bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Turbine Blade Manufacturing through MEMS-Based Milling Monitoring
Authors: Rahman Achmad Zaki , Jauhari Khairul , Huda Mahfudz Al , Sumantri Dede , Prahasto Toni , Rusnaldy  , Widodo Achmad 
Published: June 2024

Abstract
Manufacturing steam turbine blades with intricate shapes poses a substantial challenge. These blades significantly impact turbine efficiency, reliability, and productivity. Their precise formation through milling processes demands vigilant monitoring, especially concerning the cutting tool’s condition, which is responsible for shaping the blade profiles and ensuring high-quality outcomes. Direct tool monitoring, however, disrupts productivity, prompting the need for an indirect Tool Condition Monitoring (TCM) system. Such a system becomes essential for detecting tool wear and damage early, ensuring dimensional accuracy and surface smoothness. This experiment monitors tool conditions by analyzing vibrations generated by an endmill cutter while machining martensitic stainless steel (MSS) AISI 420. The setup employs a cost-effective MEMS vibration sensor, the ADXL 345, and Raspberry Pi for real-time online TCM functionality as the signal processor. This study delves into the sensor’s ability to capture vibrations representing actual tool conditions, focusing on the affordability and effectiveness of MEMS-based monitoring. The successful real-time implementation of MEMS-based monitoring could be an easily accessible gateway for manufacturing industries to embrace cloud-based monitoring systems, aligning with current technological trends. This research aims to underscore the viability and potential adoption of affordable MEMS sensors for comprehensive tool condition monitoring, offering a seamless transition toward cloud-integrated monitoring systems for manufacturing industries.
Two-step ALD process for non-oxide ceramic deposition: the example of boron nitride
Authors: Hossain Ali , Souvignet Thomas , Innis Neil R , Hao Wenjun , Boisron Olivier , Florea Ileana , Xiao Peng , Sledzinska Marianna , Journet Catherine , Marichy Catherine 
Published: June 2024

Abstract
Atomic layer deposition (ALD) based on polymer-derived ceramics (PDCs) chemistry is used for the fabrication of boron nitride thin films from reaction between trichloroborazine and hexamethyldisilazane. The transposition of the PDCs route to ALD is highly appealing for depositing ceramics, especially non-oxide ones, as it offers various molecular precursors. From a two-step approach composed of an ALD process forming a so-called preceramic film and its subsequent ceramization, conformal and homogenous BN layers are successfully synthesized on various inorganic substrates. In the first stage, smooth polyborazine coatings are obtained at a temperature as low as 90 °C. The saturation and self-limitation of the ALD gas-surface reactions are verified. Intriguingly, three ALD windows seem to exist and are attributed to change in ligand exchange. After the ceramization stage using a heat treatment, conformal near-stoichiometric BN layers are obtained. Their structure in terms of crystallinity can be adjusted from amorphous to well-crystalline sp2 phase by controlling the treatment temperature. In particular, a crystallization onset occurs at 1000 °C and well defined sp2 crystalline planes oriented parallel to the surface are noted after ceramization at 1350 °C. Finally, side-modification of the substrate surface induced by the thermal treatment appears to impact on the final BN topography and defect generation.
Production and mechanical characterization of Carbon fiber laminated composites modified by Graphene nano platelets (GnP) for high performance application
Authors: Srivastava Amit Kumar , Singh Abhishek , Verma Rajesh Kumar 
Published: June 2024

Abstract
Polymeric materials play a pivotal role in diverse high-performance engineering domains, including aerospace, marine, automotive, and defense sectors. Their applications span from essential protective gear to intricate components vital for aircraft missiles, showcasing their versatility and significance in modern technology. The Graphene nano platelets (GnP) have the exceptional properties of a high contact area with the reinforcement material and enhanced synergistic effect, which is highly desired to improve the material performance. The present work describes the production of Carbon fiber laminated composites enhanced by Graphene nano Platelets (GnP) using a cost-effective Hand layup method (HLM). Herein, three different concentrations of GnP at 0.25, 1.0, and 1.75 wt% were used to modify the CFRP laminates. This is primarily performed to examine the viscoelastic and mechanical properties of the proposed GnP/CFRP sample. The findings of mechanical testing reveal that GnP nanofiller addition of 1.00 wt% significantly enhances the tensile and flexural properties by 20.7% and 10.05% respectively in comparison to neat sample. Also, the composites show satisfactory improvement in impact strength by 31.60% and enhanced viscoelastic properties at a 0.25 wt% of GnP loading. The XRD and DMA findings support GnP loading for high performance applications.
Experimental investigations on coated copper wire and annealed copper wire electrodes in wire EDM machining through a comparative assessment employing the TOPSIS technique
Authors: Prathap Varma Uddarraju Dhana Satya , Harish Ippile , Battina N Malleswararao , Prasadaraju Kantheti , Chandu Prasad S 
Published: June 2024

Abstract
Metals that are electrically conductive but are difficult to work with are typically machined using WEDM (Wire Electric Discharge Machining). The production of tiny components, which are challenging to mill using earlier non-traditional machining techniques, has undergone a substantial transformation thanks to these machines. Two wire electrodes coated copper wire and annealed copper wire electrodes are used for the testing in this investigation, and the analysis is completed utilizing the tool material. The workpiece material, D2 Steel, was cut into cubes that were 10 mm thick for the experiment, and a wire electrode with a diameter of 0.25 mm was used. Using an L27 orthogonal array, six input variables were employed at three different levels in the testing. The TOPSIS technique is utilized to optimize the output variables, leading to the determination of various output parameters, including Material removal rate (MRR), Kerf width (KW), tool wear rate (TWR), and surface roughness (SR). The chosen input variables include wire tension (WT), a gap voltage (GV), input current (IP), wire feed rate (WF), pulse off time (Toff), and pulse on time (Ton). The 10 mm-wide D2 steel was milled using two wire electrodes, and an ANOVA table was made to identify the factors that had the greatest impact on the WEDM parameters. The rank and ideal Wire EDM parameter values must be calculated using main effect plots and tables of replies. From this investigation, we can also draw the conclusion that for machining D2 steel, annealed wire electrodes provide greater MRR than coated wire.
End-to-end simulation of particle physics events with flow matching and generator oversampling
Authors: Vaselli F , Cattafesta F , Asenov P , Rizzi A 
Published: June 2024

Abstract
The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both discrete and continuous Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name oversampling, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.
The Genesis and Evolution of EU Food Law
Authors: Schebesta Hanna , Purnhagen Kai 
Published: June 2024

Abstract
This chapter sketches the history and emergence of EU food law in terms of politics and policy. It describes the EU Treaties as the political foundations of European integration and, at the same time, the cornerstone of EU food law (section 1.2). The chapter then discusses the main policy paradigms that have shaped EU food legislative harmonization (section 1.3), namely agriculture (section 1.3.1), the completion of the internal market (section 1.3.2), food safety (section 1.3.3), transparency (section 1.3.4), and most recently, sustainability (section 1.3.5). In addition, it sets out a major premise of the book, namely that the food domain has emerged as a distinctive regulatory domain within EU law.
Internal Market Law and Food
Authors: Schebesta Hanna , Purnhagen Kai 
Published: June 2024

Abstract
This chapter provides an overview of food in the internal market. In particular, this includes the free movement of goods and significant landmark case law related to Articles 34–36 TFEU, where the restrictions and justifications are discussed with examples from the food domain (sections 4.2. and 4.3). The principle of mutual recognition has been subsequently operationalized by legislation, namely Directive (EU) 2015/1535 on the duty to notify technical regulations and Regulation (EC) 764/2008 on mutual recognition for products not subject to harmonization (section 4.4). The chapter shows that the free movement Treaty articles have lost much of their significance due to both the legislative operationalization of the internal market and the extensive harmonization of EU food law. However, authorization procedures represent a notable exception in practical importance. The conclusions challenge the textbook narrative of the successful completion of the internal market, and instead highlight a reality where the free movement of foods is administratively managed (section 4.5).
Chemical Safety of Food Products
Authors: Schebesta Hanna , Purnhagen Kai 
Published: June 2024

Abstract
This chapter addresses EU law on the chemical safety of foodstuffs. The legal frameworks broadly distinguish unintentionally (section 11.2) from intentionally added substances (section 11.3). The chapter therefore examines EU legislation on unintentionally added contaminants and their maximum levels (MLs) (sections 11.2.1 to 11.2.3) as well as legislation on the use of avoidable substances and their maximum residue levels (MRLs), notably pesticides (section 11.3.1), veterinary medicinal products (section 11.3.2), biocides (section 11.3.3), and hormones in meat (section 11.3.4). It concludes with a discussion on the need for a regulatory overhaul of the current system to regulate chemical substances in foodstuffs (section 11.4).
Organic Food
Authors: Schebesta Hanna , Purnhagen Kai 
Published: June 2024

Abstract
This chapter focuses on the complex regulatory system of the Organics Regulation (EU) 2018/848 and general questions regarding the organic food trade in the EU market. It explains and analyses the rules of Regulation (EU) 2018/848 (section 15.2), specifically the scope of application (section 15.2.1), objectives, and general principles (section 15.2.2), as well as rules on production (section 15.2.3), labelling (section 15.2.6), and certification (section 15.2.7). The chapter further discusses additional rules on official controls of the Organics Regulation (section 15.3), trade with third countries (section 15.4), and procedural aspects (section 15.5). It concludes with a final discussion of the general advantages and disadvantages of the system (section 15.6).
EU Food Quality Schemes
Authors: Schebesta Hanna , Purnhagen Kai 
Published: June 2024

Abstract
This chapter discusses several labelling options for food quality in the EU. Its main focus is on EU Regulation 1151/2012 (section 16.2), noting the Regulation’s objective and scope (section 16.2.1). Relevant food quality labels, namely the protected designation of origin (PDO) and the protected geographical indication (PGI) (section 16.2.2), their right to use and their protection (section 16.2.3), as well as the traditional specialty guarantee (TSG) (section 16.2.4) and optional quality terms (OQTs) (section 16.2.5) are discussed. The application and registration procedure for PDO, PGIs, and TSGs and their placing on the market are elaborated (section 16.3). The chapter concludes with a general discussion on the topic (section 16.4).
The Humane Perspective
Authors: Cottingham John 
Published: June 2024

AbstractThis book brings together fourteen essays from the work of John Cottingham on moral philosophy and philosophy of religion spanning the past fifteen years. The chapters are closely related in so far as they all deal with the perennial challenges of human existence—the drive to understand the world we live in, the limits of scientific inquiry, the search for a good and meaningful life, and the human quest for transcendence. As well as being thematically linked, they share a common style and methodology, illustrating the distinctive goal that has informed the author’s work in recent years, that of promoting a more ‘humane’ conception of philosophizing. While in no way discarding the technical tools of the professional philosopher such as abstract argumentation and analysis, whose value is unquestionable, this approach is notable for drawing on the full range of resources available to the human mind, including those that depend on literary, poetic, imaginative, aesthetic, and emotional modes of awareness. In contrast to the model of the philosopher as a kind of detached scrutineer, the essays exemplify the belief that there is also a distinctive and valuable kind of philosophical understanding that requires a more involved and engaged stance. The topics dealt with fall broadly within the familiar domains of moral philosophy and philosophy of religion, but the reflections offered on these areas of human thought and practice always aim to be sensitive to how morality and religion actually operate in the lives of the human beings involved.
Confronting the Cosmos
Authors: Cottingham John 
Published: June 2024

AbstractThis chapter starts with the Pauline idea that knowledge of God is naturally available to any human being without special divine grace or revelation. Against this, those who do not see the universe as the work of a divine creator can scarcely be judged guilty of any error of logic or observation. It is argued that defending the idea of natural knowledge of God will need a different kind of religious epistemology—one that, rather than trying to make religious knowledge conform to a neutral, secular-style epistemic template, takes account of the special conditions under which God, assuming his existence, might be expected to manifest himself. The chapter concludes by pointing to our experiences of natural beauty and moral goodness as ‘intimations of the transcendent’. These do not qualify as scientific evidence, but are a part of our human heritage whose significance, if we are attentive, cannot easily be ignored.
Transcending Science
Authors: Cottingham John 
Published: June 2024

AbstractIn many contemporary debates, religion and science are cast as rivals, supposedly offering competing explanations of the origins and nature of the cosmos. Religion often appears at a disadvantage here: given the magnificent achievements of science, theistic speculations about the activities of an immaterial divine agent are apt to seem radically impoverished by comparison. This chapter argues that understanding the world religiously is less about subscribing to explanatory hypotheses than about a certain mode of engagement with reality, requiring a moral and spiritual transformation of the subject. This has crucial implications for the appropriate way to philosophize about religion. Instead of an ‘epistemology of control’, based on the detached evaluation of evidence, we may need to substitute an ‘epistemology of receptivity’. In religion, as in many areas of human life, authentic understanding may require a process of attunement in order for the relevant evidence to become manifest.
Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information
Authors: Teng Jun , Zhai Tingting , Zhang Xinyi , Zhao Changheng , Wang Wenwen , Tang Hui , Wang Dan , Shang Yingli , Ning Chao , Zhang Qin 
Published: June 2024

Abstract
In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).
Substrate-directed novel direct amidation of resin-bound amino acids
Authors: Ozasa Hiroki , Mikamiyama Hidenori , Yamamoto Hisashi 
Published: June 2024

Abstract
In peptide drug discovery, the C-terminal structure of peptide compounds is crucial for enhancing the affinity with target proteins and for optimizing the compound's dynamics and physical properties. In conventional solid-phase synthesis, two steps are necessary to modify the C-terminal structure: cleavage from the resin and subsequent amidation involving activation of the resulting carboxylic acid. This process can potentially lead to racemization of the C-terminal amino acid side chain. To address this issue, we developed a novel method that, in the presence of a Ta catalyst, directly allows amines to react with amino acid-loaded resins, thereby accomplishing both resin cleavage and amidation in a single step.
Discriminative Subspace Learning With Adaptive Graph Regularization
Authors: Huang Zhuojie , Zhao Shuping , Liang Zien , Wu Jigang 
Published: June 2024

Abstract
Many subspace learning methods based on low-rank representation employ the nearest neighborhood graph to preserve the local structure. However, in these methods, the nearest neighborhood graph is a binary matrix, which fails to precisely capture the similarity between distinct samples. Additionally, these methods need to manually select an appropriate number of neighbors, and they cannot adaptively update the similarity graph during projection learning. To tackle these issues, we introduce Discriminative Subspace Learning with Adaptive Graph Regularization (DSL_AGR), an innovative unsupervised subspace learning method that integrates low-rank representation, adaptive graph learning and nonnegative representation into a framework. DSL_AGR introduces a low-rank constraint to capture the global structure of the data and extract more discriminative information. Furthermore, a novel graph regularization term in DSL_AGR is guided by nonnegative representations to enhance the capability of capturing the local structure. Since closed-form solutions for the proposed method are not easily obtained, we devise an iterative optimization algorithm for its resolution. We also analyze the computational complexity and convergence of DSL_AGR. Extensive experiments on real-world datasets demonstrate that the proposed method achieves competitive performance compared with other state-of-the-art methods.
The Immunopeptidomics Ontology (ImPO)
Authors: Faria Daniel , Eugénio Patrícia , Contreiras Silva Marta , Balbi Laura , Bedran Georges , Kallor Ashwin Adrian , Nunes Susana , Palkowski Aleksander , Waleron Michal , Alfaro Javier A , Pesquita Catia 
Published: June 2024

Abstract
The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of short peptides by major histocompatibility complex Class I molecules on the cell surface. Immunopeptidomics, the study of peptides displayed on cells, delves into the wide variety of these peptides. Understanding the mechanisms behind antigen processing and presentation is crucial for effectively evaluating cancer immunotherapies. As an emerging domain, immunopeptidomics currently lacks standardization—there is neither an established terminology nor formally defined semantics—a critical concern considering the complexity, heterogeneity, and growing volume of data involved in immunopeptidomics studies. Additionally, there is a disconnection between how the proteomics community delivers the information about antigen presentation and its uptake by the clinical genomics community. Considering the significant relevance of immunopeptidomics in cancer, this shortcoming must be addressed to bridge the gap between research and clinical practice. In this work, we detail the development of the ImmunoPeptidomics Ontology, ImPO, the first effort at standardizing the terminology and semantics in the domain. ImPO aims to encapsulate and systematize data generated by immunopeptidomics experimental processes and bioinformatics analysis. ImPO establishes cross-references to 24 relevant ontologies, including the National Cancer Institute Thesaurus, Mondo Disease Ontology, Logical Observation Identifier Names and Codes and Experimental Factor Ontology. Although ImPO was developed using expert knowledge to characterize a large and representative data collection, it may be readily used to encode other datasets within the domain. Ultimately, ImPO facilitates data integration and analysis, enabling querying, inference and knowledge generation and importantly bridging the gap between the clinical proteomics and genomics communities. As the field of immunogenomics uses protein-level immunopeptidomics data, we expect ImPO to play a key role in supporting a rich and standardized description of the large-scale data that emerging high-throughput technologies are expected to bring in the near future.
Ontology URL: https://zenodo.org/record/10237571
Project GitHub: https://github.com/liseda-lab/ImPO/blob/main/ImPO.owl