bluuebunny/crossref_metadata_2025_split huggingface dataset:
An empirical study of object detection models for the detection of iron deficiency anemia using peripheral blood smear images
Authors: Navya K T , Akshatha K R , Prasad Keerthana , Singh Brij Mohan Kumar 
Published: December 2024

Abstract
Iron Deficiency Anemia (IDA) is the nutritional disorder that occurs when the body does not contain enough iron, an essential component of hemoglobin (Hb). The World Health Organization (WHO) estimated that IDA is the main cause of anemia in 1.62 billion cases worldwide [1]. Although IDA rarely results in death, it has significant adverse impacts on human health. During diagnosis, the hemoglobin indices show low mean corpuscular hemoglobin and mean corpuscular hemoglobin volume. On Peripheral Blood Smear (PBS) images viewed under a microscope by hematologists, IDA shows hypochromic and microcytic red cells. The purpose of the proposed research is to develop a computer-aided system that will allow hematologists to diagnose and detect diseases more accurately and quickly, therefore saving them time and effort. In order to diagnose or detect clinical disorders, non-invasive techniques like machine learning algorithms are employed. This work aims to identify IDA by utilizing the RetinaNet-Disentangled Dense Object Detector (DDOD) to localize hypochromic microcytes in PBS images. To the best of our knowledge, this is the first work using the object detection technique to detect IDA based on the Red Blood Cell (RBC) morphology. We carried out an extensive quantitative and qualitative evaluation of the model. Additionally, a comparison was made between the performance of our model and other object detection models. The results showed that our approach outperformed state-of-the-art techniques, with a mean average precision that was more than 8% higher.
Digital Twin for EEG seizure prediction using time reassigned Multisynchrosqueezing transform-based CNN-BiLSTM-Attention mechanism model
Authors: Ghosh Antara , Dey Debangshu 
Published: December 2024

Abstract
The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorders. There is active research into digital twins (DT) for various healthcare applications, as they can transform research into customized and personalized healthcare. The widespread adoption of DT technology relies on ample patient data to ensure precise monitoring and decision-making, leveraging Machine Learning (ML) and Deep Learning (DL) algorithms. Given the non-stationarity of EEG recordings, characterized by substantial frequency variations over time, there is a notable preference for advanced time-frequency methods in seizure prediction. This research proposes a DT-based seizure prediction system by applying an advanced time-frequency analysis approach known as Time-Reassigned MultiSynchroSqueezing Transform (TMSST) to EEG data to extract patient-specific impulse features and subsequently, a Deep Learning strategy, CNN-BiLSTM-Attention mechanism model is utilized in learning and classifying features for seizure prediction. The proposed architecture is named as ‘Digital Twin-Net’. By estimating the group delay in the time direction, TMSST produces the frequency components that are responsible for the EEG signal's temporal behavior and those time-frequency signatures are learned by the developed CNN-BiLSTM-Attention mechanism model. Thus the combination acts as a digital twin of a patient for the prediction of epileptic seizures. The experimental results showed that the suggested approach achieved an accuracy of 99.70% when tested on 22 patients from the publicly accessible CHB-MIT dataset. The proposed method surpasses previous solutions in terms of overall performance. Consequently, the suggested method can be regarded as an efficient approach to EEG seizure prediction.
Quantification of urinary albumin in clinical samples using smartphone enabled LFA reader incorporating automated segmentation
Authors: Bhatt Sunita , Gupta Richa , Prabhakar Vijay R N , Shukla Prashant Kumar , Datta Sudip Kumar , Dubey Satish Kumar 
Published: December 2024

Abstract
Smartphone-assisted urine analyzers estimate the urinary albumin by quantifying color changes at sensor pad of test strips. These strips yield color variations due to the total protein present in the sample, making it difficult to relate to color changes due to specific analyte. We have addressed it using a Lateral Flow Assay (LFA) device for automatic detection and quantification of urinary albumin. LFAs are specific to individual analytes, allowing color changes to be linked to the specific analyte, minimizing the interference. The proposed reader performs automatic segmentation of the region of interest (ROI) using YOLOv5, a deep learning-based model. Concentrations of urinary albumin in clinical samples were classified using customized machine learning algorithms. An accuracy of 96% was achieved on the test data using the k-Nearest Neighbour (k-NN) algorithm. Performance of the model was also evaluated under different illumination conditions and with different smartphone cameras, and validated using standard nephelometer.
Investigation of organs dosimetry precision using ATOM phantom and optically stimulated luminescence detectors in computed tomography
Authors: Khallouqi Abdellah , Sekkat Hamza , El Rhazouani Omar , Halimi Abdellah 
Published: December 2024

Abstract
The primary objective of this study was to compare organ doses measured using optically stimulated luminescent dosimeters (OSLDs) with those estimated by the CT-EXPO software for common CT protocols. An anthropomorphic ATOM phantom was employed to measure organ doses across head, chest, and abdominal CT scans performed on a Hitachi Supria 16-slice CT scanner. These OSLD measurements were then compared to the estimates provided by the widely used CT-EXPO software. Organ doses were assessed using OSLDs placed in an adult anthropomorphic phantom, with calibration performed through a comprehensive process involving multiple tube potentials and sensitivity corrections. Results from three CT acquisitions per protocol were compared to estimates provided by CT-EXPO software. Findings reveal significant discrepancies between measured and estimated organ doses, with p-values consistently below 0.05 across all organs. For head CT, measured eye lens doses averaged 33.51 mGy, 6.0% lower than the estimated 35.65 mGy. In chest CT, the thyroid dose was 9.82 mGy, 13.5% higher than the estimated 8.65 mGy. For abdominal CT, the liver dose measured 12.11 mGy, 9.6% higher than the estimated 11.05 mGy. Measured doses for the rest of organs were generally lower than those predicted by CT-EXPO, showing some limitations in current estimation models and the importance of precise dosimetry. This study highlights the potential of OSLD measurements as a complementary method for organ dose assessment in CT imaging, emphasizing the need for more accurate organ dose measurement to optimize patient care.
Emerging trends in fluorescent nanodiamond quantum sensing
Authors: Hui Yuen Yung , Le Trong-Nghia , Yang Teng-I , Chen Jeson , Chang Huan-Cheng 
Published: December 2024

Abstract
Diamond is the natural hardest material and has an exceptionally high thermal conductivity, wide optical transparency, and extreme chemical resistance. Most of these outstanding properties are still preserved for diamond at the nanoscale. Nanodiamond (ND) is highly biocompatible and inherently low in cytotoxicity, genotoxicity, and animal toxicity. Moreover, ND often contains atomic defects or impurities known as color centers. In this review article, we highlight the diverse applications of nitrogen-vacancy (NV) color centers in ND, which is also called fluorescent nanodiamond (FND). This review article summarizes recent advances and achievements in applying NV-containing FND for sensing and imaging including, nanoscale magnetic field sensing, NV axis orientation tracking, magnetic resonance, relaxometry for biosensing, nanoscale temperature sensing, spin-enhanced immunosensors for ultrasensitive in vitro diagnosis, and extreme ultraviolet imaging.
Combining effective Hamiltonians and Brillouin–Wigner approach: a perturbative approach to spectroscopy
Authors: Bindech Oussama , Valentin Bastien , Yalouz Saad , Robert Vincent 
Published: December 2024

Abstract
The numerical cost of variational methods suggests using perturbative approaches to determine the electronic structure of molecular systems. In this work, a sequential construction of effective Hamiltonians drives the definition of approximate model functions and energies in a multi-state Rayleigh–Schrödinger perturbative scheme. A second step takes advantage of an updated partitioning of the Hamiltonian to perform a state-specific Brillouin–Wigner energy correction based on a well-tempered perturbation expansion. The multi-step RSBW method is exemplified on model-Hamiltonians to stress its robustness, efficiency and applicability to spectroscopy determination.
Low-temperature-field-assisted fabrication of cross-scale tissue engineering scaffolds
Authors: Ye Jing , Zhou Xingyu , Huang Zhuo , Zhang Xianglin , Huang Wei , Wu Bin , Zhou Huamin 
Published: December 2024

Abstract
In tissue engineering (TE), tissue-inducing scaffolds are a promising solution for organ and tissue repair owing to their ability to attract stem cells in vivo, thereby inducing endogenous tissue regeneration through topological cues. An ideal TE scaffold should possess biomimetic cross-scale structures, similar to that of natural extracellular matrices, at the nano- to macro-scale level. Although freeform fabrication of TE scaffolds can be achieved through 3D printing, this method is limited in simultaneously building multiscale structures. To address this challenge, low-temperature fields were adopted in the traditional fabrication processes, such as casting and 3D printing. Ice crystals grow during scaffold fabrication and act as a template to control the nano- and micro-structures. These microstructures can be optimized by adjusting various parameters, such as the direction and magnitude of the low-temperature field. By preserving the macro-features fabricated using traditional methods, additional micro-structures with smaller scales can be incorporated simultaneously, realizing cross-scale structures that provide a better mimic of natural organs and tissues. In this paper, we present a state-of-the-art review of three low-temperature-field-assisted fabrication methods—freeze casting, cryogenic 3D printing, and freeze spinning. Fundamental working principles, fabrication setups, processes, and examples of biomedical applications are introduced. The challenges and outlook for low-temperature-assisted fabrication are also discussed.
Life cycle assessment and thermodynamic performance of biomass-based combined cogeneration
Authors: Çetinkaya Afşın Yusuf , Yılmaz Fatih , Bilgili Levent 
Published: December 2024

Abstract
This study presents a thermodynamic and life cycle analysis of a biomass-based integrated energy system, comprising a biomass gasification cycle, a steam Rankine cycle, and an ammonia fluid Organic Rankine cycle. The primary objective of the system is to efficiently convert olive husk, a sustainable biomass source, into electricity and heating. The gasification process generates syngas, which fuels a gas turbine, producing high-temperature exhaust that drives a Rankine cycle. The thermodynamic analysis indicates that the overall system achieves an energy efficiency of 20.85% and an exergy efficiency of 11.70%. The Rankine cycle exhibits an energy efficiency of 23.91%, while the Organic Rankine cycle demonstrates a higher exergy efficiency of 68.87%. Parametric studies reveal that increasing the combustion chamber temperature significantly enhances system efficiency and output, with energy efficiency rising linearly as the temperature increases from 800 °C to 1000 °C. Additionally, the analysis emphasizes the importance of the compressor’s compression ratio on system performance. A life cycle assessment conducted using the ReCiPe methodology evaluates the environmental impact of the system throughout its lifecycle, revealing critical insights into its sustainability. This comprehensive evaluation highlights the potential of utilizing agricultural waste, such as olive husk, to produce renewable energy, thereby supporting environmental sustainability and waste management goals. The findings underscore the viability of the proposed system for enhancing energy production while minimizing environmental impacts, making it a promising solution for renewable energy generation in regions with abundant biomass resources.
High-fidelity simulation of FRP-confined concrete-filled steel tubes: the synergy of empirical and machine learning techniques
Authors: Alqubaysi Tariq , Ghazouani Nejib , Mabrouk Abdelkader , Raza Ali , Ahmed Mohd 
Published: December 2024

Abstract
The present research addresses a significant gap in the current literature by overcoming the limitations associated with small, noisy datasets commonly used to predict the axial load-carrying capacity (ALC) of fiber-reinforced polymer (FRP)-encased concrete-filled steel tube compression examples (FCFST). Specifically, the authors present a refined, large-scale database that facilitates the evaluation of the prediction accuracies of three modeling techniques: finite element modeling (FEM), analytical modeling, and artificial neural networks (ANN). This comprehensive comparative analysis, underpinned by a robust experimental dataset, not only enhances predictive accuracy but also provides valuable engineering insights. Unlike previous studies, which often lack data refinement or fail to compare multiple modeling approaches, our work offers a more rigorous and holistic evaluation. The current study aims to recommend and compare the estimates of FEM, analytical model, and ANN model for capturing the ALC of FCFST examples. A database comprising 335 FCFST columns was constructed from previous studies to propose FEM and ANN models while the analytical model was proposed based on a database of 698 samples and encasing mechanics of steel tube and FRP wraps. The concrete damage plastic model was used for concrete along with bilinear and linear elastic models for steel tubes and FRP wraps, respectively. Analytical and ANN models effectively considered the lateral encasing mechanism of FCFST columns for accurate predictions. The FEM exhibited high accuracy with statistical parameters: MAE = 223.76, MAPE = 285.32, R2 = 0.943, RMSE = 210.43, and a20-index = 0.83. In contrast, the ANN model outperformed, with MAE = 195, MAPE = 229.67, R2 = 0.981, RMSE = 174, and a20-index = 0.89. The R2 values between the models indicated strong correlations: FEM versus analytical (0.876), analytical versus ANN (0.914), and ANN versus FEM (0.945), with the ANN model showing the best accuracy.
Entropy production analysis of energy loss of centrifugal pump impeller with multi-objective optimization
Authors: Zhang Runqiang , Nan Lingbo , Chen Diyi , Han Wei , Huang Weining 
Published: December 2024

Abstract
Efficiency is the core index for evaluating centrifugal pumps, which reduces the overall energy power loss of the pump and enhances the economics. The efficiency and energy-saving performance of the pump can be improved with an optimal designed impeller. In this paper, the efficiency improvement optimization design method for pumps is proposed via the pump impeller multi-parameter multi-objective optimization. Firstly, the high accuracy simulation model of the pump is modelled and verified. Comparison with the experimental results verifies the accuracy of the numerical method. Then, the design parameters of the impeller blades are hub inlet angle of impeller β
1h, shroud inlet angle of impeller β
1s, outlet angle of impeller β
2, shroud leading edge LEs, hub leading edge LEh for as the optimal parameters for orthogonal design. As the result, the optimized pump efficiency is increased by 1.902% and head is increased by 0.931 m. The optimized impeller mitigates unstable vortex clusters at the trailing edges of the blades, resulting in a more uniform velocity distribution and reduced flow separation within the impeller. The vortex transport equation was employed to investigate changes in the vortex structure. Additionally, the energy loss associated with the optimized impeller was evaluated using entropy production. Results show that optimizing the impeller reduces entropy production in both the impeller and the pump, thereby minimizing overall energy loss. These findings highlight the primary sources of energy loss in the impeller as potential focal points for further optimization research.
Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility
Authors: Jacobs Ryan , Schultz Lane E , Scourtas Aristana , Schmidt KJ , Price-Skelly Owen , Engler Will , Foster Ian , Blaiszik Ben , Voyles Paul M , Morgan Dane 
Published: December 2024

Abstract
One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.
The possible long-term periodic variability of the extremely luminous quasar WISE J090924.01+000211.1
Authors: Horiuchi Takashi , Toba Yoshiki , Misawa Toru , Murata Katsuhiro L , Isogai Keisuke , Yatsu Yoichi , Takahashi Ichiro , Sasada Mahito , Niwano Masafumi , Higuchi Narikazu , Hayatsu Shunsuke , Seki Hibiki , Oasa Yumiko , Sato Rikuto 
Published: December 2024

Abstract
The extremely luminous infrared galaxy (ELIRG), WISE J090924.01$+$000211.1 (hereafter WISE J0909$+$0002, $z=1.87$) is an extraordinary object with a quasar aspect. This study performed monitoring observations of WISE J0909$+$0002 with the 105$\:$cm Murikabushi telescope, Okayama and Akeno 50$\:$cm telescopes/MITSuME ($g^{\,\prime}$, $R_{\rm c}$, and $I_{\rm c}$ bands), and the SaCRA 55$\:$cm telescope/MuSaSHI (r, i, and z bands) for three years. We obtain the following results by combining the UV/optical light curves of the CRTS, Pan-STARRS, and ZTF archive data, and our observational data: (1) the light curves of WISE J0909$+$0002 present quasi-periodic (sinusoidal) oscillations with the rest-frame period of $\sim \!660$–$689\:$d; (2) the structure functions of WISE J0909$+$0002 do not show a damped random walk (DRW) trend; (3) the mock DRW light curves present periodic-like trend on rare occasions in 10000 simulations; (4) the relativistic boost scenario is favored, since the relation between variability amplitude and power-law slope ratio is consistent with the theoretical prediction of this scenario, and a substantial parameter space exists between the inclination angles and the black hole mass; (5) the circumbinary disk model is insufficient to explain the spectral energy distribution of our target; (6) the significant radio flux density of WISE J0909+0002 is not detected from the VLA FIRST Survey, thus the radio jet precession scenario is ruled out. The Doppler boost scenario is, from our results, likely as a cause of the periodic variability; consequently the quasi-periodic oscillations in WISE J0909$+$0002 is possibly interpreted by a supermassive black hole binary. Additional observations to investigate the continuity of the periodic trend would bring new insights into mechanisms of the quasi-periodic oscillations and/or ELIRGs.
Recent TIPS increases postoperative mortality: A national cohort study
Authors: Tang Helen , Kaplan David E. , Abu-Gazala Samir , Mahmud Nadim 
Published: December 2024

Background:
Patients with cirrhosis have an increased risk of postoperative mortality, which is partially attributable to portal hypertension. Preoperative TIPS placement may reduce operative risk. Studies suggesting the benefits of preoperative TIPS are limited by residual confounding and lack of longitudinal laboratory data. To address these limitations, we used granular longitudinal data from the Veterans Health Administration.

Methods:
This retrospective cohort study of Veterans Health Administration patients with cirrhosis who underwent major surgery from 2008 to 2022 identified patients who underwent TIPS placement within 6 months before surgery. Demographics, comorbidities, surgery type, and longitudinal laboratory data were incorporated into a propensity score using 5:1 caliper matching for receipt of TIPS. The propensity-matched cohort included 39 patients with preoperative TIPS and 171 without.

Results:
Baseline characteristics were similar between groups. In Cox regression, recent TIPS was associated with an increased risk of postoperative mortality (HR: 2.69, 95% CI: 1.37–5.30, p = 0.004), redemonstrated in 500 random resampling events (median HR: 1.71). TIPS and non-TIPS patients had similar albumin, bilirubin, and international normalized ratio 6 months before surgery; however, immediately before surgery, TIPS patients had lower albumin (p = 0.009), higher bilirubin (p = 0.001), and higher international normalized ratio (p = 0.001).

Conclusions:
In a propensity-matched analysis of patients with cirrhosis undergoing major surgery, recent TIPS was associated with increased postoperative mortality and worsened liver synthetic function in the immediate preoperative period. TIPS placement should be carefully considered in patients with cirrhosis who may undergo surgery.