bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining Endocrine Therapy with Trastuzumab Emtansine Improves Progression-Free Survival and Overall Survival in HER2-Positive, Hormone Receptor-Positive Metastatic Breast Cancer
Authors: Kınıkoğlu Oğuzcan , Odabas Hatice , Altıntaş Yunus Emre , Yıldız Anıl , Çakan Burçin , Akdağ Goncagül , Yıldırım Sedat , Bal Hamit , Kaya Tuğba , Tünbekici Salih , Işık Deniz , Başoğlu Tuğba , Yıldırım Mahmut Emre , Turan Nedim
Published: June 2024

Background and Objectives: Patients with human epidermal growth factor receptor 2 (HER2) -positive, hormone receptor-positive (HR-positive) metastatic breast cancer (MBC) usually undergo trastuzumab emtansine (T-DM1) therapy in subsequent lines. Combining endocrine therapy (ET) with T-DM1 can improve treatment outcomes in this subtype. Therefore, this study aimed to investigate the benefits of using T-DM1 with ET in HER2-positive and HR-positive MBC. This study was the first to investigate the benefits of combining ET with T-DM1. Material and Methods: This study analyzed the medical records of patients with HER2-positive and HR-positive MBC who were treated with T-DM1 from June 2010 to December 2021. The patients were divided into groups based on whether they received concomitant ET with T-DM1. The primary endpoint was to determine the progression-free survival (PFS), while the secondary endpoints were overall survival (OS), objective response rate, and safety of the treatment. Results: Our analysis examined 88 patients, of whom 32 (36.4%) were treated with T-DM1 in combination with ET. The combination therapy showed a significant improvement in median PFS (15.4 vs. 6.4 months; p = 0.00004) and median OS (35.0 vs. 23.1 months; p = 0.026) compared to T-DM1 alone. The ORR was also higher in the combination group (65.6% vs. 29.3%; p = 0.026). Patients treated with pertuzumab priorly had reduced median PFS on T-DM1 compared to those who were not treated with pertuzumab (11.7 vs. 5.4 months, respectively; p < 0.01). T-DM1 demonstrated better median PFS in HER2 3+ patients compared to HER2 2+ patients, with an amplification ratio of >2.0 (10.8 vs 5.8 months, respectively; p = 0.049). The safety profiles were consistent with previous T-DM1 studies. Conclusions: The combination of T-DM1 with ET can significantly improve PFS and OS in patients with HER2-positive and HR-positive MBC. Our study suggests that prior pertuzumab treatment plus trastuzumab treatment might decrease T-DM1 efficacy.
Synergistic Effect of Sodium Dodecyl Benzene Sulfonate on Flotation Separation of Magnesite and Dolomite with Sodium Oleate Collector
Authors: Luo Na , Yan Baobao , Shi Jingyang , Li Dahu , Huang Zhiqiang
Published: June 2024

The synergistic effect of sodium dodecyl benzene sulfonate (SDBS) on the flotation separation of magnesite and dolomite using sodium oleate (NaOL) as a collector has been studied through flotation experiments, zeta potential measurements, contact angle measurements, Fourier transformation infrared spectroscopy analysis (FT-IR), particle size measurements and transmittance measurements. The flotation experiments show that when the synergist, SDBS, is added to the collector, NaOL, the collecting ability and ion resistance of NaOL can be improved so that the flotation separation of magnesite and dolomite can be realized. Zeta potential measurements, contact angle measurements and FT-IR analysis indicate that SDBS and NaOL can co-adsorb on the surface of magnesite. Particle size measurements and transmittance measurements show that SDBS can also improve the dispersion and solubility of NaOL in an aqueous solution, so as to achieve a synergistic effect.
Magnetic Barium Hexaferrite Nanoparticles with Tunable Coercivity as Potential Magnetic Heating Agents
Authors: Zahn Diana , Diegel Marco , Valitova Alina , Dellith Jan , Dutz Silvio
Published: June 2024

Using magnetic nanoparticles (MNPs) for extracorporeal heating applications results in higher field strength and, therefore, particles of higher coercivity can be used, compared to intracorporeal applications. In this study, we report the synthesis and characterization of barium hexa-ferrite (BaFe12O19) nanoparticles as potential particles for magnetic heating. Using a precipitation method followed by high-temperature calcination, we first studied the influence of varied synthesis parameters on the particles’ properties. Second, the iron-to-barium ratio (Fe/Ba = r) was varied between 2 and 12. Vibrating sample magnetometry, scanning electron microscopy and X-ray diffraction were used for characterization. A considerable influence of the calcination temperature (Tcal) was found on the resulting magnetic properties, with a decrease in coercivity (HC) from values above 370 kA/m for Tcal = 800–1000 °C to HC = 45–70 kA/m for Tcal = 1200 °C. We attribute this drop in HC mainly to the formation of entirely multi-domain particles at high Tcal. For the varying Fe/Ba ratios, increasing amounts of BaFe2O4 as an additional phase were detected by XRD in the small r (barium surplus) samples, lowering the particles’ magnetization. A decrease in HC was found in the increased r samples. Crystal size ranged from 47 nm to 240 nm and large agglomerates were seen in SEM images. The reported particles, due to their controllable coercivity, can be a candidate for extracorporeal heating applications in the biomedical or biotechnological field.
Exogenous Nucleotides Ameliorate Insulin Resistance Induced by Palmitic Acid in HepG2 Cells through the IRS-1/AKT/FOXO1 Pathways
Authors: Song Lixia , Li Yong , Xu Meihong
Published: June 2024

Nucleotides (NTs) act as pivotal regulatory factors in numerous biological processes, playing indispensable roles in growth, development, and metabolism across organisms. This study delves into the effects of exogenous NTs on hepatic insulin resistance using palmitic-acid-induced HepG2 cells, administering interventions at three distinct dosage levels of exogenous NTs. The findings underscore that exogenous NT intervention augments glucose consumption in HepG2 cells, modulates the expression of glycogen-synthesis-related enzymes (glycogen synthase kinase 3β and glycogen synthase), and influences glycogen content. Additionally, it governs the expression levels of hepatic enzymes (hexokinase, phosphoenolpyruvate carboxykinase, and glucose-6-phosphatase). Moreover, exogenous NT intervention orchestrates insulin signaling pathway (insulin receptor substrate-1, protein kinase B, and forkhead box protein O1) and AMP-activated protein kinase (AMPK) activity in HepG2 cells. Furthermore, exogenous NT intervention fine-tunes the expression levels of oxidative stress-related markers (malondialdehyde, glutathione peroxidase, and NADPH oxidase 4) and the expression of inflammation-related nuclear transcription factor (NF-κB). Lastly, exogenous NT intervention regulates the expression levels of glucose transporter proteins (GLUTs). Consequently, exogenous NTs ameliorate insulin resistance in HepG2 cells by modulating the IRS-1/AKT/FOXO1 pathways and regulate glucose consumption, glycogen content, insulin signaling pathways, AMPK activity, oxidative stress, and inflammatory status.
A Systematic Review of Moderators of the Association between Dietary Restraint and Binge Eating
Authors: Mason Tyler B. , Dolgon-Krutolow Anna , Smith Kathryn E.
Published: June 2024

Obesity interventions typically involve some form of dietary restraint (i.e., intentional limiting of food intake), yet the restraint model of binge eating proposes that engaging in dietary restraint is a causal factor for binge eating symptoms. Evidence for this model has been mixed and differs by measures, study design, and sample. Further, there also may be moderators of the association between dietary restraint and binge eating. The purpose of this systematic review was to compile the current evidence on moderators of the association between dietary restraint and binge eating. A literature search was conducted across electronic databases, resulting in the inclusion of 16 papers (with 15 different samples). The reviewed studies were primarily heterosexual White college student females and were primarily cross-sectional studies with self-report measures. There were no consistent moderators across the studies. However, there was some evidence for interactions between affective constructs and dietary restraint in relation to binge eating, as well as three-way interactions between affect, cognitive constructs, and dietary restraint. Although there were few studies, there was little current evidence for social, biological, and demographic factors as moderators. Overall, our systematic review shows a need for further research to clarify and validate the moderators, and to understand the complex interactions as well as the potential causal relationships between restraint and binge eating behaviors.
A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru
Authors: Acuña-Guzman Victor , Montoya-Alfaro María E. , Negrón-Ballarte Luisa P. , Solis-Calero Christian
Published: June 2024

Background: Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure–property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru. Methods: A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM–RF–GBM model. Results: An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39–0.40 and R2 = 0.73–0.74. Notably, the SVM–RF–GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness. Conclusions: Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.
Relationality and Metaphor—Doctrine of Signatures, Ecosemiosis, and Interspecies Communication
Authors: Williams Keith , Bédard Andrée-Anne
Published: June 2024

The Doctrine of Signatures (DoS) figures prominently in both contemporary and historic herbal traditions across a diversity of cultures. DoS—conceptualized beyond its conventional interpretation as “like cures like”, which relies solely on plant morphology—can be viewed as a type of ecosemiotic communication system. This nuanced form of interspecies communication relies on the presence of “signatures”, or signs, corresponding to the therapeutic quality of different plants based on their morphology but also their aroma, taste, texture, and even their context in the landscape. Despite its widespread contemporary dismissal by mainstream science as overly simplistic, childlike, primitive, and generally of limited value, we suggest that the recognition of “signatures” in plants may be considered as a form of communication between humans and plants. Drawing upon Indigenous thought, ecosemiotic theory, and lyric philosophy, we posit that understanding “signatures” metaphorically, as a reflection of the “shape of the world”, offers insights into the interconnectedness of all life forms—a profound affirmation of relational coherence between humans and the more-than-human. We advocate for another perspective on DoS: one which holds potential towards reorienting and restoring our relationships in the vibrant world of the Anthropocene.
High-Performance Terahertz Coherent Perfect Absorption with Asymmetric Graphene Metasurface
Authors: Chen Jintao , Hong Lujun , Lei Jiangtao , Shen Yun , Deng Xiaohua , Chen Jing , Guo Tianjing
Published: June 2024

In this work, we introduce a novel coherent perfect absorber, accentuating its novelty by emphasizing the broad bandwidth, reduced thickness, tunable property, and straightforward design achieved through the use of an asymmetric graphene metasurface. This design incorporates both square and circular graphene patches arranged on either side of a silicon substrate. With an optimized structural design, this absorber consistently captures over 90% of incoming waves across the frequency range of 1.65 to 4.49 THz, with a graphene Fermi level of 0.8 eV, and the whole device measures just 1.5 um thick. This makes our absorber significantly more effective and compact than previous designs. The absorber’s effectiveness can be significantly enhanced by combining the metasurface’s geometric design with the graphene Fermi level. It is anticipated that this ultrathin, wideband coherent perfect absorption device will play a crucial role in emerging on-chip THz communication technologies, including light modulators, photodetectors, and so on.
Study of the Floristic, Morphological, and Genetic (atpF–atpH, Internal Transcribed Spacer (ITS), matK, psbK–psbI, rbcL, and trnH–psbA) Differences in Crataegus ambigua Populations in Mangistau (Kazakhstan)
Authors: Imanbayeva Akzhunis , Duisenova Nurzhaugan , Orazov Aidyn , Sagyndykova Meruert , Belozerov Ivan , Tuyakova Ainur
Published: June 2024

This article studies the morphological parameters of vegetative and generative organs of different age groups of Crataegus ambigua from four populations in Western Karatau (Mangistau region, Kazakhstan). In this study, we examined four populations: Sultan Epe, Karakozaiym, Emdikorgan, and Samal, all located in various gorges of Western Karatau. Several phylogenetic inference methods were applied, using six genetic markers to reconstruct the evolutionary relationships between these populations: atpF–atpH, internal transcribed spacer (ITS), matK, psbK–psbI, rbcL, and trnH–psbA. We also used a statistical analysis of plants’ vegetative and generative organs for three age groups (virgin, young, and adult generative). According to the age structure, Samal has a high concentration of young generative plants (42.3%) and adult generative plants (30.9%). Morphological analysis showed the significance of the parameters of the generative organs and separated the Samal population into a separate group according to the primary principal component analysis (PCoA) coordinates. The results of the floristic analysis showed that the Samal populations have a high concentration of species diversity. Comparative dendrograms using UPGMA (unweighted pair group method with arithmetic mean) showed that information gleaned from genetic markers and the psbK–psbI region can be used to determine the difference between the fourth Samal population and the other three.
Influence of the Manufacturing Method (3D Printing and Injection Molding) on Water Absorption and Mechanical and Thermal Properties of Polymer Composites Based on Poly(lactic acid)
Authors: Mukoroh Paul Forbid , Gouda Fathi , Skrifvars Mikael , Ramamoorthy Sunil Kumar
Published: June 2024

The manufacturing method influences the properties of the produced components. This work investigates the influence of manufacturing methods, such as fused deposition modeling (3D printing) and injection molding, on the water absorption and mechanical and thermal properties of the specimens produced from neat bio-based poly(lactic acid) (PLA) polymer and poly(lactic acid)/wood composites. Acrylonitrile butadiene styrene (ABS) acts as the reference material due to its low water absorption and good functional properties. The printing layer thickness is one of the factors that affects the properties of a 3D-printed specimen. The investigation includes two different layer thicknesses (0.2 mm and 0.3 mm) while maintaining uniform overall thickness of the specimens across two manufacturing methods. 3D-printed specimens absorb significantly higher amounts of water than the injection-molded specimens, and the increase in the layer thickness of the 3D-printed specimens contributes to further increased water absorption. However, the swelling due to water absorption in 3D-printed specimens decreases upon increased layer thickness. The tensile, flexural, and impact properties of all of the specimens decrease after water absorption, while the properties improve upon decreasing the layer thickness. Higher porosity upon increasing the layer thickness is the predominant factor. The results from dynamic mechanical analysis and microscopy validate the outcomes. The results from this experimental study highlight the limitations of additive manufacturing.
Febrile Rash: An Early Diagnostic Clue to Infectious Illness in Travelers Returning from Thailand
Authors: Imad Hisham Ahmed , Putri Anastasia , Charoenwisedsil Ratchata , Charoensakulchai Sakarn , Caumes Eric
Published: June 2024

The eruption of a rash along with spiking fever in travelers returning from the tropics may be suspicious of arboviral diseases, and isolation prevent further transmission in non-endemic countries. The case presented here was seen at the Fever Clinic at the Hospital for Tropical Diseases in Bangkok, Thailand. The presenting complaints were fever, headache, myalgia, and a distinctive erythematous blanching rash. Despite a negative dengue NS1 test on the initial day, anti-dengue IgM and IgG were detectable on day five of illness. Dengue, a leading cause of traveler’s fever with rash, is of particular concern, especially during outbreaks like the one in Thailand in 2023, when the number of cases exceeded one hundred thousand over a nine-month period. The influx of 28 million travelers in 2023, many with naive immunity to many arboviruses, raises fear of transmission to temperate regions, including to countries like France, where Aedes albopictus establishment can lead to autochthonous dengue cases and clusters. Enhanced surveillance is crucial, urging the consideration of dengue as a potential diagnosis in travelers with febrile rash, even prior to lab confirmation. Immediate isolation of patients is essential to prevent autochthonous transmission, reduce outbreak risks, and avert public health crises.
Modeling the Effect of Vegetation Coverage on Unmanned Aerial Vehicles-Based Object Detection: A Study in the Minefield Environment
Authors: Baur Jasper , Dewey Kyle , Steinberg Gabriel , Nitsche Frank O.
Published: June 2024

An important consideration for UAV-based (unmanned aerial vehicle) object detection in the natural environment is vegetation height and foliar cover, which can visually obscure the items a machine learning model is trained to detect. Hence, the accuracy of aerial detection of objects such as surface landmines and UXO (unexploded ordnance) is highly dependent on the height and density of vegetation in a given area. In this study, we develop a model that estimates the detection accuracy (recall) of a YOLOv8 object’s detection implementation as a function of occlusion due to vegetation coverage. To solve this function, we developed an algorithm to extract vegetation height and coverage of the UAV imagery from a digital surface model generated using structure-from-motion (SfM) photogrammetry. We find the relationship between recall and percent occlusion is well modeled by a sigmoid function using the PFM-1 landmine test case. Applying the sigmoid recall-occlusion relationship in conjunction with our vegetation cover algorithm to solve for percent occlusion, we mapped the uncertainty in detection rate due to vegetation in UAV-based SfM orthomosaics in eight different minefield environments. This methodology and model have significant implications for determining the optimal location and time of year for UAV-based object detection tasks and quantifying the uncertainty of deep learning object detection models in the natural environment.
Hyperspectral Image Classification Based on Double-Branch Multi-Scale Dual-Attention Network
Authors: Zhang Heng , Liu Hanhu , Yang Ronghao , Wang Wei , Luo Qingqu , Tu Changda
Published: June 2024

Although extensive research shows that CNNs achieve good classification results in HSI classification, they still struggle to effectively extract spectral sequence information from HSIs. Additionally, the high-dimensional features of HSIs, the limited number of labeled samples, and the common sample imbalance significantly restrict classification performance improvement. To address these issues, this article proposes a double-branch multi-scale dual-attention (DBMSDA) network that fully extracts spectral and spatial information from HSIs and fuses them for classification. The designed multi-scale spectral residual self-attention (MSeRA), as a fundamental component of dense connections, can fully extract high-dimensional and intricate spectral information from HSIs, even with limited labeled samples and imbalanced distributions. Additionally, this article adopts a dataset partitioning strategy to prevent information leakage. Finally, this article introduces a hyperspectral geological lithology dataset to evaluate the accuracy and applicability of deep learning methods in geology. Experimental results on the geological lithology hyperspectral dataset and three other public datasets demonstrate that the DBMSDA method exhibits superior classification performance and robust generalization ability compared to existing methods.
Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm
Authors: Xu Jiawei , Liu Yuteng , Yan Changxiang , Yuan Jing
Published: June 2024

Soil moisture strongly interferes with the spectra of soil organic matter (SOM) in the near-infrared region, which reduces the correlation between organic matter and spectra and decreases accuracy in the prediction of SOM. In this study, we explored the feasibility of two types of spectral indices, two- and three-band mixed (SI) and three-band spectral indices (SI3), and two water removal algorithms, direct standardization (DS) and external parameter orthogonalization (EPO), to estimate SOM in wet soils using a total of 192 soil samples at six water content gradients. The estimation accuracies of spectral indices combined with water removal algorithms were better than those of full spectral data combined with water removal algorithms: the prediction accuracies of SI-EPO (R2 = 0.735, RMSEp = 3.4102 g/kg) were higher than those of EPO (R2 = 0.63, RMSEp = 4.1021 g/kg), and those of SI-DS (R2 = 0.70, RMSEp = 3.7085 g/kg) were higher than those of DS (R2 = 0.61, RMSEp = 4.2806 g/kg); SI3-EPO (R2 = 0.752, RMSEp = 3.1344 g/kg) was better than SI-EPO; both EPO and DS effectively mitigated the influence of soil moisture, with EPO demonstrating superior performance in small-sample prediction scenarios. This study introduces a novel approach to counteract the impact of soil moisture on SOM estimation.
Methodology for Severe Convective Cloud Identification Using Lightweight Neural Network Model Ensembling
Authors: Zhang Jie , He Mingyuan
Published: June 2024

This study introduces an advanced ensemble methodology employing lightweight neural network models for identifying severe convective clouds from FY-4B geostationary meteorological satellite imagery. We have constructed a FY-4B based severe convective cloud dataset by a combination of algorithms and expert judgment. Through the ablation study of a model ensembling combination of multiple specialized lightweight architectures—ENet, ESPNet, Fast-SCNN, ICNet, and MobileNetV2—the optimal EFNet (ENet- and Fast-SCNN-based network) not only achieves real-time processing capabilities but also ensures high accuracy in severe weather detection. EFNet consistently outperformed traditional, heavier models across several key performance indicators: achieving an accuracy of 0.9941, precision of 0.9391, recall of 0.9201, F1 score of 0.9295, and computing time of 18.65 s over the test dataset of 300 images (~0.06 s per 512 × 512 pic). ENet shows high precision but misses subtle clouds, while Fast-SCNN has high sensitivity but lower precision, leading to misclassifications. EFNet’s ensemble approach balances these traits, enhancing overall predictive accuracy. The ensemble method of lightweight models effectively aggregates the diverse strengths of the individual models, optimizing both speed and predictive performance.
Hyperspectral Image Denoising Based on Deep and Total Variation Priors
Authors: Wang Peng , Sun Tianman , Chen Yiming , Ge Lihua , Wang Xiaoyi , Wang Liguo
Published: June 2024

To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning and a total variation (TV) prior. The method minimizes the first-order moment distance between the deep prior of a Fast and Flexible Denoising Convolutional Neural Network (FFDNet) and the Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement and reinforce each other’s advantages. Specifically, the original HSI is initially processed with a random binary sparse observation matrix to achieve a sparse representation. Subsequently, the plug-and-play (PnP) algorithm is employed within the framework of generalized alternating projection (GAP) to denoise the sparsely represented HSI. Experimental results demonstrate that, compared to existing methods, this method shows significant advantages in both quantitative and qualitative assessments, effectively enhancing the quality of HSIs.
Flow Training Program: Mindfulness, Decision Making, and Mental Well-Being of Young and Adult Elite Handball Athletes
Authors: Martiny Luis , Dias Gonçalo , Ferreira José Pedro , Mendes Rodrigo , Mendes Rui
Published: June 2024

This study aimed to analyze the effect of a flow training program based on mindfulness applied to young and adult elite handball athletes. A quantitative, quasi-experimental, descriptive data analysis approach was carried out. The sample included 105 athletes (51 female and 54 male). The athletes were divided into two groups: (i) experimental (n = 53) and (ii) control (n = 52). The results of the repeated ANOVA indicated that the experimental group achieved significant improvements compared to the control group in decision making (n2p = 0.086), mental well-being (n2p = 0.045), dispositional flow state (n2p = 0.103), non-judgment (n2p = 0.040), refocusing (n2p = 0.052), and mindful traits in daily life (n2p = 0.058). A Pearson analysis demonstrated a positive correlation between dispositional flow state and mindfulness in sport and mental well-being. The correlation analysis also showed an inverse correlation between decision making and sense of control and mindful traits in daily life. The findings revealed that the program can be effective in decision making, mental well-being, dispositional flow state, mindfulness in sport, and mindful traits in daily lives of athletes.
A Spatial Gaussian-Process Boosting Analysis of Socioeconomic Disparities in Wait-Listing of End-Stage Kidney Disease Patients across the United States
Authors: Chakraborty Sounak , Dey Tanujit , Xiang Lingwei , Adler Joel T.
Published: June 2024

In this study, we employed a novel approach of combining Gaussian processes (GPs) with boosting techniques to model the spatial variability inherent in End-Stage Kidney Disease (ESKD) data. Our use of the Gaussian processes boosting, or GPBoost, methodology underscores the efficacy of this hybrid method in capturing intricate spatial dynamics and enhancing predictive accuracy. Specifically, our analysis demonstrates a notable improvement in out-of-sample prediction accuracy regarding the percentage of the population remaining on the wait list within geographic regions. Furthermore, our investigation unveils race and gender-based factors that significantly influence patient wait-listing. By leveraging the GPBoost approach, we identify these pertinent factors, shedding light on the complex interplay between demographic variables and access to kidney transplantation services. Our findings underscore the imperative for a multifaceted strategy aimed at reducing spatial disparities in kidney transplant wait-listing. Key components of such an approach include mitigating gender disparities, bolstering access to healthcare services, fostering greater awareness of transplantation options, and dismantling structural barriers to care. By addressing these multifactorial challenges, we can strive towards a more equitable and inclusive landscape in kidney transplantation.
Applying Artificial Intelligence to Promote Sustainability
Authors: Ta Miriam Du-Phuong , Wendt Stefan , Sigurjonsson Throstur Olaf
Published: June 2024

This study reviews the application of artificial intelligence (AI) throughout the food value chain and how it can be leveraged to help companies become more sustainable. A literature review across different parts of the food value chain was conducted to provide an overview of the main themes of current and future AI applications throughout the food industry. Moreover, the paper focuses on the benefits and challenges of change management when integrating AI. A documentary Systematic Review using PRISMA research was conducted to find and analyze the aforementioned applications. The key insight is that change progress varies significantly. Today’s applications are primarily found within food inspection and quality assurance due to relatively straightforward AI applications in the value chain. Such technology is mainly image-based. Companies can use the interconnectedness of AI and sustainability by becoming more efficient through AI and simultaneously saving emissions and resources through optimizing processes.
More Than 30 Years of PVC Recycling—Need for Regulation
Authors: Lahl Uwe , Zeschmar-Lahl Barbara
Published: June 2024

Building on our “Critical Inventory”, we analyse the need for the regulation of PVC plastics in the EU and its member states. To this end, we checked the three phases of the life cycle of PVC plastics: production, use and end-of-life. In the production phase, we focus on the economic relationships between PVC and chlor-alkali electrolysis, in particular, the dependence on the chlorine market and PVC sales. For the use phase, the health and environmental risks posed by many PVC additives are particularly relevant. The European Chemicals Agency (ECHA) has submitted well-founded proposals for the regulation of individual or defined groups of substances (e.g., ortho-phthalates), which we support. Problems that put a ban on the agenda stem in particular from the end-of-life phase of PVC plastics (PVC compounds), especially in the construction sector. Due to their long service life, a stock of around 160 million tonnes of PVC products in the EU has built up, increasingly finding its way into the waste management sector. Currently, there are no waste management infrastructures or facilities capable of disposing of these amounts. Without a phasing-out the production of virgin PVC (“PVC ban”), these quantities will continue to increase. We, therefore, come to the conclusion that post-consumer PVC should be collected separately and canalized into a disposal infrastructure designed for chlorine recovery. The European PVC industry “would welcome to make this separate collection mandatory”. Including the associated costs, PVC will probably lose its status as a particularly economically favourable plastic.
Molecular Dynamics-Based Car-Following Safety Characteristics and Modeling for Connected Autonomous Vehicles
Authors: Wang Kedong , Qu Dayi , Meng Yiming , Wang Tao , Yang Ziyi
Published: June 2024

To characterize the dynamic interaction properties of heterogeneous traffic flow in the complex human–vehicle–road environment and to enhance the safety and efficiency of connected autonomous vehicles (CAVs), this study analyzes the self-driven particle characteristics and safety interaction behavior of CAVs based on molecular interaction potential. The molecular dynamics of potential interaction functions are employed to establish a dynamic quantization model for car-following (CF) safety potential, referred to as the molecular force field quantization model. To calibrate the model parameters, the Artificial Bee Colony Algorithm and the highD dataset are utilized, subsequently validating the reasonableness and effectiveness of the molecular dynamics model for vehicle tracking. The simulation results demonstrate that the proposed model can more accurately fit actual CF data, significantly improving vehicle travel safety and efficiency. Moreover, the profile of vehicle acceleration shows a lower mean absolute error and root mean square error compared to actual data, indicating that the model provides superior anti-interference fluctuation resistance and stability in CF scenarios. Overall, the proposed model effectively captures the microscopic CF behavior and vehicle–vehicle safety interactions, offering a theoretical foundation for further research into vehicle-following dynamics.
A Max–Min Fairness-Inspired Approach to Enhance the Performance of Multimodal Transportation Networks
Authors: Moshebah Osamah Y. , Rodríguez-González Samuel , González Andrés D.
Published: June 2024

Disruptions in multimodal transportation networks can lead to significant damage and loss, affecting not only the networks’ efficiency but also their sustainability. Given the size, dynamics, and complex nature of these networks, it is essential to understand and enhance their resilience against disruptions. This not only ensures their functionality and performance but also supports sustainable development by maintaining equitable service across various communities and economic sectors. Therefore, developing efficient techniques to increase the robustness and resilience of transportation networks is crucial for both operational success and sustainability. This research introduces a multicriteria mixed integer linear programming (MCMILP) model aimed at enhancing the resilience and performance of multimodal–multi-commodity transportation networks. By ensuring effective distribution of commodities, alongside a cost-efficient distribution strategy in the wake of disruptive events, our model contributes significantly to sustainable transportation practices. The proposed MCMILP model demonstrates that integrating equality considerations while seeking a cost-efficient distribution strategy significantly mitigates the impact of disruptions, thereby bolstering the resilience of multimodal transportation networks. To illustrate the capabilities of the proposed modeling approach, we present a case study based on the multimodal transportation network in Colombia. The results show a significant improvement in the number of nodes that satisfy their demand requirements with respect to other approaches based on reducing total unsatisfied demand and transportation costs.
The Innovation Plight and Operational Efficiency of Chinese Manufacturing Enterprises: From the Perspective of Risk Tolerance, Expectation, and Profitability
Authors: Shu Yanfei , Yang Yaxin
Published: June 2024

With uncertainty intensifying the international technological innovation environment, the innovation situation of Chinese manufacturing enterprises has been impacted. Based on 12,781 micro panel data in 2011–2020 of 2347 Chinese A-share manufacturing listed enterprises, this paper empirically analyzes the impact of innovation plight faced by enterprises on operational efficiency. The innovation plight in this article refers to the degree to which the actual innovation performance of enterprises has not reached the expected innovation performance or is introduced by an innovation gap, measured by the difference when actual innovation performance is lower than the expected innovation performance. The empirical results show that the innovation plight of manufacturing enterprises significantly inhibits operational efficiency by reducing their risk tolerance, development ability expectation, and profitability. After using a series of tests, such as the instrumental variable method, replacing the dependent variable, and changing the parameters for measuring the independent variable, the conclusion is still robust. In addition, the results illustrate that the inhibitory effect of innovation plight on operational efficiency is more obvious for non-state-owned enterprises, small and medium-sized enterprises, high-tech enterprises, and enterprises in the eastern region. Finally, we formulate some relevant management suggestions.
Motion Control of Macro–Micro Linear Platform Based on Adaptive Fuzzy Active Disturbance Rejection Control
Authors: Wang Mingyi , Kang Tianrun , Kang Kai , Zhang Chengming , Li Liyi
Published: June 2024

To ensure precise positioning of the macro–micro platform with a symmetrical structure, it is crucial to mitigate the impact of various perturbations, including disturbances, as well as complex factors such as external loads, electrical noise, and model parameter variations. This paper proposes a novel macro–micro master–slave control structure that incorporates adaptive fuzzy linear active disturbance rejection control (AFLADRC). The Kp and Kd parameters of the linear state error feedback (LSEF) are dynamically tuned and adjusted using fuzzy reasoning. This approach enhances the robustness of the system and simplifies the tuning process. In addition, this paper also analyzes the symmetry of the coupling effect between macro and micro, as the coupling will affect the motor force and the reaction potential of the motor. The macro–micro platform adopts a symmetric design; the macro stage is driven by a permanent magnet synchronous linear motor (PMLSM), and the micro stage is driven by a voice coil motor. Finally, we built the macro–micro linear motion experimental platform to verify the control effect of the proposed method by conducting trajectory tracking experiments and comparison experiments.
Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control
Authors: Jin Mengtao , Li Junmin , Chen Te
Published: June 2024

The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability.
Electron Energy-Loss Spectroscopy Method for Thin-Film Thickness Calculations with a Low Incident Energy Electron Beam
Authors: Jaber Ahmad M. D. (Assa’d) , Alsoud Ammar , Al-Bashaish Saleh R. , Al Dmour Hmoud , Mousa Marwan S. , Trčka Tomáš , Holcman Vladimír , Sobola Dinara
Published: June 2024

In this study, the thickness of a thin film (tc) at a low primary electron energy of less than or equal to 10 keV was calculated using electron energy-loss spectroscopy. This method uses the ratio of the intensity of the transmitted background spectrum to the intensity of the transmission electrons with zero-loss energy (elastic) in the presence of an accurate average inelastic free path length (λ). The Monte Carlo model was used to simulate the interaction between the electron beam and the tested thin films. The total background of the transmitted electrons is considered to be the electron transmitting the film with an energy above 50 eV to eliminate the effect of the secondary electrons. The method was used at low primary electron energy to measure the thickness (t) of C, Si, Cr, Cu, Ag, and Au films below 12 nm. For the C and Si films, the accuracy of the thickness calculation increased as the energy of the primary electrons and thickness of the film increased. However, for heavy elements, the accuracy of the film thickness calculations increased as the primary electron energy increased and the film thickness decreased. High accuracy (with 2% uncertainty) in the measurement of C and Si thin films was observed at large thicknesses and 10 keV, where tλ≈1. However, in the case of heavy-element films, the highest accuracy (with an uncertainty below 8%) was found for thin thicknesses and 10 keV, where tλ≤0.29. The present results show that an accurate film thickness measurement can be obtained at primary electron energy equal to or less than 10 keV and a ratio of tλ≤2. This method demonstrates the potential of low-loss electron energy-loss spectroscopy in transmission electron microscopy as a fast and straightforward method for determining the thin-film thickness of the material under investigation at low primary electron energies.
Characterization of Taxonomic and Functional Dynamics Associated with Harmful Algal Bloom Formation in Recreational Water Ecosystems
Authors: Saleem Faizan , Atrache Rachelle , Jiang Jennifer L. , Tran Kevin L. , Li Enze , Paschos Athanasios , Edge Thomas A. , Schellhorn Herb E.
Published: June 2024

Harmful algal bloom (HAB) formation leads to the eutrophication of water ecosystems and may render recreational lakes unsuitable for human use. We evaluated the applicability and comparison of metabarcoding, metagenomics, qPCR, and ELISA-based methods for cyanobacteria/cyanotoxin detection in bloom and non-bloom sites for the Great Lakes region. DNA sequencing-based methods robustly identified differences between bloom and non-bloom samples (e.g., the relative prominence of Anabaena and Planktothrix). Shotgun sequencing strategies also identified the enrichment of metabolic genes typical of cyanobacteria in bloom samples, though toxin genes were not detected, suggesting deeper sequencing or PCR methods may be needed to detect low-abundance toxin genes. PCR and ELISA indicated microcystin levels and microcystin gene copies were significantly more abundant in bloom sites. However, not all bloom samples were positive for microcystin, possibly due to bloom development by non-toxin-producing species. Additionally, microcystin levels were significantly correlated (positively) with microcystin gene copy number but not with total cyanobacterial 16S gene copies. In summary, next-generation sequencing-based methods can identify specific taxonomic and functional targets, which can be used for absolute quantification methods (qPCR and ELISA) to augment conventional water monitoring strategies.
Evolution of the Antigenic Landscape in Children and Young Adults with COVID-19 and MIS-C
Authors: Bellusci Lorenza , Grubbs Gabrielle , Sait Shaimaa , Herbst Katherine , Salazar Juan , Khurana Surender ,   The Connecticut Children’s COVID Collaborative
Published: June 2024

There is minimal knowledge regarding the durability of neutralization capacity and level of binding antibody generated against the highly transmissible circulating Omicron subvariants following SARS-CoV-2 infection in children with acute COVID-19 and those diagnosed with multisystem inflammatory syndrome in children (MIS-C) in the absence of vaccination. In this study, SARS-CoV-2 neutralization titers against the ancestral strain (WA1) and Omicron sublineages were evaluated in unvaccinated children admitted for COVID-19 (n = 32) and MIS-C (n = 32) at the time of hospitalization (baseline) and at six to eight weeks post-discharge (follow-up) between 1 April 2020, and 1 September 2022. In addition, antibody binding to the spike receptor binding domain (RBD) from WA1, BA.1, BA.2.75, and BA.4/BA.5 was determined using surface plasmon resonance (SPR). At baseline, the children with MIS-C demonstrated two-fold to three-fold higher binding and neutralizing antibodies against ancestral WA1 compared to those with COVID-19. Importantly, in children with COVID-19, the virus neutralization titers against the Omicron subvariants at six to eight weeks post-discharge reached the same level as those with MIS-C had at baseline but were higher than titers at 6–8 weeks post-discharge for MIS-C cases. Cross-neutralization capacity against recently emerged Omicron BQ.1, BQ.1.1, and XBB.1 variants was very low in children with either COVID-19 or MIS-C at all time points. These findings about post-infection immunity in children with either COVID-19 or MIS-C suggest the need for vaccinations in children with prior COVID-19 or MIS-C to provide effective protection from emerging and circulating SARS-CoV-2 variants.
A Pilot Study to Evaluate the Usefulness of Optical Coherence Tomography for Staging Iris Pigmented Lesions in Cats
Authors: Komatsu Hiroyuki , Akasaka Minori , Morita Maresuke , Usami Kensuke , Inagaki Mao , Kumashiro Kayo , Tsubota Kinya , Usui Yoshihiko , Goto Hiroshi , Kobayashi Yoshitaka
Published: June 2024

This study investigated the utility of optical coherence tomography (OCT) for staging iris pigmented lesions in cats. Eighteen cats that underwent OCT examination for unilateral iris pigmented lesion were included. The cats were either suspected of melanosis due to clinical features (n = 8) or had been definitively diagnosed through histopathology with iris melanosis (n = 3), early feline diffuse iris melanoma (FDIM) (n = 4), or mid-stage or advanced FDIM (n = 3). From OCT images, mean iris thickness (MIT) was measured, and the ratio of pigmented lesion to normal iris (PN) was calculated. OCT images depicted the entire iris layer in all eyes with suspected melanosis, iris melanosis, and early FDIM, but observing the entire lesion in mid-stage/advanced FDIM was challenging. No significant difference in MIT was observed among the groups. Conversely, PN ratio was significantly higher (p < 0.05) in early FDIM (1.29 ± 0.16) than in suspected melanosis (1.02 ± 0.10) or iris melanosis (0.99 ± 0.09). Furthermore, OCT imaging revealed hyperreflective lines in 75% of eyes with suspected melanosis and in all the eyes with iris melanosis, corresponding to the pigmented lesions. Our results demonstrate that OCT is capable of detecting subtle differences in iris thickness and features in early-stage FDIM, indicating its potential utility in distinguishing between iris melanosis and early FDIM. Further study is warranted to verify the reliability of such OCT findings.