bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Deterministic Method for Computing Bertini Type Invariants of Parametric Ideals
Authors: Tajima Shinichi , Nabeshima Katsusuke 
Published: July 2024

AbstractA new framework is proposed for computing general numerical invariants, called Bertini type invariants, associated to a family of polynomial ideals depending on deformation parameters. A deterministic algorithm is introduced for computing parameter dependency of Bertini type invariants. As an application, an algorithm for computing Chern–Schwartz–MacPherson classes of a family of singular projective hypersurfaces is obtained.
“Palamism” and “Barlaamism” in the Russian Name-Glorifiers Controversy of the 1910s: The Philosophical Background
Authors: Biriukov Dmitry 
Published: July 2024

AbstractI find two lines of interpretation of the philosophical status of “Palamism” and “Barlaamism” in the Russian thought of the late 19th to early twentieth centuries. One of these lines links Palamism with Aristotelianism and nominalism, and Barlaamism with Platonism and realism. The other line, conversely, connects Palamism with Platonism and Barlaamism with nominalism. I trace in detail the development and transformation of these lines in the course of the Name-Glorifiers controversy of the 1910s. I show the impact of the academic Byzantine studies of that time on the course of the theological debate.
What is spirituality? The challenges of a philosophical definition
Authors: Reisinger Doris 
Published: July 2024

Abstract
In recent years, there have been a number of philosophical publications focusing on spirituality. But even in pertinent philosophical texts it is rare to find attempts at shaping a workable definition of spirituality, despite the obvious need for a clear definition for the philosophical debate on spirituality. This paper addresses the major issues in shaping a satisfactory definition of spirituality: an understanding of spirituality as transcendence of critical reasoning, the broadness of the concept, the implications of various scholarly backgrounds and the challenge of avoiding a circular definition. It suggests a rough clustering of common approaches into ontological, ethical, and functional definitions and discusses their weaknesses. It is argued that an inclusive, metaphysically parsimonious, and stipulative definition is the best way to address typical difficulties and avoid weaknesses of common definitions. Finally, it proposes a definition based on meaning, a term that often appears in the discussion of spirituality. It is suggested that understanding meaning in the ordinary sense of the word might be a good starting point to arrive at a satisfactory definition of spirituality.
“There Is No Script”: Police Teachers’ Experiences of Training Investigative Interviewing
Authors: Ericsson Sarah , Öman Ekervhén Lisa , Nyström Markus , Stenlund Tova , Davis Paul 
Published: July 2024

AbstractSwedish police are implementing a new model for investigative interviewing. Previous research has highlighted that investigative interviewing is difficult to operationalize and teach to recruits. This study aimed to understand what Swedish police teachers value as important interpersonal skills for investigative interviewing as well as what strategies and struggles these teachers face when teaching recruits. Interviews were conducted with six experienced interviewing teachers at Swedish police training programs. Data were analyzed using thematic analysis. The teachers described that taking the interviewee’s perspective and accepting responsibility to build rapport, as well as social and situational flexibility were important skills for interviewers to process. When describing their strategies and struggles in teaching, the teachers described the need to bridge theory and practice, train flexibility through diverse scenarios, adjust to students’ social maturity and ability to self-reflect, and work with limited resources. This study also highlights how interviewing teachers express a need for adequate time and resources to be able to help recruits develop the necessary skills for interviewing.
A modified Levenberg–Marquardt algorithm for low order-value optimization problem
Authors: Lv Xiaochen , Yu Zhensheng 
Published: July 2024

AbstractIn this paper, we consider a modified Levenberg–Marquardt algorithm for Low Order Value Optimization problems(LOVO). In the algorithm, we obtain the search direction by a combination of LM steps and approximate LM steps, and solve the subproblems therein by QR decomposition or cholesky decomposition. We prove the global convergence of the algorithm theoretically and discuss the worst-case complexity of the algorithm. Numerical results show that the algorithm in this paper is superior in terms of number of iterations and computation time compared to both LM-LOVO and GN-LOVO algorithm.
A parameter uniform numerical method on a Bakhvalov type mesh for singularly perturbed degenerate parabolic convection–diffusion problems
Authors: Kumar Shashikant , Kumar Sunil , Ramos Higinio , Kuldeep  
Published: July 2024

AbstractWe are focused on the numerical treatment of a singularly perturbed degenerate parabolic convection–diffusion problem that exhibits a parabolic boundary layer. The discretization and analysis of the problem are done in two steps. In the first step, we discretize in time and prove its uniform convergence using an auxiliary problem. In the second step, we discretize in space using an upwind scheme on a Bakhvalov-type mesh and prove its uniform convergence using the truncation error and barrier function approach, wherein several bounds derived for the mesh step sizes are used. Numerical results for a couple of examples are presented to support the theoretical bounds derived in the paper.
Investigation of distributed learning for automated lesion detection in head MR images
Authors: Yamada Aiki , Hanaoka Shouhei , Takenaga Tomomi , Miki Soichiro , Yoshikawa Takeharu , Nomura Yukihiro 
Published: July 2024

AbstractIn this study, we investigated the application of distributed learning, including federated learning and cyclical weight transfer, in the development of computer-aided detection (CADe) software for (1) cerebral aneurysm detection in magnetic resonance (MR) angiography images and (2) brain metastasis detection in brain contrast-enhanced MR images. We used datasets collected from various institutions, scanner vendors, and magnetic field strengths for each target CADe software. We compared the performance of multiple strategies, including a centralized strategy, in which software development is conducted at a development institution after collecting de-identified data from multiple institutions. Our results showed that the performance of CADe software trained through distributed learning was equal to or better than that trained through the centralized strategy. However, the distributed learning strategies that achieved the highest performance depend on the target CADe software. Hence, distributed learning can become one of the strategies for CADe software development using data collected from multiple institutions.
The Disperse Dyeing of Polyetherimide Fibers Using Orthogonal Array (Taguchi) Design
Authors: Kalayci Ece , Avinc Ozan 
Published: July 2024

AbstractPolyetherimide (PEI) fibers are classified as high-performance fibers due to their notable characteristics, including chemical resistance, heat resistance, and flame resistance. However, the amorphous structure and intrinsic dyeability of polyetherimide (PEI) fibers set them apart from other high-performance fibers. The fibers possess considerable potential due to their remarkable performance features and are utilized in a wide range of industries, containing fashion and technical textiles, for varied purposes. The present work utilized the Orthogonal Array Taguchi method to systematically improve the dyeing process parameters for fabric samples made of polyetherimide (PEI). The use of a systematic methodology enabled effective experimentation and enabled the determination of the most favorable values of factors, while simultaneously reducing the utilization of resources. The study involved conducting rigorous experiments to evaluate the influence of several parameters on the color strength, tensile qualities, and washing fastness properties of the fabric samples. Furthermore, the utilization of the Taguchi approach facilitated the anticipation of these pivotal fabric properties by considering certain parameters. The objectives of this research encompass not only the progression of textile industry, but also the provision of beneficial perspectives for augmenting the caliber and efficacy of PEI fabrics in various applications.
Spinocerebellar Ataxias: Phenotypic Spectrum of PolyQ versus Non-Repeat Expansion Forms
Authors: Moura João , Oliveira Jorge , Santos Mariana , Costa Sara , Silva Lénia , Lemos Carolina , Barros José , Sequeiros Jorge , Damásio Joana 
Published: July 2024

AbstractSpinocerebellar ataxias (SCA) are most frequently due to (CAG)n (coding for polyglutamine, polyQ) expansions and, less so, to expansion of other oligonucleotide repeats (non-polyQ) or other type of variants (non-repeat expansion SCA). In this study we compared polyQ and non-repeat expansion SCA, in a cohort of patients with hereditary ataxia followed at a tertiary hospital. From a prospective study, 88 patients (51 families) with SCA were selected, 74 (40 families) of whom genetically diagnosed. Thirty-eight patients (51.4%, 19 families) were confirmed as having a polyQ (no other repeat-expansions were identified) and 36 (48.6%, 21 families) a non-repeat expansion SCA. Median age-at-onset was 39.5 [30.0-45.5] for polyQ and 7.0 years [1.00-21.50] for non-repeat expansion SCA. PolyQ SCA were associated with cerebellar onset, and non-repeat expansion forms with non-cerebellar onset. Time to diagnosis was longer for non-repeat expansion SCA. The most common polyQ SCA were Machado-Joseph disease (MJD/SCA3) (73.7%) and SCA2 (15.8%); whereas in non-repeat expansion SCA ATX-CACNA1A (14.3%), ATP1A3-related ataxia, ATX-ITPR1, ATX/HSP-KCNA2, and ATX-PRKCG (9.5% each) predominated. Disease duration (up to inclusion) was significantly higher in non-repeat expansion SCA, but the difference in SARA score was not statistically significant. Cerebellar peduncles and pons atrophy were more common in polyQ ataxias, as was axonal neuropathy. SCA had a wide range of genetic etiology, age-at-onset and presentation. Proportion of polyQ and non-repeat expansion SCA was similar; the latter had a higher genetic heterogeneity. While polyQ ataxias were typically linked to cerebellar onset in adulthood, non-repeat expansion forms associated with early onset and non-cerebellar presentations.
Combined use of niraparib enhanced the inhibitory effect of Anti-GD2 antibody on osteosarcoma cells
Authors: Wenyao Chen , Shuai Ma , Yifeng Fan , Xinzhi Li , Xiangyong Que 
Published: July 2024

Abstract
Purpose
This study aims to investigate the effect of Niraparib in combination with an Anti-GD2 Antibody on osteosarcoma cells.

Methods
Scratch test was utilized to assess cell migration capacity, while the Transwell experiment was utilized to evaluate cell invasion potential. Cell proliferation was measured using the CCK8 experiment. The affinity between the anti-GD2 antibody and its antigen was determined via ELISA. Tumor growth was evaluated through animal experiments. Western blotting, QRT-PCR, and histological analysis were conducted to examine the expression of relevant proteins and mRNAs.

Results
MG63 cell line was used for an example. The scratch test showed that the migration rate of osteosarcoma cells in Niraparib + Anti-GD2 group was 1.07 ± 0.04 after 48 h, and 0.34 ± 0.04 in the Control group. Transwell experiment showed that the invasion ability of osteosarcoma cells in Niraparib + Anti-GD2 group was 21.0 ± 1.5, and that in Control group was 87.7 ± 2.9. CCK8 experiment showed that the absorbance value of Niraparib + Anti-GD2 group was 0.16 ± 0.10 on day 5, and that of the Control group was 0.76 ± 0.09. Western blotting showed that the expression levels of BALP and CICP in Niraparib + Anti-GD2 group were 0.751 ± 0.135 and 1.086 ± 0.115, respectively, and those in Control group were 1.025 ± 0.143 and 1.216 ± 0.168, respectively. QRT-PCR results showed that the absorbance values of Niraparib + Anti-GD2 group were 0.173 ± 0.065 and 0.170 ± 0.078 on day 14. The results of animal experiments showed that on day 5, the tumor volume of the Control group was 2433 ± 391, and that of the Niraparib + Anti-GD2 group was 1137 ± 148. Histological analysis showed that the mean density values of Niraparib + Anti-GD2 group were 0.19 ± 0.08 and 0.22 ± 0.07, and those of Control group were 0.26 ± 0.09 and 0.29 ± 0.10.

Conclusion
The combination of Niraparib and Anti-GD2 antibody significantly inhibits Osteosarcoma cells.
Characterize molecular signatures and establish a prognostic signature of gastric cancer by integrating single-cell RNA sequencing and bulk RNA sequencing
Authors: Wang Zhiwei , Weng Zhiyan , Lin Luping , Wu Xianyi , Liu Wenju , Zhuang Yong , Jian Jinliang , Zhuo Changhua 
Published: July 2024

AbstractGastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players’ risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient’s unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.
Tribological properties of the beetle leg joints
Authors: Nadein Konstantin , Kovalev Alexander , Gorb Stanislav N. 
Published: July 2024

AbstractTribological properties of femoro-tibial leg joints in two beetles, darkling beetle Zophobas morio and Congo rose chafer Pachnoda marginata were studied. Very low friction of 0.004 was revealed by the direct measurements in the joint. It is assumed that semi-solid lubricant functioning as in technical bearings is one of the leading factors of the friction minimization. Dependence of the surface texture and physical chemical properties (hydrophobicity) on the cuticle friction was analysed. Contribution of the surface texture to the tribological properties of contacting surfaces was examined by the measurement in the tribosystem “contacting surface/glass”. It is supposed that coefficient of friction (COF) decreases with decrease of surface roughness. At the same time, no statistically significant correlation was found between the hydrophobicity of the surface and the value of the friction coefficient.
Hydroxychloroquine blood concentrations and effects in Chinese patients with IgA nephropathy
Authors: Yang Ting , Shi Yaotong , Wang Ye , Feng Yuan , Shao Qiuyuan , Jiang Chunming , Wang Lulu , Liu Jing 
Published: July 2024

Abstract
Background
Hydroxychloroquine (HCQ) is recommended for Chinese patients with immunoglobulin A nephropathy (IgAN). However, the relationship between HCQ blood concentration and the therapeutic effect for IgAN has not yet been defined. This study investigates the optimal and efficacious range of HCQ blood concentrations in Chinese patients with IgAN.

Methods
Seventy-three patients with biopsy-proven IgAN who were at risk of progression were included in this study. Thirty-eight patients with IgAN were treated with HCQ plus an optimized renin–angiotensin–aldosterone system inhibitor (RAASi), and thirty-five patients received only RAASi. Blood HCQ concentration and 24-h proteinuria were examined at three and six months after treatment.

Results
The baseline proteinuria levels were comparable between the RAASi and HCQ groups. The HCQ group had lower 24-h proteinuria than the RAASi group three months after treatment, though the difference was not significant (p = 0.38). After six months, the median proteinuria level was significantly lower in the HCQ group than in the RAASi group (p < 0.05). The percentage reduction in 24-h proteinuria in the HCQ group was greater than that in the RAASi group at three (p < 0.05) and six months (p < 0.05). Hydroxychlorquine blood concentration and efficacy were positively correlated at three months (r = 0.428, p < 0.05) and six months (r = 0.48, p < 0.05). Moreover, the optimal blood concentration of HCQ for three-month efficacy was 418.96 ng/mL and that for six-month efficacy was 582.48 ng/mL. No serious adverse events were reported during HCQ treatment.

Conclusions
Hydroxyhloroquine safely reduces proteinuria in Chinese patients with IgAN. The efficacy of HCQ is positively correlated with its blood concentration.

Graphical abstract
Automatic recognition of excavator working cycles using supervised learning and motion data obtained from inertial measurement units (IMUs)
Authors: Molaei Amirmasoud , Kolu Antti , Lahtinen Kalle , Geimer Marcus 
Published: July 2024

Abstract
This paper proposes an automatic method for excavator working cycle recognition using supervised classification methods and motion information obtained from four inertial measurement units (IMUs) attached to moving parts of an excavator. Monitoring and analyzing tasks that have been performed by heavy-duty mobile machines (HDMMs) are significantly required to assist management teams in productivity and progress monitoring, efficient resource allocation, and scheduling. Nevertheless, traditional methods depend on human observations, which are costly, time-consuming, and error-prone. There is a lack of a method to automatically detect excavator major activities. In this paper, a data-driven method is presented to identify excavator activities, including loading, trenching, grading, and idling, using motion information, such as angular velocities and joint angles, obtained from moving parts, including swing body, boom, arm, and bucket. Firstly, a dataset lasting 3 h is collected using a medium-rated excavator. One experienced and one inexperienced operator performed tasks under different working conditions, such as different types of material, swing angle, digging depth, and weather conditions. Four classification methods, including support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and naive Bayes, are off-line trained. The results show that the proposed method can effectively identify excavator working cycles with a high accuracy of 99%. Finally, the impacts of parameters, such as time window, overlapping configuration, and feature selection methods, on the classification accuracy are comprehensively analyzed.
Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models
Authors: Krishna Shwetank , Irfan Sayed Ameenuddin , Keshavarz Sahar , Thonhauser Gerhard , Ilyas Suhaib Umer 
Published: July 2024

AbstractPredicting pore pressure in the formation is crucial for assessing reservoir geomechanical characteristics, designing drilling schemes/mud programs, and strategies to enhance oil recovery. Accurate predictions are vital for safe and cost-effective exploration and development. Recent research has seen the emergence of intelligent models utilizing machine learning (ML) and deep learning (DL) algorithms, offering promising outcomes. However, there remains a need to identify the most accurate and dependable model among these. This study aims to address this gap by comparing the performance of various ML and DL models, as reported in existing literature, to determine the optimal approach for pore pressure prediction. The sorted machine learning (ML) and deep learning (DL) regression algorithms used for the comparative analysis are decision tree (DT), extreme gradient boosting (XGBoost), random forest (RF), recurrent neural network (RNN), and convolutional neural network (CNN). A total dataset of 22,539 is gathered from five wells (15/9-F-1 A, 15/9-F-1 B, 15/9-F-11 A, 15/9-F-11 T2, and 15/9-F-14) drilled at North-sea Volve oil field, Norway. The first four wells are used to train and test the ML and DL algorithm, and the remaining well (15/9-F-14) is used to evaluate the best-performing algorithm’s universality in predicting pore pressure at the field of study. Seven different petrophysical parameters are used as input parameters to develop the predictive models. Statistical performance metrics are carried out to analyze the applied ML and DL performance. Based on performance indicators, the RF algorithm showed superior results compared to other predictive models with R2 and RMSE values of 0.97 and 2.70 MPa, respectively. Furthermore, the best-performing predictive model with low prediction error RMSE value is applied to the other well dataset from the field of study to access the universality of the RF algorithm to predict pore pressure in the field of study. The results of the universality analysis show a satisfactory prediction accuracy with R2 and RMSE values of 0.905 and 6.48 MPa, respectively.
Boosted device performance of perovskite solar cells via KI processing additives
Authors: Sawwan Hussain , Shen Lening , Cao Zikun , Liu Lei , Zhang Xiyao , Wang He , Gong Xiong 
Published: July 2024

AbstractPerovskite solar cells (PSCs) have attracted great attention in both academic and industrial sectors in the past years. Studies demonstrated that processing additive engineering was a facile way to improve the crystallinity and minimize the defect of metal halide perovskites (MHPs). In this study, we report efficient and stable PSCs, where the MHPs thin film is processed with KI additives. It is found that the KI processing additives could not only enhance the crystallization and suppress the defects of MHP thin film, but also boost charge transport, suppress non-radiative recombination, and enhance the hydrophobic properties of MHP thin film. As a result, the PSCs based on the MHPs thin film processed with KI additives exhibit more than 10% enhancement in efficiency and dramatically boosted stability compared to that based on pristine MHPs thin film. Our results indicated that the MHPs processed with processing additives are a simple engineering technique to boost the device performance of PSCs.