bluuebunny/crossref_metadata_2025_split huggingface dataset:
Copper supported Dowex50WX8 resin utilized for the elimination of ammonia and its sustainable application for the degradation of dyes in wastewater
Authors: Khamis Mohamed M. , Elsherbiny Abeer S. , Salem Ibrahim A. , El-Ghobashy Marwa A. 
Published: August 2024

AbstractTo obtain high efficient elimination of ammonia (NH4+) from wastewater, Cu(II), Ni(II), and Co(II)) were loaded on Dowex-50WX8 resin (D-H) and studied their removal efficiency towards NH4+ from aqueous solutions. The adsorption capacity of Cu(II)-loaded on D-H (D-Cu2+) towards NH4+ (qe = 95.58 mg/g) was the highest one compared with that of D-Ni2+ (qe = 57.29 mg/g) and D-Co2+ (qe = 43.43 mg/g). Detailed studies focused on the removal of NH4+ utilizing D-Cu2+ were accomplished under various experimental conditions. The pseudo-second-order kinetic model fitted well the adsorption data of NH4+ on D-Cu2+. The non-linear Langmuir model was the best model for the adsorption process, producing a maximum equilibrium adsorption capacity (qmax = 280.9 mg/g) at pH = 8.4, and 303 K in less than 20 min. The adsorption of NH4+ onto D-Cu2+ was an exothermic and spontaneous process. In a sustainable step, the resulting D-Cu(II)-ammine composite from the NH4+ adsorption process displayed excellent catalytic activity for the degradation of aniline blue (AB) and methyl violet 2B (MV 2B) dyes utilizing H2O2 as an eco-friendly oxidant.
Formulation of a stable diesel microemulsion using eco-friendly ionic liquids and investigation of particle size and fuel properties as an alternative fuel
Authors: El Nagy H. A. , Abd El-Aziz Mohamed Mahmoud 
Published: August 2024

AbstractEcofriendly ionic liquids (ILs) were synthesized through amidation of ricinoleic acid, the main fatty acid in castor oil, followed by a quaternization reaction to solubilize ethanol in IL/diesel blends at different ratios. As a result, stable and highly renewable, low viscous microemulsion biofuels with high oxygen content were prepared. The prepared fuel samples combine the advantages of green ionic liquids and microemulsion properties. The chemical structures of ILs were confirmed with the aid of NMR and FTIR spectroscopy. DLS analysis revealed that the ethanol particles ranged in size from 8 to 18.1 nm in all samples. As ILs ratios decrease in microemulsion from 37 to 69%, the ethanol particle sizes increase from 10 to 25%. Ethanol shows good solubilization in diesel and IL-1 is more effective than IL-2 in ethanol solubilization at low percentages of ethanol due to more oxygen atoms besides three hydroxyl groups. The ternary phase diagram indicated that the microemulsion area in the case of using IL-1 is larger than that of IL-2. The fuel properties of the prepared microemulsions are nearly close to those of neat diesel and fall within the permitted range of ASTM D975. The viscosity and density values at low ratios of ILs are found to be very close to the values of the neat diesel at different temperatures. The prepared samples show a slight decrease in cetane number and heating value compared to diesel. However, they have improved flash points, cloud points, sulfur content, and acid value. The particle sizes were checked every week and the prepared samples showed high stability with the aid of the synthesized ILs. Moreover, the prepared microemulsions stayed in a transparent appearance for more than a year and no phase separation was observed.
Adaptively triggered comparisons enhance perceptual category learning: evidence from face learning
Authors: Jacoby Victoria L. , Massey Christine M. , Kellman Philip J. 
Published: August 2024

AbstractCategorical learning is important and often challenging in both specialized domains, such as medical image interpretation, and commonplace ones, such as face recognition. Research has shown that comparing items from different categories can enhance the learning of perceptual classifications, particularly when those categories appear highly similar. Here, we developed and tested novel adaptively triggered comparisons (ATCs), in which errors produced during interactive learning dynamically prompted the presentation of active comparison trials. In a facial identity paradigm, undergraduate participants learned to recognize and name varying views of 22 unknown people. In Experiment 1, single-item classification trials were compared to a condition in which ATC trials were generated whenever a participant repeatedly confused two faces. Comparison trials required discrimination between simultaneously presented exemplars from the confused categories. In Experiment 2, an ATC condition was compared to a non-adaptive comparison condition. Participants learned to accuracy and speed criteria, and completed immediate and delayed posttests. ATCs substantially enhanced learning efficiency in both experiments. These studies, using a novel adaptive procedure guided by each learner’s performance, show that adaptively triggered comparisons improve category learning.
Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning
Authors: Vidyadharan Sreejith , Rao B. V. V. S. N. Prabhakar , Yogeeswari P. , Kesavadas C. , Rajagopalan Venkateswaran 
Published: August 2024

AbstractGlioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.
How users make online privacy decisions in work and personal contexts of use
Authors: Klütsch Jennifer , Böffel Christian , Abraham Lukas , Mooser Janina , Thüs Dominik , Tawanger Parwana , von Salm-Hoogstraeten Sophia , Schlittmeier Sabine J. 
Published: August 2024

AbstractWith the rising usage of contactless work options since COVID-19, users increasingly share their personal data in digital tools at work. Using an experimental online vignette study (N = 93), we examined users’ willingness to use a video conferencing tool, while systematically varying the context of use (personal vs. low trustworthiness work vs. high trustworthiness work) and the type of information shared (low vs. medium vs. high sensitivity). We also assessed users’ perceived responsibility in work and personal contexts of use and their self-assessed digital competence. Our results highlight employer trustworthiness as an important factor in the willingness to use a third-party video conferencing tool, with increased willingness to use these tools in work contexts of use with high trustworthiness compared to those with low trustworthiness. This effect seems to be reduced when the data to be shared is of high sensitivity, compared to medium and low sensitivity data. Furthermore, despite reduced responsibility for data protection in work compared to personal contexts of use, the willingness to use a video conferencing tool did not decrease between trustworthy work and personal contexts of use. We discuss our findings and their methodological implications for future research and derive implications for privacy decisions at work.
Active learning-based machine learning approach for enhancing environmental sustainability in green building energy consumption
Authors: Mahmood Shahid , Sun Huaping , Ali Alhussan Amel , Iqbal Asifa , El-kenawy El-Sayed M. 
Published: August 2024

AbstractGreen building (GB) techniques are essential for reducing energy waste in the construction sector, which accounts for almost 40% of global energy consumption. Despite their importance, challenges such as occupant behavior and energy management gaps often result in GBs consuming up to 2.5 times more energy than intended. To address this, Building Automation Systems (BAS) play a crucial role in enhancing energy efficiency. This research develops a predictive model for GB design using machine learning to minimize energy consumption and improve indoor sustainability. The dataset is utilized to predict cooling and heating individually, with data visualization by graphically illustrating dataset features and preprocessing through Z-Score normalization and dataset splitting. The proposed model, based on active learning and utilizing ML regressors such as Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN), and Logistic Regressor (LR), shows significant performance improvements. The CBR-AL model achieves impressive results with values of 0.9975 for cooling (Y1) and 0.9883 for heating (Y2), indicating a high level of accuracy. The model’s success in reducing energy consumption and improving sustainability has potential ripple effects, including substantial cost savings, reduced carbon footprints, and improved operational efficiency in green buildings. This approach not only enhances environmental sustainability but also sets a benchmark for future advancements in predictive modelling for energy management.
COVID-19 severity detection using chest X-ray segmentation and deep learning
Authors: Singh Tinku , Mishra Suryanshi , Kalra Riya , Satakshi  , Kumar Manish , Kim Taehong 
Published: August 2024

AbstractCOVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus’s variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework’s reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings.
Low frequency ultrasound elicits broad cortical responses inhibited by ketamine in mice
Authors: Shi Linli , Mastracchio Christina , Saytashev Ilyas , Ye Meijun 
Published: August 2024

AbstractThe neuromodulatory effects of >250 kHz ultrasound have been well-demonstrated, but the impact of lower-frequency ultrasound, which can transmit better through air and the skull, on the brain is unclear. This study investigates the biological impact of 40 kHz pulsed ultrasound on the brain using calcium imaging and electrophysiology in mice. Our findings reveal burst duration-dependent neural responses in somatosensory and auditory cortices, resembling responses to 12 kHz audible tone, in vivo. In vitro brain slice experiments show no neural responses to 300 kPa 40 kHz ultrasound, implying indirect network effects. Ketamine fully blocks neural responses to ultrasound in both cortices but only partially affects 12 kHz audible tone responses in the somatosensory cortex and has no impact on auditory cortex 12 kHz responses. This suggests that low-frequency ultrasound’s cortical effects rely heavily on NMDA receptors and may involve mechanisms beyond indirect auditory cortex activation. This research uncovers potential low-frequency ultrasound effects and mechanisms in the brain, offering a path for future neuromodulation.
Charting sustainable urban development through a systematic review of SDG11 research
Authors: Almulhim Abdulaziz I. , Sharifi Ayyoob , Aina Yusuf A. , Ahmad Shakil , Mora Luca , Filho Walter Leal , Abubakar Ismaila Rimi 
Published: August 2024

AbstractThe Sustainable Development Goal (SDG) 11 underscores the imperative of creating inclusive, safe, resilient and sustainable cities and communities by 2030. Here we employ bibliometric techniques to assess the evolving landscape of SDG11 research. Using a comprehensive dataset of over 21,000 scholarly publications, we investigate publication trends, thematic focus areas, authorship patterns, keyword co-occurrences and citation networks related to SDG11 research. The results reveal a consistent increase in research output, reflecting the growing global interest in urban sustainability studies. We identify influential authors, organizations and countries shaping the research landscape, highlighting existing global collaborative networks and emerging research hubs. Core thematic areas emphasize critical topics and interdisciplinary connections. Citation networks underscore the impacts of disseminating research outputs, including seminal works. This study offers insights for policymakers, academics and practitioners to align their collective efforts toward sustainable, inclusive and climate-resilient urban development. Moreover, it advances SDG11 by noting opportunities for further research, knowledge dissemination and international collaboration.
STIC2 selectively binds ribosome-nascent chain complexes in the cotranslational sorting of Arabidopsis thylakoid proteins
Authors: Stolle Dominique S , Osterhoff Lena , Treimer Paul , Lambertz Jan , Karstens Marie , Keller Jakob-Maximilian , Gerlach Ines , Bischoff Annika , Dünschede Beatrix , Rödiger Anja , Herrmann Christian , Baginsky Sacha , Hofmann Eckhard , Zoschke Reimo , Armbruster Ute , Nowaczyk Marc M , Schünemann Danja 
Published: August 2024

AbstractChloroplast-encoded multi-span thylakoid membrane proteins are crucial for photosynthetic complexes, yet the coordination of their biogenesis remains poorly understood. To identify factors that specifically support the cotranslational biogenesis of the reaction center protein D1 of photosystem (PS) II, we generated and affinity-purified stalled ribosome-nascent chain complexes (RNCs) bearing D1 nascent chains. Stalled RNCs translating the soluble ribosomal subunit uS2c were used for comparison. Quantitative tandem-mass spectrometry of the purified RNCs identified around 140 proteins specifically associated with D1 RNCs, mainly involved in protein and cofactor biogenesis, including chlorophyll biosynthesis, and other metabolic pathways. Functional analysis of STIC2, a newly identified D1 RNC interactor, revealed its cooperation with chloroplast protein SRP54 in the de novo biogenesis and repair of D1, and potentially other cotranslationally-targeted reaction center subunits of PSII and PSI. The primary binding interface between STIC2 and the thylakoid insertase Alb3 and its homolog Alb4 was mapped to STIC2’s β-sheet region, and the conserved Motif III in the C-terminal regions of Alb3/4.
Substituted fullerenes as a promising capping ligand towards stabilization of exohedral Dy(iii) based single-ion magnets: a theoretical study
Authors: Kumari Kusum , Singh Saurabh Kumar 
Published: August 2024

Using state-of-the-art computational methods, we have thoroughly investigated the coordination ability of substituted fullerene {C55X5}− (X = CCH3/B/N) towards stabilizing novel Dy(iii) exohedral complexes showcasing giant barrier height >2200 K.
Connecting dots between nucleotide biosynthesis and DNA lesion repair/bypass in cancer
Authors: Lin Jackson C. , Oludare Ayobami , Jung Hunmin 
Published: August 2024

Abstract
Purine and pyrimidine nucleotides are crucial building blocks for the survival of cells, and there are layers of pathways to make sure a stable supply of them including de novo nucleotide biosynthesis. Fast-growing cells including cancer cells have high demand for nucleotide, and they highly utilize the nucleotide biosynthesis pathways. Due to the nature of the fast-growing cells, they tend to make more errors in replication compared with the normal cells. Naturally, DNA repair and DNA lesion bypass are heavily employed in cancer cells to ensure fidelity and completion of the replication without stalling. There have been a lot of drugs targeting cancer that mimic the chemical structures of the nucleobase, nucleoside, and nucleotides, and the resistance toward those drugs is a serious problem. Herein, we have reviewed some of the representative nucleotide analog anticancer agents such as 5-fluorouracil, specifically their mechanism of action and resistance is discussed. Also, we have chosen several enzymes in nucleotide biosynthesis, DNA repair, and DNA lesion bypass, and we have discussed the known and potential roles of these enzymes in maintaining genomic fidelity and cancer chemotherapy.
Variability and stellar pulsation incidence in Am and Fm stars using TESS and Gaia data
Authors: Dürfeldt-Pedros O. , Antoci V. , Smalley B. , Murphy S. , Posilek N. , Niemczura E. 
Published: August 2024

Aims. We aim to study chemically peculiar Am and Fm stars, distinguished by their unique abundance patterns, which are crucial for studying mixing processes in intermediate-mass stars. These stars provide a window into the atomic diffusion in their stellar envelopes, the evolution-dependent changes in mixing, and the resulting effects on pulsation mechanisms.
Methods. This study examines the pulsation characteristics of the Am and Fm star group. Our analysis encompasses 1276 stars (available as catalogues on GitHub), utilising data from TESS and Gaia and focussing on stars from the Renson catalogue.
Results. In our sample, 51% of stars (649) display no variability, and are thus categorised as constant stars. Among those that remain, 25% (318 stars) are pulsating Am, Fm, and ρ Puppis stars, including 20% (261 stars) that are exclusively Am and Fm stars. Additionally, 17% of stars (210) show variability indicative of binarity and/or rotational modulation and 7% (93 stars) are eclipsing binaries. Of the pulsating stars, 10% (32 stars) are γ Doradus type, 54% (172 stars) δ Scuti type, and 36% (114 stars) are hybrids, underlining a diverse pulsational behaviour of Am and Fm stars.
Conclusions. Our findings indicate that pulsating stars predominantly occupy positions near the red edge of the classical instability strip, allowing us to ascertain the incidence of pulsations in this stellar population.
Energetic seed particles in self-consistent particle acceleration modeling at interplanetary shock waves
Authors: Nyberg S. , Vuorinen L. , Afanasiev A. , Trotta D. , Vainio R. 
Published: August 2024

Aims. The study investigates the relevance of the seed particle population in the results of particle acceleration in interplanetary shock waves, when wave–particle interactions are treated self-consistently.
Methods. We employed the SOLar Particle Acceleration in Coronal Shocks (SOLPACS) model, which is a proton acceleration simulation in shocks with self-consistent nonlinear wave–particle interactions. We compared a suprathermal monoenergetic injection with a two-component injection, including the suprathermal monoenergetic component and a broad-spectrum energetic component corresponding to the observed background particle spectrum. Energetic particles in the beginning of the simulation could increase the local wave intensities sufficiently to increase the rate of acceleration for injected particles and even reshape the resulting particle energy spectra and spatial distributions. The resulting particle energy spectra, particle spatial distributions, and wave intensity spectra are compared to observations made by Solar Orbiter’s instrument suite of the 2021 October 30 energetic storm particle (ESP) event to evaluate the relevance of the seed particle population in the acceleration model.
Results. The energetic component of the seed particle population shortens the needed acceleration time for particles and enhances the tail of the spectrum to a level that matches the observations. The highest compared energies (> 1 MeV) match only when an energetic component is included in the seed particle population. The wave intensities and spatial distributions, on the other hand, showed no significant differences with the monoenergetic and two-component injection. While the simulated and observed wave intensities match within five minutes before the shock passing, the simulated wave field is too intense farther out from the shock, probably due to a lack of wave damping and/or decay processes in the simulation, leading to particles being slightly overly trapped to regions closer to the shock.