bluuebunny/crossref_metadata_2025_split huggingface dataset:
An assessment of the implications of distribution remuneration and taxation policies on the final prices of prescription medicines: evidence from 35 countries
Authors: Leon Giovanny , Carbonel Christophe , Rampuria Aparajit , Rajpoot Ravindra Singh , Joshi Parth , Kanavos Panos 
Published: September 2024

AbstractThis paper analyses the structure of and variability in taxation and prescription drug distribution policies and quantifies the impact of such policies on the cost of prescription drugs to health systems in 35 countries. Taxes on prescription drugs remain highly prevalent (83% of the sample) although 63% of the sample countries implement a lower than standard VAT rate. Three remuneration types of the wholesale and retail distribution chain have been identified. Wholesale and retail distributors are remunerated on a regressive mark-up basis, which is price-dependent, although fixed fees and fixed percentages, which are non-price dependent, are also highly prevalent. Price component analysis for three groups of products classed as high-, medium- and low-priced suggests that mark-ups plus taxes varied significantly across countries and products, and ranged from 5% to 187% of ex-factory prices. Average margins also vary significantly by countries and products ranging 5–65% of retail prices. The cost of distribution and taxation contributes significantly to prescription drug costs for health systems. Although distribution chain remuneration raises efficiency and overall affordability questions, these need to be considered together with the regulatory framework shaping market structure of the distribution chain, as well as any prevailing horizontal and vertical integration policies. The overall cost of prescription drugs could be reduced immediately by eliminating taxation; this could go some way to alleviate fiscal pressures on health budgets, whilst avoiding resource re-allocation from health to other sectors.
Autistic and non-autistic adults use discourse context to determine a speaker’s intention to request
Authors: Frost Faith , Nagano Marisa , Zane Emily 
Published: September 2024

AbstractThe current study focuses on how autistic adults utilize context to determine whether ambiguous utterances (e.g., “I’m thirsty”) are intended as indirect requests or as literal comment/questions. Two questions are addressed: (1) How do autistic adults compare to neurotypical adults in using context to interpret an utterance’s intention as either literal or a request? (2) What cognitive mechanisms correlate with indirect request interpretation, and are these different for participants in each group? Twenty-six autistic and 26 neurotypical college students participated, engaging in an online experiment where they read narratives that ended with utterances open to literal or request interpretations, based on context. After each narrative, participants selected the best paraphrase of the utterance from two options, literal versus request. Following this task, participants completed two mentalizing measures (a false belief and emotion-identification task) and several executive functioning tests. The best model for predicting paraphrase choice included scores on the emotion-identification task and context as main effects, along with the interaction between both. Participants with higher emotion-identification test scores were more likely to provide correct paraphrases. Models including group as a main effect and/or interaction were not better at fitting the data, nor were any models that included executive functioning measures as main effects or interactions. Emotion-identification test scores, but not autism diagnosis, predict how adults infer whether an utterance is a request. Findings suggest that autistic adults use context similarly to neurotypical adults when interpreting requests, and that similar processes underlie performance for each group.
Modification of the lesser curvature incision line enhanced gastric conduit perfusion as determined by indocyanine green fluorescence imaging and decreased the incidence of anastomotic leakage following esophagectomy
Authors: Zhao Hongbo , Koyanagi Kazuo , Ninomiya Yamato , Kazuno Akihito , Yamamoto Miho , Shoji Yoshiaki , Yatabe Kentaro , Kanamori Kohei , Tajima Kohei , Mori Masaki 
Published: September 2024

Abstract
Aim
This study aimed to investigate the effectiveness of a modified incision line on the lesser curvature for gastric conduit formation during esophagectomy in enhancing the perfusion of gastric conduit as determined by indocyanine green fluorescence imaging and reducing the incidence of anastomotic leakage.

Methods
A total of 272 patients who underwent esophagectomy at our institute between 2014 and 2022 were enrolled in this study. These patients were divided based on two different types of cutlines on the lesser curvature: conventional group (n = 141) following the traditional cutline and modified group (n = 131) adopting a modified cutline. Gastric conduit perfusion was assessed by ICG fluorescence imaging, and clinical outcomes after esophagectomy were evaluated.

Results
The distance from the pylorus to the cutline was significantly longer in the modified group compared with the conventional group (median: 9.0 cm vs. 5.0 cm, p < 0.001). The blood flow speed in the gastric conduit wall was significantly higher in the modified group than that in the conventional group (median: 2.81 cm/s vs. 2.54 cm/s, p = 0.001). Furthermore, anastomotic leakage was significantly lower (p = 0.024) and hospital stay was significantly shorter (p < 0.001) in the modified group compared with the conventional group. Multivariate analysis identified blood flow speed in the gastric conduit wall as the only variable significantly associated with anastomotic leakage.

Conclusions
ICG fluorescence imaging is a feasible, reliable method for the assessment of gastric conduit perfusion. Modified lesser curvature cutline could enhance gastric conduit perfusion, promote blood circulation around the anastomotic site, and reduce the risk of anastomotic leakage after esophagectomy.
The unexpected power of linear programming: an updated collection of surprising applications
Authors: Golden Bruce , Schrage Linus , Shier Douglas , Apergi Lida Anna 
Published: September 2024

AbstractLinear programming has had a tremendous impact in the modeling and solution of a great diversity of applied problems, especially in the efficient allocation of resources. As a result, this methodology forms the backbone of introductory courses in operations research. What students, and others, may not appreciate is that linear programming transcends its linear nomenclature and can be applied to an even wider range of important practical problems. The objective of this article is to present a selection, and just a selection, from this range of problems that at first blush do not seem amenable to linear programming formulation. The exposition focuses on the most basic models in these selected applications, with pointers to more elaborate formulations and extensions. Thus, our intent is to expand the modeling awareness of those first encountering linear programming. In addition, we hope this article will be of interest to those who teach linear programming and to seasoned academics and practitioners, alike.
Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models
Authors: de Arriba-Pérez Francisco , García-Méndez Silvia , Otero-Mosquera Javier , González-Castaño Francisco J. 
Published: September 2024

Abstract
Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial for timely diagnosis and early intervention. We propose using Large Language Models to extract features from free dialogues to detect cognitive decline. These features comprise high-level reasoning content-independent features (such as comprehension, decreased awareness, increased distraction, and memory problems). Our solution comprises (i) preprocessing, (ii) feature engineering via Natural Language Processing techniques and prompt engineering, (iii) feature analysis and selection to optimize performance, and (iv) classification, supported by automatic explainability. We also explore how to improve Chatgpt’s direct cognitive impairment prediction capabilities using the best features in our models. Evaluation metrics obtained endorse the effectiveness of a mixed approach combining feature extraction with Chatgpt and a specialized Machine Learning model to detect cognitive decline within free-form conversational dialogues with older adults. Ultimately, our work may facilitate the development of an inexpensive, non-invasive, and rapid means of detecting and explaining cognitive decline.
Linearly convergent nonoverlapping domain decomposition methods for quasilinear parabolic equations
Authors: Engström Emil , Hansen Eskil 
Published: September 2024

AbstractWe prove linear convergence for a new family of modified Dirichlet–Neumann methods applied to quasilinear parabolic equations, as well as the convergence of the Robin–Robin method. Such nonoverlapping domain decomposition methods are commonly employed for the parallelization of partial differential equation solvers. Convergence has been extensively studied for elliptic equations, but in the case of parabolic equations there are hardly any convergence results that are not relying on strong regularity assumptions. Hence, we construct a new framework for analyzing domain decomposition methods applied to quasilinear parabolic problems, based on fractional time derivatives and time-dependent Steklov–Poincaré operators. The convergence analysis is conducted without assuming restrictive regularity assumptions on the solutions or the numerical iterates. We also prove that these continuous convergence results extend to the discrete case obtained when combining domain decompositions with space-time finite elements.
CAR T-cells for pediatric solid tumors: where to go from here?
Authors: Trautmann Tina , Yakobian Natalia , Nguyen Rosa 
Published: September 2024

AbstractDespite the great success that chimeric antigen receptor (CAR) T-cells have had in patients with B-cell malignancies and multiple myeloma, they continue to have limited efficacy against most solid tumors. Especially in the pediatric population, pre- and post-treatment biopsies are rarely performed due to ethical reasons, and thus, our understanding is still very limited regarding the mechanisms in the tumor microenvironment by which tumor cells exclude effectors and attract immune-suppressive cells. Nevertheless, based on the principles that are known, current T-cell engineering has leveraged some of these processes and created more potent CAR T-cells. The recent discovery of new oncofetal antigens and progress made in CAR design have expanded the potential pool of candidate antigens for therapeutic development. The most promising approaches to enhance CAR T-cells are novel CAR gating strategies, creative ways of cytokine delivery to the TME without enhancing systemic toxicity, and hijacking the chemokine axis of tumors for migratory purposes. With these new modifications, the next step in the era of CAR T-cell development will be the clinical validation of these promising preclinical findings.
Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection
Authors: Schlechtweg Dominik , Zamora-Reina Frank D. , Bravo-Marquez Felipe , Arefyev Nikolay 
Published: September 2024

AbstractThere has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.
A comparative analysis of the ethics of gene editing: ChatGPT vs. Bard
Authors: Burright Jack , Al-khateeb Samer 
Published: September 2024

AbstractRecently, there has been a growing trend in using large language models (LLMs) to develop diverse applications suitable for a wide range of tasks. These tasks range from solving programming bugs to helping teach elementary school students how to enhance their writing. Even with all these beneficial use cases, researchers worry about the potential bias these tools could produce and their effect on society. In this research, we compared responses that resulted from prompting two chatbots, namely OpenAI ChatGPT and Google Bard, about the issue of gene editing. Twelve prompts that are part of two subgroups were used to generate responses (text) about the issue of gene editing when the political affiliation (Democrat, Republican, and Communist) or geographical areas (United States, China, and Europe) of the prompter is provided. The Twelve responses were then analyzed semantically using three dictionary-based tools, i.e., Linguistic Inquiry and Word Count, the Moral Foundation Theory and Biblical Ethics dictionary, and Google’s Perspective API, to test and analyze the semantic and linguistic differences (measured via the Mann–Whitney U test) in the responses returned from the two chatbots. The results suggest that there are semantic and linguistic differences in responses per chatbots and prompts.
Quality of institutions, rent-seeking, and inter-jurisdictional cooperation
Authors: Di Liddo Giuseppe , Giuranno Michele G. 
Published: September 2024

AbstractThe differing fiscal needs of municipalities introduce bias into yardstick competition among local administrators driven by pure rent-seeking motives. The influence of institutional quality on this bias varies across cases, sometimes alleviating it, while in other instances exacerbating it. Inter-municipal cooperation provides incumbents with control over political yardstick competition. However, this alliance compromises the quality of public services when rent extraction is a priority. To address the issue of collusion and enhance accountability, the central government may find it counterproductive to uniformly improve institutional quality across all districts. Instead, a more effective approach may involve targeted policy interventions in specific districts, considering the varying levels of fiscal and institutional disparities. By doing so, the central government can disrupt the incentive for collusion and rent extraction among local incumbents.
Introduction of a Framework for the Integration of a Kinematic Robot Arm Model in an Artificial Neural Network - Extended Kalman Filter Approach
Authors: Horvath Sabine , Neuner Hans 
Published: September 2024

AbstractThe aim of this paper is to introduce a model in which systematic effects can be assigned according to their origin or mode of action. The approach intends to improve the positional accuracy of a robot arm. We show the impact of unaccounted model biases on estimated parameters when applying sequential approaches and conclude the necessity of jointly determining all influencing variables. Therefore, we propose a simultaneous estimation of transformation parameters, robot’s kinematic parameters and non-geometric parameters modelled by an artificial neural network (ANN) in further consequence. Thus, the main contribution of this paper is a new approach of the simultaneous estimation of the geometric and non-geometric components of a robot arm model. The integration of the geometric model (transformations, kinematic robot model) with the non-geometric one (ANN) is realised in the extended Kalman filter. The functionality of the algorithm has been proven on simulated data. The adaptive behaviour of machine learning approaches is made possible by an additional iteration of the ANN. The initialisation of the ANN parameters must not deviate from the nominal parameters by more than 10% so that the ANN can learn the non-geometric part. In this setup, the robot arm position corrections are reduced by 32.5%. A final sensitivity analysis proves the estimability of most kinematic parameters in the course of a future adaptive extension of the approach.
Older Adult Family Homicide in Victoria, Australia 2001–2015: A Description of Family and Family Violence Homicide Victimisation
Authors: Kennedy Briohny , Ibrahim Joseph Elias , Koppel Sjaan , Bugeja Lyndal 
Published: September 2024

Abstract
Purpose
This study examined factors present among older adult family homicide victims and their offenders compared to younger adult family homicide using the social-ecological model.

Method
The study used a population-based single-jurisdiction cross-sectional design of consecutive homicide cases of adults (aged 18 years and older) reported to the coroner in Victoria, Australia for the period 2001–2015. Included were homicides involving an intimate or familial relationship. The data source was the Victorian Homicide Register, maintained by the Coroners Court of Victoria. Descriptive and multivariate analysis was performed to compare the presence of factors between younger and older adult family homicides.

Results
During the study period 37/283 (13.1%) family homicides occurred among older adult victims (aged 65 years and older) and 246/283 (86.9%) occurred among younger adults (aged 18–64 years). The deceased older adult was often male (57%), with a documented physical illness (54%). Offenders against the older adult were frequently male (68%), non-intimately related (73%), with diagnosed mental illness, a history of substance use and/or historical exposure to violence (all 57%). There were 15 older adult family violence (FV) homicide victims, eight that were previously victims of the homicide offender, primarily enduring emotional, physical and psychological violence as well as the perpetrator assaulting another family member. Seven older adults had perpetrated FV against their offender.

Conclusions
The history of violence perpetration in the deceased and mental health factors in their offender, as well as the greater proportion of parent-child and other familial relationships offer interesting focal points for future prevention research.
Exploring Therapists’ Perspectives on Acute Dynamic Risk Factors in Intimate Partner Violence by Family-Only Perpetrators: A Qualitative Study
Authors: Serno Carlijn , Bouman Yvonne H. A. , Hoed Joseline J. den , Kip Hanneke , de Ruiter Corine 
Published: September 2024

Abstract
Purpose
Intimate partner violence (IPV) is a common problem with severe consequences for perpetrators, their families, and society. IPV perpetrators are a heterogeneous group of which family-only (FO) perpetrators constitute the largest subgroup. Previous research suggests that FO perpetrators have a different risk profile than other subgroups, implying that they may require different treatment programs. Most studies have focused on static and stable risk factors; however, acute dynamic risk factors (ADRFs) – i.e., factors that constitute an immediate risk for offending behavior and can rapidly change over hours, days, or weeks – help to explain offending behavior. This study explored ADRFs for FO perpetration according to therapists to improve our understanding of FO perpetration, which could help in intervention planning for this group.

Methods
Ten semi-structured interviews were conducted with therapists in a community setting. Each interview included one FO case. Data were analyzed using a combination of inductive and deductive thematic analysis.

Results
ADRFs were categorized into personal and situational factors. Six ADRFs for FO perpetration were identified: mental exhaustion, perceived cheating/abandonment, psychological hurt, uncertainty, feelings of inadequacy, and intoxication. According to therapists, the dyadic relationship between the perpetrator and their partner plays an important role. Additionally, ADRFs were found to be often interconnected with each other and with static and stable risk factors.

Conclusions
This exploratory study revealed which types of ADRFs might play a role in FO perpetration. Future research should focus on the interrelationships between ADRFs and their relation to static and stable dynamic risk factors.
Impacts of generative AI on user contributions: evidence from a coding Q &A platform
Authors: Li Xinyu , Kim Keongtae 
Published: September 2024

AbstractThis study investigates the short-term impact of generative AI, exemplified by the introduction of ChatGPT, on user contributions in a coding Q&A platform. We find that the introduction of ChatGPT led to a reduction in the number of high-quality answers provided by users, particularly among highly engaged contributors, despite an overall increase in answers. We identify two key mechanisms: (1) increased perceived question sophistication despite no actual change in content and (2) reduced motivation of loyal users in providing answers in the face of AI-generated alternatives. The findings suggest that while generative AI can facilitate value creation on user-generated content (UGC) platforms, it also poses challenges in retaining core contributors and managing content quality. The paper contributes to the literature on the impact of AI adoption on platforms and suggests practical implications for UGC platform management, such as the need for AI content disclosure measures to retain engaged users.
Multimodal emotion recognition based on a fusion of audiovisual information with temporal dynamics
Authors: Salas-Cáceres José , Lorenzo-Navarro Javier , Freire-Obregón David , Castrillón-Santana Modesto 
Published: September 2024

AbstractIn the Human-Machine Interactions (HMI) landscape, understanding user emotions is pivotal for elevating user experiences. This paper explores Facial Expression Recognition (FER) within HMI, employing a distinctive multimodal approach that integrates visual and auditory information. Recognizing the dynamic nature of HMI, where situations evolve, this study emphasizes continuous emotion analysis. This work assesses various fusion strategies that involve the addition to the main network of different architectures, such as autoencoders (AE) or an Embracement module, to combine the information of multiple biometric cues. In addition to the multimodal approach, this paper introduces a new architecture that prioritizes temporal dynamics by incorporating Long Short-Term Memory (LSTM) networks. The final proposal, which integrates different multimodal approaches with the temporal focus capabilities of the LSTM architecture, was tested across three public datasets: RAVDESS, SAVEE, and CREMA-D. It showcased state-of-the-art accuracy of 88.11%, 86.75%, and 80.27%, respectively, and outperformed other existing approaches.