bluuebunny/crossref_metadata_2025_split huggingface dataset:
Production of Autoclaved Cementitious Composites Using Recycled Waste Glass
Authors: Kariem Mohamed Asef , Frag Eman Yossri , El‐Sherif Ahmed A. , Abdelrazak Taha A. , Hazem Mahmoud M. 
Published: October 2024

ABSTRACTOne of the major negative environmental implications of economic growth and the advancement of information technology is the large quantity of electronic waste dumped in landfills. Cathode ray tubes (CRTs) from outdated televisions and computer monitors are a significant source of electrical waste. The CRT funnel primarily consists of silica, significant alkalis (Na2O‐K2O), and heavy metals like barium‐strontium, along with a substantial lead (Pb) content that may contaminate the soil. Owing to its heavy metal content, CRT is considered hazardous waste, and regulations require its glass to be recycled or repurposed instead of landfill disposal. The low pozzolanic activity of CRT silica suggests that its high content, when paired with an optimized particle size and specific curing conditions, can enhance the mechanical properties of cement‐based products. Hydrothermal treatment has been found to speed up both the hydration of ordinary Portland cement (OPC) and the pozzolanic reactions. Since the main objective was to safely recycle large amounts of CRT, three mixes were proposed with 10%, 20%, and 30% OPC + 90%, 80%, and 70% CRT, respectively, and the effect of hydrothermal curing conditions on mechanical properties and durability of these blends was investigated. CRT‐70, a blend containing 70% CRT glass waste, showed enhanced strength due to the formation of zeolitic phases and calcium silicate hydrate (CSH). These phases also provided CRT‐70 with notable fire resistance, ensuring its structural stability under elevated temperatures. These results demonstrate the possibility of production of precast building products via high‐volume recycling of hazardous CRT waste.
Bailout test, HYDRUS‐2D, and analytical modeling for estimating permeability of ephemeral stream bed
Authors: Al‐Yaqoubi Shahad , Al‐Maktoumi Ali , Kacimov Anvar , Al‐Ismaily Said , Al‐Mayahi Ahmed 
Published: October 2024

AbstractAccurate estimation of effective saturated hydraulic conductivity (Ksat) of a vadose zone with an underlying shallow perched aquifer is challenging. Standard pumping tests and double‐ring infiltrometers are unsuitable for this purpose. Therefore, a bailout test (BOT) was conducted in nine soil pits to evaluate the hydraulic properties of the vadose zone and the subjacent shallow‐perched aquifer in a porous bed of a wadi in Oman. The analytical (Kirkham's type) and numerical (HYDRUS‐2D) models were also used. Upon instantaneous emptying, the water level rises in a pit, owing to seepage from the ambient shallow aquifer. The draw up was monitored and compared with modeling, which assumes a homogeneous or layered van Genuchten's soil. The soils of the excavated pits were characterized as sandy‐textured, gleyed, and containing calcareous and clay‐enriched layers. A good match between HYDRUS‐analytical results for layered soils and an idealized homogeneous one illustrates that BOT is a robust and quick technique for the estimating Ksat = 2–3 cm/h in the coarse‐textured wadi bed vadose zone overlaying a transient water table. BOT is better than classical auger hole tests because they test a larger soil volume through induced seepage. BOT is also more suitable for drainage trenches and other excavations, allowing for easier observation, soil sampling from the banks, and rapid dewatering.
Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders
Authors: Stoffl Lucas , Bonnetto Andy , d’Ascoli Stéphane , Mathis Alexander 
Published: October 2024

AbstractNatural behavior is hierarchical. Yet, there is a paucity of benchmarks addressing this aspect. Recognizing the scarcity of large-scale hierarchical behavioral benchmarks, we create a novel synthetic basketball playing benchmark (Shot7M2). Beyond synthetic data, we extend BABEL into a hierarchical action segmentation benchmark (hBABEL). Then, we develop a masked autoencoder framework (hBehaveMAE) to elucidate the hierarchical nature of motion capture data in an unsupervised fashion. We find that hBehaveMAE learns interpretable latents on Shot7M2 and hBABEL, where lower encoder levels show a superior ability to represent fine-grained movements, while higher encoder levels capture complex actions and activities. Additionally, we evaluate hBehaveMAE on MABe22, a representation learning benchmark with short and long-term behavioral states. hBehaveMAE achieves state-of-the-art performance without domain-specific feature extraction. Together, these components synergistically contribute towards unveiling the hierarchical organization of natural behavior. Models and benchmarks are available at https://github.com/amathislab/BehaveMAE.
Challenges for AI in Healthcare Systems
Authors: Bertl Markus , Lamo Yngve , Leucker Martin , Margaria Tiziana , Mohammadi Esfandiar , Mukhiya Suresh Kumar , Pechmann Ludwig , Piho Gunnar , Rabbi Fazle 
Published: October 2024

AbstractThis paper overviews the challenges of using artificial intelligence (AI) methods when building healthcare systems, as discussed at the AIsola Conference in 2023. It focuses on the topics (i) medical data, (ii) decision support, (iii) software engineering for AI-based health systems, (iv) regulatory affairs as well as (v) privacy-preserving machine learning and highlights the importance and challenges involved when utilizing AI in healthcare systems.
Future Opportunities for Systematic AI Support in Healthcare
Authors: Bertl Markus , Piho Gunnar , Draheim Dirk , Ross Peeter , Pechmann Ludwig , Bucciarelli Nicholas , Sharma Rahul 
Published: October 2024

AbstractArtificial Intelligence (AI) holds transformative potential to revolutionize healthcare delivery and outcomes. However, the literature suggests that focusing solely on AI algorithms leads to low adoption rates. AI needs to be introduced systematically into healthcare. This paper builds on this approach and synthesizes existing literature and authors’ insights to critically examine the current landscape and future opportunities for systematic AI support in healthcare. The multifaceted applications of AI, ranging from disease prediction to personalized medicine, are explored with a focus on AI’s potential to optimize employee performance, alleviate healthcare staff burdens, and enhance patient care. However, challenges such as limited access to unbiased data sets, connectivity issues, and ethical concerns pose significant barriers to AI adoption in healthcare.
Leveraging Actionable Explanations to Improve People’s Reactions to AI-Based Decisions
Authors: Langer Markus , Valera Isabel 
Published: October 2024

AbstractThis paper explores the role of explanations in mitigating negative reactions among people affected by AI-based decisions. While existing research focuses primarily on user perspectives, this study addresses the unique needs of people affected by AI-based decisions. Drawing on justice theory and the algorithmic recourse literature, we propose that actionability is a primary need of people affected by AI-based decisions. Thus, we expected that more actionable explanations – that is, explanations that guide people on how to address negative outcomes – would elicit more favorable reactions than feature relevance explanations or no explanations. In a within-participants experiment, participants (N = 138) imagined being loan applicants and were informed that their loan application had been rejected by AI-based systems at five different banks. Participants received either no explanation, feature relevance explanations, or actionable explanations for this decision. Additionally, we varied the degree of actionability of the features mentioned in the explanations to explore whether features that are more actionable (i.e., reduce the amount of loan) lead to additional positive effects on people’s reactions compared to less actionable features (i.e., increase your income). We found that providing any explanation led to more favorable reactions, and that actionable explanations led to more favorable reactions than feature relevance explanations. However, focusing on the supposedly more actionable feature led to comparably more negative effects possibly due to our specific context of application. We discuss the crucial role that perceived actionability may play for people affected by AI-based decisions as well as the nuanced effects that focusing on different features in explanations may have.
AI and Democratic Equality: How Surveillance Capitalism and Computational Propaganda Threaten Democracy
Authors: Black Ashton 
Published: October 2024

AbstractIn this paper, I argue that surveillance capitalism and computational propaganda can undermine democratic equality. First, I argue that two types of resources are relevant for democratic equality: 1) free time, which entails time that is free from systemic surveillance, and 2) epistemic resources. In order for everyone in a democratic system to be equally capable of full political participation, it’s a minimum requirement that these two resources are distributed fairly. But AI that’s used for surveillance capitalism can undermine the fair distribution of these resources, thereby threatening democracy. I further argue that computational propaganda undermines the democratic aim of collective self-determination by normalizing relations of domination and thereby disrupting the equal standing of persons. I conclude by considering some potential solutions.
Towards ML-Integration and Training Patterns for AI-Enabled Systems
Authors: Peldszus Sven , Knopp Henriette , Sens Yorick , Berger Thorsten 
Published: October 2024

AbstractMachine learning (ML) has improved dramatically over the last decade. ML models have become a fundamental part of intelligent software systems, many of which are safety-critical. Since ML models have complex lifecycles, they require dedicated methods and tools, such as pipeline automation or experiment management. Unfortunately, the current state of the art is model-centric, disregarding the challenges of engineering systems with multiple ML models that need to interact to realize complex functionality. Consider, for instance, robotics or autonomous driving systems, where perception architectures can easily incorporate more than 30 ML models. Developing such multi-ML model systems requires architectures that can integrate and chain ML components. Maintaining and evolving them requires tackling the combinatorial explosion when re-training ML components, often exploring different (hyper-)parameters, features, training algorithms, or other ML artifacts. Addressing these problems requires systems-centric methods and tools. In this work, we discuss characteristics of multi-ML-model systems and challenges of engineering them. Inspired by such systems in the autonomous driving domain, our focus is on experiment-management tooling, which supports tracking and reasoning about the training process for ML models. Our analysis reveals their concepts, but also their limitations when engineering multi-ML-model systems, especially due to their model-centric focus. We discuss possible integration patterns and ML training to facilitate the effective and efficient development, maintenance, and evolution of multi-ML-model systems. Furthermore, we describe real-world multi-ML-model systems, providing early results from identifying and analyzing open-source systems from GitHub.
Coding Historical Causes of Death Data with Large Language Models
Authors: Pedersen Bjørn-Richard , Islam Maisha , Kristoffersen Doris Tove , Bongo Lars Ailo , Garrett Eilidh , Reid Alice , Sommerseth Hilde 
Published: October 2024

AbstractThis paper investigates the feasibility of using pre-trained generative Large Language Models (LLMs) to automate the assignment of ICD-10 codes to historical causes of death. Due to the complex narratives often found in historical causes of death, this task has traditionally been manually performed by coding experts. We evaluate the ability of GPT-3.5, GPT-4, and Llama 2 LLMs to accurately assign ICD-10 codes on the HiCaD dataset that contains causes of death recorded in the civil death register entries of 19,361 individuals from Ipswich, Kilmarnock, and the Isle of Skye in the UK between 1861–1901. Our findings show that GPT-3.5, GPT-4, and Llama 2 assign the correct code for 69%, 83%, and 40% of causes, respectively. However, we achieve a maximum accuracy of 89% by standard machine learning techniques. All LLMs performed better for causes of death that contained terms still in use today, compared to archaic terms. Also, they performed better for short causes (1–2 words) compared to longer causes. LLMs therefore do not currently perform well enough for historical ICD-10 code assignment tasks. We suggest further fine-tuning or alternative frameworks to achieve adequate performance.
Using Passive Sensing to Identify Depression
Authors: Zafeiridi Evi , Qirtas Malik Muhammad , Bantry White Eleanor , Pesch Dirk 
Published: October 2024

AbstractDepression is a common mental health issue that affects people’s thoughts, behaviours, and feelings. However, depression can often be under-diagnosed or under-treated. Early identification of depression can help to reduce the severity of the condition. Passive sensing, which captures data through mobile applications and wearable devices, has been shown effective in monitoring and identifying mental health problems, including depression. In line with the scope of AISoLA for Digital Humanities to explore the challenges and opportunities of interdisciplinary action to develop better practices in research, this paper explores the efficacy of passive sensing through mobile applications and fitness trackers to identify signs of depression among 52 adults in a three-week study. Sensing data captures calls, text messages, locations, nearby devices, usage of social applications, physical activity, sleep duration and quality through the AWARE and FitBit applications. The paper also investigates differences in the behaviour between people without depression and people with symptoms of depression, and it explores which sensor data can help to accurately identify depression. The results show high accuracy of certain sensing data to identify symptoms of depression. Depression is associated with reduced physical activity, higher sleep efficiency, increased number of incoming calls, increased number of visited places and reduced application use. Differences between behaviours show that people with symptoms of depression are less active, have a higher sleep score and receive more calls compared to people without symptoms. These findings should be interpreted within the methodological issues that are discussed in this paper in relation to wider research in sensing technology that aims to identify and monitor depression, including small sample sizes and lack of information about participants.
Empirische Studie: Interviews über Nahtoderfahrungen
Authors: Nadile Stefan 
Published: October 2024

ZusammenfassungIm Folgenden wird die Fragestellung erläutert und es werden methodische Überlegungen zur Durchführung der Datenerhebung angestellt. Zudem wird geschildert, wie die erhobenen Daten auswertbar gemacht und ausgewertet wurden.
Typologie der NTE-Deutungen im biografischen Kontext
Authors: Nadile Stefan 
Published: October 2024

ZusammenfassungDass die Erfahrungen tatsächlich gemacht wurden, daran scheint für die Betroffenen kein Zweifel zu bestehen. Wie dieser Eindruck jedoch bewertet wird und welche Relevanz seine Bewertung im biografischen Kontext hat, wird in den folgenden Kapiteln behandelt.
Measurement of Healthy Adult Brain Temperature Using 1H Magnetic Resonance Spectroscopy Thermometry
Authors: Tan Yahong , Liu Wenjia , Li Yanhua , Zhang Nan , Wang Mingxiao , Sun Shuo , Ma Lin 
Published: October 2024

Abstract

Purpose
The purpose of this study is to measure the brain temperature (T
br) by using 1H magnetic resonance spectroscopy (1H MRS) thermometry and investigate its age and gender differences in healthy adults. The brain temperature was further compared with the body temperature (T
bo) to investigate the possible existence of brain-body temperature gradient (∆T).

Methods
A total of 80 subjects were included in this study. 1H MRS data were collected on a 3.0T MR scanner using Point Resolved Selective Spectroscopy (PRESS) sequence. Voxels were positioned in the right frontal (RF) lobe and left frontal (LF) lobe, respectively. The temperature of each voxel was calculated by chemical shift difference (∆δ) between H2O and NAA which was obtained by LCModel software. The average temperature of bilateral frontal lobe voxels was defined as T
br for each subject. The average forehead temperature was acquired before MR scanning, defined as T
bo, in this study. The difference between T
br and T
bo, denoted as the brain-body temperature gradient (∆T), was calculated. Age and gender characteristics of T
br, ∆T and T
bo were analyzed.

Results
Tbr (38.51 ± 0.59℃) was higher than T
bo (36.47 ± 0.26℃) (P < 0.05). Negative correlations were observed between T
br and age (r = −0.49, P < 0.05) and between ∆T and age (r = −0.44, P < 0.05), whereas no correlation existed between T
bo and age (r = −0.03, P = 0.79).

Conclusion
Our observation demonstrated that the brain temperature, derived from 1H MRS thermometry, is significantly higher than the body temperature, indicating the existence of a brain-body temperature gradient, and the brain temperature gradually decreases with age.
Effect of fenofibrate on residual beta cell function in adults and adolescents with newly diagnosed type 1 diabetes: a randomised clinical trial
Authors: Hostrup Pernille E. , Schmidt Tobias , Hellsten Simon B. , Gerwig Rebekka H. , Størling Joachim , Johannesen Jesper , Sulek Karolina , Hostrup Morten , Andersen Henrik U. , Buschard Karsten , Hamid Yasmin , Pociot Flemming 
Published: October 2024

Abstract
Aims/hypothesis
Fenofibrate, a peroxisome proliferator-activated receptor alpha agonist, shows some promise in alleviating beta cell stress and preserving beta cell function in preclinical studies of type 1 diabetes. The aim of this phase 2, placebo-controlled, double-blinded, randomised clinical trial was to investigate the efficacy and safety of fenofibrate in adults and adolescents with newly diagnosed type 1 diabetes.

Methods
We enrolled 58 individuals (aged 16 to 40 years old) with newly diagnosed type 1 diabetes and randomised them to daily oral treatment with fenofibrate 160 mg or placebo for 52 weeks (in a block design with a block size of 4, assigned in a 1:1 ratio). Our primary outcome was change in beta cell function after 52 weeks of treatment, assessed by AUC for C-peptide levels following a 2 h mixed-meal tolerance test. Secondary outcomes included glycaemic control (assessed by HbA1c and continuous glucose monitoring), daily insulin use, and proinsulin/C-peptide (PI/C) ratio as a marker of beta cell stress. We assessed outcome measures before and after 4, 12, 26 and 52 weeks of treatment. Blinding was maintained for participants, their healthcare providers and all staff involved in handling outcome samples and assessment.

Results
The statistical analyses for the primary outcome included 56 participants (n=27 in the fenofibrate group, after two withdrawals, and n=29 in the placebo group). We found no significant differences between the groups in either 2 h C-peptide levels (mean difference of 0.08 nmol/l [95% CI −0.05, 0.23]), insulin use or glycaemic control after 52 weeks of treatment. On the contrary, the fenofibrate group showed a higher PI/C ratio at week 52 compared with placebo (mean difference of 0.024 [95% CI 0.000, 0.048], p<0.05). Blood lipidome analysis revealed that fenofibrate repressed pathways involved in sphingolipid metabolism and signalling at week 52 compared with placebo. The 52 week intervention evoked few adverse events and no serious adverse events. Follow-up in vitro experiments in human pancreatic islets demonstrated a stress-inducing effect of fenofibrate.

Conclusions/interpretation
Contrary to the beneficial effects of fenofibrate found in preclinical studies, this longitudinal, randomised, placebo-controlled trial does not support the use of fenofibrate for preserving beta cell function in individuals with newly diagnosed type 1 diabetes.

Trial registration
EudraCT number: 2019-004434-41

Funding
This study was funded by the Sehested Hansens Foundation.

Graphical Abstract
Optimal procurement auctions with audit
Authors: Chillemi Ottorino , Galavotti Stefano 
Published: October 2024

AbstractOne solution to limit the contractor’s default risk in first-price procurement auctions is to audit the winning firm in order to assess the reliability of its bid, and, in case of an unsatisfactory response, move to the firm that ranked second, and so on. Focussing on a situation in which firms with cheaper technologies have a higher risk of default ex-post, we provide conditions that ensure that a first-price auction with audit is indeed optimal. These conditions are likely to be satisfied when the relation between firms’ cost and default risk is not too steep and when the cost of performing the audit is not too large. We also characterize the optimal threshold that determines which firms are to be audited and which are not. We finally discuss cases in which, instead, a first-price auction with audit is dominated by a mechanism in which the allocation (and the audit) is done randomly, at least at some point of the awarding process.
Sustainable production of the drug precursor tyramine by engineered Corynebacterium glutamicum
Authors: Poethe Sara-Sophie , Junker Nora , Meyer Florian , Wendisch Volker F. 
Published: October 2024

Abstract
Tyramine has attracted considerable interest due to recent findings that it is an excellent starting material for the production of high-performance thermoplastics and hydrogels. Furthermore, tyramine is a precursor of a diversity of pharmaceutically relevant compounds, contributing to its growing importance. Given the limitations of chemical synthesis, including lack of selectivity and laborious processes with harsh conditions, the biosynthesis of tyramine by decarboxylation of l-tyrosine represents a promising sustainable alternative. In this study, the de novo production of tyramine from simple nitrogen and sustainable carbon sources was successfully established by metabolic engineering of the l-tyrosine overproducing Corynebacterium glutamicum strain AROM3. A phylogenetic analysis of aromatic-l-amino acid decarboxylases (AADCs) revealed potential candidate enzymes for the decarboxylation of tyramine. The heterologous overexpression of the respective AADC genes resulted in successful tyramine production, with the highest tyramine titer of 1.9 g L−1 obtained for AROM3 overexpressing the tyrosine decarboxylase gene of Levilactobacillus brevis. Further metabolic engineering of this tyramine-producing strain enabled tyramine production from the alternative carbon sources ribose and xylose. Additionally, up-scaling of tyramine production from xylose to a 1.5 L bioreactor batch fermentation was demonstrated to be stable, highlighting the potential for sustainable tyramine production.

Key points
• Phylogenetic analysis revealed candidate l-tyrosine decarboxylases
• C. glutamicum was engineered for de novo production of tyramine
• Tyramine production from alternative carbon substrates was enabled