bluuebunny/crossref_metadata_2025_split huggingface dataset:
Economics of Electrowinning Iron from Ore for Green Steel Production
Authors: Humbert Matthew S. , Brooks Geoffrey A. , Duffy Alan R. , Hargrave Chad , Rhamdhani M. Akbar 
Published: August 2024

AbstractThe transition to green steel production is pivotal for reducing global carbon emissions. This study presents a comprehensive techno-economic analysis of various green steel production methods, including hydrogen reduction and three different electrolysis techniques: aqueous hydroxide electrolysis (AHE), molten salt electrolysis, and molten oxide electrolysis (MOE). By comparing process flow diagrams, capital and operational expenditures, specific energy consumption, and production footprint, this work provides a high-level assessment of the economic viability of these processes as they mature. The analysis reveals that MOE, despite its ongoing development, offers a promising route for iron production given its ability to process a wide range of ore qualities and the potential to sell electrolyte as a cement product. However, the best balance between deployment ready technology and economic benefit is AHE. Operational challenges are also discussed, such as electrolyte loss and slag handling. We suggest that the sale of by-products like oxygen may not significantly impact the economics due to market saturation. The findings underscore the importance of continued research and development in process optimization to realize the full potential of green steel technologies. All the calculations have been released as supplementary electronic material (MS Excel workbook). The format has been inspired by the techno-economic assessment template (TECHTEST) distributed by the US Dept. of Energy.
Graphical Abstract
Cryo-Assisted Nitrogen Treatment for the Fabrication of Nanoengineered, Mixed Transition Metal Oxide Anode from Inorganic Domestic Waste, for Lithium-Ion Batteries
Authors: Ashraf Humza , Karahan B. Deniz 
Published: August 2024

AbstractA novel method for the fabrication of nanoengineered, mixed transition metal oxide anode active material is proposed based on implementing liquid nitrogen treatment during the chemical precipitation process, for the first time in open literature. Such interference in the precipitation is believed to change the surface energy of the nuclei leading to differentiation in the growth process. To exemplify this hypothesis with an environmentally friendly approach, kitchen scourer pads, an existing waste, are used as a starting material instead of using a mixture of primary quality metals’ salts. Therefore, in this study, firstly, an optimization is realized to leach the scouring pad with 100% efficiency. Then, by applying a conventional chemical precipitation to this leachate at pH 5.5, Sample 1-P is produced. Herein, innovatively liquid nitrogen treatment is carried out during the chemical precipitation to produce Sample 2-P. Lastly, these precipitates (Samples 1-P, 2-P) are calcinated in the air to form mixed transition metal oxide powders: Samples 1 and 2, respectively. Structural, chemical, and morphological characterizations are carried out to examine the effect of liquid nitrogen treatment on the powders’ properties. To discuss the effect of nitrogen treatment on the electrochemical performances of the anode active materials (Sample 1 and Sample 2), galvanostatic tests are realized. The results show that Sample 2 demonstrates a higher 1st discharge capacity (1352 mAh/g) and retains 62% of its performance after 200 cycles when 50 mA/g current load is applied. Moreover, this electrode delivers around 500 mAh/g at 1 A/g current load. The remarkable cycle performance of Sample 2 is believed to be related to the superior chemical, structural, and physical properties of the electrode active material.
Graphical Abstract
Die aktuelle Guideline Nebenniereninzidentalom
Authors: Braun Leah 
Published: August 2024

ZusammenfassungNebenniereninzidentalome werden in den letzten Jahren durch die Zunahme der abdominellen Bildgebung häufig diagnostiziert, sodass bereits 2016 eine dezidierte Guideline der European Society of Endocrinology erstellt wurde, welche nun 2023 in revidierter Form publiziert wurde. Ein multidisziplinäres Assessment dieser Inzidentalome sollte bei Verdacht auf Malignität oder bei hormoneller Sekretion erfolgen. Bis zu 10 % der Inzidentalome sind maligne. Die wichtigste Bildgebung zur Beurteilung der Dignität ist die Computertomographie ohne Kontrastmittel, da sich mit dieser Homogenität und Hounsfield Units (HU) ideal beurteilen lassen. Bis zu 50 % aller Nebennierenadenome sind durch eine milde autonome Cortisolsekretion gekennzeichnet. Daher wird empfohlen, in allen Fällen einen 1‑mg-Dexamethason-Hemmtest durchzuführen. Weitere biochemische Diagnostik (Ausschluss eines Phäochromozytoms, Bestimmung des Aldosteron-Renin-Quotienten, Steroidprofiling) ist in ausgewählten Fällen angezeigt. Ein hormonell inaktives und bildgebend eindeutig gutartiges Inzidentalom bedarf keiner Therapie und keiner weiteren Nachsorge. Bei Verdacht auf Malignität sollte eine Adrenalektomie erfolgen. Diese ist minimal-invasiv durchzuführen, wenn die Raumforderung ≤ 6 cm und nicht invasiv ist, anderenfalls muss offen operiert werden. Eine weitere Bildgebung nach 6–12 Monaten ist bei allen uneindeutigen Fällen vorgesehen; hierfür sollte eine CT oder MRT durchgeführt werden. Bei einem signifikanten Wachstum von mehr als 20 % muss in der Regel eine Operation erfolgen. Eine erneute hormonelle Abklärung ist nur bei klinischen Veränderungen vorgesehen. Forschungsbedarf in der Versorgung von Nebenniereninzidentalomen besteht vor allem in der Versorgung von Patienten und Patientinnen mit milder autonomer Cortisolsekretion, da es hier an Studien mangelt für evidenzbasierte Empfehlungen.
Bacillus subtilis as an effective tool for bioremediation of lead, copper and cadmium in water
Authors: Rocco Debora H. E. , Freire Bruna Moreira , Oliveira Thais Jardim , Alves Patricia Lius Melo , de Oliveira Júnior José Martins , Batista Bruno Lemos , Grotto Denise , Jozala Angela Faustino 
Published: August 2024

AbstractBioremediation is a promising technique for the removal and recovery of contaminated areas, which is based on the ability of organisms to convert toxic substances into less harmful or inert compounds. Here, we evaluate the capacity of the Bacillus subtilis (BS) bacteria to bioadsorved heavy metals such as lead (Pb), copper (Cu) and cadmium (Cd) in water samples. Water samples were laboratory contaminated Pb at 500 ppm, Cu and Cd at 100 ppm. First, the growth curve of BS was plotted using Tryptone Soy Broth (TSB) at 100% (TSB100) and 33% (TSB33). Later, BS was studied in water containing all three metals separately and simultaneously. All solutions were stirred at 150 rpm, 35 ℃ for periods that ranged from 1 to 144 h. The heavy metal analyses were performed by X-ray fluorescence and Inductively Coupled Plasma Mass Spectrometry. The results showed that BS was resistant to Cu, Cd and Pb in water, with active multiplication and reduction in the concentration of the metals. The highest removal efficiency, in the presence of each heavy metal, reached 100% with Pb, 92.3% with Cd and 89% with Cu. In the solution with the mixture of heavy metals, Cd and Pb levels reduced significantly. However, the mixture of metals negatively affected the removal of Cu. In conclusion, the implement using Bacillus-type bacteria can be efficient in the biosorption of heavy metals from aqueous solutions and can be employed as a cost-effective treatment for the bioremediation of industrial effluents and contaminated areas.
Driving sustainability: assessing KPI effectiveness in the Saudi chemical industry
Authors: Alfarsi Alaa , Sherif Ziyad , Jagtap Sandeep , Gupta Sumit , Salonitis Konstantinos 
Published: August 2024

AbstractThis study explores the relationship between Key Performance Indicators (KPIs) and environmental performance improvement within the Saudi chemical industry. Against the backdrop of global sustainability imperatives and Saudi Arabia’s Vision 2030, which promotes sustainability for economic diversification, this research aims to assess the effectiveness of KPIs in driving environmental sustainability practices. The motivation for this study stems from the identified gaps in the systematic implementation and utilisation of KPIs and the lack of awareness regarding certain aspects of environmental impact management within the industry in the Kingdom. The methodology involved a structured survey administered to a diverse range of chemical manufacturing companies, followed by rigorous data analysis using descriptive evaluation, Analysis of Variance (ANOVA), reliability analysis, and t-tests. The results revealed insights into pollution areas, KPI utilisation, methods for pollution assessment, alignment with strategic goals, and governance regulations. Descriptive analysis highlighted air quality management as a priority, with notable attention to water and land pollution, while quantitative analysis confirmed the significance of KPIs in driving environmental performance improvement in the area. However, it also unveiled the absence of a systematic approach to implementing and utilising KPIs effectively, coupled with a lack of awareness regarding certain aspects of environmental impact management, consequently leading to uncertainty. Overall, this study contributes to advancing sustainability efforts within the Saudi chemical sector, providing actionable insights for industry stakeholders and policymakers.
MLAWSMOTE: Oversampling in Imbalanced Multi-label Classification with Missing Labels by Learning Label Correlation Matrix
Authors: Mao Jian , Huang Kai , Liu Jinming 
Published: August 2024

AbstractMissing labels in multi-label datasets are a common problem, especially for minority classes, which are more likely to occur. This limitation hinders the performance of classifiers in identifying and extracting information from minority classes. Oversampling is an effective method for addressing imbalanced multi-label problems by generating synthetic instances to create a class-balanced dataset. However, the existing oversampling algorithms mainly focus on the location of the generated data, and there is a lack of design on how to complete the labels of the synthetic data. To address this issue, we propose MLAWSMOTE, a synthetic data generation algorithm based on matrix factorization weights. We introduce a weak supervised learning method in the oversampling method, optimize the weights of features and labels by using label correlation, and iteratively learn the ideal label weights. The mapping relationship between features and labels is learned from the dataset and the label correlation matrix. The oversampling ratio is defined based on the discrepancy between observed labels and the ideal label of synthetic instances. It mitigates the impact of missing minority labels on the model’s predictions. The labeling of synthetic instances is performed based on label prediction, and the potential labeling distribution is complemented. Experimental results on multiple multi-label datasets under different label missing ratios demonstrate the effectiveness of the proposed method in terms of ACC, Hamming loss, MacroF1 and MicroF1. In the validation of the four classifiers, MacroF1 decreased by 24.78%, 17.81%, 3.8% and 19.56%, respectively, with the increase of label loss rate. After applying MLAWSMOTE only decreased by 15.79%, 13.63%, 3.78% and 15.21%.
Dissolvable microneedle patch enables local delivery of immunomodulatory microparticles containing bifunctional molecules for periodontal tissue regeneration
Authors: Zhang Xuexiang , Hasani-Sadrabadi Mohammad Mahdi , Dashtimighadam Erfan , Fahimipour Farahnaz , Shokeen Bhumika , Bezouglaia Olga , Fu Ruxing , Hong Isabelle , Yang Yang , Aghaloo Tara , Wu Benjamin M. , Gu Zhen , Lux Renate , Li Song 
Published: August 2024

AbstractPeriodontitis is initiated by dysbiosis of the oral microbiome. Pathogenic bacteria elicit ineffective immune responses, which damage surrounding tissues and lead to chronic inflammation. Although current treatments typically aim for microbial eradication, they fail to address the significance of immune cell reactions in disease progression. Here, we searched for small molecules as drug candidates and identified a bifunctional antibiotic, azithromycin (AZM), that not only inhibits bacterial growth but also modulates immune cells to suppress inflammation. We further engineered a dissolvable microneedle patch loaded with biodegradable microparticles for local and painless delivery of AZM to the gingival tissues. Inflammatory cytokines were decreased while anti-inflammatory cytokines and M2 macrophage were increased with AZM treatments in vitro. In vivo delivery of the AZM-loaded microneedle patch demonstrated the same effects on cytokine secretion and the promotion of tissue healing and bone regeneration. In addition, microparticles containing anti-inflammatory interleukin-4 alone or in combination with separately-formulated AZM microparticles, had similar or slightly enhanced therapeutic outcomes respectively. The bimodal action of AZM obviates the necessity for separate antibacterial and immunomodulatory agents, providing a practical and streamlined approach for clinical treatment. Our findings also demonstrate the therapeutic efficacy of microparticles delivery into the soft tissues by a minimally invasive and fast-degrading microneedle patch and offer a novel therapeutic approach for the treatment of periodontitis and other diseases through immunomodulation.
Graphical Abstract
Sustainable cement mortar production using rice husk and eggshell powder: a study of strength, electrical resistivity, and microstructure
Authors: Paul Suvash Chandra , Basit Md Abdul , Hasan Noor Md. Sadiqul , Islam M. Shariful 
Published: August 2024

AbstractIn order to preserve the natural fine aggregates and proper waste resources recycling, rice husk and eggshell powders were used as filler materials in production of standard M30 grade mortar. Natural fine aggregate was replaced with rice husk powder (RHP) at 3%, 5%, 7%, 10%, 12%, and 15%, and with eggshell powder (EP) at 3%, 5%, and 7%, both at a constant water-to-cement ratio of 0.40. Compressive strength, split tensile strength, water absorption, pore distribution, and electrical resistivity of the mortar samples were investigated. Also, XRD, SEM, and EDS were performed to observe the samples’ chemical phases and microstructure. The results show that up to 15% RHP reduced the compressive strength by 20–35%, whereas the up to 7% EP obtained a similar (varies by 0–5%) result compared to the control sample at 28 days. Furthermore, the results suggest that sand replacement with RHP and EP had no impact on the water absorption and pore volume of mortars. Also, the samples made with up to 15% RHP and 7% EP improved the resistivity by up to 80% and 15%, respectively, providing better resistance to chloride ion migration. Based on the results, it is recommended to use the RHP and EP up to a level of 5% and 7%, respectively, in sustainable M30 grade mortar production.
The Will to Chaos and Disorder: The Behemoth as a Model of Political Economy
Authors: Harcourt Bernard E. 
Published: August 2024

AbstractThe history of political economy is tormented by beasts. The most famous is the Leviathan, the giant serpentine monster that figures in Hobbes’s masterpiece of modern political theory. Robert Fredona and Sophus Reinert spotlight another sea monster, the Kraken, that giant octopus or squid with a particular morphology (i.e., its tentacles) that so fittingly describes the grip of multinational corporations, stateless financial capital, social media, and tech giants today. But there are still other monsters in the bestiary of political economy. In this essay, I highlight the Behemoth, a land monster that captures another critical dimension of political economy: the willful and intentional deployment of chaos and disorder as a way of governing. Franz Neumann, political and legal theorist and lawyer, Columbia University professor, and member of the Frankfurt School in exile, placed the Behemoth at the heart—and in the title—of his analysis of Germany’s political economy under the Nazi regime. Alongside the Leviathan surveillance state and the many tentacular grips of multinational, social media, and tech Krakens, the Behemoth remains a key model to better understand current forms of capitalism.
Ireland’s Role in British Colonial Capitalism: “Men of Capitals” and Pitt’s Irish Proposals, 1784–1785
Authors: O’Sullivan Mary 
Published: August 2024

AbstractIn April 1785, the British prime minister, William Pitt, proposed to give Ireland “compleat liberty and equality” with Britain “in matters of trade.” Historians cast the stakes around Pitt’s “Irish” proposals in terms of ideologies about trade, but this paper focuses on the concrete economic issues involved. It shows that Pitt’s proposals emerged from years of debates in which contemporaries conceived of the British Atlantic economy in terms of an integration of trade, shipping, and credit that evokes a British system of colonial capitalism. Ireland’s dependent relationship to that system, and the perceived failure of “free trade” to overcome its poverty, generated a battle among Irish “improvers” over rival plans to attract “men of capitals” to Ireland. Pitt played an important role in this fierce Irish debate by favoring one plan, but the British prime minister and his main Irish advisor, Thomas Orde, were never convinced by that plan’s logic of improvement, supporting it instead for fiscal reasons. That calculus made Pitt’s proposals vulnerable to attack from economic interests in Britain that took Ireland’s plans for economic improvement more seriously.
A Parameterization Scheme for Correcting All‐Sky Surface Longwave Downward Radiation Over Rugged Terrain
Authors: Yang Feng , Zeng Zhenzhong , Cheng Jie 
Published: August 2024

AbstractAccurate surface longwave downward radiation (SLDR) is crucial for understanding mountain climate dynamics. While existing algorithms notably improve the accuracy of clear‐sky SLDR, a terrain correction algorithm that can correct remotely sensed and model‐simulated all‐sky SLDR on a large scale remains largely unexplored. Here, we propose a parameterization scheme for estimating all‐sky SLDR in rugged terrain. We primarily improve the estimation of nearby terrain thermal contribution by considering topographic asymmetry and incorporate the effects of ice cloud thermal scattering under low water vapor conditions. We validate the reliability of our model using the Discrete Anisotropic Radiative Transfer (DART) model, demonstrating a good agreement with a bias value of −12.8 W/m2 and a RMSE value of 28.2 W/m2. Further evaluation against the Essential Thermal Infrared Remote Sensing (ELITE) SLDR product at three TIPEX‐III in situ sites, located near the bottom of deep valleys with predominantly flat surfaces, indicates significant improvement in our model, reducing the mean bias by 7.4 W/m2 and the mean RMSE by 4.1 W/m2. Post‐terrain correction, the ELITE SLDR difference map exhibits a spatial pattern of “small in the northwest and large in the southeast” in the study area, with the maximum differences reaching 67 W/m2 in the daytime and 54 W/m2 at nighttime. Comparison with existing methods reveals similar improvements due to the consideration of terrain effects. Overall, our SLDR correction model shows enormous potential for correcting remotely sensed and model‐simulated SLDR products on a large scale.
Representative Sample Size for Estimating Saturated Hydraulic Conductivity via Machine Learning: A Proof‐Of‐Concept Study
Authors: Ahmadisharaf Amin , Nematirad Reza , Sabouri Sadra , Pachepsky Yakov , Ghanbarian Behzad 
Published: August 2024

AbstractMachine learning (ML) has been extensively applied in various disciplines. However, not much attention has been paid to data heterogeneity in databases and number of samples used to train ML models in hydrology. In this study, we addressed these issues and their impacts on the accuracy and reliability of ML models in the estimation of saturated hydraulic conductivity, Ks. We selected 17,990 soil samples from the USKSAT database and created random subsets N = 2,000, 4,000, 6,000, 8,000, 10,000, 12,000, 14,000, 16,000, and 17,990, 80% of which were used for training. The random subset selection was repeated 50 times. The extreme gradient boosting (XGBoost) algorithm was used to estimate Ks from other soil properties, such as bulk density, soil depth, texture, and organic content. For each subset, we conducted the learning curve analysis on the training and cross‐validation data sets. Results showed that for all training sample sizes the number of samples was not enough for the training and cross‐validation curves to reach a plateau. We also applied the concept of representative elementary volume by plotting the average coefficient of determination, R2, and root mean square log‐transformed error, RMSLE, against the training sample size. For the testing data set, as the number of training sample size increased from 1,600 to 14,392 the average R2 value increased from 0.74 to 0.90, while the average RMSLE value decreased from 1.08 to 0.69. Either the learning curve or representative sample size analysis is required to investigate whether the number of samples is enough or not.
Slow rewarming after hypothermia does not ameliorate white matter injury after hypoxia-ischemia in near-term fetal sheep
Authors: McDouall Alice , Zhou Kelly Q. , Davies Anthony , Wassink Guido , Jones Timothy L. M. , Bennet Laura , Gunn Alistair J. , Davidson Joanne O. 
Published: August 2024

Abstract
Background
The optimal rate to rewarm infants after therapeutic hypothermia is unclear. In this study we examined whether slow rewarming after 72 h of hypothermia would attenuate white matter injury.

Methods
Near-term fetal sheep received sham occlusion (n = 8) or cerebral ischemia for 30 min, followed by normothermia (n = 7) or hypothermia from 3–72 h, with either spontaneous fast rewarming (n = 8) within 1 h, or slow rewarming at ~0.5 °C/h (n = 8) over 10 h. Fetuses were euthanized 7 days later.

Results
Ischemia was associated with loss of total and mature oligodendrocytes, reduced expression of myelin proteins and induction of microglia and astrocytes, compared with sham controls (P < 0.05). Both hypothermia protocols were associated with a significant increase in numbers of total and mature oligodendrocytes, area fraction of myelin proteins and reduced numbers of microglia and astrocytes, compared with ischemia-normothermia (P < 0.05). There was no difference in the number of oligodendrocytes, microglia or astrocytes or expression of myelin proteins between fast and slow rewarming after hypothermia.

Conclusion
The rate of rewarming after a clinically relevant duration of hypothermia had no apparent effect on white matter protection by hypothermia after cerebral ischemia in near-term fetal sheep.

Impact

Persistent white matter injury is a major contributor to long-term disability after neonatal encephalopathy despite treatment with therapeutic hypothermia.

The optimal rate to rewarm infants after therapeutic hypothermia is unclear; current protocols were developed on a precautionary basis.

We now show that slow rewarming at 0.5 °C/h did not improve histological white matter injury compared with rapid spontaneous rewarming after a clinically established duration of hypothermia in near-term fetal sheep.