bluuebunny/crossref_metadata_2025_split huggingface dataset:
Observation of Atmospheric and Ionospheric Anomalies before the Nepal Earthquakes on 25th April and 12th May 2015
Authors: Simha C.Prasanna , Rao K. Madhusudhana , Dumka Rakesh. K. 
Published: April 2024

Abstract

Identifying pre-seismic atmospheric and ionospheric anomalies holds significant research importance but is fraught with challenges, particularly for earthquakes characterized by diverse magnitudes, focal depths, and focal mechanisms. This study investigates atmospheric-ionospheric disturbances associated with the Nepal earthquakes of April 25, 2015 (M=7.8) and May 12, 2015 (M=7.3), utilizing atmospheric and ionospheric parameters. Ionospheric parameters such as vertical total electron content (VTEC) and atmospheric parameters including outgoing long-wave radiation (OLR), cloud cover, and vertical temperature gradient (VTG) were collected from IGS GPS stations and INSAT 3D data provided by the Indian Meteorological Department (IMD). Notable VTEC anomalies were detected 3 and 10 days prior to the April 25, 2015 event and 2 and 6 days before the May 12, 2015 event. The study employed the inter-quartile range (IQR) and running median over one day to establish upper limit references for VTEC signatures during the 51-day period surrounding the Nepal earthquakes. The analysis revealed pronounced increases in VTEC near the earthquake epicentre, such as at stations LCK-4 and LHAZ, compared to more distant stations like IISC, HYDE, SGOC, and URUM. Prior to the earthquakes, a 54‒60% relative amplitude increase in VTEC was observed at these upper bound (UB) stations. Furthermore, examination of the global planetary index (Kp) and storm time disturbance index (Dst) over the 51-day period did not reveal any geomagnetic signatures attributable to geomagnetic storms during the seismic activity. OLR ranged from 240 to 340 watts/m2, observed four days preceding the event, while the vertical temperature gradient varied from 4.3 to 23.2°K. Daily OLR variations over the 51-day period exhibited significant anomalous atmospheric responses a few days prior to the earthquakes. The shallow depth of the earthquakes facilitated enhanced energy release from the seismic zones, potentially contributing to the augmentation of anomalous VTEC patterns.
Gossypiboma Mimicking an Ovarian Cyst, Discovered Two Years Post- Cesarean Section: A Case Report
Authors: OSMAN HAGIR , Masoud M. Sayed , MohamedAli Khattab Saeed Elkhazin , Fadl Hiba Awadelkareem Osman , Hamza Abdelrahman , Basheer Hind Abashar Mohamed , alfaraja Mohamed 
Published: April 2024

Abstract

This case report presents a unique instance of gossypiboma, a surgical complication characterized by the unintentional retention of foreign objects such as gauze or sponges within the peritoneal cavity. Despite its rarity, gossypiboma poses significant risks, including intestinal obstruction and abscess formation. We describe the case of a 37-year-old multiparous female who presented with abdominal pain two years post-cesarean section, initially diagnosed with an ovarian cyst. However, intraoperative exploration revealed a gauze-filled cyst adherent to the ovary and jejunum, highlighting the diagnostic challenge and the need for meticulous surgical evaluation. This is the first reported case of gossypiboma in Sudan in 2024, emphasizing the global prevalence of this medical error. Root cause analysis and curriculum modifications are essential to address this issue. Multidisciplinary collaboration is crucial for successful management, emphasizing the importance of prompt recognition and intervention. This case underscores the diagnostic complexities and the necessity for enhanced protocols to prevent similar occurrences, advocating for improved communication among healthcare teams to ensure optimal patient outcomes
Robust Distributed Learning under Least Squares Approximation
Authors: Zhang Zhaodu , Chao Yue , Ma Xuejun 
Published: April 2024

Abstract

Massive datasets pose a serious challenge to traditional statistical methods. Modal regression has greater robustness and high inference efficiency compared to mean regression and likelihood-based methods. In this paper, we present a robust distributed least squares approximation (RDLSA) method for heterogeneously distributed massive datasets under the modal regression framework. Specifically, we first approximate the local objective function for each worker/server/machine by using the Taylor expansion, where it is necessary to remain the main quadratic term. Then, each of local machines calculate their corresponding estimates and upload them to a master machine for obtaining the approximated aggregated estimator. This process yields a one-step global estimator such that one round of communication is required. Correspondingly, the consistency and asymptotic normality of the estimator are rigorously established under some mild conditions. To further reduce the estimation bias, we perform one Newton-Raphson iteration to obtain a two-step global aggregated estimator. In addition, we develop a variable selection procedure for the distributed modal regression under the robust least squares approximation procedure. Finally, we provide simulation experiments and a real data application to verify the superiority of our method.
A resilient type-III broken gap Ga 2 O 3 /SiC van der Waals heterogeneous bilayer with band-to-band tunneling effect and tunable electronic property
Authors: Ferdous Naim , Islam Md. Sherajul , Park Jeongwon 
Published: April 2024

Abstract
The potential of van der Waals (vdW) heterostructure to incorporate the outstanding features of stacked materials to meet a variety of application requirements has drawn considerable attention. Due to the unique quantum tunneling mechanisms, a type-III broken-gap obtained from vdW heterostructure is a promising design strategy for tunneling field-effect transistors. Herein, a unique Ga2O3/SiC vdW bilayer heterostructure with inherent type-III broken gap band alignment has been revealed through first-principles calculation. The underlying physical mechanism to form the broken gap band alignment is thoroughly studied. Due to the overlapping band structures, a tunneling window of 0.609 eV has been created, which enables the charges to tunnel from the VBM of the SiC layer to the CBM of the Ga2O3 layer and fulfills the required condition for band-to-band tunneling. External electric field and strain can be applied to tailor the electronic behavior of the bilayer heterostructure. Positive external electric field and compressive vertical strain enlarge the tunneling window and enhance the band-to-band tunneling (BTBT) scheme while negative electric field and tensile vertical strain shorten the BTBT window. Under external electric field as well as vertical and biaxial strain, the Ga2O3/SiC vdW hetero-bilayer maintains the type-III band alignment, revealing its capability to tolerate the external electric field and strain with resilience. All these results provide a compelling platform of the Ga2O3/SiC vdW bilayer to design high performance tunneling field effect transistor.
DQM Analysis of multi-Directional FGM Micro Plats for buckling and vibration
Authors: Rajasekaran Sundaramoorthy 
Published: April 2024

Abstract
In the realm of Micro Electro-Mechanical Systems (MEMS) ,comprehending their behaviour is paramount importance for practical applications. Analsing these structures often becomes intricate due to size effets,rendering traditional methods like Finite Element Analysis inadequate. This study aims to address this challenge by developing a micro-plate model integrating the modified couple stress theory, which capturesinherent size effects in MEMS. The primary objective is to investigate critical loads and resonant frequencies of mirco-plates through meticulous parametric and comparative analysis, considering length scale parameters, material gradient indices, and aspect ratios as per the modified couple stress theory. The research focuses on multi-layer multi-directional functionally graded sandwich thin rectangular microplates under mechanical loading, employing the principle of virtual displacement and Kirchhoff's hypothesis. Physical properties across thickness, length and width diections, along side various boundary conditions are examined usig Power law, Exponential and Sigmoidal law functions. This study models various types of functionally graded thin micro plates depending on the material variation in axial, depth and multi-directions using the modified differential quadrature method showing effectiveness, simplicity, and high accuracy. The results presented herein serve as a benchmark for validating future experimental findings. The notable advantage of using sandwich plates is the mitigation odelamination issues typically encountered in composite structures. Micro plate of complexgeometry with curved boundary has also been analyzed.
Inquisitive Motion Generation System based on a Free-Energy Model of Curiosity and Interest
Authors: Honda Shimon , Yanagisawa Hideyoshi 
Published: April 2024

Abstract
Product motion is a crucial design element, blending functionality with aesthetic appeal. Such motions establish an emotional bond between the product and its user. Engaging with the product stimulates curiosity and interest in its motion, encouraging repeated use. This study aims to formulate indicators for curiosity and interest in motion and to implement a computational system to generate motions that provoke inquiry. This study situates curiosity and interest as emotions crucial to the inquiry cycle, proposing that optimal prediction error (deviation from expected motion patterns) cultivates interest, while fluctuations in prediction error spark curiosity. Our analysis is based on the inquiry cycle, a mathematical model of emotions rooted in the free energy principle of brain function. We developed curiosity, motion, and interest measures from the model based on prediction error variation and reduction. The developed system can independently control prediction errors and their variations for a given open-loop mechanism. This system generates motions that control the magnitude of prediction errors by deviating from the trajectory of energy-minimizing motions. Using the semantic differential method and eye-tracking experiments measuring gaze duration, we found that interest correlates with prediction error in an upward-convex manner, and its variability induces curiosity. These results align with our model. The novelty of this study lies in experimentally verifying that applying moderate prediction errors and fluctuations in prediction errors is effective in increasing interest and curiosity in motion design. This research contributes design insights for engineers and designers seeking to enhance the appeal of product motions.
Combined with the insulin resistance index in assessing subclinical systolic function in newly diagnosed type 2 diabetes mellitus:An analysis based on automated functional imaging echocardiography
Authors: Li Shengnan , Chen Run , Cao Yan , Hu Zhongshun , Ni Danfeng , Du Zihui , Zhang Chaoxue 
Published: April 2024

Abstract
Background: In recent years, simpler and more practical indicators based on routine biochemical tests or anthropometric measurements have been widely utilized for the assessment of insulin sensitivity. However, limited research has been conducted to investigate the predictive value of these novel simplified measures in relation to subclinical left ventricular systolic dysfunction.
Methods: A total of 160 newly diagnosed patients with type 2 diabetes mellitus (T2DM) and 70 healthy subjects matched by age and sex were included in the study. Left ventricular function parameters were assessed using AFI echocardiography. Four indicators of insulin resistance (IR) were computed: Triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-c), the product of fasting triglycerides and glucose levels(TyG), TyG multiplied by the body mass index(TyG-BMI)and the Insulin resistance metabolic score(METS-IR). The binary logistic regression analysis identified clinical and ultrasonic risk factors associated with abnormal GLPS-AVG in patients with T2DM. Develop a multiple-index-based log P model for integrated application. The diagnostic efficacy of the log P model in predicting left ventricular systolic function impairment was assessed using ROC analysis.
Results: Competing risk regression revealed that BMI, IVSD, SBP and LA were significant risk factors for the reduction of GLPS-AVG in individuals with T2DM. Additionally, two IR index models were found to be closely associated with abnormal GLPS-AVG: TyG-BMI (6.227,p=0.000); METS-IR(7.436,p=0.000). ROC analysis results indicate that TyG-BMI, METS-IR, IVSD, SBP, LA and a combination of five other indexes have demonstrated certain efficacy in predicting and evaluating diabetic heart function reduction. Its ROC-AUC (0.95CI) are 0.750 (0.564 ~ 0.934), 0.774 (0.582 ~ 0.944), 0.702 (0.461 ~ 0.948), 0.737 (0.478 ~ 0.983), 0.726 (0.483 ~ 0.951), 0.878 (0.770 ~ 0.987) respectively.
Conclusion: Approximately 20% of newly diagnosed patients with T2DM exhibit early-stage left ventricular systolic dysfunction.The Log P model exhibited the highest predictive efficiency when applied in combination, with significantly higher sensitivity, specificity and accuracy than each individual application.
A Comparative Investigation of Disease Detection in Plant Pathology: A Study on the YOLOv3 and Gaussian YOLOv3Models
Authors: Haripriya M. , Radhika A. , Jeslin J. 
Published: April 2024

Abstract

Leaf disease detection is a critical task in precision agriculture, aiming to monitor and control the spread of plant diseases for sustainable crop management. Object detection models have shown promise in accurately identifying and localizing diseases on plant leaves in recent years. This paper explores the effectiveness of YOLOv3 (You Only Look Once) and a variant known as Gaussian YOLOv3 in the context of leaf disease detection. YOLOv3 is known for its real-time object detection capabilities and high accuracy. However, it may face challenges in accurately localizing subtle disease patterns and handling uncertainties in complex leaf images. To address these challenges, Gaussian YOLOv3 incorporates Gaussian components to model uncertainty and improves localization accuracy. The comparative analysis involves evaluating the performance of YOLOv3 and Gaussian YOLOv3 in terms of localization accuracy, speed, adaptability to diverse conditions, and training requirements. Experiments are conducted using a dataset comprising various leaf diseases under different environmental conditions. They enable timely interventions and agricultural decision-making, reducing crop losses and ensuring effective disease management.
Adaptive Multi-Scale Feature Fusion with Spatial Translation for semantic Segmentation
Authors: Wang Hongru , Wang Haoyu 
Published: April 2024

Abstract

In image segmentation tasks,the extraction of multi-scale features enables models to better adapt to targets of diverse scales and capture semantic information more comprehensively. Additionally, the rational utilization of receptive fields assists models in better comprehending both local and global image structures.In this work, we combine the advantages of these two approaches and propose a novel adaptive module termed the MFFM. Module similar to the human eye’s visual system adaptively adjusts the focus and perceptual range to maximize the capture of target features. However, fixed-size convolutional kernels may result in information loss or confusion, leading to inaccuracies in segmentation outcomes, especially when dealing with highly similar images. To address this issue,we introduce spatial shift mechanism to perform pixel-level translation of the feature map,and by taking into account the relative relationship between pixels, the network can learn more discriminative features, thereby enhancing segmentation accuracy. Based on this, we propose a network model called AMFFNet. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and ADE20K datasets, achieving the test set performance of 91.7% and 46.76% MIoU without any post-processing.
The Association Between Adolescent Adhd and Callous-unemotional Traits is Moderated by Electrophysiological Reinforcement Sensitivity
Authors: Koppány Domonkos , Hámori György , Réthelyi János M. , Bunford Nóra 
Published: April 2024

Abstract

Reward processing is a hypothesized ADHD intermediate phenotype and, as such, is suggested to improve prediction of relevant outcomes, above and beyond the clinical phenotype. Although both reward and punishment processing and ADHD are associated with CU traits, gaps in knowledge remain about adolescents, electrophysiological indices, and longitudinal relations. We examined, in N = 297 adolescents, whether accounting for ODD and sex, ERPs to gain and loss moderate the prospective association between ADHD symptoms and CU traits and whether any moderational effects are driven by ADHD risk status and domain of symptoms. Findings indicated ADHD symptoms were positively associated with Uncaring traits at low (b = .255, SE = .109, p = .020; 95% CI[.040, .469]), but not at moderate (b = .136, SE = .078, p = .085; 95% CI[-.019, .291]) or high (b = .016, SE = .080, p = .842; 95% CI[-.142, .174]) levels of Target P3, with this moderational effect apparent in adolescents at-risk for ADHD, for inattention symptoms (F(6, 92) = 2.299, ΔR2 = .138, p = .010). Electrophysiological reward and punishment processing improves prediction of CU traits above and beyond the ADHD clinical phenotype; in adolescents at-risk for ADHD, low electrophysiological response links elevated inattention symptoms to greater Uncaring traits whereas moderate and higher response may be protective.
Exploring the Interactive Effects of ZDDP, Metal Sulfides, and Carbon Nanotubes on the Lubrication Performance of a PAO Oil
Authors: Prieto Germán , Abdelnabe Juan Pablo , Ovando Enzo , Cárdenas Ignacio Silva , Antonelli María Mercedes , Tuckart Walter Roberto 
Published: April 2024

Abstract

This study investigates the frictional behavior and wear protection capacity of polyalphaolephin (PAO) oils additivated with combinations of zinc dialkyldithiophosphate (ZDDP), metal sulfides, and carbon nanotubes (CNTs) on steel surfaces. Various oil formulations, comprising PAO base oil with differing proportions of ZDDP, metal sulfides, and CNTs, were utilized to lubricate steel-steel contacts in block-on-ring tests. Both the blocks and rings were made of SAE 52100 bearing steel. The testing conditions selected for all the tests were 1 GPa of Hertzian contact stress, 1 m/s of sliding speed, and a total sliding distance of 3,600 m. Surface analysis was performed by means of SEM-EDS, laser confocal microscopy, and Raman microspectrometry. Results indicated that combining metal sulfides or CNTs with ZDDP notably reduced friction compared to ZDDP-only oil, with reductions ranging from 24 to 60%, depending on the formulation. However, wear rates varied considerably among formulations. Those containing ZDDP and Bi2S3 exhibited significant wear rate increases of 160 to 180% compared to ZDDP-only oil. Optimal wear protection for steel surfaces was achieved with ZDDP + CNTs and ZDDP + MoS2 combinations, resulting in wear rate reductions of 19 to 14% respectively.
Platelet (PLT), eosinophil (EOS), and albumin (ALB) are useful indicators for evaluating the risk of developing lupus nephritis (LN) in patients with systemic lupus erythematosus (SLE) Running head: indicators for evaluating the risk of developing lupus nephritis
Authors: Zeng Wanxin , Huang Cancan , Yi Sha , Xiong Xia , Zhong Guishu , Chen Yan 
Published: April 2024

Abstract

Objectives Although there have been extensive investigations of platelet (PLT), eosinophil (EOS), and albumin (ALB) in many diseases, their roles in systemic lupus erythematosus (SLE) with lupus nephritis (LN) remain unclear. Thus, the present study aimed to evaluate the value of PLT, EOS, and ALB levels and provide guidance for the clinical application of PLT, EOS, and ALB detection in Chinese SLE patients with LN. Methods Among 2060 enrolled SLE patients undergoing hospitalization, we included a total of 73 patients diagnosed with LN and 325 SLE patients without LN who completed the measurement of blood and LN screening between 2018 and 2022. All clinical characteristics and the blood measurement information of SLE patients were extracted and analyzed from the medical records. Univariate and multivariate logistic regression analyses were used to evaluate the possible relationship of PLT, EOS, and ALB to LN. Receiver operating characteristic (ROC) curve analysis was also performed to assess the discriminative ability of three ratios in predicting LN. The nomogram was performed to facilitate an individualized estimation of the risk of lupus nephritis in SLE patients. Results The LN group had lower PLT, EOS, and ALB levels than the SLE group (P < 0.01). Univariate logistic regression analysis indicated that three risk factors for LN were identified, including PLT (OR = 0.393, 95% CI 0.172–0.896,P = 0.026), EOS (OR = 0.108, 95% CI 0.027–0.439,P = 0.002), and ALB (OR = 0.351, 95% CI 0.127–0.972,P = 0.044). Multivariate logistic regression analysis also showed that, compared with the low groups, the high PLT group, high EOS group, and high ALB group had a lower risk of LN. In addition, ROC analysis and the nomogram comprised of PLT, EOS, and ALB revealed that these three predictors were determined as predictive indicators of LN in SLE patients and exhibited sufficient predictive accuracy, with the area under the characteristic curve (AUC) of 0.720 [95% confidence interval (CI) 0.658–0.782]. Conclusions Decreased levels of PLT, EOS, and ALB might be correlated with an increased risk of LN in Chinese SLE patients.
A novel capsule neural network for identification of rice leaf disease
Authors: Chen Yahong , Xiao Qingquan , Tang Huazhu , Xie Quan 
Published: April 2024

Abstract

A novel network model (RESCapsNet) for identification of rice leaf diseases was proposed by combining the traditional convolutional neural network and capsule networks. The results show the identification accuracy and F1_score of the proposed RESCapsNet is 99.63% and 99.6% on the public dataset from Kaggle, respectively. The accuracy of RESCapsNet is improved by 12.54% compared to the capsule network (CapsNet), although CapsNet only required 110 epochs to reach its optimal state, RESCapsNet required training 140 epochs to reach its optimal state. This method is effective in identifying and classifying rice leaf diseases, as shown by the experimental results, and can achieve early defense against rice leaf diseases.