bluuebunny/crossref_metadata_2025 huggingface dataset:
Porphyrin-Based Aluminum Metal-Organic Framework with Copper: Pre-Adsorption of Water Vapor, Dynamic and Static Sorption of Diethyl Sulfide Vapor, and Sorbent Regeneration
Authors: Ahmad Mohammad , Samokhvalov Alexander
Published: December 2024

Metal–organic frameworks (MOFs) are hybrid inorganic–organic 3D coordination polymers with metal sites and organic linkers, which are a “hot” topic in the research of sorption, separations, catalysis, sensing, and environmental remediation. In this study, we explore the molecular mechanism and kinetics of interaction of the new copper porphyrin aluminum metal–organic framework (actAl-MOF-TCPPCu) compound 4 with a vapor of the volatile organic sulfur compound (VOSC) diethyl sulfide (DES). First, compound 4 was synthesized by post-synthetic modification (PSM) of Al-MOF-TCPPH2 compound 2 by inserting Cu2+ ions into the porphyrin ring and characterized by complementary qualitative and quantitative chemical, structural, and spectroscopic analysis. Second, the interaction of compound 4 with DES vapor was analyzed dynamically by the novel method of in situ time-dependent attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy at controlled humidity levels. The sorbent–adsorbate interactions, as analyzed by the shifts in IR peaks, indicate that the bonding includes the hydroxy O-H, carboxylate COO−, and phenyl groups. The kinetics of sorption obeys the Langmuir pseudo-first-order rate law. The pre-adsorption of water vapor by compound 4 at the controlled relative humidity under static (equilibrium) conditions yields the binary stoichiometric adsorption complex (Al-MOF-TCPPCu)1.0(H2O)8.0. The pre-adsorption of water vapor makes the subsequent sorption of DES slower, while the kinetics obey the same rate law. Then, static pre-adsorption of water vapor was followed by static sorption of DES vapor, and the ternary adsorption complex (Al-MOF-TCPPCu)1.0(H2O)8.0(DES)3.8 was obtained. Despite the pre-adsorption of significant amounts of water, the binary complex adsorbs a large amount of DES: ca. 36.6 wt. % (per compound 4). Finally, the ternary complex is facilely regenerated by gentle heating under vacuum. Compound 4 and related MOFs are promising for adsorptive removal of vapor of DES and related VOSCs from dry and humid air.
Res-UNet Ensemble Learning for Semantic Segmentation of Mineral Optical Microscopy Images
Authors: Jiang Chong , Abdul Halin Alfian , Yang Baohua , Abdullah Lili Nurliyana , Manshor Noridayu , Perumal Thinagaran
Published: December 2024

In geology and mineralogy, optical microscopic images have become a primary research focus for intelligent mineral recognition due to their low equipment cost, ease of use, and distinct mineral characteristics in imaging. However, due to their close reflectivity or transparency, some minerals are not easily distinguished from other minerals or background. Secondly, the number of background pixels often vastly exceeds the number of pixels for individual mineral particles, and the number of pixels of different mineral particles in the image also varies significantly. These have led to the issue of data imbalance. This imbalance results in lower recognition accuracy for categories with fewer samples. To address these issues, a flexible ensemble learning for semantic segmentation based on multiple optimized Res-UNet models is proposed, introducing dice loss and focal loss functions and incorporating a pre-positioned spatial transformer networks block. Twelve optimized Res-UNet models were used to construct multiple Res-UNet ensemble learnings using heterogeneous ensemble strategies. The results demonstrate that the system integrated with five learners using the weighted voting fusion method (RUEL-5-WV) achieved the best performance with a mean Intersection over Union (mIOU) of 91.65 across all nine categories and an IOU of 84.33 for the transparent mineral (gangue). The results indicate that this ensemble learning scheme outperforms individual optimized Res-UNet models. Compared to the classical Deeplabv3 and PSPNet, this scheme also exhibits significant advantages.
Platinum Compound on Gold–Magnesia Hybrid Structure: A Theoretical Investigation on Adsorption, Hydrolysis, and Interaction with DNA Purine Bases
Authors: Song Zhenjun , Liu Mingyue , Zhong Aiguo , Yang Meiding , He Zhicai , Wang Wenmin , Li Hongdao
Published: December 2024

Cisplatin-based platinum compounds are important clinical chemotherapeutic agents that participate in most tumor chemotherapy regimens. Through density-functional theory calculations, the formation and stability of the inorganic oxide carrier, the mechanisms of the hydrolysis reaction of the activated platinum compound, and its binding mechanism with DNA bases can be studied. The higher the oxidation state of Pt (II to IV), the more electrons transfer from the magnesia–gold composite material to the platinum compound. After adsorption on the composite carrier, 5d←2p coordination bonds of Pt-N are strengthened. For flat and oblique adsorption modes of cisplatin, there is no significant difference in the density of states of the gold and magnesium oxide film, indicating the maintenance of the heterojunction structural framework. However, there are significant changes in the electronic states of cisplatin itself with different adsorption configurations. In the flat configuration, the band gap width of cisplatin is larger than that of the oblique configuration. The Cl-Pt bond range in the Pt(III) compound shows a clear charge reduction on the magnesia film, indicating the Cl-Pt bond is an active site with the potential for decomposition and hydrolysis. The substitution of chloride ions by water can lead to hydrolysis products, enhancing the polarization of the composite and showing strong charge separation. The hydrolysis of the free platinum compound is endothermic by 0.309 eV, exceeding the small activation energy barrier of 0.399 eV, indicating that hydrolysis of this platinum compound is easily achievable. ADME (absorption, distribution, metabolism, and excretion) prediction parameters indicate that hydrolysis products have good ESOL (Estimated SOLubility) solubility and high gastrointestinal absorption, consistent with Lipinski’s rule. During the coordination reaction process, there are significant changes in the distribution of frontier molecular orbitals, with the HOMO (highest occupied molecular orbital) of the initial state primarily located on the purine base, providing the possibility for electron transfer to the empty orbitals of the platinum compound in the LUMO (lowest unoccupied molecular orbital). The HOMO and HOMO-1 of the transition state and product are mainly distributed on the platinum compound, indicating clear electron transfer and orbital rearrangement. The activation energy barrier for the purine coordination reaction with the hydrolysis products is reduced to 0.61 eV, and the dipole moment gradually decreases to 6.77 Debye during the reaction, indicating a reduction in the system’s charge separation and polarization. This contribution is anticipated to provide a new theoretical clue for developing inorganic oxide carriers of platinum compounds.
Sulfur Amino Acid Restriction Mitigates High-Fat Diet-Induced Molecular Alterations in Cardiac Remodeling Primarily via FGF21-Independent Mechanisms
Authors: Pinheiro Filipe , Lail Hannah , Neves João Sérgio , Negrão Rita , Wanders Desiree
Published: December 2024

Background/Objectives: Dietary sulfur amino acid restriction (SAAR) elicits various health benefits, some mediated by fibroblast growth factor 21 (FGF21). However, research on SAAR’s effects on the heart is limited and presents mixed findings. This study aimed to evaluate SAAR-induced molecular alterations associated with cardiac remodeling and their dependence on FGF21. Methods: Male C57BL/6J wild-type and FGF21 knockout mice were randomized into four dietary regimens, including normal fat and high-fat diets (HFDs) with and without SAAR, over five weeks. Results: SAAR significantly reduced body weight and visceral adiposity while increasing serum FGF21 levels. In the heart, SAAR-induced molecular metabolic alterations are indicative of enhanced lipid utilization, glucose uptake, and mitochondrial biogenesis. SAAR also elicited opposing effects on the cardiac gene expression of FGF21 and adiponectin. Regarding cellular stress responses, SAAR mitigated the HFD-induced increase in the cardiac expression of genes involved in oxidative stress, inflammation, and apoptosis, while upregulating antioxidative genes. Structurally, SAAR did not induce alterations indicative of cardiac hypertrophy and it counteracted HFD-induced fibrotic gene expression. Overall, most alterations induced by SAAR were FGF21-independent, except for those related to lipid utilization and glucose uptake. Conclusions: Altogether, SAAR promotes cardiac alterations indicative of physiological rather than pathological remodeling, primarily through FGF21-independent mechanisms.
Bioelectrical Impedance Vector Analysis in Extremely Low-Birth-Weight Infants to Assess Nutritional Status: Breakthroughs and Insights
Authors: Núñez-Ramos Raquel , Escuder-Vieco Diana , Rico Cruz Carolina , Diego-Poncela Cristina De , Vázquez-Román Sara , Germán-Díaz Marta , García-Lara Nadia Raquel , Pallás-Alonso Carmen
Published: December 2024

Background/Objectives: To obtain bioelectrical data to assess nutritional status for extremely low-birth-weight (ELBW) infants upon reaching term-corrected age. Methods: A descriptive, observational, prospective, and single-center study, which included ELBW preterm infants was performed. The study variables collected were gestational age, sex, and anthropometry at birth and at term-corrected age. Bioelectrical impedance vector analysis (BIVA) was performed by a phase-sensitive device (BIA 101 BIVA PRO AKERN srl, Pisa, Italy). The components of the impedance vector—resistance (R) and reactance (Xc)—were normalized for body height (H). For each subject, the measurement was taken between the 36th and 44th weeks of postmenstrual age (PMA). A semi-quantitative analysis of body composition was performed using the vector modality of the BIA. Using the RXc graph method, the bivariate 95% confidence intervals of the mean vectors were constructed. From the bivariate normal distribution of R/H and Xc/H, the bivariate 95%, 75%, and 50% tolerance intervals for this cohort were drawn. The individual impedance vectors were compared with the distribution of the vectors from other populations. Results: 85 ELBW infants (40 male, 45 female) were included, with a mean gestational age at birth of 26 + 6 weeks (±1.76). Mean R/H was 870.33 (±143.21) Ohm/m and Xc/H was 86.84 (±19.05) Ohm/m. We found differences in the bioelectrical data with regard to gender, with resistance values being significantly higher in females. Our ellipses align closely with those from other term neonatal cohorts. Conclusions: Bioelectrical data and the confidence and tolerance ellipses of an ELBW infant cohort are presented and can be used as a reference standard for nutritional assessment at discharge.
Dietary Patterns and Fibre Intake Are Associated with Disease Activity in Australian Adults with Inflammatory Bowel Disease: An Exploratory Dietary Pattern Analysis
Authors: Cosier Denelle , Lambert Kelly , Charlton Karen , Batterham Marijka , Little Robert , Wu Nan , Tavakoli Paris , Ghaly Simon , Pipicella Joseph , Connor Susan , Leach Steven , Lemberg Daniel , Houshyar Yashar , Jayawardana Thisun , Koentgen Sabrina ,   on behalf of the Australian IBD Microbiome Study Consortium, Hold Georgina
Published: December 2024

Background: Few studies have explored the relationship between habitual dietary patterns and disease activity in people with Inflammatory Bowel Disease (IBD). This cross-sectional study explored the association between dietary patterns and clinical and objective markers of inflammation in adults from the Australian IBD Microbiome Study. Methods: Dietary patterns were derived using principal component analysis (PCA) of baseline food frequency questionnaire data. Food intake was quantified using 3-day food record data. Associations between dietary intake and both clinical disease activity index (CDAI) and faecal calprotectin (FCP) were analysed. Results: Participants included 412 adults (IBD = 223, Healthy controls (HC) = 189). Both cohorts consumed poor-quality diets with inadequate servings of most food groups compared to Australian reference standards. IBD participants without FCP inflammation had significantly higher fibre intake than those with moderate FCP. In the Crohn’s Disease group, high adherence to ‘High plant diversity’ and ‘Meat eaters’ dietary patterns were associated with increased CDAI and FCP, respectively. In the combined IBD cohort, high adherence to a ‘Vegan-style’ dietary pattern was associated with increased FCP. Conclusions: There is a need for dietary modifications among Australian adults, both with and without IBD, to improve dietary fibre intake and adherence to dietary guidelines. Dietary patterns characterised by a high intake of plant foods or meat products were both positively associated with indicators of active IBD. It is possible that some participants with active IBD were modifying their diet to try to manage their disease and reduce symptoms, contributing to the association between healthier dietary patterns and active disease. Further clinical and longitudinal studies are needed to expand upon the findings. This study offers a unique contribution by utilising FCP as an objective marker of intestinal inflammation and applying dietary pattern analysis to investigate the relationship between diet and inflammatory markers.
Evaluating the Impact of Multimodal Prehabilitation with High Protein Oral Nutritional Supplementation (HP ONS) with Beta-Hydroxy Beta-Methylbutyrate (HMB) on Sarcopenic Surgical Patients—Interim Analysis of the HEROS Study
Authors: Koh Frederick Hong-Xiang , Yik Vanessa , Chin Shuen-Ern , Kok Shawn Shi-Xian , Lee Hui-Bing , Tong Cherie , Tay Phoebe , Chean Esther , Lam Yi-En , Mah Shi-Min , Foo Li-Xin , Yan Clement C , Chua Wei-Tian , Jamil Haziq bin , G Khasthuri , Ong Lester Wei-Lin , Tan Alvin Yong-Hui , Chue Koy-Min , Ho Leonard Ming-Li , Chong Cheryl Xi-Zi , Ladlad Jasmine , Tan Cheryl Hui-Min , Khoo Nathanelle Ann Xiaolian , Ng Jia-Lin , Tan Winson Jianhong , Foo Fung-Joon
Published: December 2024

Background: Multimodal prehabilitation programs, which may incorporate nutritional supplementation and exercise, have been developed to combat sarcopenia in surgical patients to enhance post-operative outcomes. However, the optimal regime remains unknown. The use of beta-hydroxy beta-methylbutyrate (HMB) has beneficial effects on muscle mass and strength. However, its effect on muscle quality in the perioperative setting has yet to be established. This study aims to explore the impact of a multimodal prehabilitation program using a bundle of care that includes high-protein oral nutritional supplementation (HP ONS) with HMB and resistance exercise on muscle quality and functional outcomes in sarcopenic surgical patients. Methods: Sarcopenic adult patients undergoing elective major gastrointestinal surgeries were recruited for this pilot interventional cohort study. They were enrolled in a 2–4-week multimodal prehabilitation program comprising resistance exercise, nutritional supplementation, vitamin supplementation, comorbid optimization and smoking cessation. Participants were provided three units of HP ONS with HMB per day pre-operatively. The primary outcome was changes in intramuscular adipose tissue (IMAT) as a proxy of muscle quality, assessed using Artificial Intelligence (AI)-aided ultrasonography. Secondary outcomes include changes in anthropometric measurements and functional characteristics. Outcomes were measured before prehabilitation, after prehabilitation and 1 month post-operatively. Results: A total of 36 sarcopenic patients, with a median age of 71.5 years, were included in this study. There was an increase in the IMAT index after two weeks of prehabilitation (p = 0.032) to 1 month after surgery (p = 0.028). Among functional parameters, improvement was observed in gait speed (p = 0.01) after two weeks of prehabilitation, which returned to baseline post-operatively. The median length of hospital stay was 7 (range: 2–75) days. Conclusions: The increase in the IMAT index in a sarcopenic surgical cohort undergoing prehabilitation may be due to altered muscle metabolism in elderly sarcopenic patients. A prehabilitation regime in sarcopenic patients incorporating HP ONS with HMB and resistance exercise is feasible and is associated with increased gait speed.
Maternal Gut Microbiome-Mediated Epigenetic Modifications in Cognitive Development and Impairments: A New Frontier for Therapeutic Innovation
Authors: Nohesara Shabnam , Abdolmaleky Hamid Mostafavi , Dickerson Faith , Pinto-Tomás Adrián A. , Jeste Dilip V. , Thiagalingam Sam
Published: December 2024

Cognitive impairment in various mental illnesses, particularly neuropsychiatric disorders, has adverse functional and clinical consequences. While genetic mutations and epigenetic dysregulations of several genes during embryonic and adult periods are linked to cognitive impairment in mental disorders, the composition and diversity of resident bacteria in the gastrointestinal tract—shaped by environmental factors—also influence the brain epigenome, affecting behavior and cognitive functions. Accordingly, many recent studies have provided evidence that human gut microbiota may offer a potential avenue for improving cognitive deficits. In this review, we provide an overview of the relationship between cognitive impairment, alterations in the gut microbiome, and epigenetic alterations during embryonic and adult periods. We examine how various factors beyond genetics—such as lifestyle, age, and maternal diet—impact the composition, diversity, and epigenetic functionality of the gut microbiome, consequently influencing cognitive performance. Additionally, we explore the potential of maternal gut microbiome signatures and epigenetic biomarkers for predicting cognitive impairment risk in older adults. This article also explores the potential roles of nutritional deficiencies in programming cognitive disorders during the perinatal period in offspring, as well as the promise of gut microbiome-targeted therapeutics with epigenetic effects to prevent or alleviate cognitive dysfunctions in infants, middle-aged adults, and older adults. Unsolved challenges of gut microbiome-targeted therapeutics in mitigating cognitive dysfunctions for translation into clinical practice are discussed, lastly.
Childhood Mediterranean Diet Compliance Is Associated with Lower Incidence of Childhood Obesity, Specific Sociodemographic, and Lifestyle Factors: A Cross-Sectional Study in Children Aged 6–9 Years
Authors: Jacovides Constantina , Pritsa Agathi , Chrysafi Maria , Papadopoulou Sousana K. , Kapetanou Maria G. , Lechouritis Eleftherios , Mato Martin , Papadopoulou Vasiliki G. , Tsourouflis Gerasimos , Migdanis Athanasios , Sampani Anastasia , Kosti Rena I. , Psara Evmorfia , Giaginis Constantinos
Published: December 2024

Background: Mediterranean diet (MD) constitutes a commonly examined dietary model. It includes a plethora of bioactive ingredients with strong antioxidant, anti-inflammatory, antithrombotic and anticancer properties. Several substantial studies support strong evidence that MD can exert preventing actions against human morbidity and mortality, promoting human well-being and quality of life. The present study aims to evaluate whether childhood MD compliance may be associated with socio-demographic, anthropometric, and lifestyle factors in children at the age of 6–9 years. Methods: This is a cross-sectional survey on 3875 children aged 6–9 years old with their matched mothers. Qualified questionnaires were used to evaluate and collect by one-to-one interviews with trained professionals the above data. Results: Elevated MD adherence was observed only in 22.2% of the enrolled children, while 37.5% of children maintained intermediate MD adherence and 40.3% of children adopted lower MD levels. Children MD compliance was related at an independent manner with maternal education level, childhood anthropometric factors such as Body Mass Index (BMI), Waist circumference to Height ratio (WtHR), quality of life, and exclusively breastfeeding behaviors after adjusting for several possible confounders (p ˂ 0.05). Conclusions: Elevated MD adherence of children aged 6–9 years old showed a lower obesity of overweight/obesity, including abdominal obesity. A higher maternal educational status and adopting exclusively breastfeeding practices were associated with greater levels of children’s MD adherence, promoting their quality of life and well-being. Based on the present results, future prospective surveys need to be performed to evaluate if there is a causality relation concerning this topic.
Dendrimers: A Themed Issue in Honor of Professor Donald A. Tomalia on the Occasion of His 85th Birthday, Recognizing His Outstanding Achievements in Advancing the Field of Dendrimers
Authors: Caminade Anne-Marie
Published: December 2024

As the main pioneer in the field of dendrimers, a term that he coined in 1985 in reference to their tree-like structure, Prof. Donald A. Tomalia has inspired several generations of researchers worldwide [...]
Synthesis of Novel Zwitterionic Surfactants: Achieving Enhanced Water Resistance and Adhesion in Emulsion Polymer Adhesives
Authors: Toan Mai , Choi Jaehyouk , Ngo Hang Thi , Bae Jin-Young , Shin Seunghan , Kwon Kiok
Published: December 2024

Recent advancements in polymer materials have enabled the synthesis of bio-based monomers from renewable resources, promoting sustainable alternatives to fossil-based materials. This study presents a novel zwitterionic surfactant, SF, derived from 10-undecenoic acid obtained from castor oil through a four-step reaction, achieving a yield of 78%. SF has a critical micelle concentration (CMC) of 1235 mg/L, slightly higher than the commercial anionic surfactant Rhodacal DS-4 (sodium dodecyl benzene sulfonate), and effectively stabilizes monomer droplets, leading to excellent conversion and stable latex formation. The zwitterionic groups in SF enhance adhesion to hydrophilic substrates (glass, stainless steel, and skin). Films produced with SF exhibit outstanding water resistance, with only 18.48% water uptake after 1800 min, compared to 81% for the control using Rhodacal DS-4. Notably, SF maintains low water uptake across various concentrations, minimizing water penetration. Thus, the synthesized SF demonstrates improved adhesive properties and excellent water resistance in emulsion polymerization applications, highlighting its potential as a sustainable, high-performance alternative to petrochemical surfactants.
Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China
Authors: Yin Gaohong , Zhang Yanling , Cao Yuxi , Park Jongmin
Published: December 2024

Precipitation plays a key control in the water, energy, and carbon cycles, and it is also an important driving force for land surface modeling. This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation estimate in Northeast China (NEC). Precipitation estimates from satellite-based IMERG and SM2RAIN-ASCAT, as well as reanalysis data from MERRA-2, were used in this study. The triple collocation (TC) approach was used to quantify the error uncertainties in each input data set, which are associated with the weights assigned to each data set in the merging procedure. The results revealed that IMERG provides a better consistency with the other two input data sets and thus was more relied on during the merging process. The accuracy of both SM2RAIN-ASCAT and MERRA-2 showed obvious spatio-temporal patterns due to their retrieval algorithms and resolution limits. The merged TC-based daily precipitation provides the highest correlation coefficient with ground-based measurements (R = 0.52), suggesting its capability to represent the temporal variation in daily precipitation. However, it largely overestimated the precipitation intensity in the summer, leading to a large positive bias.
Wildfire Severity to Valued Resources Mitigated by Prescribed Fire in the Okefenokee National Wildlife Refuge
Authors: Ross C. Wade , Loudermilk E. Louise , O’Brien Joseph J. , Flanagan Steven A. , Snitker Grant , Hiers J. Kevin
Published: December 2024

Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to highly valued resources and assets (HVRAs)—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and HVRAs impacted by the 2017 West Mims event, which burned across both prescribed-fire treated and untreated areas within the Okefenokee National Wildlife Refuge. Wildfire severity was quantified using the differenced normalized burn ratio (dNBR) index, while treatment records were used to calculate the prescribed frequency and post-treatment duration, which is defined as the time elapsed between the last treatment and the West Mims event. A generalized additive model (GAM) was fit to model dNBR as a function of post-treatment duration, fire frequency, and vegetation type. Although dNBR exhibited considerable heterogeneity both within and between HVRAs and vegetation classes, areas treated with prescribed fire demonstrated substantial reductions in burn severity. The beneficial effects of prescribed fire were most pronounced within approximately two years post-treatment with up to an 88% reduction in mean wildfire severity. However, reductions remained evident for approximately five years post-treatment according to our model. The mitigating effect of prescribed fire was most pronounced in Introduced Upland Vegetation-Shrub, Eastern Floodplain Forests, and Longleaf Pine Woodland when the post-treatment duration was within 12 months. Similar trends were observed in areas surrounding red-cockaded woodpecker nesting sites, which is an HVRA of significant ecological importance. Our findings support the frequent application of prescribed fire (e.g., one- to two-year intervals) as an effective strategy for mitigating wildfire severity to HVRAs.
The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics
Authors: Lisańczuk Marek , Mitelsztedt Krzysztof , Stereńczak Krzysztof
Published: December 2024

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m2 were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty.
Comparing and Optimizing Four Machine Learning Approaches to Radar-Based Quantitative Precipitation Estimation
Authors: Liu Miaomiao , Zuo Juncheng , Tan Jianguo , Liu Dongwei
Published: December 2024

To improve radar-based quantitative precipitation estimation (QPE) methods, this study investigated the relationship between radar reflectivity (Z) and hourly rainfall intensity (R) using data from 289 precipitation events in Shanghai between September 2020 and March 2024. Two Z-R relationship models were compared in terms of their fitting performance: Z = 270.81 R1.09 (empirically fitted relationship) and Z = 300 R1.4 (standard relationship). The results show that the Z = 270.81 R1.09 model outperforms the Z = 300 R1.4 model in terms of fitting accuracy. Specifically, the Z = 270.81 R1.09 model more effectively captures the nonlinear relationship between radar reflectivity and rainfall intensity, with a higher degree of agreement between the fitted curve and the observed data points. This model demonstrated superior performance across all 289 precipitation events. This study evaluated the performance of four machine learning approaches while incorporating five meteorological features: specific differential phase shift (KDP), echo-top height (ET), vertical liquid water content (VIL), differential reflectivity (ZDR), and correlation coefficient (CC). Nine QPE models were constructed using these inputs. The key findings are as follows: (1) For models with a single-variable input, the KAN deep learning model outperformed Random Forest, Gradient Boosting Decision Trees, Support Vector Machines, and the traditional Z-R relationship. (2) When six features were used as inputs, the accuracy of the machine learning models improved significantly, with the KAN deep learning model outperforming other machine learning methods. Compared to using only radar reflectivity, the KAN deep learning model reduced the MRE by 20.78%, MAE by 4.07%, and RMSE by 12.74%, while increasing the coefficient of determination (R2) by 18.74%. (3) The integration of multiple meteorological features and machine learning optimization significantly enhanced QPE accuracy, with the KAN deep learning model performing best under varying meteorological conditions. This approach offers a promising method for improving radar-based QPE, particularly considering seasonal, weather system, and precipitation stage differentiation.
A 3D Surface Reconstruction Pipeline for Plant Phenotyping
Authors: Stausberg Lina , Jost Berit , Klingbeil Lasse , Kuhlmann Heiner
Published: December 2024

Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods rely on camera systems, including RGB, multi-spectral, and hyper-spectral cameras, which offer valuable insights into plant physiology. In recent years, 3D sensing systems such as laser scanners have gained popularity due to their ability to capture structural plant parameters that are difficult to obtain using spectral sensors. Unlike images, point clouds are not structured and require pre-processing steps to extract precise information and handle noise or missing points. One approach is to generate mesh-based surface representations using triangulation. A key challenge in the 3D surface reconstruction of plants is the pre-processing of point clouds, which involves removing non-plant noise from the scene, segmenting point clouds from populations to individual plants, and further dividing individual plants into their respective organs. In this study, we will not focus on the segmentation aspect but rather on the other pre-processing steps, like denoising parameters, which depend on the data type. We present an automated pipeline for converting high-resolution point clouds into surface models of plants. The pipeline incorporates additional pre-processing steps such as outlier removal, denoising, and subsampling to ensure the accuracy and quality of the reconstructed surfaces. Data were collected using three different sensors: a handheld scanner, a terrestrial laser scanner (TLS), and a mobile mapping platform, under varying conditions from controlled laboratory environments to complex field settings. The investigation includes five different plant species, each with distinct characteristics, to demonstrate the potential of the pipeline. In a next step, phenotypic traits such as leaf area, leaf area index (LAI), and leaf angle distribution (LAD) were calculated to further illustrate the pipeline’s potential and effectiveness. The pipeline is based on the Open3D framework and is available open source.
Realisation of an Application Specific Multispectral Snapshot-Imaging System Based on Multi-Aperture-Technology and Multispectral Machine Learning Loops
Authors: Wunsch Lennard , Hubold Martin , Nestler Rico , Notni Gunther
Published: December 2024

Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi aperture imaging, which enables a simultaneous capture (snapshot) of spectrally selective and spatially resolved scene information. There are some limiting factors for the spectral resolution when implementing this imaging principle, e.g., usable sensor resolutions and area, and required spatial scene resolution or optical complexity. Careful analysis is therefore needed for the specification of the multispectral system properties and its realisation. In this work we present a systematic approach for the application-related implementation of this kind of MSI. We focus on spectral system modeling, data analysis, and machine learning to build a universally usable multispectral loop to find the best sensor configuration. The approach presented is demonstrated and tested on the classification of waste, a typical application for multispectral imaging.
Method for Correcting Error Due to Self-Heating of Resistance Temperature Detectors Suitable for Metrology in Industry 4.0
Authors: Li Jiyun , Pei Hongxing , Kochan Orest , Wang Chunzhi , Kochan Roman , Ivanyshyn Alla
Published: December 2024

This study contributes to improving the accuracy of temperature measurements with a platinum resistance temperature detector (RTD) by proposing techniques to mitigate the error due to self-heating by the operating current. An assessment of the measurement errors of the platinum RTD was carried out to study ways to improve their accuracy. High accuracy can be achieved by individual calibration using a voltage divider circuit to measure resistance, the substitution method, and the transitional measure. It was shown that each of these approaches offers potential improvements in the accuracy of temperature measurements using RTDs. However, one of the genuine limitations is the error due to heating the RTD by the operating current. To address this, both linear and nonlinear methods for correcting the error due to heating by the operating current were studied. This paper examines how these methods can be applied to mitigate the influence of self-heating on measurement accuracy. Moreover, the residual errors associated with these methods of correction were estimated. The analysis showed that while these methods can reduce the errors significantly, there remain limitations below which it is not possible to mitigate the error.
Vertex-Oriented Method for Polyhedral Reconstruction of 3D Buildings Using OpenStreetMap
Authors: Liu Hanli , Hellín Carlos J. , Tayebi Abdelhamid , Calles Francisco , Gómez Josefa
Published: December 2024

This work presents the mathematical definition and programming considerations of an efficient geometric algorithm used to add roofs to polyhedral 3D building models obtained from OpenStreetMap. The algorithm covers numerous roof shapes, including some well-defined shapes that lack an explicit reconstruction theory. These shapes include gabled, hipped, pyramidal, skillion, half-hipped, gambrel, and mansard. The input data for the developed code consist of latitude and longitude coordinates defining the target area. Geospatial data necessary for the algorithm are obtained through a request to the overpass-turbo service. The findings showcase outstanding performance for buildings with straightforward footprints, but they have limitations for the ones with intricate footprints. In future work, further refinement is necessary to solve the mentioned limitation.
Flexible Mechanical Sensors for Plant Growth Monitoring: An Emerging Area for Smart Agriculture
Authors: Phan Thi Thu Hien , Ngo Thi Mai Vi , Phan Hoang-Phuong
Published: December 2024

The last decade has seen significant progress in the development of flexible electronics and sensors, particularly for display technologies and healthcare applications. Advancements in scalable manufacturing, miniaturization, and integration have further extended the use of this new class of devices to smart agriculture, where multimodal sensors can be seamlessly attached to plants for continuous and remote monitoring. Among the various types of sensing devices for agriculture, flexible mechanical sensors have emerged as promising candidates for monitoring vital parameters, including growth rates and water flow, providing a new avenue for understanding plant health and growth under varied environmental conditions. This perspective provides a snapshot of recent progress in this exciting and unconventional area of research and highlights potential opportunities for the future.
Are Junior Tennis Players Less Exposed to Shocks and Vibrations than Adults? A Pilot Study
Authors: Le Solliec Tom , Hautier Christophe , Gassier Robin , Trama Robin , Gilbert Benoit , Song Lin , Zhang Qingshan
Published: December 2024

This study investigated muscle activation, shocks, and vibrations of the upper extremities during tennis serves between junior and adult tennis players. Thirty-five well-trained tennis players (15 juniors and 20 adults) performed 10 maximal successful tennis serves. Two triaxial accelerometers recorded the shock and vibration on the racket and the hand on the dominant side. Eight surface EMG electrodes were also used to measure the arm muscles’ activities. Linear mixed models were used to test the fixed effect of age on muscular activation and vibration. Statistical non-Parametric Mapping was employed to make statistical inferences on the EMG and accelerometer data obtained from the continuous wavelet transform. Comparing EMG parameters between junior and adult players reveals similar upper limb intermuscular coordination. The junior players experienced lower racket and hand vibration amplitudes, which were partially explained by a lower ball velocity. This study revealed that young players showed no difference in EMG parameters in the tennis serve but were as exposed to shocks and vibrations as adults when compared based on a given speed and a given handgrip force. These vibrations apply to an immature skeleton, which can increase the risk of injuries caused by overuse. In addition, differences in the racket vibration frequency provide original knowledge to engineers who need to develop innovative sports equipment for tennis.
A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen
Authors: Lu Naiwei , Liu Yiru , Cui Jian , Xiao Xiangyuan , Luo Yuan , Noori Mohammad
Published: December 2024

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time–frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle–bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage.
Investigation of Separating Temperature-Induced Structural Strain Using Improved Blind Source Separation (BSS) Technique
Authors: Gu Hao’an , Zhang Xin , Sumarac Dragoslav , Peng Jiayi , Dunai László , Zhang Yufeng
Published: December 2024

The strain data acquired from structural health monitoring (SHM) systems of large-span bridges are often contaminated by a mixture of temperature-induced and vehicle-induced strain components, thereby complicating the assessment of bridge health. Existing approaches for isolating temperature-induced strains predominantly rely on statistical temperature–strain models, which can be significantly influenced by arbitrarily chosen parameters, thereby undermining the accuracy of the results. Additionally, signal processing techniques, including empirical mode decomposition (EMD) and others, frequently yield unstable outcomes when confronted with nonlinear strain signals. In response to these challenges, this study proposes a novel temperature-induced strain separation technique based on improved blind source separation (BSS), termed the Temperature-Separate Second-Order Blind Identification (TS-SOBI) method. Numerical verification using a finite element (FE) bridge model that considers both temperature loads and vehicle loads confirms the effectiveness of TS-SOBI in accurately separating temperature-induced strain components. Furthermore, real strain data from the SHM system of a long-span bridge are utilized to validate the application of TS-SOBI in practical engineering scenarios. By evaluating the remaining strain components after applying the TS-SOBI method, a clearer understanding of changes in the bridge’s loading conditions is achieved. The investigation of TS-SOBI introduces a novel perspective for mitigating temperature effects in SHM applications for bridges.
Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State
Authors: Nagiub Mena , Beuth Thorsten , Sistu Ganesh , Gotzig Heinrich , Eising Ciarán
Published: December 2024

Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor scenes. The method trains on static datasets to learn unwrapped depth map prediction and then adapts to dynamic datasets using continuous learning methods. Additionally, blind deconvolution is introduced to mitigate the blur. The combined use of these methods produces high-quality depth maps with reduced blur noise.
Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management
Authors: Duan Yuntao , Zhang Tao , Shi Dunhuang
Published: December 2024

Aero-engines, particularly turbofan engines, are highly complex systems that play a critical role in the aviation industry. As core components of modern aircraft, they provide the thrust necessary for flight and are essential for safe and efficient operations. However, the complexity and interconnected nature of these engines also make them vulnerable to failures and, in the context of intelligent systems, potential cyber-attacks. Ensuring the secure and reliable operation of these engines is crucial as disruptions can have significant consequences, ranging from costly maintenance issues to catastrophic accidents. The innovation of this article lies in a proposed method for obtaining key points. The research method is based on convolution and the basic shape of convolution. Through feature fusion, a self-convolution operation, a half operation, and derivative operation on the original feature data of the engine, two key points of the engine in the entire lifecycle are obtained, and these key points are analyzed in detail. Finally, the key point-based acquisition method and statistical data analysis were applied to the engine’s health planning and lifespan prediction, and the results were validated on the test set. The results indicate that the key point-based method proposed in this paper has promising prospects.
Network Dismantling on Signed Network by Evolutionary Deep Reinforcement Learning
Authors: Ou Yuxuan , Xiong Fujing , Zhang Hairong , Li Huijia
Published: December 2024

Network dismantling is an important question that has attracted much attention from many different research areas, including the disruption of criminal organizations, the maintenance of stability in sensor networks, and so on. However, almost all current algorithms focus on unsigned networks, and few studies explore the problem of signed network dismantling due to its complexity and lack of data. Importantly, there is a lack of an effective quality function to assess the performance of signed network dismantling, which seriously restricts its deeper applications. To address these questions, in this paper, we design a new objective function and further propose an effective algorithm named as DSEDR, which aims to search for the best dismantling strategy based on evolutionary deep reinforcement learning. Especially, since the evolutionary computation is able to solve global optimization and the deep reinforcement learning can speed up the network computation, we integrate it for the signed network dismantling efficiently. To verify the performance of DSEDR, we apply it to a series of representative artificial and real network data and compare the efficiency with some popular baseline methods. Based on the experimental results, DSEDR has superior performance to all other methods in both efficiency and interpretability.