bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stresses in repair welding of high-strength steels—part 2: heat control and stress optimization
Authors: Schröpfer Dirk , Witte Julien , Kromm Arne , Kannengiesser Thomas 
Published: March 2024

AbstractIn welding of high-strength steels, e.g. for foundations and erection structures of wind energy plants, unacceptable defects can occasionally be found in the weld area, which should be removed by thermal gouging and subsequent re-welding. High shrinkage restraint of repair welds may lead to crack formation and component failure, predominantly in interaction with degraded microstructures and mechanical properties due to repair cycles. This study aims for elaboration of recommendations for repair concepts appropriate to the stresses and materials involved to avoid cold cracking, damage and expensive reworking. In part 1 [1] of this study, systematic investigations of influences of shrinkage restraint on residual stresses and cold cracking risk during repair welding of two high-strength steels S500MLO for offshore application and S960QL for mobile crane structures were focussed. In this part 2, the microstructure, particularly hardness, and residual stresses due to gouging and influences of heat control parameters in repair welding are analysed. A clear reduction in residual stress after gouging can be observed, especially for the specimens with restrained transverse shrinkage. Gouging to a depth of approx. 2/3 of the seam height does not lead to a complete relaxation of the observed reaction forces. Particularly for the higher strength steel S960QL, there are pronounced areas influenced by the gouging process in which a degradation of the microstructure and properties should be assumed. Overall, the repair welds show a significant increase in the width of the weld and HAZ compared to the original weld, especially in the case of S960QL/G89. The repair welds show higher welding-induced stresses than the original welds, especially in the areas of the HAZ and the base metal close to the weld seam. This behaviour can be attributed overall to increased restraint conditions due to the remaining root weld or shorter gouge grooves. In good agreement with earlier investigations, the residual stresses transverse to the weld can be significantly reduced by upwardly limited working or interpass temperatures, and the reaction stresses resulting from high restraint conditions can be effectively counteracted. The influence of the heat input on the stress formation is low compared to the interpass temperature for both test materials.
Fatigue strength of single-sided fillet welds in overlapping ultra-high-strength steel sheets
Authors: Ahola Antti , Salerto Samu , Loisa Timo , Lipiäinen Kalle , Björk Timo 
Published: March 2024

AbstractFrom the manufacturing viewpoint, overlapping thin sheets can provide a substantial geometrical improvement in welded hollow sections compared to butt-welded cross-sectional details. However, the plate eccentricity and non-penetrating fillet welds make the joints susceptible to fatigue failures under transversal cyclic loads. This work experimentally investigates the fatigue strength of overlap joints prepared with gas metal arc welding in the single-sided fillet weld configuration. Fatigue tests were carried out on the lap joints made of S960 ultra-high-strength steel (UHSS) grade under uniaxial constant amplitude axial loading employing both pulsating tension (applied stress ratio of R ≈ 0) and pulsating compression (R ≈ -∞). In addition, the lap joints were prepared with both straight welds (the welds transverse to the loading direction) and inclined welds (the welds with a 30° inclement angle from the transversal direction) to investigate the shear stress effects on the joints’ fatigue performance. Plasma butt-welded samples were tested as a reference join type. For the plasma butt-welded joints, the recommended detail category of FAT71 in the nominal stress system for weld root failures in single-sided butt welds was observed clearly conservative—a mean fatigue strength of Δσc,50% = 130 MPa with a fixed slope parameter of m = 3 was obtained. Compared to the butt-welded joints, a significant decrease in fatigue strength capacity was found in the lap joint specimens with misalignment factors of km > 3.0. The failure locations were also different in joints subjected to the tension and compression loads. The shear load did not majorly contribute to the changes in the fatigue strength capacity compared to the joints subjected to the transversal normal stress.
Assessment of diffuse bone marrow involvement on 18F-fluoro-D-glucose PET/computed tomography
Authors: Zhang Tong , Wang Lei , Yang Funing , Wang Hongjia , Li Ping 
Published: March 2024

Abstract
Purpose
This study aims to investigate the role of bone marrow (BM) FDG uptake distribution in assessing pathological status of BM with diffusely increased FDG uptake.

Methods
We retrospectively analyzed one hundred and thirty-four PET/CT scans with diffusely hypermetabolic BM, which involved forty-nine patients with BM malignant infiltration (BMI) and eighty-five patients with benign BM disorders. The maximum standardized uptake values (SUVmax) of axial skeletons, appendicular skeletons, and the range of humerus FDG uptake were measured. The clinical and laboratory data were collected. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were used to evaluate the risk factors for BMI and discriminative ability of above indicators for the pathology status of BM.

Results
In patients with diffusely hypermetabolic BM, both the glucose metabolism of axial and appendicular skeletons was higher in BMI than BM benign disorders. The multivariate logistic regression analysis (stepwise) revealed age (odds ratio [OR] 1.073; 95%CI, 1.031–1.117; P = 0.001), femurs SUVmax (OR 2.058; 95%CI, 1.317–3.218; P = 0.002), neutrophil count (OR 0.805; 95%CI, 0.718–0.902; P < 0.001) and range of humerus FDG uptake (OR 11.335; 95%CI, 2.831–45.377; P = 0.001) were associated with BMI. Combined diagnosis had the highest ROC value (AUC 0.918; 95%CI, 0.864–0.973; P < 0.001) with a sensitivity of 89.8% and specificity of 85.9%.

Conclusion
The BM activity of the appendicular skeleton was more significant in distinguishing BM malignant and benign disorders. Range of humerus FDG uptake combined femurs SUVmax, neutrophil count and age was reliable for assessing diffuse BM involvement.
The fuzzy support vector data description based on tightness for noisy label detection
Authors: Wu Xiaoying , Liu Sanyang , Bai Yiguang 
Published: March 2024

AbstractMachine learning (ML) is an approach driven by data, and as research in machine learning progresses, the issue of noisy labels has garnered widespread attention. Noisy labels can significantly reduce the accuracy of supervised classification models, making it important to address this problem. Therefore, it is a very meaningful task to detect as many noisy labels as possible from the big data. In this study, a new method is proposed for detecting noisy labels in datasets. This method leverages a deep pre-trained network to extract a feature set from the image data first which can extract more accurate data features. Then, a membership degree based on tightness into the support vector data description (SVDD) model named TF-SVDD is introduced to detect noisy data in the dataset. In order to simulate different types of label noise more accurately, we first assumed that the labels of the datasets used were all correct, and in addition constructed the noise set using two method: the density peak noise set and the random noise set. Experimental results demonstrate that the TF-SVDD can effectively detect noisy label data, surpassing traditional support vector data description algorithms and other methods in terms of outlier detection accuracy, with the average accuracy mostly exceeding 50$$\%$$%, and even reaching 80$$\%$$%. Furthermore, one novel measure called ‘confidence’ is employed to rectify noisy labels in the data. Following the correction of noisy labels, the accuracy of image classification experiences a significant improvement, with the average promotion ratio mostly exceeding 10$$\%$$%, and reaching 30$$\%$$%.
An approach for reaching consensus in large-scale group decision-making focusing on dimension reduction
Authors: Bakhshi Fatemeh , Ashtiani Mehrdad 
Published: March 2024

AbstractGroup decision-making and consensus modeling have always been important research topics. With the widespread use of the Internet, group decisions can be made online, in which a large number of decision-makers participate. Most of the existing studies on large-scale group decision-making consider 20–50 decision-makers. Therefore, there is a need for a framework that focuses on situations where thousands of decision-makers exist. As dimension reduction is one of the five primary challenges in large-scale group decision-making, in this study, after reviewing the existing approaches, a new model is presented using a statistical approach along with complex network analysis techniques. The opinions are generalized first, and then the network of opinions is built. This new method reduces the dimensions of the problem by considering a hierarchy of opinions. Different scenarios were designed for the evaluation. The results show that the effect of this generalization on dimension reduction depends on the parameters of the problem. We have shown that in a group decision scenario with 3000 decision-makers and 6 alternatives, 99% of the data was reduced. As dimension reduction is the main focus of the current research, the effect of consistency on the diversity of opinions has also been investigated, and the results show that opinion consistency affects opinion generalization, which in turn affects dimension reduction. In addition, in the performed simulations, three types of functions were used to calculate similarity. The aim was to determine the best similarity function for the decision problems whose purpose was to rank the available alternatives. The results show that Euclidean similarity is a strict criterion compared with Cosine similarity.
Lower Bounds on the Homology of Vietoris–Rips Complexes of Hypercube Graphs
Authors: Adams Henry , Virk Žiga 
Published: March 2024

AbstractWe provide novel lower bounds on the Betti numbers of Vietoris–Rips complexes of hypercube graphs of all dimensions and at all scales. In more detail, let$$Q_n$$Qnbe the vertex set of$$2^n$$2nvertices in then-dimensional hypercube graph, equipped with the shortest path metric. Let$$\textrm{VR}(Q_n;r)$$VR(Qn;r)be its Vietoris–Rips complex at scale parameter$$r \ge 0$$r≥0, which has$$Q_n$$Qnas its vertex set, and all subsets of diameter at mostras its simplices. For integers$$r<r'$$r<r′the inclusion$$\textrm{VR}(Q_n;r)\hookrightarrow \textrm{VR}(Q_n;r')$$VR(Qn;r)↪VR(Qn;r′)is nullhomotopic, meaning no persistent homology bars have length longer than one, and we therefore focus attention on the individual spaces$$\textrm{VR}(Q_n;r)$$VR(Qn;r). We provide lower bounds on the ranks of homology groups of$$\textrm{VR}(Q_n;r)$$VR(Qn;r). For example, using cross-polytopal generators, we prove that the rank of$$H_{2^r-1}(\textrm{VR}(Q_n;r))$$H2r-1(VR(Qn;r))is at least$$2^{n-(r+1)}\left( {\begin{array}{c}n\\ r+1\end{array}}\right) $$2n-(r+1)nr+1. We also prove a version ofhomology propagation: if$$q\ge 1$$q≥1and ifpis the smallest integer for which$$\textrm{rank}H_q(\textrm{VR}(Q_p;r))\ne 0$$rankHq(VR(Qp;r))≠0, then$$\textrm{rank}H_q(\textrm{VR}(Q_n;r)) \ge \sum _{i=p}^n 2^{i-p} \left( {\begin{array}{c}i-1\\ p-1\end{array}}\right) \cdot \textrm{rank}H_q(\textrm{VR}(Q_p;r))$$rankHq(VR(Qn;r))≥∑i=pn2i-pi-1p-1·rankHq(VR(Qp;r))for all$$n \ge p$$n≥p. When$$r\le 3$$r≤3, this result and variants thereof provide tight lower bounds on the rank of$$H_q(\textrm{VR}(Q_n;r))$$Hq(VR(Qn;r))for alln, and for each$$r \ge 4$$r≥4we produce novel lower bounds on the ranks of homology groups. Furthermore, we show that for each$$r\ge 2$$r≥2, the homology groups of$$\textrm{VR}(Q_n;r)$$VR(Qn;r)for$$n \ge 2r+1$$n≥2r+1contain propagated homology not induced by the initial cross-polytopal generators.
Smoothed Bootstrap Methods for Hypothesis Testing
Authors: Al Luhayb Asamh S. M. , Coolen-Maturi Tahani , Coolen Frank P. A. 
Published: March 2024

AbstractThis paper demonstrates the application of smoothed bootstrap methods and Efron’s methods for hypothesis testing on real-valued data, right-censored data and bivariate data. The tests include quartile hypothesis tests, two sample medians and Pearson and Kendall correlation tests. Simulation studies indicate that the smoothed bootstrap methods outperform Efron’s methods in most scenarios, particularly for small datasets. The smoothed bootstrap methods provide smaller discrepancies between the actual and nominal error rates, which makes them more reliable for testing hypotheses.
Effect of Biochar, Potting Mixture and their Blends to Improve Ocimum basilicum Growth in Sandy Soil
Authors: Danish Mohammad , Pradhan Snigdhendubala , McKay Gordon , Al-Ansari Tareq , Mansour Said , Mackey Hamish R. 
Published: March 2024

AbstractBiochar has received significant interest as a soil amendment but its relative performance to addition of high-quality soil is not well assessed. This study compared different loadings of cabbage-waste biochar produced at 360 °C against potting mixture (PM) as amendments to enhance basil (Ocimum basilicum) growth in sandy soil. Pot tests for six conditions of biochar, PM or their blend were tested at both 2% or 6% loading, plus a control, with soil and plant characteristics monitored. The combination of 6% biochar and 6% PM increased seed germination by 85% compared to the control, while 6% biochar applied in sandy soil reduced seed germination by 29%. However, significant benefits were realized later in the growing stage in comparison to the control and most other tested conditions. The 6% application of biochar to the soil enhanced leaf chlorophyll and carotenoid contents, membrane stability index, and leaf relative water content by 198%, 150%, 15%, and 5% over the control, respectively. The highest shoot and total biomass were achieved with the combined application of 6% biochar and PM, followed by 6% biochar only. When comparing only application of biochar or PM, biochar was the more favorable soil amendment at both 2% and 6% loadings. Biochar was also effective at enhancing plant uptake of beneficial nutrients, while immobilizing Al, which is detrimental, in the soil. Overall, biochar is a better soil amendment for sandy soil than equivalent quantities of PM, but mild benefits occur through their combined application.
Action-guidance and AI ethics: the case of fair machine learning
Authors: Sahlgren Otto 
Published: March 2024

AbstractA prominent approach to implementing AI ethics involves translating ethical principles, such as fairness and transparency, into practical frameworks and tools that responsible agents, such as ML developers, can use to ensure that machine learning systems act according to the relevant principles. Fair machine learning research exemplifies this approach by producing frameworks and software toolkits that responsible agents could apply to align machine learning systems with principles such as fairness, equality, and justice. However, the application of available frameworks and tools has proven challenging both due to ambiguous operationalization of the relevant principles and many real-life obstacles that agents face in the context of machine learning system design and development, such as lack of access to proper evaluation data. This article conceptualizes these problems as instances of a more general “action-guidance gap” in AI ethics. The article addresses the action-guidance gap by outlining a philosophical account of action-guidance that can be used to identify and address problems related to the specification and practical implementation of AI ethics principles. Centering on fair machine learning practice as a case example, the article presents a set of detailed requirements for action-guidance in fair machine learning practice which explain problems that previous studies have identified with regard to the real-life application of fair machine learning frameworks and tools. Paving a way forward, the article presents theoretical and practical lessons for ensuring action-guidance in fairness-sensitive design, with implications for AI ethics more generally.
The moral decision machine: a challenge for artificial moral agency based on moral deference
Authors: Gudmunsen Zacharus 
Published: March 2024

AbstractHumans are responsible moral agents in part because they can competently respond to moral reasons. Several philosophers have argued that artificial agents cannot do this and therefore cannot be responsible moral agents. I present a counterexample to these arguments: the ‘Moral Decision Machine’. I argue that the ‘Moral Decision Machine’ responds to moral reasons just as competently as humans do. However, I suggest that, while a hopeful development, this does not warrant strong optimism about ‘artificial moral agency’. The ‘Moral Decision Machine’ (and similar agents) can only respond to moral reasons by deferring to others, and there are good reasons to think this is incompatible with responsible moral agency. While the challenge to artificial moral agency based on moral reasons-responsiveness can be satisfactorily addressed; the challenge based on moral deference remains an open question. The right way to understand the challenge, I argue, is as a route to the claim that artificial agents are unlikely to be responsible moral agents because they cannot be authentic.
Evaluation of the executive functions and quality of life in a sample of Egyptian male adolescents with substance use disorder: A case-control study
Authors: Sonbol Hassan Mohammed , Sabri Youmna , Shahda Mohamed , Shouman Eman Abdallah 
Published: March 2024

Abstract
Background
Adolescent substance use is a major problem that has serious medical, psychological, and legal consequences later in life. Substance use disorder is closely linked to deficits in executive functions. Impaired executive functions (EFs) have been linked to all stages of the substance use disorder (SUD) life cycle, increasing the likelihood of commencing use, escalating use more quickly, and increasing the likelihood of relapsing following treatment. The current study aimed at evaluating of the executive functions and quality of life in a sample of adolescent Egyptian males with substance use disorder.

Results
A significantly higher mean Trail Making Test-A, B (TMT-A and TMT-B) scores among studied cases than the control group (equals lower executive functions) with a mean score of TMT-A is 74.38 versus 63.2 among controls and for TMT-B; the mean score for control is 97.22 versus 142.04 among cases. A statistically significant difference between the case and control groups on all quality of life scores measuring the following domains: general health and well-being, physical health, psychological health, social interactions, and the environment, also there has been a negative correlation between TMT-A and the environmental domain (r = − 0.279) and TMT-B with the same variable (r = − 0.414).

Conclusions
Substance use disorders are a major health problem among youth. Deficits in executive functions are strongly associated with adolescent substance use. The more affected executive functions are associated with more affected quality of life of these patients.
Understanding of Causes of Spurious Associations: Problems and Prospects
Authors: Ghouse Ghulam , Rehman Atiq Ur , Bhatti Muhammad Ishaq 
Published: March 2024

AbstractThis paper contributes to the social science literature when analyzing survey or time series data social scientists use spurious regression without due consideration of its assumptions and the data structure. This results in misinterpretation and misleading conclusions about the population. The paper reviews basic statistical and econometrics literature which led to the development of modern time series analysis in the presence of spurious regression. It concludes that the term ‘Spurious’ was well known before the Granger and Yule’s work in time series context rather than cross-sectional data. The same reasons can produce spurious regression today and surely the solution doesn’t exist in the cointegration analysis. Social scientists and applied econometrician investigators need more serious thinking and care to avoid spurious regression, if it is necessary even if data is stationary or cross-sectional. In this study, we extended the Ghouse experiment which is based on simulated data by employing real-world data to assess the effectiveness of the newly proposed Ghouse Equation in comparison to conventional approaches. The findings demonstrate that the Ghouse Equation produces the lowest probability of spurious regression as compared to its counterparts. Moreover, in forecasting performance, Ghouse Equation outperformed its counterparts. These results highlight the Ghouse Equation as a valuable and better tool for econometric analysis for nonstationary time series.
Underwater object detection and datasets: a survey
Authors: Jian Muwei , Yang Nan , Tao Chen , Zhi Huixiang , Luo Hanjiang 
Published: March 2024

AbstractThe rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond.
Exploring the persistence of adults on secondary education courses: occupational status, satisfaction and self-efficacy as key factors
Authors: Daza-Pérez Lidia , Llanes-Ordóñez Juan , Figuera-Gazo Pilar 
Published: March 2024

AbstractOne of the most worrying issues in Spanish education is the high school dropout rate, especially for those students who leave compulsory secondary education with no qualifications. Some of these students re-enter the system via adult education centres (AECs), where they can obtain the minimum qualification required by the labour market (the Secondary Education Graduate Certificate, the equivalent of GCSE in UK education). Entry into and adaptation to the AECs was explored in a non-probabilistic sample of 234 individuals from a total population of 2033 enrolled in 14 Catalan AECs, and the roles of a range of factors in shaping successful trajectories were analysed. The aim was to contribute to the design of strategies boosting students’ well-being and raising the probability of their persisting in their studies. The results showed that when study was full-time or combined with a part-time job of half a day or fewer working hours, when there was high academic satisfaction with the centre, and when there was a feeling of empowerment and efficacy in studying, the bond to the centre and the will to continue studying there were enhanced.