bluuebunny/crossref_metadata_2025 huggingface dataset:
Describing Biological Vulnerability in Small, Vulnerable Newborns in Urban Burkina Faso (DenBalo): Gut Microbiota, Immune System, and Breastmilk Assembly
Authors: Ouédraogo Lionel Olivier , Deng Lishi , Ouattara Cheick Ahmed , Compaoré Anderson , Ouédraogo Moctar , Argaw Alemayehu , Lachat Carl , Houpt Eric R. , Saidi Queen , Haerynck Filomeen , Sonnenburg Justin , Azad Meghan B. , Tavernier Simon J. , Bastos-Moreira Yuri , Toe Laeticia Celine , Dailey-Chwalibóg Trenton
Published: December 2024

Background: Small vulnerable newborns (SVNs), including those born preterm, small for gestational age, or with low birth weight, are at higher risk of neonatal mortality and long-term health complications. Early exposure to maternal vaginal microbiota and breastfeeding plays a critical role in the development of the neonatal microbiota and immune system, especially in low-resource settings like Burkina Faso, where neonatal mortality rates remain high. Objectives: The DenBalo study aims to investigate the role of maternal and neonatal factors, such as vaginal and gut microbiota, immune development, and early nutrition, in shaping health outcomes in SVNs and healthy infants. Methods: This prospective cohort observational study will recruit 141 mother-infant pairs (70 SVNs and 71 healthy controls) from four health centers in Bobo-Dioulasso, Burkina Faso. The mother-infant pairs will be followed for six months with anthropometric measurements and biospecimen collections, including blood, breast milk, saliva, stool, vaginal swabs, and placental biopsies. Multi-omics approaches, encompassing metagenomics, metabolomics, proteomics, and immune profiling, will be used to assess vaginal and gut microbiota composition and functionality, immune cell maturation, and cytokine levels at critical developmental stages. Conclusions: This study will generate comprehensive data on how microbiota, metabolomic, and proteomic profiles, along with immune system development, differ between SVNs and healthy infants. These findings will guide targeted interventions to improve neonatal health outcomes and reduce mortality, particularly in vulnerable populations.
The Effect of Fluid Restriction and Intake Conditions on the Shooting Performance of Competitive Adolescent Handball Players
Authors: Uylas Erdem , Mancı Egemen , Rodriguez-Sanchez Nidia , Bediz Cem Şeref , Günay Erkan
Published: December 2024

(1) Background: This study aimed to investigate the effects of fluid restriction and intake (water vs. sports drink) on shooting accuracy and speed in adolescent handball players, a population with high sensitivity to hydration levels yet understudied in this context. (2) Methods: A total of 47 adolescent competitive handball players (15.04 ± 1.5 years) were included in this study, and the participants were divided into low, average, and high performance according to their shooting performance in the familiarization session. All participants were exposed to fluid restriction and intake conditions during handball training on different days. Before and after the training sessions, changes in shooting accuracy and speed were evaluated. (3) Results: The training protocol resulted in body mass loss in the Average Performer group (p = 0.001). Compared to fluid restriction, fluid intake (water intake and sports drink intake) post-training had a positive main effect on shooting accuracy, F(2, 88) = 34.32, p < 0.001, ηp2 = 0.44, and shooting speed, F(1, 61) = 4.05, p = 0.35, ηp2 = 0.84. (4) Conclusions: Hydration level plays an important role in shooting accuracy and speed performance in adolescent handball players. Therefore, fluid intake integrated into training or match sessions may contribute to the maintenance and improvement of shooting performance.
Advancements in Detection Methods for Salmonella in Food: A Comprehensive Review
Authors: Patel Aayushi , Wolfram Andrew , Desin Taseen S.
Published: December 2024

Non-typhoidal Salmonella species are one of the leading causes of gastrointestinal disease in North America, leading to a significant burden on the healthcare system resulting in a huge economic impact. Consequently, early detection of Salmonella species in the food supply, in accordance with food safety regulations, is crucial for protecting public health, preventing outbreaks, and avoiding serious economic losses. A variety of techniques have been employed to detect the presence of this pathogen in the food supply, including culture-based, immunological, and molecular methods. The present review summarizes these methods and highlights recent updates on promising emerging technologies, including aptasensors, Surface Plasmon Resonance (SPR), and Surface Enhanced Raman Spectroscopy (SERS).
In Vitro Evaluation, Chemical Profiling, and In Silico ADMET Prediction of the Pharmacological Activities of Artemisia absinthium Root Extract
Authors: Alsaleh Asma N. , Aziz Ibrahim M. , Aljowaie Reem M. , Alshalan Rawan M. , Alkubaisi Noorah A. , Aboul-Soud Mourad A. M.
Published: December 2024

Artemisia absinthium L., is a plant with established pharmacological properties, but the A. absinthium root extract (AARE) remains unexplored. The aim of this study was to examine the chemical composition of AARE and assess its biological activity, which included antidiabetic, antibacterial, anticancer, and antioxidant properties. GC-MS was used to analyze the chemical components. The antioxidant activity of the total phenolic and flavonoid content was evaluated. Antibacterial activity and cytotoxic effects were identified. Enzyme inhibition experiments were performed to determine its antidiabetic potential. Molecular docking was utilized to evaluate the potential antioxidant, antibacterial, and anticancer activities of the compounds from AARE using Maestro 11.5 from the Schrödinger suite. AARE exhibited moderate antioxidant activity in DPPH (IC50: 172.41 ± 3.15 μg/mL) and ABTS (IC50: 378.94 ± 2.18 μg/mL) assays. Cytotoxicity tests on MCF-7 and HepG2 cancer cells demonstrated significant anticancer effects, with IC50 values of 150.12 ± 0.74 μg/mL and 137.11 ± 1.33 μg/mL, respectively. Apoptotic studies indicated an upregulation of pro-apoptotic genes (caspase-3, 8, 9, Bax) and a downregulation of anti-apoptotic markers (Bcl-2 and Bcl-Xl). AARE also inhibited α-amylase and α-glucosidase, suggesting potential antidiabetic effects, with IC50 values of 224.12 ± 1.17 μg/mL and 243.35 ± 1.51 μg/mL. Antibacterial assays revealed strong activity against Gram-positive bacteria. Molecular docking and pharmacokinetic analysis identified promising inhibitory effects of key AARE compounds on NADPH oxidase, E. coli Gyrase B, and Topoisomerase IIα, with favorable drug-like properties. These findings suggest AARE’s potential in treating cancer, diabetes, and bacterial infections, warranting further in vivo and clinical studies.
Experiences from Clinical Research and Routine Use of Florbetaben Amyloid PET—A Decade of Post-Authorization Insights
Authors: Jovalekic Aleksandar , Bullich Santiago , Roé-Vellvé Núria , Kolinger Guilherme Domingues , Howard Lorelei R. , Elsholz Floriana , Lagos-Quintana Mariana , Blanco-Rodriguez Beatriz , Pérez-Martínez Esther , Gismondi Rossella , Perrotin Audrey , Chapleau Marianne , Keegan Richard , Mueller Andre , Stephens Andrew W. , Koglin Norman
Published: December 2024

Florbetaben (FBB) is a radiopharmaceutical approved by the FDA and EMA in 2014 for the positron emission tomography (PET) imaging of brain amyloid deposition in patients with cognitive impairment who are being evaluated for Alzheimer’s disease (AD) or other causes of cognitive decline. Initially, the clinical adoption of FBB PET faced significant barriers, including reimbursement challenges and uncertainties regarding its integration into diagnostic clinical practice. This review examines the progress made in overcoming these obstacles and describes the concurrent evolution of the diagnostic landscape. Advances in quantification methods have further strengthened the traditional visual assessment approach. Over the past decade, compelling evidence has emerged, demonstrating that amyloid PET has a strong impact on AD diagnosis, management, and outcomes across diverse clinical scenarios, even in the absence of amyloid-targeted therapies. Amyloid PET imaging has become essential in clinical trials and the application of new AD therapeutics, particularly for confirming eligibility criteria (i.e., the presence of amyloid plaques) and monitoring biological responses to amyloid-lowering therapies. Since its approval, FBB PET has transitioned from a purely diagnostic tool aimed primarily at excluding amyloid pathology to a critical component in AD drug development, and today, it is essential in the diagnostic workup and therapy management of approved AD treatments.
Effects of Semaglutide and Tirzepatide on Bone Metabolism in Type 2 Diabetic Mice
Authors: Lv Fang , Cai Xiaoling , Lin Chu , Yang Wenjia , Ji Linong
Published: December 2024

Background/Objectives: Type 2 diabetes and weight loss are associated with detrimental skeletal health. Incretin-based therapies (GLP-1 receptor agonists, and dual GIP/GLP-1 receptor agonists) are used clinically to treat diabetes and obesity. The potential effects of semaglutide and tirzepatide on bone metabolism in type 2 diabetic mice remain uncertain. Methods: Combined streptozotocin and high fat feeding were employed in female C57BL/6J mice to promote hyperglycemia. Mice were administered for 4 weeks with a saline vehicle (sc., once-daily), semaglutide (40 μg/kg/d, sc., every three days), or tirzepatide (10 nmol/kg, sc., once-daily). Bone strength was assessed by three-point bending. Femur microarchitecture was determined by micro-CT, and bone formation and resorption parameters were measured by histomorphometric analysis. Serum was collected to measure bone resorption (C-telopeptide fragments of type I collagen, CTX) and formation (procollagen type 1 N-terminal propeptide, P1NP) biomarkers, respectively. The expression of bone metabolism-related genes was evaluated in the bone using RT-PCR. Results: Glucose levels significantly reduced after 4 weeks of semaglutide and tirzepatide treatment (both p < 0.05) compared with vehicle treatment. Tirzepatide led to more weight loss than semaglutide. Compared to saline-treated diabetic mice, the mean femur length was shorter in the tirzepatide group. After treatment with tirzepatide or semaglutide, cortical bone and trabecular bone parameters did not change significantly compared to saline-treated diabetic mice, except that cortical thickness was lower in the semaglutide group compared to the saline group (p = 0.032). Though CTX and P1NP levels decreased, however, the change in CTX and P1NP levels did not differ among the four groups during the 4 weeks of treatment (all p > 0.05). Semaglutide affected RANKL and OPG mRNA expression and increased the ratio of OPG/RANKL. No significant difference was found in the quantity of Col1a1, RANKL, OPG, and RUNX2 between tirzepatide- and saline-treated diabetic mice. Conclusions: The 4-week treatment with semaglutide and tirzepatide had a neutral effect on bone mass compared with the controls, and most of the bone microarchitecture parameters were also comparable between groups in diabetic mice. A better understanding of incretin-based therapies on bone metabolism in patients with diabetes requires further evaluation in large clinical trials.
Editorial 15th Anniversary of Pharmaceutics—Improvement of Drug Bioavailability
Authors: Vandamme Thierry F.
Published: December 2024

Improving the health of humans and animals depends on the discovery of new active molecules as well as improving the bioavailability of molecules already marketed [...]
Concomitant Inhibition and Collaring of Dual-Species Biofilms Formed by Candida auris and Staphylococcus aureus by Triazole Based Small Molecule Inhibitors
Authors: Parveen Humaira , Mukhtar Sayeed , Albalawi Mona O. , Khasim Syed , Ahmad Aijaz , Wani Mohmmad Younus
Published: December 2024

Background/Objectives: Biofilm-associated infections, particularly those involving Candida auris and Staphylococcus aureus, pose significant challenges in clinical settings due to their resilience and resistance to conventional treatments. This study aimed to synthesize novel triazole derivatives containing a piperazine ring via click chemistry and evaluate their efficacy in disrupting biofilms formed by these pathogens. Methods: Triazole derivatives were synthesized using click chemistry techniques. The antimicrobial activity of the compounds was tested against planktonic cells of C. auris and S. aureus in single and dual-species culture conditions. Biofilm disruption efficacy was assessed, alongside the evaluation of physicochemical properties, oral bioavailability potential, and toxicity profiles. Results: The compound T3 demonstrated potent antimicrobial activity against planktonic cells of C. auris and S. aureus in both single and dual-species cultures. T3 exhibited significant efficacy in reducing microbial viability within biofilms formed by these pathogens. Physicochemical analyses revealed favorable solubility and permeability profiles, supporting its potential for oral bioavailability. Toxicity assessments showed a non-toxic profile, highlighting a promising safety margin for further development. Conclusions: This study underscores the anti-biofilm properties of novel triazole-piperazine derivatives, particularly T3, against single and dual-species biofilms of C. auris and S. aureus. These findings position T3 as a promising candidate for developing therapies targeting polymicrobial infections and provide a foundation for future research into alternative strategies for combating biofilm-associated infections.
Enhanced Stability and In Vitro Biocompatibility of Chitosan-Coated Lipid Vesicles for Indomethacin Delivery
Authors: Abu Koush Angy , Popa Eliza Gratiela , Pricop Daniela Angelica , Nita Loredana , Foia Cezar-Ilie , Pauna Ana-Maria Raluca , Buca Beatrice Rozalina , Pavel Liliana Lacramioara , Mititelu-Tartau Liliana
Published: December 2024

Background: Lipid vesicles, especially those utilizing biocompatible materials like chitosan (CHIT), hold significant promise for enhancing the stability and release characteristics of drugs such as indomethacin (IND), effectively overcoming the drawbacks associated with conventional drug formulations. Objectives: This study seeks to develop and characterize novel lipid vesicles composed of phosphatidylcholine and CHIT that encapsulate indomethacin (IND-ves), as well as to evaluate their in vitro hemocompatibility. Methods: The systems encapsulating IND were prepared using a molecular droplet self-assembly technique, involving the dissolution of lipids, cholesterol, and indomethacin in ethanol, followed by sonication and the gradual incorporation of a CHIT solution to form stable vesicular structures. The vesicles were characterized in terms of size, morphology, Zeta potential, and encapsulation efficiency and the profile release of drug was assessd. In vitro hemocompatibility was evaluated by measuring erythrocyte lysis and quantifying hemolysis rates. Results: The IND-ves exhibited an entrapment efficiency of 85%, with vesicles averaging 317.6 nm in size, and a Zeta potential of 24 mV, indicating good stability in suspension. In vitro release kinetics demonstrated an extended release profile of IND from the vesicles over 8 h, contrasting with the immediate release observed from plain drug solutions. The hemocompatibility assessment revealed that IND-ves exhibited minimal hemolysis, comparable to control groups, indicating good compatibility with erythrocytes. Conclusions: IND-ves provide a promising approach for modified indomethacin delivery, enhancing stability and hemocompatibility. These findings suggest their potential for effective NSAID delivery, with further in vivo studies required to explore clinical applications.
From Traditional to Digital: The Evolution of Business Models in Hospitality Through Platforms
Authors: Zeqiri Adelina
Published: December 2024

The hospitality business model has undergone profound changes influenced by successive waves of technological advances and platforms. This paper explores how different waves of technologies and platforms are shaping the evolution of the hospitality business model. Employing a systematic literature review, this research analyzes how platform innovation has driven significant shifts in the industry. The analysis yielded four key findings. Firstly, the incorporation of digital technologies resulted in a radical transformation of conventional business models, prompting substantial alterations in marketing strategies, distribution processes, and consumer behavior patterns. Secondly, the industry has undergone five significant technological revolutions, evolving from steam-powered transportation to the integration of artificial intelligence (AI) and the Internet of Things (IoT), thereby driving a shift towards innovation-centric business models. Thirdly, the advent of digital platforms, most notably online travel agencies (OTAs), posed a challenge to traditional hospitality models by introducing personalized experiences and disrupting established market norms. While digital platforms offer advantages such as enhanced convenience and customization, they also present challenges, including intensified competition and declining profit margins. Nonetheless, technological advancements such as AI, virtual reality (VR), and augmented reality (AR) present a potential avenue for redefining value propositions and enhancing customer experiences. Fourth, the future of the hospitality business model is poised for further disruption, influenced by the emergence of new virtual platforms and services. This could potentially result in a reshaping of the industry’s value chain and value proposition.
The Finite Element Method in Thermosetting Polymers’ and FRPs’ Supramolecular Structure and Thermomechanical Properties’ Modeling
Authors: Korolev Alexander , Zadorin Alexander , Mishnev Maxim
Published: December 2024

The object of research is cured thermosetting epoxy polymer and FRP on the base of the same polymer matrix. The purpose of this research is to develop the finite element (FE) method in the modeling of cured thermosetting polymers and FRPs to predict their mechanical and thermal properties. The structural mathematical modeling with subsequent computer FE modeling was performed. The results of FE modeling were compared with the experimental data of cured polymer’s and FRP’s tensile strength and deformations under mechanical load at different temperatures. The design of the polymer’s FE model was based on the tetrahedral supramolecular structure and then transformed into FRP’s model by integrating glass fiber rods. Using the structural density as the structure model’s parameter, the relative size and disposition of the finite elements were determined. The viscoelastic properties are set in the model by regulating the structural density and compressive/tensile properties of joints. The long-term plastic deformation and stress relaxation were determined as the result of the supramolecular structure’s inner shearing with the decrease of its structural density. The FE models of the cured epoxy polymer and FRP were developed, making it possible to predict short-term and long-term deformations under load with high accuracy considering the temperature factor.
Influence of Modified PVA Fiber on Ultra-High Performance Concrete and Its Enhancing Mechanism
Authors: Chen Zhiyuan , Fan Hongyu , Zheng Wanying , Zhang Siheng , Wu Xi , Fu Tengfei , Yu Demei
Published: December 2024

In this study, the properties of ultra-high-performance concrete (UHPC) were enhanced by adding modified polyvinyl alcohol (PVA) fibers. The specimens with different curing ages were evaluated in various aspects to investigate the effects of different dosages, lengths, and surface treatments of PVA fibers on the performance of UHPC. The performance was compared with that of steel fiber-reinforced UHPC with the same ratio and multiple dosages. At the same time, the distribution of fibers and the morphology of fibers were observed by a scanning electron microscope, and the mechanism of fiber reinforcement was discussed. The results showed that the mechanical properties were significantly affected by the fiber dosage, length, and surface treatment. Based on the test results, the optimum PVA fiber addition can increase the compressive strength and flexural strength by 12.0% and 6.0% compared to the control UHPC without fibers. A comprehensive evaluation was carried out and indicated that the optimum PVA fiber addition has the potential to replace 0.5% steel fiber in certain conditions.
Too Gay for the Evangelicals, Too Evangelical for the Gays: A Narrative and Autoethnographic Study of a Celibate–Gay Testimony
Authors: Aylen Luke
Published: December 2024

The ecclesiastical discourse in Britian over homosexuality has included a significant focus on the narratives and experience of LGBTQ+ people. However, the relationship between and respective authority given to human experience and the Bible within church debates remains a matter of contention, especially among evangelicals committed to ‘biblicism’. This study considers how even those unconvinced about experience as a ‘source’ of theology might still engage with queer narratives as an invitation for personal and cultural reflexivity and how the plausibility of theological claims might be tested whilst still prioritising Scripture. I examine testimony through a three-stage study. First, I conduct a narrative analysis of audiovisual recordings of my own prior practice of testimony as a celibate gay evangelical. Second, I offer up new, autoethnographic, thick descriptions of three pivotal crisis moments. Third, I theologically reflect upon these in relation to Romans 12.1–2 and the meta-theme of identity formation. I argue that LGBTQ+ testimonies have the potential to illuminate and thus transform heteronormative cultural patterns within the church; I argue that Christian identity formation must include the central integration of God’s identification of a person in Christ; and I attempt to model how Christians might cautiously discern God’s activity within a practice of testimony.
Arctic Sea Ice Surface Temperature Retrieval from FengYun-3A MERSI-I Data
Authors: Li Yachao , Liu Tingting , Wang Zemin , Shokr Mohammed , Yuan Menglin , Yuan Qiangqiang , Wu Shiyu
Published: December 2024

Arctic sea-ice surface temperature (IST) is an important environmental and climatic parameter. Currently, wide-swath sea-ice surface temperature products have a spatial resolution of approximately 1000 m. The Medium Resolution Spectral Imager (MERSI-I) offers a thermal infrared channel with a wide-swath width of 2900 km and a high spatial resolution of 250 m. In this study, we developed an applicable single-channel algorithm to retrieve ISTs from MERSI-I data. The algorithm accounts for the following challenges: (1) the wide range of incidence angle; (2) the unstable snow-covered ice surface; (3) the variation in atmospheric water vapor content; and (4) the unique spectral response function of MERSI-I. We reduced the impact of using a constant emissivity on the IST retrieval accuracy by simulating the directional emissivity. Different ice surface types were used in the simulation, and we recommend the sun crust type as the most suitable for IST retrieval. We estimated the real-time water vapor content using a band ratio method from the MERSI-I near-infrared data. The results show that the retrieved IST was lower than the buoy measurements, with a mean bias and root-mean-square error (RMSE) of −1.928 K and 2.616 K. The retrieved IST is higher than the IceBridge measurements, with a mean bias and RMSE of 1.056 K and 1.760 K. Compared with the original algorithm, the developed algorithm has higher accuracy and reliability. The sensitivity analysis shows that the atmospheric water vapor content with an error of 20% may lead to an IST retrieval error of less than 1.01 K.
Shallow Water Bathymetry Inversion Based on Machine Learning Using ICESat-2 and Sentinel-2 Data
Authors: Ye Mengying , Yang Changbao , Zhang Xuqing , Li Sixu , Peng Xiaoran , Li Yuyang , Chen Tianyi
Published: December 2024

Shallow water bathymetry is essential for maritime navigation, environmental monitoring, and coastal management. While traditional methods such as sonar and airborne LiDAR provide high accuracy, their high cost and time-consuming nature limit their application in remote and sensitive areas. Satellite remote sensing offers a cost-effective and rapid alternative for large-scale bathymetric inversion, but it still relies on significant in situ data to establish a mapping relationship between spectral data and water depth. The ICESat-2 satellite, with its photon-counting LiDAR, presents a promising solution for acquiring bathymetric data in shallow coastal regions. This study proposes a rapid bathymetric inversion method based on ICESat-2 and Sentinel-2 data, integrating spectral information, the Forel-Ule Index (FUI) for water color, and spatial location data (normalized X and Y coordinates and polar coordinates). An automated script for extracting bathymetric photons in shallow water regions is provided, aiming to facilitate the use of ICESat-2 data by researchers. Multiple machine learning models were applied to invert bathymetry in the Dongsha Islands, and their performance was compared. The results show that the XG-CID and RF-CID models achieved the highest inversion accuracies, 93% and 94%, respectively, with the XG-CID model performing best in the range from −10 m to 0 m and the RF-CID model excelling in the range from −15 m to −10 m.
Differential Study on Estimation Models for Indica Rice Leaf SPAD Value and Nitrogen Concentration Based on Hyperspectral Monitoring
Authors: Zhang Yufen , Liang Kaiming , Zhu Feifei , Zhong Xuhua , Lu Zhanhua , Chen Yibo , Pan Junfeng , Lu Chusheng , Huang Jichuan , Ye Qunhuan , Yin Yuanhong , Peng Yiping , Mo Zhaowen , Fu Youqiang
Published: December 2024

Soil and plant analyzer development (SPAD) value and leaf nitrogen concentration (LNC) based on dry weight are important indicators affecting rice yield and quality. However, there are few reports on the use of machine learning algorithms based on hyperspectral monitoring to synchronously predict SPAD value and LNC of indica rice. Meixiangzhan No. 2, a high-quality indica rice, was grown at different nitrogen rates. A hyperspectral device with an integrated handheld leaf clip-on leaf spectrometer and an internal quartz-halogen light source was conducted to monitor the spectral reflectance of leaves at different growth stages. Linear regression (LR), random forest (RF), support vector regression (SVR), and gradient boosting regression tree (GBRT) were employed to construct models. Results indicated that the sensitive bands for SPAD value and LNC were displayed to be at 350–730 nm and 486–727 nm, respectively. Normalized difference spectral indices NDSI (R497, R654) and NDSI (R729, R730) had the strongest correlation with leaf SPAD value (R = 0.97) and LNC (R = −0.90). Models constructed via RF and GBRT were markedly superior to those built via LR and SVR. For prediction of leaf SPAD value and LNC, the model constructed with the RF algorithm based on whole growth periods of spectral reflectance performed the best, with R2 values of 0.99 and 0.98 and NRMSE values of 2.99% and 4.61%. The R2 values of 0.98 and 0.83 and the NRMSE values of 4.88% and 12.16% for the validation of leaf SPAD value and LNC were obtained, respectively. Results indicate that there are significant spectral differences associated with SPAD value and LNC. The model built with RF had the highest accuracy and stability. Findings can provide a scientific basis for non-destructive real-time monitoring of leaf color and precise fertilization management of indica rice.
Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications
Authors: Feng Pengju , Song Kaishan , Wen Zhidan , Tao Hui , Yu Xiangfei , Shang Yingxin
Published: December 2024

Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintaining the stability of river ecosystems and driving the global carbon cycle. In this study, the in situ samples of aCDOM(355) and DOC collected along the main stream of the Songhua River were matched with Sentinel-2 imagery. Multiple linear regression and five machine learning models were used to analyze the data. Among these models, XGBoost demonstrated a superior, highly stable performance on the validation set (R2 = 0.85, RMSE = 0.71 m−1). The multiple linear regression results revealed a strong correlation between CDOM and DOC (R2 = 0.73), indicating that CDOM can be used to indirectly estimate DOC concentrations. Significant seasonal variations in the CDOM distribution in the Songhua River were observed: aCDOM(355) in spring (6.23 m−1) was higher than that in summer (5.3 m−1) and autumn (4.74 m−1). The aCDOM(355) values in major urban areas along the Songhua River were generally higher than those in non-urban areas. Using the predicted DOC values and annual flow data at the sites, the annual DOC flux in Harbin was calculated to be approximately 0.2275 Tg C/Yr. Additionally, the spatial variation in annual CDOM was influenced by both natural changes in the watershed and human activities. These findings are pivotal for a deeper understanding of the role of river systems in the global carbon cycle.
Recognition of Ground Clutter in Single-Polarization Radar Based on Gated Recurrent Unit
Authors: Wang Jiaxin , Zou Haibo , Zhong Landi , Hu Zhiqun
Published: December 2024

A new method is proposed for identifying ground clutter in single-polarization radar data based on the gated recurrent unit (GRU) neural network. This method needs five independent input variables related to radar reflectivity structure, which are the reflectivity at current tilt, the reflectivity at the upper tilt, the reflectivity at 3.5 km, the echo top height, and the texture of reflectivity at current tilt, respectively. The performance of the new method is compared with that of the traditional method used in the Weather Surveillance Radar 1988-Doppler system in four cases with different scenarios. The results show that the GRU method is more effective than the traditional method in capturing ground clutter, particularly in situations where ground clutter exists at two adjacent elevation angles. Furthermore, in order to assess the new method more comprehensively, 709 radar scans from Nanchang radar in July 2019 and 708 scans from Jingdezhen radar in June 2019 were collected and processed by the two methods, and the frequency map of radar reflectivity exceeding 20 dBZ was analyzed. The results indicate that the GRU method has a stronger ability than the traditional method to identify and remove ground clutter. Meanwhile, the GRU method can also preserve meteorological echoes well.
A Weakly Supervised Multimodal Deep Learning Approach for Large-Scale Tree Classification: A Case Study in Cyprus
Authors: Amin Arslan , Kamilaris Andreas , Karatsiolis Savvas
Published: December 2024

Forest ecosystems play an essential role in ecological balance, supporting biodiversity and climate change mitigation. These ecosystems are crucial not only for ecological stability but also for the local economy. Performing a tree census at a country scale via traditional methods is resource-demanding, error-prone, and requires significant effort by a large number of experts. While emerging technologies such as satellite imagery and AI provide the means for achieving promising results in this task with less effort, considerable effort is still required by experts to annotate hundreds or thousands of images. This study introduces a novel methodology for a tree census classification system which leverages historical and partially labeled data, employs probabilistic data imputation and a weakly supervised training technique, and thus achieves state-of-the-art precision in classifying the dominant tree species of Cyprus. A crucial component of our methodology is a ResNet50 model which takes as input high spatial resolution satellite imagery in the visible band and near-infrared band, as well as topographical features. By applying a multimodal training approach, a classification accuracy of 90% among nine targeted tree species is achieved.
A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width
Authors: Li Wenqi , Zhang Chendi , Puhl David , Pan Xiao , Hassan Marwan A. , Bird Stephen , Yang Kejun , Zhao Yang
Published: December 2024

River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. To address this issue, we aimed to develop an automatic framework specifically for delineating river channels and measuring the bankfull widths at small spatial intervals along the channel. The DeepLabV3+ Convolutional Neural Network (CNN) model was employed to accurately delineate channel boundaries and a Voronoi Diagram approach was complemented as the river width algorithm (RWA) to calculate river bankfull widths. The CNN model was trained by images across four river types and performed well with all the evaluating metrics (mIoU, Accuracy, F1-score, and Recall) higher than 0.97, referring to the accuracy over 97% in prediction. The RWA outperformed other existing river width calculation methods by showing lower errors. The application of the framework in the Lillooet River, Canada, presented the capacity of this methodology to obtain detailed distributions of hydraulic and hydrological parameters, including flow resistance, flow energy, and sediment transport capacity, based on high-resolution channel widths. Our work highlights the significant potential of the newly developed framework in acquiring high-resolution channel width information and characterizing fluvial dynamics based on these widths along river channels, which contributes to facilitating cost-effective integrated river management.
Concurrent Validity and Relative Reliability of the RunScribe™ System for the Assessment of Spatiotemporal Gait Parameters During Walking
Authors: Ráfales-Perucha Andrés , Bravo-Viñuales Elisa , Molina-Molina Alejandro , Cartón-Llorente Antonio , Cardiel-Sánchez Silvia , Roche-Seruendo Luis E.
Published: December 2024

The evaluation of gait biomechanics using portable inertial measurement units (IMUs) offers real-time feedback and has become a crucial tool for detecting gait disorders. However, many of these devices have not yet been fully validated. The aim of this study was to assess the concurrent validity and relative reliability of the RunScribe™ system for measuring spatiotemporal gait parameters during walking. A total of 460 participants (age: 36 ± 13 years; height: 173 ± 9 cm; body mass: 70 ± 13 kg) were asked to walk on a treadmill at 5 km·h−1. Spatiotemporal parameters of step frequency (SF), step length (SL), step time (ST), contact time (CT), swing time (SwT), stride time (StT), stride length (StL) and normalized stride length (StL%) were measured through RunScribe™ and OptoGait™ systems. Bland–Altman analysis indicated small systematic biases and random errors for all variables. Pearson correlation analysis showed strong correlations (0.70–0.94) between systems. The intraclass correlation coefficient supports these results, except for contact time (ICC = 0.64) and swing time (ICC = 0.34). The paired t-test showed small differences in SL, StL and StL% (≤0.25) and large in CT and SwT (1.2 and 2.2, respectively), with no differences for the rest of the variables. This study confirms the accuracy of the RunScribe™ system for assessing spatiotemporal parameters during walking, potentially reducing the barriers to continuous gait monitoring and early detection of gait issues.
Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System
Authors: Liu Shing-Hong , Wu Bo-Yan , Zhu Xin , Chin Chiun-Li
Published: December 2024

Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users. Now, ballistocardiogram (BCG) and impedance plethysmogram (IPG) could be used to perform the cuffless BP measurement. Thus, the aim of this study is to realize edge computing for the BP measurement in real time, which includes measurements of BCG and IPG signals, digital signal process, feature extraction, and BP estimation by machine learning algorithm. This system measured BCG and IPG signals from a bodily weight-fat scale with the self-made circuits. The signals were filtered to reduce the noise and segmented by 2 s. Then, we proposed a flowchart to extract the parameter, pulse transit time (PTT), within each segment. The feature included two calibration-based parameters and one calibration-free parameter was used to estimate BP with XGBoost. In order to realize the system in STM32F756ZG NUCLEO development board, we limited the hyperparameters of XGBoost model, including maximum depth (max_depth) and tree number (n_estimators). Results show that the error of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in server-based computing are 2.64 ± 9.71 mmHg and 1.52 ± 6.32 mmHg, and in edge computing are 2.2 ± 10.9 mmHg and 1.87 ± 6.79 mmHg. This proposed method significantly enhances the feasibility of bodily weight-fat scale in the BP measurement for effective utilization in mobile health applications.
Air Pollution Measurement and Dispersion Simulation Using Remote and In Situ Monitoring Technologies in an Industrial Complex in Busan, South Korea
Authors: Dehkhoda Naghmeh , Sim Juhyeon , Shin Juseon , Joo Sohee , Cho Sung Hwan , Kim Jeong Hun , Noh Youngmin
Published: December 2024

Rapid industrialization and the influx of human resources have led to the establishment of industrial complexes near urban areas, exposing residents to various air pollutants. This has led to a decline in air quality, impacting neighboring residential areas adversely, which highlights the urgent need to monitor air pollution in these areas. Recent advancements in technology, such as Solar Occultation Flux (SOF) and Sky Differential Optical Absorption Spectroscopy (SkyDOAS) used as remote sensing techniques and mobile extraction Fourier Transform Infrared Spectrometry (MeFTIR) used as an in situ technique, now offer enhanced precision in estimating the pollutant emission flux and identifying primary sources. In a comprehensive study conducted in 2020 in the Sinpyeong Jangrim Industrial Complex in Busan City, South Korea, a mobile laboratory equipped with SOF, SkyDOAS, and MeFTIR technologies was employed to approximate the emission flux of total alkanes, sulfur dioxide (SO2), nitrogen dioxide (NO2), formaldehyde (HCHO), and methane (CH4). Using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) diffusion model, pollutant dispersion to residential areas was simulated. The highest average daily emission flux was observed for total alkanes, with values of 69.9 ± 71.6 kg/h and 84.1 ± 85.8 kg/h in zones S1 and S2 of the Sinpyeong Jangrim Industrial Complex, respectively. This is primarily due to the prevalence of metal manufacturing and mechanical equipment industries in the area. The HYSPLIT diffusion model confirmed elevated pollution levels in residential areas located southeast of the industrial complex, underscoring the influence of the dominant northwesterly wind direction and wind speed on pollutant dispersion. This highlights the urgent need for targeted interventions to address and mitigate air pollution in downwind residential areas. The total annual emission fluxes were estimated at 399,984 kg/yr and 398,944 kg/yr for zones S1 and S2, respectively. A comparison with the Pollutant Release and Transfer Registers (PRTRs) survey system revealed that the total annual emission fluxes in this study were approximately 24.3 and 4.9 times higher than those reported by PRTRs. This indicates a significant underestimation of the impact of small businesses on local air quality, which was not accounted for in the PRTR survey system.
The Development of a Forceps-Adaptable Pressure Device for Instrumental Delivery: A Proof-of-Concept Study for Clinical and Educational Applications
Authors: Hivert Mathieu , Bengler Cyril , De Jonckheere Julien , Gaultier Franck , Pécout Marie , Mayeur Olivier , Rubod Chrystèle
Published: December 2024

Objective: To develop and validate a device that measures the pressure exerted by forceps on the fetal head for clinical use. Background: The lack of clinical tools to quantify forceps pressure on the fetal head may impact maternal and neonatal outcomes. Existing studies have not measured the direct contact pressure between forceps blades and the fetal head, highlighting the need for innovation. Methods: We integrated fluid pressure transducers into obstetric forceps using fluid-filled tubing encased in flexible silicone socks attached to the blades. Tubing materials—polyvinyl chloride (PVC) and polyurethane (PU)—and fluids (air and water) were tested with both biocompatible and non-biocompatible silicone socks. An onboard electronic board collected pressure data and transmitted them via Bluetooth for real-time analysis. The system was evaluated on a custom-built bench simulating forceps application. Results: Air-filled tubing exhibited significant drift and low accuracy due to air compressibility. Water-filled PU tubing reduced drift but was still suboptimal. Water-filled PVC tubing with both types of silicone socks provided the best results, showing minimal drift and a strong correlation between measured pressures and applied forces. Conclusions: The developed device represents a significant advancement, as no existing system measures the pressure exerted by forceps blades on the fetal head. By effectively measuring pressure across the entire contact surface in real time, it offers applications in both training and clinical practice. The device allows for objective feedback, potentially improving the safety and efficacy of forceps deliveries. Future work includes comprehensive mannequin tests and eventual in vivo studies to validate its effectiveness in realistic settings, aiming to enhance obstetric training and reduce maternal and neonatal complications.
Cloud Removal in the Tibetan Plateau Region Based on Self-Attention and Local-Attention Models
Authors: Zheng Guoqiang , Zhao Tianle , Liu Yaohui
Published: December 2024

Optical remote sensing images have a wide range of applications but are often affected by cloud cover, which interferes with subsequent analysis. Therefore, cloud removal has become indispensable in remote sensing data processing. The Tibetan Plateau, as a sensitive region to climate change, plays a crucial role in the East Asian water cycle and regional climate due to its snow cover. However, the rich ice and snow resources, rapid snow condition changes, and active atmospheric convection in the plateau as well as its surrounding mountainous areas, make optical remote sensing prone to cloud interference. This is particularly significant when monitoring snow cover changes, where cloud removal becomes essential considering the complex terrain and unique snow characteristics of the Tibetan Plateau. This paper proposes a novel Multi-Scale Attention-based Cloud Removal Model (MATT). The model integrates global and local information by incorporating multi-scale attention mechanisms and local interaction modules, enhancing the contextual semantic relationships and improving the robustness of feature representation. To improve the segmentation accuracy of cloud- and snow-covered regions, a cloud mask is introduced in the local-attention module, combined with the local interaction module to modulate and reconstruct fine-grained details. This enables the simultaneous representation of both fine-grained and coarse-grained features at the same level. With the help of multi-scale fusion modules and selective attention modules, MATT demonstrates excellent performance on both the Sen2_MTC_New and XZ_Sen2_Dataset datasets. Particularly on the XZ_Sen2_Dataset, it achieves outstanding results: PSNR = 29.095, SSIM = 0.897, FID = 125.328, and LPIPS = 0.356. The model shows strong cloud removal capabilities in cloud- and snow-covered areas in mountainous regions while effectively preserving snow information, and providing significant support for snow cover change studies.
Performance Recovery and Stability Analysis of Disturbance Observer Under Unmodeled Dynamics
Authors: Joo Youngjun
Published: December 2024

Feedback system design is often achieved by neglecting the unmodeled dynamics, such as the actuator and sensor, to reduce design complexity. It is based on an assumption that the unmodeled dynamics are fast enough to be negligible. However, it may cause severe problems for the stability or performance of the overall system, especially, when the controller contains the fast dynamics or uses the high-gain feedback term. A disturbance observer has been widely employed in many industrial applications due to its simple structure and powerful ability to reject disturbances and compensate plant uncertainties. However, since the disturbance observer contains fast dynamics in its structure, the analysis of the effect of the unmodeled dynamics on the disturbance observer-based control is mandatory. This paper reveals the robustness and disturbance rejection performance of the disturbance observer based on the singular perturbation theory and proposes its design guideline for robust stability in the presence of unmodeled dynamics. In addition, this paper presents that the disturbance observer recovers a nominal performance designed for a nominal model of the plant.
Wearable Surface Electromyography System to Predict Freeze of Gait in Parkinson’s Disease Patients
Authors: Moore Anna , Li Jinxing , Contag Christopher H. , Currano Luke J. , Pyles Connor O. , Hinkle David A. , Patil Vivek Shinde
Published: December 2024

Freezing of gait (FOG) is a disabling yet poorly understood paroxysmal gait disorder affecting the vast majority of patients with Parkinson’s disease (PD) as they reach advanced stages of the disorder. Falling is one of the most disabling consequences of a FOG episode; it often results in injury and a future fear of falling, leading to diminished social engagement, a reduction in general fitness, loss of independence, and degradation of overall quality of life. Currently, there is no robust or reliable treatment against FOG in PD. In the absence of reliable and effective treatment for Parkinson’s disease, alleviating the consequences of FOG represents an unmet clinical need, with the first step being reliable FOG prediction. Current methods for FOG prediction and prevention cannot provide real-time readouts and are not sensitive enough to detect changes in walking patterns or balance. To fill this gap, we developed an sEMG system consisting of a soft, wearable garment (pair of shorts and two calf sleeves) embedded with screen-printed electrodes and stretchable traces capable of picking up and recording the electromyography activities from lower limb muscles. Here, we report on the testing of these garments in healthy individuals and in patients with PD FOG. The preliminary testing produced an initial time-to-onset commencement that persisted > 3 s across all patients, resulting in a nearly 3-fold drop in sEMG activity. We believe that these initial studies serve as a solid foundation for further development of smart digital textiles with integrated bio and chemical sensors that will provide AI-enabled, medically oriented data.