSM’s mechanical properties, highlighting the potential of TG and CWS in creating low carbon CLSM.
Bond–Slip Behavior of High-Strength Stainless Steel Wire Mesh in Engineered Cementitious Composites: Numerical and Theoretical Analysis
Authors: Zou Xuyan , Zhang Tao , Li Ziyuan , Zhu Juntao , Li Ke , Peng Minghao
Published: November 2024

This study introduces high-strength non-prestressed steel strands as reinforcement materials into Engineered Cementitious Composites (ECCs) and developed a novel high-strength stainless-steel-strand-mesh (HSSWM)-reinforced ECC with enhanced toughness and corrosion resistance. The bonding performance between HSSWM and an ECC is essential for facilitating effective cooperative behavior. The bond behavior between the HSSWM and ECC was investigated through theoretical analysis. A local bond–slip model was proposed based on the average bond–slip model for HSSWM and ECCs. The results indicated that the local bond–slip model provided a more accurate analysis of the bonding performance between HSSWM and the ECC compared to the average bond–slip model. The effects of the ECC’s tensile strength, steel strand diameter, and transverse strand spacing on local bond–slip mechanical behavior were investigated through FEA. The results showed that the local bond–slip model and FE results aligned well with the experimental data. Additionally, the distribution of bond stress between the HSSWM and the ECC was analyzed using the micro-element method based on the local bond–slip model. A prediction model for the critical anchorage length and bond capacity of HSSWM in the ECC was established, and the accuracy of the model was verified.
A Survey of Planar Underactuated Mechanical System
Authors: Huang Zixin , Yu Chengsong , Zeng Ba , Gong Xiangyu , Zhou Hongjian
Published: November 2024

Planar underactuated mechanical systems have been a popular research issue in the area of mechanical systems and nonlinear control. This paper reviews the current research status of control methods for a class of planar underactuated manipulator (PUM) systems containing a single passive joint. Firstly, the general dynamics model and kinematics model of the PUM are given, and its control characteristics are introduced; secondly, according to the distribution position characteristics of the passive joints, the PUM is classified into the passive first joint system, the passive last joint system, and the passive intermediate joint system, and the analysis and discussion are carried out in respect to the existing intelligent control methods. Finally, in response to the above discussion, we provide a brief theoretical analysis and summarize the challenges faced by PUM, i.e., uncertainty and robustness of the system, unified control methods and research on underactuated systems with uncontrollable multi-passive joints; at the same time, the practical applications have certain limitations that need to be implemented subsequently, i.e., anti-jamming, multi-planar underactuated robotic arm co-control and spatial underactuated robotic arm system development. Aiming at the above challenges and problems in the control of PUM systems, we elaborate on them in points, and put forward the research directions and related ideas for future work, taking into account the contributions of the current work.
Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm
Authors: Hsieh Nan-Kai , Yu Tsung-Yu
Published: November 2024

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions.
Multidimensional Inequality Metrics for Sustainable Business Development
Authors: Ahelegbey Daniel Felix , Giudici Paolo
Published: November 2024

This paper introduces a novel methodology for measuring and decomposing multidimensional inequality, allowing for the breakdown of overall inequality into contributions from distinct dimensions. This approach offers valuable insights for sustainable business development by identifying economic sectors where Environmental, Social, or Governance (ESG) practices are most unequal and thus may benefit from policy intervention. Additionally, it explores the relationship between company size and sustainability, highlighting that the most concentrated sectors tend to exhibit the greatest ESG inequality. Applying this methodology to a sample of over 1000 small and medium-sized enterprises across various sectors in Italy, our analysis of the multidimensional index for 2022 reveals notable disparities in corporate financial performance and ESG adoption. Specifically, the manufacturing sector exhibited the highest inequality in financial performance, with an index of 0.77, indicating significant variation in financial health and firm size. Conversely, the financial services sector showed lower financial inequality, with an index of 0.23, suggesting more uniform outcomes. On the ESG front, the financial sector demonstrated the highest inequality, particularly in environmental and social dimensions, with an average Gini coefficient of 0.26, while the manufacturing sector displayed a more consistent ESG performance, with an average Gini of 0.19. These findings underscore sector-specific patterns in financial and ESG dimensions, providing a foundation for targeted interventions to reduce disparities.
Computational Insights into the Unstable Fixed Point of the Fractional Difference Logistic Map
Authors: Uzdila Ernestas , Telksniene Inga , Telksnys Tadas , Ragulskis Minvydas
Published: November 2024

Thedivergence from the unstable fixed point of the fractional difference logistic map is investigated in this paper. In contrary to the classical logistic map, the memory horizon of the fractional difference logistic map reaches the initial condition. And though higher order orbits do not exist in the fractional difference logistic map, a trajectory started at the unstable fixed point may continuously remain at the fixed point as the number of iterations tends to infinity. Such an effect is well known for the classical logistic map, but less so in the fractional difference logistic map. It appears that this effect depends on the accuracy of the floating point arithmetic. It is demonstrated that the divergence from the unstable fixed point of the fractional difference logistic map is a completely computational artifact. Using double precision, approximately 32% values of a from the interval 2.7<a≤3.7 diverge from the unstable fixed point.
MFAN: Multi-Feature Attention Network for Breast Cancer Classification
Authors: Nasir Inzamam Mashood , Alrasheedi Masad A. , Alreshidi Nasser Aedh
Published: November 2024

Cancer-related diseases are some of the major health hazards affecting individuals globally, especially breast cancer. Cases of breast cancer among women persist, and the early indicators of the diseases go unnoticed in many cases. Breast cancer can therefore be treated effectively if the detection is correctly conducted, and the cancer is classified at the preliminary stages. Yet, direct mammogram and ultrasound image diagnosis is a very intricate, time-consuming process, which can be best accomplished with the help of a professional. Manual diagnosis based on mammogram images can be cumbersome, and this often requires the input of professionals. Despite various AI-based strategies in the literature, similarity in cancer and non-cancer regions, irrelevant feature extraction, and poorly trained models are persistent problems. This paper presents a new Multi-Feature Attention Network (MFAN) for breast cancer classification that works well for small lesions and similar contexts. MFAN has two important modules: the McSCAM and the GLAM for Feature Fusion. During channel fusion, McSCAM can preserve the spatial characteristics and extract high-order statistical information, while the GLAM helps reduce the scale differences among the fused features. The global and local attention branches also help the network to effectively identify small lesion regions by obtaining global and local information. Based on the experimental results, the proposed MFAN is a powerful classification model that can classify breast cancer subtypes while providing a solution to the current problems in breast cancer diagnosis on two public datasets.
Properties of the SURE Estimates When Using Continuous Thresholding Functions for Wavelet Shrinkage
Authors: Kudryavtsev Alexey , Shestakov Oleg
Published: November 2024

Wavelet analysis algorithms in combination with thresholding procedures are widely used in nonparametric regression problems when estimating a signal function from noisy data. The advantages of these methods lie in their computational efficiency and the ability to adapt to the local features of the estimated function. It is usually assumed that the signal function belongs to some special class. For example, it can be piecewise continuous or piecewise differentiable and have a compact support. These assumptions, as a rule, allow the signal function to be economically represented on some specially selected basis in such a way that the useful signal is concentrated in a relatively small number of large absolute value expansion coefficients. Then, thresholding is performed to remove the noise coefficients. Typically, the noise distribution is assumed to be additive and Gaussian. This model is well studied in the literature, and various types of thresholding and parameter selection strategies adapted for specific applications have been proposed. The risk analysis of thresholding methods is an important practical task, since it makes it possible to assess the quality of both the methods themselves and the equipment used for processing. Most of the studies in this area investigate the asymptotic order of the theoretical risk. In practical situations, the theoretical risk cannot be calculated because it depends explicitly on the unobserved, noise-free signal. However, a statistical risk estimate constructed on the basis of the observed data can also be used to assess the quality of noise reduction methods. In this paper, a model of a signal contaminated with additive Gaussian noise is considered, and the general formulation of the thresholding problem with threshold functions belonging to a special class is discussed. Lower bounds are obtained for the threshold values that minimize the unbiased risk estimate. Conditions are also given under which this risk estimate is asymptotically normal and strongly consistent. The results of these studies can provide the basis for further research in the field of constructing confidence intervals and obtaining estimates of the convergence rate, which, in turn, will make it possible to obtain specific values of errors in signal processing for a wide range of thresholding methods.
LMPSeizNet: A Lightweight Multiscale Pyramid Convolutional Neural Network for Epileptic Seizure Detection on EEG Brain Signals
Authors: Alsaadan Arwa , Alzamel Mai , Hussain Muhammad
Published: November 2024

Epilepsy is a chronic disease and one of the most common neurological disorders worldwide. Electroencephalogram (EEG) signals are widely used to detect epileptic seizures, which provide specialists with essential information about the brain’s functioning. However, manual screening of EEG signals is laborious, time-consuming, and subjective. The rapid detection of epilepsy seizures is important to reduce the risk of seizure-related implications. The existing automatic machine learning techniques based on deep learning techniques are characterized by automatic extraction and selection of the features, leading to better performance and increasing the robustness of the systems. These methods do not consider the multiscale nature of EEG signals, eventually resulting in poor sensitivity. In addition, the complexity of deep models is relatively high, leading to overfitting issues. To overcome these problems, we proposed an efficient and lightweight multiscale convolutional neural network model (LMPSeizNet), which performs multiscale temporal and spatial analysis of an EEG trial to learn discriminative features relevant to epileptic seizure detection. To evaluate the proposed method, we employed 10-fold cross-validation and three evaluation metrics: accuracy, sensitivity, and specificity. The method achieved an accuracy of 97.42%, a sensitivity of 99.33%, and a specificity of 96.51% for inter-ictal and ictal classes outperforming the state-of-the-art methods. The analysis of the features and the decision-making of the method shows that it learns the features that clearly discriminate the two classes. It will serve as a useful tool for helping neurologists and epilepsy patients.
Solution of Internal Forces in Statically Indeterminate Structures Under Localized Distributed Moments
Authors: Wei Pengyun , Hu Junhong , Man Haizhong , Hong Shunjun
Published: November 2024

Classical methods for manually solving internal forces in statically indeterminate structures mainly include force and displacement methods. While the force method involves substantial work when solving the internal forces of structures with higher degrees of indeterminacy, the displacement method offers a fixed and easily understood approach. However, the displacement method requires prior knowledge of load constant formulas. Common methods for deriving load constant formulas include the force method, virtual beam method, and energy method. Nevertheless, deriving load constant formulas for localized distributed moments using these methods proves to be highly challenging. This study aims to derive load constant formulas for localized distributed moments. Firstly, the load constant formula for a single concentrated moment is derived using the formula for a single concentrated force. Then, the load constant formulas for localized uniform moments and localized linearly distributed moments are derived via the integral method, leveraging the load constant formula for a single concentrated moment. This approach addresses the problem of solving internal forces in statically indeterminate structures under distributed moments via the displacement method. Finally, the proposed approach is verified using three typical examples. The promotion of the research results in this article in teaching can deepen students’ understanding of load constants and the displacement method, enrich teaching content, and have certain engineering applications and teaching practical significance.
MCADNet: A Multi-Scale Cross-Attention Network for Remote Sensing Image Dehazing
Authors: Tao Tao , Xu Haoran , Guan Xin , Zhou Hao
Published: November 2024

Remote sensing image dehazing (RSID) aims to remove haze from remote sensing images to enhance their quality. Although existing deep learning-based dehazing methods have made significant progress, it is still difficult to completely remove the uneven haze, which often leads to color or structural differences between the dehazed image and the original image. In order to overcome this difficulty, we propose the multi-scale cross-attention dehazing network (MCADNet), which offers a powerful solution for RSID. MCADNet integrates multi-kernel convolution and a multi-head attention mechanism into the U-Net architecture, enabling effective multi-scale information extraction. Additionally, we replace traditional skip connections with a cross-attention-based gating module, enhancing feature extraction and fusion across different scales. This synergy enables the network to maximize the overall similarity between the restored image and the real image while also restoring the details of the complex texture areas in the image. We evaluate MCADNet on two benchmark datasets, Haze1K and RICE, demonstrating its superior performance. Ablation experiments further verify the importance of our key design choices in enhancing dehazing effectiveness.
Fully Integrated MEMS Micropump and Miniaturized Mass Flow Sensor as Basic Components for a Microdosing System
Authors: Seidl Martin , Schrag Gabriele
Published: November 2024

Despite major advances in the field of actuator technology for microsystems, miniaturized microfluidic actuation systems for mobile devices are still not common in the market. We present a micropump concept and an associated mass flow sensor design, which, in combination, have the potential to form the basis for an integrated microfluidic development platform for microfluidic systems in general and microdosing systems in particular. The micropump combines the use of active valves with an electrostatic drive principle for the pump membrane and the valves, respectively. With a size of only 1.86 mm × 1.86 mm × 0.3 mm, the first prototypes are capable of pumping gaseous media at flow rates of up to 110 μL/min. A specific feature of the presented micropump is that the pumping direction is perpendicular to the chip surface. The corresponding flow sensor combines the principle of hot-wire anemometry with a very small footprint of only 1.4 mm × 1.4 mm × 0.4 mm. The main innovation is that the hot wires are fixed inside a through-hole in the substrate of the microchip, so that the flow direction of the fluid to be measured is perpendicular to the chip surface, which enables direct integration with the presented micropump. Detection thresholds of around 10 μL/min and measuring ranges of up to 20 mL/min can be achieved with the first prototypes, without dedicated evaluation electronics.
Bacillus subtilis PE7-Mediated Alleviation of Phosphate Starvation and Growth Promotion of Netted Melon (Cucumis melo L. var. reticulatus Naud.)
Authors: Han Seong Eun , Kim Kil Yong , Maung Chaw Ei Htwe
Published: November 2024

Members of Bacillus species are able to enhance the level of available phosphorus (P) for plant absorption through mechanisms of P solubilization and mineralization. In our study, B. subtilis PE7 showed P-solubilizing activity in simple phosphate broth (SPB) medium, and acetic acid, iso-butyric acid, and iso-valeric acid were major organic acids responsible for the increase in soluble P and decrease in pH of SPB medium. In addition, strain PE7 released phytase on phytase-screening agar (PSA) medium, and analysis of semi-quantitative reverse transcription and polymerase chain reaction (sqRT-PCR) revealed that the phyC gene expression was the highest at 1 day after incubation. A low concentration of KH2PO4 in SPB medium induced more biofilm formation than a high concentration of KH2PO4. Strain PE7 showed swimming and swarming motilities in TY and TrA agar media. Under P starvation, inoculation with higher cell numbers of strain PE7 enhanced biomass and nutrient acquisition by melon plants, resulting in higher values of growth parameters and nutrient contents. Moreover, the persistence of bacterial cells on the root surface and in the rhizosphere of melon plants indicated colonization of the plants by strain PE7. Due to its capacity for P solubilization and mineralization, B. subtilis PE7 could be utilized as an alternative to synthetic fertilizer for P deficient-stress management in crop plantation.
Biological Characteristics and Fungicide Screening of Colletotrichum fructicola Causing Mulberry Anthracnose
Authors: Li Ping , Dai Xue , Wang Siyi , Luo Qian , Tang Qingqing , Xu Zijing , Zhao Weiguo , Wu Fuan
Published: November 2024

Mulberry is an important economic crop in China that is widely planted and has important edible and medicinal value. Anthracnose, a critical leaf disease, severely compromises the yield and quality of mulberry trees. However, there are many kinds of pathogens causing mulberry anthracnose and it is difficult to control. This study was undertaken to elucidate the biological characteristics of Colletotrichum fructicola, the pathogen responsible for mulberry leaf spot in Zhejiang Province, and to screen out effective fungicides for its management. The biological characteristics of the pathogen were studied using the cross method and spore counting method, while the sensitivity of the pathogen to seven different fungicides was determined using the growth rate method. The findings indicated that potato dextrose agar (PDA) is the optimal growth medium for the pathogen. The pathogen was capable of growing across a temperature range of 5 to 40 °C, with optimum growth observed at 25 °C. Exposure to a 56 °C water bath for 10 min resulted in the death of the pathogen. It was also found to grow and sporulate within a pH range of 4 to 12, with an optimum pH of 7. Under alternating 12 h light and dark cycles, the colonies grew rapidly and produced abundant spores. Among the fungicides tested, 97% carbendazim WP exhibited the best inhibitory effect, with an EC50 (concentration for 50% of maximal effect) value of 0.0242 μg/mL. This was followed by 35% propiconazole SC, which had an EC50 of 0.4180 μg/mL. The fungicidal effect of 25 g/L fludioxonil SSCC was relatively poor, with an EC50 value of 103.0170 μg/mL. This study clarifies the optimal conditions for the growth and sporulation of the mulberry anthracnose pathogen and identifies fungicides with effective inhibitory properties. These findings will provide valuable guidance for field applications and disease management in controlling mulberry anthracnose.
Evolution of Cognitive Disorders in Patients with Mild Cognitive Impairment (MCI) After Ischemic Stroke: Secondary Data Analysis from the Improved Health Care in Neurology and Psychiatry—Longer Life (IHCNP) Study
Authors: Jianu Dragoș-Cătălin , Petrica Ligia , Dan Traian Flavius , Munteanu Georgiana , Bora Bianca , Arnăutu Sergiu Florin , Ursoniu Sorin , Chira Diana , Strilciuc Ștefan , Falup-Pecurariu Cristian , Mureșanu Dafin Fior
Published: November 2024

Background: The Improved Health Care in Neurology and Psychiatry—Longer Life (IHCNP) study was an 18-month prospective, observational, non-interventional research study focused on patients with mild cognitive impairment (MCI) following ischemic stroke. Objectives: Our secondary analysis of the IHCNP data aimed to document the progression of MCI in this patient group. Methods: A total of 100 patients from Romania were recruited, all of whom underwent cognitive assessments using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Rey Auditory Verbal Learning Test (RAVLT). Clinical evaluations were also conducted as part of the study. Baseline cognitive scores were recorded, and subsequent follow-ups documented cognitive changes over time. Results: At baseline, cognitive scores indicated mild impairment, with averages of MMSE 25.41, MoCA 23.27, and RAVLT 33.63. By the end of the study, patients exhibited a significant cognitive decline, with MMSE scores dropping by 8.7%, MoCA by 10.0%, and RAVLT by 29.5% (p < 0.0001 for all measures), reflecting the progressive nature of MCI post-stroke. Conclusions: These findings highlight the importance of early diagnosis and intervention to mitigate cognitive decline in post-stroke patients. The study underscores the need for ongoing cognitive monitoring to improve patient outcomes and manage MCI progression effectively.