bluuebunny/crossref_metadata_2025_split huggingface dataset:
QAmplifyNet: pushing the boundaries of supply chain backorder prediction using interpretable hybrid quantum-classical neural network
Authors: Jahin Md Abrar , Shovon Md Sakib Hossain , Islam Md. Saiful , Shin Jungpil , Mridha M. F. , Okuyama Yuichi 
Published: October 2023

AbstractSupply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet’s superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression’s performance on each preprocessed dataset. The model’s interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting “Not Backorder” and 75% for predicting “backorder,” outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.
An analysis of impact load and fragmentation dimension to explore energy dissipation patterns in coal crushing
Authors: Wang Xiao-He , Jing Wu , Zhang Wen-Bo , Wang Jiang-Hao , Yun Qing-Long , Wang Yi-Qing , Yi Sui 
Published: October 2023

AbstractThis research delineates the energy dissipation characteristics in coal crushing under impact loads, leveraging the capabilities of Separated Hopkinson Pressure Bar experimental system. A meticulous examination of both burst-prone and non-burst-prone coal samples during destruction processes was undertaken to decipher the dynamic compression mechanical attributes from perspectives of energy and fragmentatio‘s fractal dimensions. Burst-prone coal showcases a more pronounced escalation in fragmentation work in comparison to non-burst-prone samples, thereby illustrating a perceptible strain-rate dependent effect correlating with enhanced strain rates. Additionally, it was observed that incident, reflected, and transmitted energy trajectories for both sample categories follow an approximately linear ascendancy, albeit exhibiting diverse magnitudes. Burst-prone coal manifests a more rapid and focused energy growth compared to its non-burst-prone counterpart. When subjected to impact loads, a notable trend was discerned where the fragmentation’s fractional dimension escalated persistently with both the incident energy and the crushing work, portraying a prominent growth effect. The insights garnered from this study pave the way for distinguishing between impacted and unimpacted coal samples using energy perspectives and fragmentation's fractal dimensions.
Improvement of thermal-stability of chondroitinase ABCI immobilized on graphene oxide for the repair of spinal cord injury
Authors: Hassanli Atefeh , Daneshjou Sara , Dabirmanesh Bahareh , Khajeh Khosro 
Published: October 2023

AbstractSpinal cord injury healing has been shown to be aided by chondroitinase ABC I (cABCI) treatment. The transport of cABCI to target tissues is complicated by the enzyme's thermal instability; however, cABCI may be immobilized on nanosheets to boost stability and improve delivery efficiency. This investigation's goal was to assess the immobilization of cABC I on graphene oxide (GO). for this purpose, GO was produced from graphene using a modified version of Hummer’s process. the immobilization of cABC I on GO was examined using SEM, XRD, and FTIR. The enzymatic activity of cABC I was evaluated in relation to substrate concentration. The enzyme was then surface-adsorption immobilized on GO, and its thermal stability was examined. As compared to the free enzyme, the results showed that the immobilized enzyme had a greater Km and a lower Vmax value. The stability of the enzyme was greatly improved by immobilization at 20, 4, 25, and 37 °C. For example, at 37 °C, the free enzyme retained 5% of its activity after 100 min, while the immobilized one retained 30% of its initial activity. The results showed, As a suitable surface for immobilizing cABC I, GO nano sheets boost the enzyme's stability, improving its capability to support axonal regeneration after CNC damage and guard against fast degradation.
Present and future of the South Asian summer monsoon’s rainy season over Northeast India
Authors: Saha Prolay , Mahanta Rahul , Goswami B. N. 
Published: October 2023

AbstractAn iconic feature of the Indian Summer Monsoon Rainfall (ISMR), a longer than June–September rainy season over Northeast India (NEI), while a much shorter one over northwest India is expected to be altered by climate change but an objective definition of the length of the monsoon rainy season (LRS) over the NEI is lacking. Here, defining the LRS objectively over NEI, we show that the El Niño and Southern Oscillation (ENSO) is a primary driver of LRS, while rainfall during LRS is poorly correlated with the ENSO. In contrast to a significant decreasing trend of LRS and LRS-rainfall during the historical period, the projected LRS under the SSP5–8.5 scenario continues to decrease while the LRS-rainfall acquires a significant increasing trend over NEI. A significant increase in the impact of hydrological disasters is expected over NEI in the future due to the increasing intensity and frequency of extreme rain events within a shorter rainy season.
A scoping review of artificial intelligence-based methods for diabetes risk prediction
Authors: Mohsen Farida , Al-Absi Hamada R. H. , Yousri Noha A. , El Hajj Nady , Shah Zubair 
Published: October 2023

AbstractThe increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.
Combined acid hydrolysis and fermentation improves bioactivity of citrus flavonoids in vitro and in vivo
Authors: König Alice , Sadova Nadiia , Dornmayr Marion , Schwarzinger Bettina , Neuhauser Cathrina , Stadlbauer Verena , Wallner Melanie , Woischitzschläger Jakob , Müller Andreas , Tona Rolf , Kofel Daniel , Weghuber Julian 
Published: October 2023

AbstractMany bioactive plant compounds, known as phytochemicals, have the potential to improve health. Unfortunately, the bioavailability and bioactivity of phytochemicals such as polyphenolic flavonoids are reduced due to conjugation with sugar moieties. Here, we combine acid hydrolysis and tailored fermentation by lactic acid bacteria (Lactiplantibacillus plantarum) to convert the biologically less active flavonoid glycosides hesperidin and naringin into the more active aglycones hesperetin and naringenin. Using a comprehensive approach, we identify the most effective hydrolysis and fermentation conditions to increase the concentration of the aglycones in citrus extracts. The higher cellular transport and bioactivity of the biotransformed citrus extract are also demonstrated in vitro and in vivo. Superior antioxidant, anti-inflammatory and cell migration activities in vitro, as well as intestinal barrier protecting and antioxidant activities in Drosophila melanogaster are identified. In conclusion, the presented biotransformation approach improves the bioactivity of flavonoids, clearly traced back to the increase in aglycone content.
Audible pain squeaks can mediate emotional contagion across pre-exposed rats with a potential effect of auto-conditioning
Authors: Packheiser Julian , Soyman Efe , Paradiso Enrica , Michon Frédéric , Ramaaker Eline , Sahin Neslihan , Muralidharan Sharmistha , Wöhr Markus , Gazzola Valeria , Keysers Christian 
Published: October 2023

AbstractFootshock self-experience enhances rodents’ reactions to the distress of others. Here, we tested one potential mechanism supporting this phenomenon, namely that animals auto-condition to their own pain squeaks during shock pre-exposure. In Experiment 1, shock pre-exposure increased freezing and 22 kHz distress vocalizations while animals listened to the audible pain-squeaks of others. In Experiment 2 and 3, to test the auto-conditioning theory, we weakened the noxious pre-exposure stimulus not to trigger pain squeaks, and compared pre-exposure protocols in which we paired it with squeak playback against unpaired control conditions. Although all animals later showed fear responses to squeak playbacks, these were weaker than following typical pre-exposure (Experiment 1) and not stronger following paired than unpaired pre-exposure. Experiment 1 thus demonstrates the relevance of audible pain squeaks in the transmission of distress but Experiment 2 and 3 highlight the difficulty to test auto-conditioning: stimuli weak enough to decouple pain experience from hearing self-emitted squeaks are too weak to trigger the experience-dependent increase in fear transmission that we aimed to study. Although our results do not contradict the auto-conditioning hypothesis, they fail to disentangle it from sensitization effects. Future studies could temporarily deafen animals during pre-exposure to further test this hypothesis.
Peptide binder design with inverse folding and protein structure prediction
Authors: Bryant Patrick , Elofsson Arne 
Published: October 2023

AbstractThe computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and AlphaFold2 (AF) in a joint framework. Foldseek generates backbone seeds for a modified version of ESM-IF1 adapted to protein complexes. The resulting sequences are evaluated with AF using an MSA representation for the receptor structure and a single sequence for the binder. We show that AF can accurately evaluate protein binders and that our bind score can select these (ROC AUC = 0.96 for the heterodimeric case). We find that designs created from seeds with more contacts per residue are more successful and tend to be short. There is a relationship between the sequence recovery in interface positions and the plDDT of the designs, where designs with ≥80% recovery have an average plDDT of 84 compared to 55 at 0%. Designed sequences have 60% higher median plDDT values towards intended receptors than non-intended ones. Successful binders (predicted interface RMSD ≤ 2 Å) are designed towards 185 (6.5%) heteromeric and 42 (3.6%) homomeric protein interfaces with ESM-IF1 compared with 18 (1.5%) using ProteinMPNN from 100 samples.
Multiwavelength observation of 1A 0535+262=HD 245770 from 2010 to 2021
Authors: Liu Wei , Yan Jingzhi , Xiao Guangcheng , Li Xiukun , Gao Bo , Liu Qingzhong 
Published: October 2023

Context. 1A 0535+262 is a high-mass X-ray binary that went into a giant X-ray outburst in 2020. During this event, the X-ray luminosity reached the highest value measured over the last 30 years.
Aims. Our aim is to study the long-term variability of 1A 0535+262 before and after the 2020 major X-ray outburst and to uncover the mechanism that led to the X-ray outburst.
Methods. We used the long-term photometric light curve and the equivalent widths of the Hα and He I λ6678 lines to monitor the state of the Be star’s circumstellar disk. The Hα line profiles show evidence for V/R variability, which we revealed by fitting the Hα spectral line profiles with two Gaussian functions. In addition, we divided our data into four periods according to the intensity of the X-ray, optical, and infrared emission.
Results. The Hα line profiles show single-peaked profiles in most cases. This is consistent with the previously reported orbital inclination of i = 37° ±2°. Unlike the Hα lines, the He I λ6678 lines show a maximal intensity in October 2020, which is one month before the giant X-ray outburst in 2020. Based on the behavior of the equivalent widths of the Hα and He I λ6678 lines, and the V-band magnitude, we find two mass ejection processes from the Be star to the Be disk on MJD 55820 and MJD 56600. The V/R quasi-period is about two years during 2011–2015, which is different from 1994 to 1995. Furthermore, the periods I → II → III → IV in the (B − V) color index versus V-band magnitude diagram constitute a cycle. From the behavior of the V/R ratio of Hα lines, and the variability of the V band, we believe that the precession of the density perturbation inside the disk is retrograde.
Unveiling Bias: Examining the Influence of Positive Results on
Ergogenic Aids in Published Sports Science Studies
Authors: Marocolo Moacir , Mota Gustavo R. , Rodrigues Alex Batista , Leite Roberto C. de Matos , Hohl Rodrigo , Paixão Rodney Coelho da , Souza Hiago L. R. , Meireles Anderson , Arriel Rhai 
Published: October 2023

AbstractWe investigated the potential for publication bias in the field of sports science
regarding studies on ergogenic aids and their effects on exercise performance.
We found evidence to suggest that journals tend to prioritize studies with
positive results (76%) while neglecting those with negative outcomes
(2.7%). Worryingly, this could lead to a discrepancy between reported
conclusions and actual study outcomes. We also identified inconsistencies
between reported outcomes and actual performance variable outcomes. Taken
together, these data highlight the need for future research to reduce bias and
encourage the publication of studies with both positive and negative results to
improve the reliability of scientific evidence in this field.
A novel integrated time-resolved array avalanche photodiode detection system for nuclear resonant scattering measurements
Authors: Bao Ziyu , Zhang Yujun , Jiang Junguo , Ma Yichao , Liu Yu , Li Shenghao , Zhang Hongyu , Liu Monan , Zhou Yangfan , Li Zhenjie , Liu Yaoguang , Li Hangxu , Shen Zhibang , Yu Can , Shi Zhan , Ou Zina , Zhou Aiyu , Li Qiuju , Liu Peng , Xu Wei , Hu Michael Y. , Zhao Jiyong , Alp Ercan Esen 
Published: October 2023

The nuclear resonant scattering (NRS) experiment requires photon-counting detectors with high time resolution, short dead time, large dynamic range, low noise, and large detection area. An 8-channel avalanche photodiode (APD) array detector system with high integrity, flexibility, and reliability has been developed to adapt to the demands of NRS experiments. The detector system mainly consists of four key parts: (i) an array-APD sensor, (ii) 8-channel integrated fast preamplifiers, (iii) the time-to-digital converter readout electronics, and (iv) a data acquisition system and EPICS support software. Remarkably, the system exhibits a time resolution of better than 500 ps and has a sufficiently low noise level, allowing for the lowest detection energy threshold of 4 keV. The performance of the new array-APD system as well as its real application in nuclear forward scattering (NFS) and nuclear resonant inelastic x-ray scattering (NRIXS) experiments was tested in two synchrotron facilities. With the new system, the NFS signal very close to the prompt electronic scattering signal can be extracted. Thanks to the customized EPICS-areaDetector-based control software, NRIXS spectra can be readily measured with time and energy information of the NRIXS signal stored in the raw data, which is promising for developing NRIXS data analysis in the time domain. The array-APD detector can be deployed for nuclear resonant scattering experiments at various synchrotron radiation facilities.
Multipoint formulas in inverse problems and their numerical implementation
Authors: Novikov Roman G , Sivkin Vladimir N , Sabinin Grigory V 
Published: October 2023

Abstract
We present the first numerical study of multipoint formulas for finding leading coefficients in asymptotic expansions arising in potential and scattering theories. In particular, we implement different formulas for finding the Fourier transform of potential from the scattering amplitude at several high energies. We show that the aforementioned approach can be used for essential numerical improvements of classical results including the slowly convergent Born–Faddeev formula for inverse scattering at high energies. The approach of multipoint formulas can be also used for recovering the x-ray transform of potential from boundary values of the scattering wave functions at several high energies. Determination of total charge (electric or gravitational) from several exterior measurements is also considered. In addition, we show that the aforementioned multipoint formulas admit an efficient regularization for the case of random noise. In particular, we proceed from theoretical works (Novikov 2020 Inverse Problems
36 095001; 2021 Russ. Math. Surv.
76 723–5).
Electron scattering and neutrino physics
Authors: Ankowski A M , Ashkenazi A , Bacca S , Barrow J L , Betancourt M , Bodek A , Christy M E , Doria L , Dytman S , Friedland A , Hen O , Horowitz C J , Jachowicz N , Ketchum W , Lux T , Mahn K , Mariani C , Newby J , Pandey V , Papadopoulou A , Radicioni E , Sánchez F , Sfienti C , Udías J M , Weinstein L , Alvarez-Ruso L , Amaro J E , Argüelles C A , Balantekin A B , Bolognesi S , Brdar V , Butti P , Carey S , Djurcic Z , Dvornikov O , Edayath S , Gardiner S , Isaacson J , Jay W , Klustová A , McFarland K S , Nikolakopoulos A , Norrick A , Pastore S , Paz G , Reno M H , Ruiz Simo I , Sobczyk J E , Sousa A , Toro N , Tsai Y-D , Wagman M. , Walsh J G , Yang G 
Published: October 2023

Abstract
A thorough understanding of neutrino–nucleus scattering physics is crucial for the successful execution of the entire US neutrino physics program. Neutrino–nucleus interaction constitutes one of the biggest systematic uncertainties in neutrino experiments—both at intermediate energies affecting long-baseline deep underground neutrino experiment, as well as at low energies affecting coherent scattering neutrino program—and could well be the difference between achieving or missing discovery level precision. To this end, electron–nucleus scattering experiments provide vital information to test, assess and validate different nuclear models and event generators intended to test, assess and validate different nuclear models and event generators intended to be used in neutrino experiments. Similarly, for the low-energy neutrino program revolving around the coherent elastic neutrino–nucleus scattering (CEvNS) physics at stopped pion sources, such as at ORNL, the main source of uncertainty in the evaluation of the CEvNS cross section is driven by the underlying nuclear structure, embedded in the weak form factor, of the target nucleus. To this end, parity-violating electron scattering (PVES) experiments, utilizing polarized electron beams, provide vital model-independent information in determining weak form factors. This information is vital in achieving a percent level precision needed to disentangle new physics signals from the standard model expected CEvNS rate. In this white paper, we highlight connections between electron- and neutrino–nucleus scattering physics at energies ranging from 10 s of MeV to a few GeV, review the status of ongoing and planned electron scattering experiments, identify gaps, and lay out a path forward that benefits the neutrino community. We also highlight the systemic challenges with respect to the divide between the nuclear and high-energy physics communities and funding that presents additional hurdles in mobilizing these connections to the benefit of neutrino programs.
Experimental study on dynamic characteristics of charged particles in vacuum arc
Authors: Liu Shan , Yuan Zhao , Liu Liming , Chen Lixue , Liu Xueliang , Li Qian , Pan Yuan 
Published: October 2023

Abstract
The vacuum arc is essentially a process of evaporation, collision and ionization of metal materials to keep burning. Electron temperature and density are important parameters to describe vacuum arc plasma transport. This paper was based on the theory of continuous spectral radiation, combined with the principle of two-colorimetric temperature measurement, and realized 2D calculation and observation of electron temperature and density. The experimental platform for vacuum arc breaking and an optical image acquisition system were established to achieve synchronous acquisition of two wavelengths of arc by imaging separation method. On the basis of partial local thermodynamic equilibrium hypothesis, and obtained the 2D dynamic distribution of the electron temperature and density. The results showed that electron density decayed along the radial direction and the density near anode was lower in the axial direction. The 2D distribution trend of electron temperature was opposite to the density. Electron temperature had a little change and the electron density expands outward in different arcing times. With the increase of current amplitude, the peak value of electron density increased, and the electron density in the arc gap also increased. The measured results could reflect the formation and movement of microscopic particles at the initial diffusion stage.