bluuebunny/crossref_metadata_2025_split huggingface dataset:
Magnetic pomegranate peels activated carbon (MG-PPAC) composite for Acid Orange 7 dye removal from wastewater
Authors: Khalil Asmaa , Nazir Muhammad Altaf , Salem Mohamed A. , Ragab Safaa , El Nemr Ahmed 
Published: July 2024

AbstractA magnetic pomegranate peels activated carbon (MG-PPAC) nanocomposite (23.46–26.75 nm) was prepared as an effective adsorbent for Acid Orange 7 (AO7) dye removal from wastewater. The effects of impregnation ratio and activation temperatures (600–900 °C) on the specific surface area and pore morphology were studied. BET, SEM, EDX, FT-IR, XRD and VSM are considered the synthesized composite MG-PPAC. The prepared magnetic composite at 700 °C activation temperature and impregnation ratio 1/2 (peels/ZnCl2) exhibited 513.34 m2/g surface area, 0.4025 cm3/g volume of the total pores, and 3.1364 nm mean diameter of the pores. The magnetization saturation, remanence and coercivity of the MG-PPA composite were 14.116 emu/g, 0.50685 emu/g, and 19.705 G, respectively. Also, the composite MG-PPAC was in a super-paramagnetic state at room temperature and could be gathered within 5 S (less than 5 S) with an external magnetic field. The impact of pH, adsorbent dose, initial concentration of adsorbate (AO7 dye), and time of contact have been studied to optimize the removal process. Langmuir, Freundlich and Temkin isotherm models were used to investigate MG-PPAC adsorption behavior for AO7 dye. Applicability of the Langmuir isotherm model demonstrates a monolayer adsorption AO7 dye removal process, and the maximum monolayer capacity (Qm) attained from linear solvation of LIM is 322.58 mg/g. Furthermore, the highest removal was 99.53% at pH 2.25. Also, the adsorption process was tested using like pseudo-first-order, pseudo-second-order, intraparticle diffusion, film diffusion, and Elovich models. The pseudo-second-order model is well-fitted to the operational data of AO7 dye removal.
A multilevel backbone extraction framework
Authors: Hmaida Sanaa , Cherifi Hocine , El Hassouni Mohammed 
Published: July 2024

AbstractAs networks grow in size and complexity, backbones become an essential network representation. Indeed, they provide a simplified yet informative overview of the underlying organization by retaining the most significant and structurally influential connections within a network. Network heterogeneity often results in complex and intricate structures, making it challenging to identify the backbone. In response, we introduce the Multilevel Backbone Extraction Framework, a novel approach that diverges from conventional backbone methodologies. This generic approach prioritizes the mesoscopic organization of networks. First, it splits the network into homogeneous-density components. Second, it extracts independent backbones for each component using any classical Backbone technique. Finally, the various backbones are combined. This strategy effectively addresses the heterogeneity observed in network groupings. Empirical investigations on real-world networks underscore the efficacy of the Multilevel Backbone approach in preserving essential network structures and properties. Experiments demonstrate its superiority over classical methods in handling network heterogeneity and enhancing network integrity. The framework is adaptable to various types of networks and backbone extraction techniques, making it a versatile tool for network analysis and backbone extraction across diverse network applications.
Gravity experiments with radio pulsars
Authors: Freire Paulo C. C. , Wex Norbert 
Published: July 2024

AbstractThe discovery of the first pulsar in a binary star system, the Hulse–Taylor pulsar, 50 years ago opened up an entirely new field of experimental gravity. For the first time it was possible to investigate strong-field and radiative aspects of the gravitational interaction. Continued observations of the Hulse–Taylor pulsar eventually led, among other confirmations of the predictions of general relativity (GR), to the first evidence for the reality of gravitational waves. In the meantime, many more radio pulsars have been discovered that are suitable for testing GR and its alternatives. One particularly remarkable binary system is the Double Pulsar, which has far surpassed the Hulse–Taylor pulsar in several respects. In addition, binary pulsar-white dwarf systems have been shown to be particularly suitable for testing alternative gravitational theories, as they often predict strong dipolar gravitational radiation for such asymmetric systems. A rather unique pulsar laboratory is the pulsar in a hierarchical stellar triple, that led to by far the most precise confirmation of the strong-field version of the universality of free fall. Using radio pulsars, it could be shown that additional aspects of the Strong Equivalence Principle apply to the dynamics of strongly self-gravitating bodies, like the local position and local Lorentz invariance of the gravitational interaction. So far, GR has passed all pulsar tests with flying colours, while at the same time many alternative gravity theories have either been strongly constrained or even falsified. New telescopes, instrumentation, timing and search algorithms promise a significant improvement of the existing tests and the discovery of (qualitatively) new, more relativistic binary systems.
Assessing the current and future trends of climate extremes at Zarima subbasin North Western Ethiopia
Authors: Kassahun Meaza , Ture Kassahun , Nedaw Dessie 
Published: July 2024

AbstractClimate extreme events in Ethiopia have increased, impacting the country’s agriculture dependent economy. Localized studies on rainfall extremes provides valuable insights to develop mitigation measures. This study focuses on the analysis of rainfall extremes for the observed (1984–2018) and the future mid time period (2031–2065) and end time period (2066–2100) projected under RCP4.5: moderate emissions reduction scenario aiming to stabilize greenhouse gas concentrations by the mid-21st century and RCP8.5: high emissions scenario with continued increase in greenhouse gas emissions throughout the twenty-first century. Trend analysis was done using R statistical software modified Man Kendell package. The result revealed that the observed total rainfall in wet days (PRCPTOT) showed insignificant declining trend across the subbasin. Under the RCP4.5 scenario, mid time period projections indicated increasing trends in PRCPTOT at Maytsebri, Adiremet and Debarik stations while decreasing trends expected at Ketema Niguse and Zarima stations. In the RCP4.5 end time period, Debarik and Zarima stations showed increasing trends while other stations exhibited decreasing trends. Under the RCP8.5 scenario, PRCPTOT showed insignificant decreasing trend except Ketema Niguse station which showed significant increasing trend during the mid time period. The observed annual maximum 1-day rainfall (RX1day) and annual RX5day rainfall had both positive and negative trends over subbasin. The RCP4.5 scenario projected declining trends in annual RX1day rainfall, while the RCP8.5 scenario projected increasing trends, except the end time projection of Debarik station. Consecutive dry days (CDD) increased and Consecutive wet days (CWD) decreased insignificantly in majority the subbasin. Projections indicated higher CDD and decreased CWD are expected in majority of the subbasin area. These finding implied that further investigation is required on the impact of climate extremes.
Command responsibility in military AI contexts: balancing theory and practicality
Authors: Oimann Ann-Katrien , Salatino Adriana 
Published: July 2024

AbstractArtificial intelligence (AI) has found extensive applications to varying degrees across diverse domains, including the possibility of using it within military contexts for making decisions that can have moral consequences. A recurring challenge in this area concerns the allocation of moral responsibility in the case of negative AI-induced outcomes. Some scholars posit the existence of an insurmountable “responsibility gap”, wherein neither the AI system nor the human agents involved can or should be held responsible. Conversely, other scholars dispute the presence of such gaps or propose potential solutions. One solution that frequently emerges in the literature on AI ethics is the concept of command responsibility, wherein human agents may be held responsible because they perform a supervisory role over the (subordinate) AI. In the article we examine the compatibility of command responsibility in light of recent empirical studies and psychological evidence, aiming to anchor discussions in empirical realities rather than relying exclusively on normative arguments. Our argument can be succinctly summarized as follows: (1) while the theoretical foundation of command responsibility appears robust (2) its practical implementation raises significant concerns, (3) yet these concerns alone should not entirely preclude its application (4) they underscore the importance of considering and integrating empirical evidence into ethical discussions.
Clinical efficacy of current treatments for high-volume metastatic hormone-sensitive prostate cancer: a systematic review and network meta-analysis
Authors: Tian Yuxuan , Liu Zhifu , Mulati Yelin , Yao Kaifeng , Jin Jie , He Zhisong , Fan Yu 
Published: July 2024

Abstract
Background
Several randomized controlled trials (RCTs) demonstrated a significant survival benefit of novel treatment regimens compared with androgen deprivation therapy (ADT) for metastatic hormone-sensitive prostate cancer (mHSPC), especially in high-volume disease (HVD, CHAARTED defined). As an influence on poor prognosis, the treatment for patients with HVD, especially visceral metastasis (VM) needed to be distinguished from mHSPC. This study was conducted to rank the treatment options for patients with HVD and VM, respectively, according to the latest data.

Methods
We synthesized current evidence based on well-designed RCTs. Only phase III trials were included. A Bayesian network meta-analysis was conducted by using R-4.2.3, and the pooled hazard ratio (HR) of overall survival (OS) and progression-free survival (PFS) with a 95% credible interval (CI) were calculated. Note that the definitions of PFS were various. The ranking plots were generated. OR of adverse events was also calculated and presented. This study was registered in the International Prospective Register of Systematic Reviews (CRD42023416334).

Results
Eleven RCTs were included through Pubmed, Embase and Cochrane. In HVD patients, all combination therapies can improve OS and PFS. Among them, The HR for Darolutamide (Daro) + Docetaxel (Doc) + androgen deprivation therapy (ADT) was most significant over ADT in both OS and PFS (hazard ratio [HR]: 0.50, 95% confidence interval [CI]: 0.39–0.63; HR: 0.25; 95% CI: 0.19–0.31). In patients with visceral metastasis, adding novel hormonal agents (NHAs) to ADT showed better survival outcome. But in analysis of treatment ranking, not alike the outcome of high-volume disease, Doc + ADT seems ranked higher than other NHA + ADT. Almost all combination therapies lead to more grade ≥ 3 adverse events.

Conclusion
Triplet therapy achieved the best effect on both HVD and visceral metastasis with a tolerable adverse effect. In HVD, our findings demonstrated that any NHA, Docetaxel or triplet combination therapy was superior to ADT alone. Ranking of combination therapy differs between patients with HVD and visceral metastases. In patients with visceral metastasis, chemotherapy has a higher priority than novel hormonal agents. Abiraterone's efficacy ranked better compared to other NHAs but still worse than docetaxel. The sensitivity treatments of bicalutamide versus placebo lead to diversity of results.
Formulation of prebiotic, low glycemic index millet soups using foxtail, barnyard and kodo millet
Authors: Peerkhan Nazni , Pandey Madan , Bhandari Yosha 
Published: July 2024

AbstractOptimization of foxtail, barnyard, and kodo millet, was carried out to formulate millet soup mix by adopting a mixture design. The physico chemical parameters such as pH, Total Soluble Solids (TSS), viscosity, color values (L*, a*, b*), quality characteristics such as soluble, insoluble total fiber, reducing sugar, total phenolic content, and various sensory parameters such as appearance, colour, aroma, taste, and overall acceptability were recorded to assess the effect of selected above three millets on the reconstituted millet soups. Optimization was done and a polynomial model was developed using Response Surface Methodology (RSM) relating to total fiber, antioxidant activity, prebiotic activity score, glycemic index and overall acceptability of the millet soups. Results revealed that there was a significant difference (p < 0.05) observed in the in the pH, TSS, viscosity among ten variations of millet soups. Similar statistical significance (p < 0.05) was noticed in the colour values. Quality characters such as reducing sugar, insoluble, and soluble dietary fiber values were also found to be different between the ten millet soup variations. Highest value of total phenolic content was recorded in MS6 variation with 7.5 gm of barnyard and 7.5 gm kodo millet flour in the millet soup. The overlay plots were used to attain the optimum formulation for the millet soups related to total dietary fiber, antioxidant activity, prebiotic score, glycemic index and overall acceptability which created the yellow region based on the optimized value. The results revealed that the optimum values for the predicted responses were 5.65 g, 24.96%, 0.36, 41.85, 7.65 for the total dietary fiber, antioxidant activity, prebiotic score, glycemic index and overall acceptability, respectively, which was attained from the optimum mixture of 2.14 gm foxtail millet flour, 5.76 gm barnyard millet flour, and 7.11 gm kodo millet flour. From the results the optimized millet soup can be categorized as low glycemic soup (GI < 55) with good prebiotic activity.
Online Learning Based on Learning Analytics in Big Data for College English Language Teaching
Authors: Liu Xuesong 
Published: July 2024

AbstractThis study addresses the challenges of enhancing the quality of education and improving the overall student experience in online English language teaching sessions. Current approaches often struggle with session initiation, real-time data processing, and personalized learning experiences. To tackle these issues, the study proposes a manifold learning data analytics model (MLDAM). This innovative method leverages classifier tree learning to distinguish between trivial and non-trivial aspects of student learning experiences and session data. MLDAM integrates multi-dimensional data extraction, classification learning, and impact evaluation to enhance the effectiveness of online English language teaching. The model analyzes data from 176,009 English phrases across 36 online teaching sessions, focusing on improving session accessibility, student comprehension, and suggestion accuracy. Using an iterative training process based on student performance and feedback, it continuously extracts and processes multiple types of data to refine teaching strategies. Results show notable improvements: a 14.74% increase in classification accuracy, an 8.73% increase in data extraction ratio, an 11.84% reduction in feature discard, a 10.57% decrease in initialization time, and a 13.17% reduction in classification time. These metrics demonstrate MLDAM’s ability to efficiently process and analyze session data, enabling real-time adjustments during lessons. The model optimizes data utilization, making learning more responsive and adaptable. It enhances the precision of impact evaluations, facilitating targeted course adjustments and personalized learning experiences. This research presents a comprehensive, data-driven methodology for improving teaching quality and student outcomes in virtual English classrooms.
Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm
Authors: Liu Miaomiao , Xu Haoran , Zhao Fengda , Zhang Qiang , Jia Ying , Xi Jiahao 
Published: July 2024

AbstractAccurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.
Acute promyelocytic leukemia: the first reported case diagnosed by immunophenotyping in Mozambique
Authors: Mongo Edson , Matola Felismina , Sigaúque Adérito , Monteiro Vânia , Guiliche Onélia , Babetine Teresa , Manhique-Coutinho Lena , Mutchamua Juliana , Dias Angelina , Mutsaka Dadirayi , Decroo Tom , Gils Tinne , Mudenyanga Chishamiso , Amodo Faizana , Terra-Granado Eugênia , Chissumba Raquel Matavele 
Published: July 2024

AbstractPatients with acute promyelocytic leukemia (APL), a subtype of acute myeloid leukemia (AML), often present with severe leucocytosis and thrombocytopenia, which may lead to symptoms that require prompt diagnosis and treatment. Immunophenotyping combined with cytomorphology is a valuable tool for identifying APL patients. We describe the case of a 9-year-old male patient who was diagnosed with APL in July 2022 using immunophenotyping combined with cytomorphology. The patient was treated with chemotherapy and is now in full remission in the maintenance phase. This case demonstrated the importance of timely differential diagnosis using the correct tools, allowing inclusion in the appropriate treatment protocol.
Influences of Space Weather Forecasting Uncertainty on Satellite Conjunction Assessment
Authors: Parker William E. , Freeman Mervyn , Chisham Gareth , Kavanagh Andrew , Mun Siew Peng , Rodriguez‐Fernandez Victor , Linares Richard 
Published: July 2024

AbstractA significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.
Vasa vasorum enhancement on optical coherence tomography in Kawasaki disease
Authors: Kakimoto Nobuyuki , Suzuki Hiroyuki , Taruya Akira , Takeuchi Takashi , Suenaga Tomohiro , Tsuchihashi Tomoya , Suzuki Takayuki , Shibuta Shoichi , Ino Yasushi , Tanaka Atsushi , Tokuhara Daisuke 
Published: July 2024

Abstract
Background
Patients with Kawasaki disease (KD) prone to develop coronary artery aneurysm (CAA) with unknown etiology. We aimed to disclose the relationship between vasa vasorum (VV) and intimal thickening using optical coherence tomography (OCT) in KD.

Methods
Forty-three coronary artery branches of 21 patients with KD were examined by OCT. The coronary arteries were classified into three groups: the CAA group (n = 9) in which CAAs remained since the acute phase, the regressed group (n = 16) in which CAAs were regressed, and the no CAA group (n = 18). The number and distribution of VV, and intimal thickening in coronary arteries were evaluated on OCT.

Results
Intimal thickening was significantly more severe in the CAA and regressed groups than in the no CAA group (median: 481, 474, and 218 μm, p = 0.001 and p < 0.001, respectively). The number of VV in the regressed group was significantly higher than that in the CAA and no CAA groups. The numbers of adventitial VV and internal VV were positively correlated with the intimal thickness (R = 0.64, p < 0.001; R = 0.62, p < 0.001, respectively). In the no CAA group, no internal VV were observed.

Conclusions
VV enhances according to intimal thickening, suggesting that VV may have some link to the healing process, such as CAA regression and intimal thickening.

Impact

Kawasaki disease (KD) is a vasculitis syndrome developing coronary artery aneurysm, however its etiology still remains unclear. Coronary artery imaging using optical coherence tomography (OCT) can reveal coronary arterial wall pathology, however OCT studies are limited in patients with KD.

Using OCT, we disclosed the closed relationship between vasa vasorum enhancement and regressed coronary arterial lesions.

Vasa vasorum enhancement is involved in the pathomechanism of the convalescent phase of KD.