bluuebunny/crossref_metadata_2025 huggingface dataset:
A Markovian Approach to Automated Detection of Product Traceability Anomalies and Gaps in Complex Discrete Manufacturing
Authors: Bakir Roudi , Blume Christine
Published: December 2024

Product traceability is essential in modern manufacturing to ensure quality standards and regulatory compliance. It also provides transparency that resonates with customers’ growing emphasis on sustainability. Traditional methods of traceability verification often involve manual inspections, which are time-consuming and prone to human error. This paper proposes an automated approach leveraging machine learning to detect anomalies and gaps in product traceability within discrete production lines. The production line is modeled as discrete sequences, with process parameter names for each station hashed and represented as states in Markov Chains. Through continuous analysis, the system updates the Markov Chain model, adapting to changing production scenarios without manual intervention. Anomalies are detected based on deviations in transition probabilities, allowing for the automatic identification of traceability anomalies and gaps. This not only ensures the integrity of the production process but also optimizes operational efficiency, significantly reducing the need for manual oversight. The proof-of-concept is demonstrated in a case study at the automotive electronic manufacturing plant of Robert Bosch Elektronik GmbH, showcasing the effectiveness and scalability of the automated approach in real-world manufacturing environments.
Multi-Sensor Data Fusion for Application of Machine Learning in Milling Processes on Press Tool Surfaces
Authors: Paturri Skender , Wendland Lasse , Gravert Marvin , Duka Enea , Mackiewicz Maik
Published: December 2024

In today’s global markets, companies must ensure product quality, transparency, and responsiveness to remain competitive. Efficient and precise milling processes are now more crucial than ever due to the utilization of advanced manufacturing technologies. Deviations of nominal geometry occurring from the milling process can result in time consuming and costly reworking or scrapping. Predicting these deviations before machining has a significant value for optimizing the workflow. Data-based methods have demonstrated their ability to surpass the limitations of classical analysis methods in optimizing machining processes in production. However, their performance depends on an extensive and diverse structured data set. In this paper, we propose a method for fusing all the necessary machine signals collected during the milling process into a large-scale data set suitable for machine learning in milling. This is achieved by recognizing patterns and reference points in various types of sensor systems to transform the signals into a unified data model. Specifically, our data set includes the milling program, internal data of the tool machine, data collected by external accelerometers and spindle sensors, as well as CAD data and 3D scans of the finalized part. Together, this serves as a digital end-to-end representation of the milling process, providing a foundation for machine learning based methods. We apply and evaluate our approach on a test geometry.
Systematic Planning and Early-Stage Development of Industrial AI Systems for Plant Optimization
Authors: Voigt Jorrit , Gillmann Marlene Judith , Dröder Klaus
Published: December 2024

Artificial Intelligence (AI) offers promising capabilities for many industrial applications and the use of AI has gained traction within manufacturing processes. However, working procedures lack criteria to decide about the benefit of AI systems for particular use cases and do not describe any uncertainty analysis in the development stage, since they assume readily available significant data. The objective of the present study is to introduce a process model, which includes decision criteria to determine the necessity of AI and evaluate the uncertainty of the system, consisting of suitable sensors and algorithms, in early development phases. The procedure is verified by analyzing a battery production line and identifying the prediction of success for MIG-welding as a suitable point of improvement by an AI system. A qualitative extraction of domain knowledge and data source selection leads to a fuzzy model and a clear distinction between the epistemic and aleatoric uncertainty of the system to estimate the prediction capability. The presented method allows the distinction between such uncertainties in a systematic development process and thus allows for a targeted optimization of AI systems. Extending the introduced systematic to other industrial fields can help to increase the implementation rates of AI in manufacturing.
Cross-Process X-ray Inspection Strategy in Battery Cell Assembly
Authors: Masuch Steffen , Gräfnitz Sophie , Dröder Klaus
Published: December 2024

The production of lithium-ion battery cells (LIBs) for electric vehicles requires a considerable amount of energy and raw materials. Due to the increasing added value along the production chain, it is essential to recognise deviations in the intermediate products as early as possible using 100% inline measurement processes. For this reason, LIBs are inspected during cell assembly using plain radiography to qualitatively check their internal geometry. For safety-critical features, such as the anode-cathode-overhang (AC-overhang) in the composite, measuring methods are required that are not fulfilled by plain radiography. The use of computer tomography (CT) offers a solution for this demand. However, to realise a 100% CT inspection, the scan time of conventional systems and the number of applications along the cell assembly must be reduced.

This research presents an approach to overcome this limitation in the form of a cross-process X-ray inspection strategy based on a technical and economic analysis of the cell assembly. To reduce scan time and the number of X-ray applications, multiple internal features are measured in a single CT scan prior to electrolyte filling. An initial feasibility assessment of this inspection position is being investigated using a state-of-the-art metal-jet tube and photon counting detector. This provides an inspection time of 1 s, enabling detailed inspection to be combined in a time-efficient process. However, moving the CT inspection downstream would contradict the key objective of early defect detection. To overcome these challenges, an upstream vision system was developed. This vision system detects outliers immediately after the stacking process by detecting the position of the external feature separator in the electrode separator composite (ESC) without X-rays and at a much lower cost than CT. This approach aligns with the primary objective of early defect detection in the production chain through the cross-process inspection strategy.
Enhancing Automotive Production Through Integrated Data Analysis and Simulation
Authors: Bublitz Enno , Warsönke Kai
Published: December 2024

In the context of automotive production, the variability in component tolerances poses a significant challenge, often leading to production downtimes. Deviations in dimensional accuracy and geometry can disrupt automated assembly processes, emphasizing the critical link between input quality and assembly outcomes. As automation gains prominence, robust process monitoring becomes imperative for quality assurance and maintenance cycle optimization. However, a prevalent issue persists data fragmentation and underutilization in production. Our approach is centred on leveraging existing production data to optimize automotive production and streamline final assembly processes. We investigate the interplay of upstream process influences on downstream assembly processes within large-scale production environments. Real-world manufacturing data is analysed and validated through finite element analysis (FEA) simulations coupled with Monte Carlo simulations. Focusing on one specific automotive assembly process—the automated assembly of cockpits—we examine the influence of various body concepts on assembly automation compatibility. Data analysis from two assembly lines identifies body concepts conducive to efficient assembly, subsequently validated using our innovative coupled simulation method. This holistic approach identifies production-suitable product concepts and integrates our novel simulation method into the design of future products, bridging the gap between production data fragmentation and optimized assembly in the automotive industry. By connecting real manufacturing processes with product concept suitability, we enhance production efficiency, advance data mining and simulation methods, and improve overall production quality.
Industrial workspace detection of a robotic arm using combined 2D and 3D vision processing
Authors: Schorr Logan , Cobilean Victor , Mavikumbure Harindra S. , Manic Milos , Hadimani Ravi L.
Published: December 2024

Automation via robotic systems is becoming widely adopted across many industries, but intelligent autonomy in dynamic environments is challenging to implement due to the difficulty of 3D vision. This paper proposes a novel method that utilizes in-situ 2D image processing to simplify 3D segmentation for robotic workspace detection in industrial applications. Using a TOF sensor mounted on a robotic arm, depth images of the workspace are collected. The algorithm identifies the contour of a table, filters extraneous data points, and converts only relevant data to a 3D pointcloud. This pointcloud is processed to identify the precise location of the workspace with regard to the robot. This method has been shown to be 10% more accurate and over 10,000% faster than a human analyzing the data in a GUI-based software using an octree region-based segmentation algorithm and provides consistent results, only limited by the resolution of the camera itself.
In-vitro activity of the novel β-lactam/β-lactamase inhibitor combinations and cefiderocol against carbapenem-resistant Pseudomonas spp. clinical isolates collected in Switzerland in 2022
Authors: Le Terrier Christophe , Bouvier Maxime , Kerbol Auriane , Dell’Acqua Chloé ,   Nara Network members, Nordmann Patrice , Poirel Laurent
Published: December 2024

To evaluate the in-vitro activity of the novel commercially-available drugs, including meropenem-vaborbactam (MEV), ceftazidime-avibactam (CZA), ceftolozane-tazobactam (C/T), imipenem-relebactam (IPR) as well as cefiderocol (FDC), against carbapenem-resistant Pseudomonas spp. (CRP) isolates. All CRP isolates collected at the Swiss National Reference Laboratory (NARA) over the year 2022 (n = 170) have been included. Most of these isolates (n = 121) were non-carbapenemase producers. Among the 49 carbapenemase producers, 47 isolates produced metallo-β-lactamases (MBL) including NDM-1 (n = 11), VIM-like (n = 28), IMP-like (n = 7), and both NDM-1 and VIM-2 (n = 1) and two isolates produced the class A carbapenemase GES-5. Susceptibility testing was determined by broth microdilution method (BMD), or disk diffusion test, and results interpreted following EUCAST guidelines. The susceptibility rates for MEV, CZA, C/T and IPR were found to be 41%, 45%, 59% and 58%, respectively, for the whole set of isolates tested. Among non-carbapenemase producers, susceptibility rates for these β-lactam/β-lactamase inhibitors (BL/BLI) combinations were higher, determined at 55%, 61%, 83%, and 82%, respectively. The overall susceptibility of carbapenemase-producing Pseudomonas spp. to novel BL/BLI was relatively low, while 80% of these isolates demonstrated susceptibility to FDC, with a similar proportion (79%) observed among MBL producers. A total of 10 MBL-producing isolates (6%), mainly NDM-1, were found to exhibit resistance to all drugs tested, with the exception of colistin. FDC exhibited an excellent in-vitro activity against this collection of CRP recovered from Switzerland in 2022, including MBL producers. The new BL/BLI combinations displayed significant activity against non-carbapenemase CRP, with IPR and C/T showing the highest susceptibility rates.
How are informal diagrams used in software engineering? An exploratory study of open-source and industrial practices
Authors: Jongeling Robbert , Cicchetti Antonio , Ciccozzi Federico
Published: December 2024

In software engineering practice, models created for communication and documentation are often informal. This limits the applicability of powerful model-driven engineering mechanisms. Understanding the motivations and use of informal diagrams can improve modelling techniques and tools, by bringing together the benefits of both informal diagramming and modelling using modelling languages and modelling tools. In this paper, we report on an initial exploration effort to investigate the use of informal diagramming in both open-source software repositories and industrial software engineering practices. We carried out a repository mining study on open-source software repositories seeking informal diagrams and classified them according to what they represent and how they are used. Additionally, we describe industrial practices that rely to some extent on informal diagramming, as gathered through unstructured interviews with practitioners. We compare the findings from these data sources and discuss how informal diagrams are used in practice.
Genetic biomarkers and machine learning techniques for predicting diabetes: systematic review
Authors: Khan Sulaiman , Mohsen Farida , Shah Zubair
Published: December 2024

Diabetes mellitus is a long-term metabolic condition marked by high blood sugar levels due to issues with insulin production, insulin effectiveness, or a combination of both. It stands as one of the fastest-growing diseases worldwide, projected to afflict 693 million adults by 2045. The escalating prevalence of diabetes and associated health complications (kidney disease, retinopathy, and neuropathy) underscore the imperative to devise predictive models for early diagnosis and intervention. These complications contribute to increased mortality rates, blindness, kidney failure, and an overall diminished quality of life in individuals living with diabetes. While clinical risk factors and glycemic control provide valuable insights, they alone cannot reliably predict the onset of vascular complications. Genetic biomarkers and machine learning techniques have emerged as promising tools for predicting diabetes development risk and associated complications. Despite the emergence of numerous smart AI models for diabetes prediction, there is still a need for a thorough review outlining their progress and challenges. To address this gap, this paper offers a systematic review of the literature on AI-based models for diabetes identification, following the PRISMA extension for scoping reviews guidelines. Our review revealed that multimodal diabetes prediction models outperformed unimodal models. Most studies focused on classical machine learning models, with SNPs being the most used data type, followed by gene expression profiles, while lipidomic and metabolomic data were the least utilized. Moreover, some studies focused on identifying genetic determinants of diabetes complications relied on familial linkage analysis, tailored for robust effect loci. However, these approaches had limitations, including susceptibility to false positives in candidate gene studies and underpowered AI models capabilities due to sample size constraints. The landscape shifted dramatically with the proliferation of genomic datasets, fueled by the emergence of biobanks and the amalgamation of global cohorts. This surge has led to a more than twofold increase in genetic discoveries related to both diabetes and its complications using AI. Our focus here is on these genetic breakthroughs, particularly those empowered by AI models. However, we also highlight the existing gaps in research and underscore the need for further advancements to propel genomic discovery to the next level.
Specification overfitting in artificial intelligence
Authors: Roth Benjamin , Luz de Araujo Pedro Henrique , Xia Yuxi , Kaltenbrunner Saskia , Korab Christoph
Published: December 2024

Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Consequently, regulatory bodies struggle with containing this technology’s potential negative side effects. High-level requirements such as fairness and robustness need to be formalized into concrete specification metrics, imperfect proxies that capture isolated aspects of the underlying requirements. Given possible trade-offs between different metrics and their vulnerability to over-optimization, integrating specification metrics in system development processes is not trivial. This paper defines specification overfitting, a scenario where systems focus excessively on specified metrics to the detriment of high-level requirements and task performance. We present an extensive literature survey to categorize how researchers propose, measure, and optimize specification metrics in several AI fields (e.g., natural language processing, computer vision, reinforcement learning). Using a keyword-based search on papers from major AI conferences and journals between 2018 and mid-2023, we identify and analyze 74 papers that propose or optimize specification metrics. We find that although most papers implicitly address specification overfitting (e.g., by reporting more than one specification metric), they rarely discuss which role specification metrics should play in system development or explicitly define the scope and assumptions behind metric formulations.
An end-to-end occluded person re-identification network with smoothing corrupted feature prediction
Authors: Zhao Caijie , Qin Ying , Zhang Bob , Zhao Yajie , Wu Baoyun
Published: December 2024

Occluded person re-identification (ReID) is a challenging task as the images suffer from various obstacles and less discriminative information caused by incomplete body parts. Most current works rely on auxiliary models to infer the visible body parts and partial-level features matching to overcome the contaminated body information, which consumes extra inference time and fails when facing complex occlusions. More recently, some methods utilized masks provided from image occlusion augmentation (OA) for the supervision of mask learning. These works estimated occlusion scores for each part of the image by roughly dividing it in the horizontal direction, but cannot accurately predict the occlusion, as well as failing in vertical occlusions. To address this issue, we proposed a Smoothing Corrupted Feature Prediction (SCFP) network in an end-to-end manner for occluded person ReID. Specifically, aided by OA that simulates occlusions appearing in pedestrians and providing occlusion masks, the proposed Occlusion Decoder and Estimator (ODE) estimates and eliminates corrupted features, which is supervised by mask labels generated via restricting all occlusions into a group of patterns. We also designed an Occlusion Pattern Smoothing (OPS) to improve the performance of ODE when predicting irregular obstacles. Subsequently, a Local-to-Body (L2B) representation is constructed to mitigate the limitation of the partial body information for final matching. To investigate the performance of SCFP, we compared our model to the existing state-of-the-art methods in occluded and holistic person ReID benchmarks and proved that our method achieves superior results over the state-of-the-art methods. We also achieved the highest Rank-1 accuracies of 70.9%, 87.0%, and 93.2% in Occluded-Duke, Occluded-ReID, and P-DukeMTMC, respectively. Furthermore, the proposed SCFP generalizes well in holistic datasets, yielding accuracies of 95.8% in Market-1510 and 90.7% in DukeMTMC-reID.
Iterative local search for preserving data privacy
Authors: Arbelaez Alejandro , Climent Laura
Published: December 2024

k-Anonymization is a popular approach for sharing datasets while preserving the privacy of personal and sensitive information. It ensures that each individual is indistinguishable from at least k-1 others in the anonymized dataset through data suppression or generalization, which inevitably leads to some information loss. The goal is to achieve k-anonymization with minimal information loss. This paper presents an efficient local search framework designed to address this challenge using arbitrary information loss metrics. The framework leverages anytime capabilities, allowing it to balance computation time and solution quality, thereby progressively improving the quality of the anonymized data. Our empirical evaluation shows that the proposed local search framework significantly reduces information loss compared to current state-of-the-art solutions, providing performance improvements of up to 54% and 43% w.r.t. the k-members and l-greedy heuristic solutions, the leading algorithms for large datasets. Additionally, our solution approach outperforms the Hun-garian-based solution, the best solution approach for small-size instances, by up to 4.7% on these instances.
Leading good digital lives
Authors: Müller-Salo Johannes
Published: December 2024

The paper develops a conception of the good life within a digitalized society. Martha Nussbaum’s capability theory offers an adequate normative framework for that purpose as it systematically integrates the analysis of flourishing human lives with a normative theory of justice. The paper argues that a theory of good digital lives should focus on everyday life, on the impact digitalization has on ordinary actions, routines and corresponding practical knowledge. Based on Nussbaum’s work, the paper develops a concept of digital capabilities. Digital capabilities are combined capabilities: To possess a digital capability, an individual must acquire certain skills and abilities (internal capabilities) and needs access to devices and external infrastructures like internet connections. If societies as a whole and everyday environments are digitalized to a certain degree, the possession of specific digital capabilities is a crucial precondition for a flourishing life. The paper likewise analyzes challenges that are connected to digital capabilities. Digital structures are constantly changing. In consequence, digital capabilities are never acquired once and for all, but always precarious and in danger of being lost—with serious consequences for individual everyday lives in digitalized environments. As digital capabilities are crucial for leading a good life, people are entitled to develop and maintain them. They describe demands of justice. Using the examples of filling in an online form and digital education, the paper finally illustrates the size of institutional changes that are necessary to meet these demands.
A Three-Level Approach to Examining the Relationship Between Categorical Beacons of Influence and Democracy in Western Europe
Authors: Cruz Erik , Torres Christopher E. , Alqahtani Mohammed
Published: December 2024

Despite the global proliferation of democracy over recent decades, many democratic governments are experiencing unprecedented levels of public dissatisfaction. This study examines how various beacons of grievance—economic dissatisfaction, safety concerns, perceptions of corruption, and attitudes toward immigration—influence individuals’ satisfaction with democratic governance in Western Europe. Utilizing nationally representative data from the 2012 European Social Survey and indicators from the Global Terrorism Database, we employ a three-level hierarchical linear model to simultaneously analyze individual, regional, and country-level variables. Our exploratory, non-directional hypothesis posits that there is a statistically significant relationship between these multilevel factors and individuals’ satisfaction with democratic governance in the European Union. The findings reveal that subjective perceptions play a more significant role than objective indicators in shaping satisfaction with democratic governance. Specifically, individuals with positive opinions on immigration and higher trust in their national parliament exhibit increased satisfaction with democratic governance. At the regional level, higher migration rates correlate positively with democratic governance satisfaction, suggesting a complex interplay between objective conditions and personal attitudes. Conversely, while economic concerns at the individual level affect satisfaction, objective economic indicators at the country level, such as the GINI coefficient, yield unexpected results, indicating higher satisfaction in more unequal societies. By integrating these diverse grievances within a unified multilevel model, this research advances the theoretical understanding of democratic resilience and highlights the importance of considering multilevel factors in assessing public opinion. The study underscores the need for policymakers to address both subjective perceptions and objective conditions to enhance satisfaction with democratic governance. It also calls for further research into cross-level interactions and additional regional variables to deepen the understanding of the dynamics influencing public contentment with democratic governance.
A new stepwise approach to minimize phrenic nerve injury during cryoballoon pulmonary vein isolation
Authors: Phkhaladze K. , Omran H. , Fink T. , Sciacca V. , Guckel D. , Khalaph M. , Braun M. , Hamriti M. El , Thale J. , Nölker G. , Vogt J. , Sohns C. , Sommer P. , Imnadze G.
Published: December 2024

Background

A phrenic nerve injury (PNI) during cryoballoon (CB) pulmonary vein isolation (PVI) continues to represent a limitation of this technique. The objective of this study was to develop a novel technique with the aim of reducing the incidence of PNI.

Methods

We performed a retrospective analysis of data from two hospitals in patients with symptomatic, drug-resistant atrial fibrillation (AF) over 7 years to evaluate the incidence and clinical characteristics of PNI during cryoballoon PVI. Patients in the intervention group were treated with a new technique consisting of the following consecutive steps: (A) phrenic nerve stimulation near stimulation threshold instead of 10 V stimulation; (B) advanced ablation to the right superior pulmonary vein (PV) using a pre-freezing technique; (C) “pulling away” of the CB after vein isolation and/or after reaching − 40 °C for both right PVs. Two subtypes of PNI were studied: persistent (no recovery to discharge) and transient (recovery to discharge) PNI.

Results

Nine hundred patients with a mean age of 62.3 (± 10.9) years (38% female) were analyzed. Transient PNI occurred in 8/250 patients (3.2%) in the intervention group compared to 39/750 patients (6%) in the control group (p = 0.09). Persistent PNI occurred in one patient (0.4%) in the intervention group compared to 18 (2.8%) in the control group (p = 0.03). Any PNI occurred in 9 patients in the intervention group (3.6%) compared to 57 patients (8.8%) in the control group (p = 0.008).

Conclusion

In this retrospective analysis, a new cryo-PVI technique significantly reduces the incidence of PNI, particularly persistent PNI.

Graphical Abstract
Using a Video Visual Scene Display Application to Improve Communication for Individuals with Autism Disorder
Authors: Boyev Alexander , Rabaev Irina , Cohen Nitzan , Chassidim Hadas
Published: December 2024

Augmentative and Alternative Communication (AAC) describes different ways to communicate that supplement or compensate for the impairment of individuals with complex communication needs. AAC can utilize computerized devices, with visual scene display (VSD) or video visual scene display (VVSD) as a communication mode. Recent literature insufficiently tackles the differences between the two modes of presentation. To address this issue, we developed a user-friendly application that can display both video clips and photographs with embedded hotspots. The efficiency of VSD vs. VVSD was evaluated by measuring the perceptions and the performance of adults with ASD during conversation. Findings showed that VVSD has advantages over VSD in improving communication. However, further research with a larger sample size is required.
Adipose tissue as target of environmental toxicants: focus on mitochondrial dysfunction and oxidative inflammation in metabolic dysfunction-associated steatotic liver disease
Authors: Lolescu Bogdan M. , Furdui-Lința Adina V. , Ilie Cosmin A. , Sturza Adrian , Zară Flavia , Muntean Danina M. , Blidișel Alexandru , Crețu Octavian M.
Published: December 2024

Obesity, diabetes, and their cardiovascular and hepatic comorbidities are alarming public health issues of the twenty-first century, which share mitochondrial dysfunction, oxidative stress, and chronic inflammation as common pathophysiological mechanisms. An increasing body of evidence links the combined exposure to multiple environmental toxicants with the occurrence and severity of metabolic diseases. Endocrine disruptors (EDs) are ubiquitous chemicals or mixtures with persistent deleterious effects on the living organisms beyond the endocrine system impairment; in particular, those known as metabolism-disrupting chemicals (MDCs), increase the risk of the metabolic pathologies in adult organism or its progeny. Being largely lipophilic, MDCs mainly target the adipose tissue and elicit mitochondrial dysfunction by interfering with mitochondrial bioenergetics, biogenesis, dynamics and/or other functions. Plastics, when broken down into micro- and nano-plastics (MNPs), have been detected in several human tissues, including the liver. The harmful interplay between inflammatory and redox processes, which mutually interact in a positive feed-back loop, hence the term oxidative inflammation ("OxInflammation"), occurs both at systemic and organ level. In both liver and adipose tissue, oxinflammation contributes to the progression of the metabolic dysfunction-associated steatotic liver disease (MASLD). Moreover, it has been reported that individuals with MASLD may be more susceptible to the harmful effects of toxicants (mainly, those related to mitochondria) and that chronic exposure to EDs/MDCs or MNPs may play a role in the development of the disease. While liver has been systematically investigated as major target organ for ambient chemicals, surprisingly, less information is available in the literature with respect to the adipose tissue. In this narrative review, we delve into the current literature on the most studied environmental toxicants (bisphenols, polychlorinated biphenyls, phthalates, tolylfluanid and tributyltin, per-fluoroalkyl and polyfluoroalkyl substances, heavy metals and MNPs), summarize their deleterious effects on adipose tissue, and address the role of dysregulated mitochondria and oxinflammation, particularly in the setting of MASLD.
The folk concept of art
Authors: Mikalonytė Elzė Sigutė , Kneer Markus
Published: December 2024

What is the folk concept of art? Does it track any of the major definitions of art philosophers have proposed? In two preregistered experiments (N = 888) focusing on two types of artworks (paintings and musical works), we manipulate three potential features of artworks: intentional creation, the possession of aesthetic value, and institutional recognition. This allows us to investigate whether the folk concept of art fits an essentialist definition drawing on one or more of the manipulated factors or whether it might be a disjunctive or cluster concept. The results suggest that none of the three manipulated properties alone suffices for an object to be considered art. The folk concept of art might thus well be a cluster concept instead of an essentialist concept.
Achieving ultralow leakage current in Schottky-MIS cascode anode lateral field-effect diode based on AlGaN/GaN HEMT
Authors: Wang Fangzhou , Gao Changhong , Ding Guojian , Yu Cheng , Wang Zhuocheng , Wang Xiaohui , Feng Qi , Yu Ping , Zuo Peng , Chen Wanjun , Wang Yang , Jia Haiqiang , Chen Hong , Zhang Bo , Wang Zeheng
Published: December 2024

In this paper, we design and fabricate a Schottky-metal-insulator-semiconductor (MIS) cascode anode GaN lateral field-effect diode (CA-LFED) to achieve ultralow reverse leakage current (ILEAK). The device based on AlGaN/GaN high-electron-mobility-transistor (HEMT) includes a normally-off MIS-controlled channel that is cascoded with a high barrier height Schottky contact. At reverse bias, the high electric-field is effectively prevented by the normally-off MIS-controlled channel edge. Together with the high barrier height Schottky contact, this feature significantly suppresses the ILEAK. Supported by the device fabrication, the CA-LFED with high breakdown voltage (BV) > 600 V shows an ultralow ILEAK of 3.6 × 10−9 A/mm as well as a low forward voltage drop (VF) of 2.2 V. The performance suggests that the CA-LFED can be a promising candidate for ultralow ILEAK and better VF-ILEAK trade-off GaN power diode applications.
Radioembolization for Hepatocellular Carcinoma: a Comparison on Dual-phase Cone-beam CT, Contrast-enhanced CT (CECT) and 99mTc-macroaggregated albumin-SPECT/CT in predicting final distribution volumes and dosimetry of the post-embolization 90Y PET/CT
Authors: di Gaeta Ettore , Olivieri Michela , Savi Annarita , Magnani Patrizia , Canevari Carla , Gusmini Simone , Palumbo Diego , Guazzarotti Giorgia , Augello Luigi , Calabrese Francesca , Steidler Stephanie , Cipriani Federica , Rimini Margherita , Casadei-Gardini Andrea , Aldrighetti Luca , Chiti Arturo , De Cobelli Francesco
Published: December 2024

Purpose

Personalized treatment schemes are being systematically applied to ensure best treatment outcome in oncologic patients. This is true also for personalized dosimetry in transarterial radioembolization (TARE) in hepatocellular carcinoma (HCC) patients. Precise and detailed volumetric and functional data derived from radiological and nuclear imaging methods are essential for personalized dosimetry. We sought to evaluate accuracy of dual-phase cone-beam CT (CBCT) in comparison to pre-treatment contrast-enhanced CT (CECT), and 99mTc-macroaggregated albumin-SPECT/CT ([99mTc]MAA SPECT/CT) to predict and assess the efficacy of TARE based on post-treatment 90Y PET/CT.

Material and methods

Thirty consecutive patients with HCC treated with TARE were included. Intraprocedural dual-phase CBCT acquisition protocol was developed to distinguish tumor volume in the early arterial phase and perfused volume of non-affected liver in the late arterial phase. Volumetric data obtained from pre-treatment CECT, dual-phase CBCT and [99mTc]MAA SPECT/CT were compared to post-treatment 90Y PET/CT considered the standard reference. Treatment simulations for final calculated dose from the different imaging derived volumes were then compared to post-treatment 90Y PET/CT.

Results

CBCT resulted as the most accurate method in predicting tumor- (R2 0.88) and perfused volumes (R2 0.82). Dosimetry prediction planning performed on derived volumes from the different methods did not show significant difference (p < 0.05), yet highest concordance with 90Y PET/CT data was observed with dual-phase CBCT.

Conclusion

Our study shows that dual-phase CBCT acquisition is a novel alternative method for correctly and safely administering more accurate and defined doses during TARE.

clinicaltrials.gov ID: NCT03981497.
Institutional innovation in response to backlash: How members are circumventing the WTO impasse
Authors: Pelc Krzysztof
Published: December 2024

In response to the impasse caused by the US blockade of the World Trade Organization’s dispute settlement body, 52 Member-states have converged on a innovative workaround: the Multi-Party Interim Appeal Arbitration Arrangement (MPIA). This article asks two questions related to this singular, yet overlooked initiative in global governance: first, what determines who joins a novel enforcement mechanism in the midst of a global “backlash” against trade liberalization? Secondly, does the MPIA work? Examining countries’ trade policy in the run-up to the MPIA’s creation suggests that the main motivation for MPIA participation is the possibility to challenge trade partners’ measures; vulnerability to others’ challenges, conversely, does not deter joining. The same analysis can be used to predict subsequent MPIA entrants, in ways that match anecdotal evidence. A corollary finding is that insofar as the US reasons similarly to other countries, its trade profile suggests it does not stand to gain much from more credible enforcement, which may explain its lack of zeal during WTO reform negotiations. Secondly, looking at dyadic trade measures over time, we offer evidence that even at this early stage, the MPIA generates deterrence among its members, and this effect is growing. From an institutional standpoint, the experiment appears to be working: the MPIA is not only an interim solution, but also a prototype for institutional innovation in the face of backlash.
Association of Social Vulnerability and Access to Higher Quality Medicare Advantage Plans
Authors: Ko Hansoo , Alsadah Ghaida , Gimm Gilbert
Published: December 2024

Background

With more than half of all beneficiaries enrolled in Medicare Advantage (MA) plans, ensuring access to high-quality MA plans is a key concern for policymakers. Access to high-quality MA plans may be limited in certain areas if private insurers are not willing to offer high-quality MA plans in local areas with greater unmet health-related social needs.

Objective

This study examined the association of a market-level social vulnerability index (SVI) score with the number of high-quality MA plans.

Design

This study conducted a retrospective cross-sectional study.

Participants

Our analysis included 3113 USA counties in 2020.

Main Measures

Our primary outcome measure, the availability of high-quality MA plans at the market level, was defined by counting the raw number of 5-star plans, plans with 4.5 or higher stars, and plans with 4 or higher stars. We also counted the number of all MA plans at the market level as an outcome measure to explore private insurers’ market entry and participation decisions.

Results

We found evidence that fewer high-quality MA plans are available in markets with greater unmet social needs (higher SVI scores). Compared to the least vulnerable markets, the most vulnerable markets had 1.5 fewer MA plans overall [95%CI −2.9, −0.1]. The most vulnerable markets also had 1.1 fewer 4 or higher star plans [95%CI −1.9, −0.3] than the least vulnerable markets. Furthermore, this negative association was concentrated in the southern region, which has a greater proportion of Black/African Americans in its market-level populations.

Conclusion

As historically marginalized groups are more likely to reside in markets with greater unmet social needs, disparities in access to high-quality MA plans may widen existing health disparities. Therefore, monitoring the availability of high-quality MA plans in areas with greater unmet social needs is needed to improve health equity for MA beneficiaries.
Flying Blind: How Thorough are IRBs when Assessing Scientific Value?
Authors: Shum Carol , Hey Spencer Phillips , Wilkes Michael S. , Powers John A. , Pighin Melissa Ann , Yarborough Mark
Published: December 2024

Background

Institutional Review Boards (IRBs) in the United States play a crucial role in ensuring the ethical conduct of clinical trials, including assessing the scientific merit of studies to justify the risks to participants. However, prior research suggests that many IRBs do not systematically evaluate scientific merit, raising concerns about the approval of low-quality trials.

Objective

To investigate whether IRBs provide adequate guidance on assessing scientific merit in their Standard Operating Procedures (SOPs) and other relevant materials.

Design

A systematic pilot investigation of IRB SOPs and related guidance documents from a sample of U.S.-based non-profit institutions.

Participants

IRB materials from 35 U.S.-based non-profit institutions selected from the FDA’s Bioresearch Monitoring Information System database, representing 39.9% of submissions between 2018 and 2021. Additionally, materials from one U.S.-based for-profit IRB were included.

Interventions

Not applicable.

Main Measures

The presence of guidance on 15 dimensions of scientific merit, clustered into four PICO (Population, Intervention, Comparator, Outcome) categories, was assessed by reviewing IRB SOPs and related documents. Evidence of guidance was determined by mention of keywords related to each dimension.

Key Results

Most IRB materials mentioned basic study elements such as study design (99%), subject recruitment (90%), and intervention justification (97%). However, critical aspects related to study quality, such as bias reduction (53%) and outcome measurement tools (57%), were less frequently mentioned. The least represented dimension was confounder control (10%).

Conclusions

IRB guidance materials vary in their coverage of scientific merit dimensions, with significant gaps in areas critical for assessing study quality. Strengthening guidance materials by including comprehensive instructions for all 15 dimensions could improve IRB assessments of scientific merit, thereby enhancing the ethical oversight of clinical trials.
Explaining Interruption Behavior in Parliament: The Role of Topic Expertise, Career Status, and Government-Opposition Dynamics
Authors: Diener Julius
Published: December 2024

Why do politicians interrupt each other? Previous research has studied how personal characteristics of politicians, especially their gender, influence interruption behavior. However, we lack knowledge regarding how strategic incentives arising from political competition influence whether two politicians will interrupt each other. I argue that interruptions are a tool to disrupt the communication of other politicians, attack their public image, and boost one’s own reputation. Based on strategic incentives, I expected three groups of factors to drive interruptions. First, politicians who are experts on the same topic should be more likely to interrupt each other. Second, career status could influence how likely new parliamentarians are to interrupt and how likely frontbenchers are to be interrupted. Third, opposition parties may use interruptions to attack members of government parties, whereas members of government parties should be least likely to interrupt their coalition partners. I used data on interruptions in the German Bundestag from 2017 to 2021, employing logistic and network regression models, and found support for shared topic expertise and government-opposition dynamics driving interruptions. New parliamentarians seem to be less likely to interrupt, but I found no robust evidence for an effect of frontbencher status. These findings enrich knowledge about the strategic role of interruptions in parliamentary communication and legislative behavior, demonstrating the roles of topic expertise and government-opposition dynamics in interruptions, which should not be disregarded in future studies.
On the Relation Between S-Spectrum and Right Spectrum
Authors: Carvalho Luís , Diogo Cristina , Mendes Sérgio , Soares Helena
Published: December 2024

We use the

$$\mathbb {R}$$

R

-linearity of

$$I\lambda -T$$

I
λ
-
T

to define

$$\sigma (T)$$

σ
(
T
)

, the right spectrum of a right

$$\mathbb {H}$$

H

-linear operator T in a right quaternionic Banach space. We show that

$$\sigma (T)$$

σ
(
T
)

coincides with the S-spectrum

$$\sigma _S(T)$$

σ
S

(
T
)

.
Reconstructing ancient Southern African mitochondrial genomes at Faraoskop
Authors: Morris Alan G. , Salie Tasneem , Mittnik Alissa , Rebello George , Barbieri Chiara , Parkington John , Krause Johannes , Ramesar Raj
Published: December 2024

Twelve human skeletons, approximately 2000 years old, were recovered from the Faraoskop archaeological site in the Western Cape Province, South Africa (Manhire 1993). Several of the skeletons were well enough preserved to determine the osteological profiles (sex, age and stature etc.). Additionally, paleopathological and traumatic changes were observed on some of these skeletal remains. Given suggested context that these human remains were drawn from a single mortuary event, this paper investigates the possibility of familial relationships between the individuals by establishing maternal profiles from mitochondrial DNA. The mitochondrial DNA analysis resulted in the identification of four full genomes from the Faraoskop (FK) individuals and the two Khoesan pastoralist individuals chosen as reference samples for the analysis. Three other FK individuals provided partial genomes which could be assigned to incomplete haplotypes. Five individuals could not be sequenced due to poor DNA preservation. Molecular sex could be confirmed for five FK and two reference individuals, adding to the sex assessment from osteological data. All but one of the mitochondrial haplotypes were L0d1 or L0d2 which is consistent with mtDNA from living Khoesan populations in southern Africa. One individual (FK1) was L0f1, a haplotype which is not present southern African Khoesan, but is currently centred in Uganda and Tanzania. It is occasionally found amongst southern African Bantu speakers which suggests that the presence of L0f1 is a remnant of an earlier distribution which is now lost. The three L0 mitochondrial haplotypes from the six Faraoskop individuals (L0d1, L0d2, and L0f) suggest a diversity of maternal lineages compatible with the diversity of Khoesan groups but given the simultaneity of the burial, it is tempting to suggest that those with similar maternal haplotypes were closely related.
Introducing Practical Discourse Analysis
Authors: Jenks Christopher J.
Published: December 2024

This introduction to discourse analysis provides students with an accessible, yet comprehensive, overview of the subject and all the skills and knowledge needed to become capable discourse analysts. Through practical coverage and advice, this book introduces discourse analysis as a set of analytical tools and perspectives that can be applied to an assignment, project, or thesis. Across seven chapters the book is divided according to practical themes and topics allowing students to establish a deeper understanding of discourse analysis. Students will be taught how to identify and categorise established theories and methodologies, including conversation analysis, critical discourse analysis and more. Through figures, examples, chapter summaries, and over thirty learning activities, this volume teaches students the foundational skills to approach the analytical process with more confidence and background knowledge, suitable for undergraduate and graduate students studying discourse analysis.
Twentieth-Century Literature and the Aftermath of War
Authors: Bryan Rachel
Published: December 2024

Students of twentieth-century literature are familiar with narratives that associate devastating wars with conceptual, societal, and aesthetic upheavals. What these accounts overlook, however, is a body of psychologically attuned modern writing that was less interested in this shattering of faith and form than in those counterfactual modes of resistance deployed by individuals and nations in response to mass violence and profound change. Twentieth-Century Literature and the Aftermath of War is an innovative study of the attention paid to such reparative, stabilising impulses in post-war writings from across the last century. Focusing on works by Henry James, Elizabeth Bowen, and Kazuo Ishiguro as case studies, it argues that to fully understand the relationship between modern warfare and literary art, we must learn to engage with texts whose modernity lies in their acknowledgement of the draw felt towards, and contested ethics of, consolatory counterfactuals.