bluuebunny/crossref_metadata_2025 huggingface dataset:
Problem of pain in the USA: evaluating the generalisability of high-impact chronic pain models over time using National Health Interview Survey (NHIS) data
Authors: Falasinnu Titilola , Hossain Md Belal , Karim Mohammad Ehsanul , Weber Kenneth Arnold , Mackey Sean
Published: January 2025

Introduction

High-impact chronic pain (HICP) significantly affects the quality of life for millions of US adults, imposing substantial economic/healthcare burdens. Disproportionate effects are observed among racial/ethnic minorities and older adults.

Methods

We leveraged the National Health Interview Survey (NHIS) from 2016 (n=32 980), 2017 (n=26 700) and 2021 (n=28 740) to validate and develop analytical models for HICP. Initial models (2016 NHIS data) identified correlates associated with HICP, including hospital stays, diagnosis of specific diseases, psychological symptoms and employment status. We assessed the models’ generalisability and drew comparisons across time. We constructed five validation scenarios to account for variations in the availability of predictor variables across datasets and different time frames for pain assessment questions. We used logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) and random forest techniques. We assessed model discrimination, calibration and overall performance using metrics such as area under the curve (AUC), calibration slope and Brier score.

Results

Scenario 1, validating the NHIS 2016 model against 2017 data, demonstrated excellent discrimination with an AUC of 0.89 (95% CI 0.88 to 0.90) for both LASSO and random forest models. Subgroup-specific performance varied, with the lowest AUC among adults aged ≥65 years (0.81, 95% CI 0.78 to 0.82) and the highest among Hispanic respondents (0.91, 95% CI 0.88 to 0.94). Model calibration was generally robust, although underfitting was observed for Hispanic respondents (calibration slope: 1.31). Scenario 3, testing the NHIS 2016 model on 2021 data, showed reduced discrimination (AUC: 0.82, 95% CI 0.81 to 0.83) and overfitting (calibration slopes <1). De novo models based on 2021 data showed comparable discrimination (AUC: 0.86, 95% CI 0.85 to 0.87) but poorer calibration when validated against older datasets.

Conclusion

These findings underscore the potential of these models to guide personalised medicine strategies for HICP, aiming for more preventive rather than reactive healthcare. However, the model’s broader applicability requires further validation in varied settings and global populations.
Protein tyrosine phosphatase delta is a STAT3-phosphatase and suppressor of metabolic liver disease
Authors: Roca Suarez Armando Andres , Jühling Frank , Moehlin Julien , Mailly Laurent , Virzì Alessia , Brignon Nicolas , Durand Sarah C , Oudot Marine A , Schaeffer Eugenie , Martin Romain , Meiss-Heydmann Laura , Bach Charlotte , Boulahtouf Zakaria , Girard Lea , Osswald Emma , Jamey Carole , Brumaru Daniel , Dali-Youcef Nassim , Mukherji Atish , Saez-Palma Maria , Testoni Barbara , Zoulim Fabien , Koneru Bhuvaneswari , Fujiwara Naoto , Hoshida Yujin , Felli Emanuele , Pessaux Patrick , Tremblay Michel L , Parent Romain , Schuster Catherine , Baumert Thomas F , Lupberger Joachim
Published: January 2025

Objective

Impaired hepatic expression of protein tyrosine phosphatase delta (PTPRD) is associated with increased STAT3 transcriptional activity and reduced survival from hepatocellular carcinoma in patients with chronic hepatitis C virus infection. However, thePTPRD-expressing hepatic cell types, signalling pathways responsive to PTPRD and their role in non-viral liver disease are largely unknown.

Methods

We studiedPTPRDexpression in single-cell and bulk liver transcriptomic data from mice and humans, and established aPtprd-deficient mouse model for metabolic dysfunction-associated steatohepatitis (MASH). Identified pathways were validated by perturbation studies in human hepatocytes and PTPRD substrates by pull-down assays. The clinical relevance was further explored in a cohort with metabolic disease by ranking patients according toPTPRDexpression and analysing its association with metabolic disease markers.

Results

The analysis of individuals ranked according toPTPRDexpression andPtprd-deficient mice, showed thatPTPRDlevels were associated with hepatic glucose/lipid signalling and peroxisome function. HepaticPTPRDexpression is impaired in aetiologies of chronic liver diseases that are associated with metabolic disease. We further validated PTPRD as a STAT3 phosphatase in the liver, acting as a regulator of peroxisomal fatty acid metabolism. During MASH, low PTPRD led to increased liver steatosis inPtprd+/− mice and a pronounced unfolded protein response, which impacts insulin signalling. Accordingly, silencing ofPTPRDblunted insulin-induced AKT phosphorylation. Patients with obesity and low hepaticPTPRDexpression exhibit increased levels of metabolic risk factors.

Conclusion

Our data revealed an important regulatory role of the hepatic PTPRD-STAT3 axis in maintaining glucose/lipid homeostasis, which is recapitulated in clinical manifestations of metabolic liver disease.
Characteristics of injury deaths among homeless and non-homeless US veterans (2017–2021)
Authors: Beydoun Hind A , Szymkowiak Dorota , Ng Ted K S , Tsai Jack
Published: January 2025

Objective

The objective of this study is to assess whether experiencing homelessness may be associated with future risk of injury death and characterise these injury deaths by homelessness status among veterans who received healthcare through the US Department of Veterans Affairs (VA).

Methods

A retrospective cohort study was conducted among 6 128 921 veterans (399 125 homeless and 5 729 796 non-homeless) who received VA healthcare between 2017 and 2020 and were followed until 2021 using linked data from VA’s Corporate Data Warehouse, Homeless Operations Management System and the VA/Department of Defense Joint Mortality Data Repository. Injury death rates were estimated by homelessness status with 95% CIs using the exact Poisson method. Multivariable Cox regression models were applied to estimate HRs with 95% CI for homelessness as a predictor of injury deaths, controlling for demographic, clinical, substance use and mental health characteristics.

Results

The injury-specific mortality rate (per 100 000 person-years) was estimated at 254.4 (95% CI 252.5 to 256.4) and was higher among homeless (453.3 (95% CI 443.3 to 463.5)) versus non-homeless (239.9 (95% CI 237.9 to 241.9)) veterans. There were disparities in anatomical sites and injury type by homelessness status. Injury-related risk of death was twice as high among veterans with versus without a homelessness experience (adjusted HR 1.93, 95% CI 1.88 to 1.98).

Conclusion

Homeless veterans may be at high risk for specific patterns of injury death. Injury prevention efforts should target exposures that distinguish this vulnerable population from other veterans seeking VA healthcare services.
Volumetric response after closure of a spinal CSF leak in patients with spontaneous intracranial hypotension: a multicompartmental longitudinal study
Authors: Zander Charlotte , Lützen Niklas , Rau Alexander , Wolf Katharina , Arnold Philipp , Mast Hansjörg , El Rahal Amir , Volz Florian , Cimflova Petra , Beck Jürgen , Urbach Horst , Demerath Theo
Published: January 2025

Background

Cerebrospinal fluid (CSF) loss in spontaneous intracranial hypotension (SIH) is accompanied by volume shifts between the intracranial compartments. This study investigated tricompartimental and longitudinal volume shifts after closure of a CSF leak.

Methods

Patients with SIH and suitable pre-therapeutic and post-therapeutic imaging for volumetric analysis were identified from our tertiary care center between 2020 and 2023. The Bern SIH score was calculated. Pre-interventional and post-interventional volumetry encompassed the CSF, parenchymal and venous compartments (ie, venous sinus and choroid plexus volumes).

Results

In total, 32 patients with SIH (49.7±16.0 years, 22 women) met inclusion criteria. The mean SIH score decreased between baseline (4.5±2.7) and early (2.7±2.3, <7 days after intervention), and also late follow-up (1.4±1.7, follow-up ≥7 days) after leak closure. This was accompanied by a significant increase in ventricular volume from 22.1 to 25.0 mL (P=0.01) at early follow-up, and 23.9 mL at later follow-up (P=0.080). In contrast, venous sinus volumes decreased from 13.8 to 9.6 mL (P=0.016) at early follow-up, and 10.0 mL (P=0.007) at late follow-up. No significant change in mean choroid plexus, total gray or total white matter volume was observed.

Conclusions

Closure of a spinal CSF leak leads to an early increase in ventricular CSF volume and a decrease in venous sinus volume. The results reflect the long-term convergence of the SIH score to normal values and indicate that permanent closure of a CSF leak induces a stable recompensation of the intracranial compartments without involving significant volume shifts within the cerebral parenchyma.
Return to duty after non-surgical treatment of a non-neurological thoracic or lumbar spine fracture in French military patients: a retrospective analysis of 54 patients
Authors: Lines J-B , Cungi P J , Da Silva C , Aigle L , Dagain A , Joubert C
Published: January 2025

Introduction

Non-surgical management of non-neurological thoracic or lumbar spine (TL) fractures seems to provide good results in the civilian population, leading to return to work in most cases. However, data on the military population are limited, particularly regarding return to duty. This study aimed to describe a population of French military patients with traumatic non-neurological TL fractures and the outcomes of non-surgical management regarding operational capacity.

Materials and methods

This retrospective single-centre observational study used data from military patients followed for non-neurological TL fractures, from the date of the trauma or after treatment in another civilian or military care facility. Medical fitness status at 1 year was the primary outcome. Medical fitness status at 1 year was defined as unrestricted resumption and restricted resumption in cases of return to duty in a different role or in the previous role with restrictions.

Results

Between January 2010 and December 2020, 93 patients presented with non-neurological traumatic TL fractures at Sainte-Anne National Military Hospital. Fifty-four records met the inclusion criteria. Falls from high heights (27.8%, n=15) and road accidents (24.1%, n=13) were the most common traumatic mechanisms. The remaining 77 injuries were compression fractures, predominantly at the thoracolumbar junction. One year after trauma, 90.7% of patients (n=49) had returned to duty, and 77.8% (n=42) were free from medical restriction. The median duration of incapacity (MDI) was 173 days. Age significantly affected medical fitness at 1 year (p=0.019); however, it did not affect the MDI. Polytrauma was significantly associated with a lower resumption rate at 1 year (53.3%, p=0.007).

Conclusion

Non-surgical management of non-neurological TL fractures allowed unrestricted return to duty in this population. These clinically relevant trends have led to perspectives for defining non-surgical treatment indications, means and goals.
Tobacco product flavour policies in the USA
Authors: Whitacre Travis R , Crippen Alyssa , Monthrope Mayah , Narine Tanisha , Liber Alex C , Friedman Abigail S
Published: January 2025

Objectives

Characterise US residents’ exposure to restrictions on sales of flavoured electronic nicotine delivery system (ENDS), cigars and menthol cigarettes across states and time, and assess correlations between these policies.

Methods

From 2022 to 2024, we compiled flavour policy locations from advocacy groups and online searches, located corresponding legal texts and reviewed these to identify policy details, including effective dates. Using census data, we calculated the proportion of state residents covered by each policy quarterly from 2009 to 2024 and estimated correlations between them and cigarette taxes.

Results

By January 2024, menthol cigarettes, flavoured cigars and flavoured ENDS sales restrictions covered 17.6%, 18.1% and 28.1% of US residents. About 1 in 10 US residents is subject to flavoured ENDS restrictions without concurrent restrictions on flavoured cigar and menthol cigarette sales. Strong correlations between flavour policy coverage and cigarette tax rates indicate a need to adjust for exposure to a range of tobacco control policies in analyses evaluating any one of these regulations’ effects.

Conclusions

While state and local adoption of restrictions on flavoured tobacco product sales has proliferated, flavour policy coverage for combustible tobacco products lags well behind that for ENDS. If this leads some people who vape flavoured ENDS to substitute towards flavoured cigars and/or menthol cigarettes, this policy combination could harm population health.

Policy implications

Rapid implementation of proposed US Food and Drug Administration rules barring flavoured cigar and menthol cigarette sales is needed to ensure that regulation of more lethal, combustible tobacco products is not more lenient than restrictions on less harmful nicotine products.
Psychological, social and cultural influences on smoking among lesbian, bisexual and queer women
Authors: Grant Ruby , Mooney-Somers Julie , McNair Ruth , Pennay Amy , Segan Catherine , Power Jennifer , Bourne Adam
Published: January 2025

Background

Smoking rates have declined markedly in Australia over time; however, lesbian, bisexual and queer (LBQ) women continue to smoke at higher rates than heterosexual women. Understanding the factors influencing smoking in this population is crucial for developing targeted cessation interventions and other supports.

Methods

Experiences of and motivations for smoking among 42 LBQ cisgender and transgender women and non-binary people in Australia who currently or previously smoked were explored through semi-structured interviews. Participants were primarily white Australian cisgender women in their 30s–40s. Thematic analysis was used to identify common psychological, social and cultural influences on smoking.

Results

While coping with minority stress was a common factor influencing some participants’ smoking behaviours, participants also described how smoking offered pleasurable opportunities for gender expression, affirmation and rebellion. Smoking also enabled participants to experience ‘marginalised connectivity’, a form of social solidarity fostered through the sharing of a stigmatised practice by an oppressed or stigmatised community.

Conclusion

This article identifies factors contributing to the ongoing socio-cultural relevance of smoking for some LBQ women in Australia. These specific psychological, social and cultural contexts remain salient for LBQ women’s smoking and must be factored into smoking cessation campaigns and programme designs for this population. Tailored messaging that emphasises alternative self-care strategies and the benefits of quitting in the context of supportive communities may be more effective in engaging LBQ women than long-term health risk messages.
Inventory and characteristics of Canadian interhospital transfer coordination structures
Authors: Desrosiers Marie-Eve , Moore Lynne , Nguyen-Tri David , Keundo Aubin Armand , Boutin Denis , Ouellet Caroline
Published: January 2025

Several studies have described interhospital transfer coordination structures in the US but there is a lack of data on Canadian coordination systems. We aimed to develop an inventory of existing interhospital coordinating structures across Canada and to detail how they operate in terms of governance, re-sources, tools, and processes. A cross-sectional survey was designed and ten managers/medical officials from seven coordination structures of interhospital transfers across Canada completed the survey. Findings indicate that coordination systems had similar communication modalities and covered similar services including the provision of medical advice. However, they differed in terms of the profile of staff processing transfers and transportation coordination. Mature structures such as Ontario and Alberta, who manage high annual volumes, distinguish themselves with very clear, standardized, and efficient processes, as well as implemented quality and performance improvement methods including key performance indicators. These results suggest that Canada may benefit from having an inter-provincial community of practice for interhospital transfer coordination structures to promote best practices, identify solutions, and harmonize services and processes. Better harmonization in transfer coordination would improve equity in the access to healthcare services. Future research should focus on the elaboration of evidence-and consensus-based guidelines that apply to the Canadian context.
A Time to Cast Away Stones: On a Family of Pebble Automata
Authors: Martynova Olga , Okhotin Alexander
Published: January 2025

Deterministic graph-walking automata equipped with finitely many “drop-once” pebbles are investigated: these automata explore a graph by following its edges using finitely many states, and they can also leave pebbles at some nodes, but cannot lift these pebbles anymore. It is proved that for every such automaton there is a graph-walking automaton without pebbles that recognizes the same set of graphs. To be precise, for an automaton with [Formula: see text] drop-once pebbles and [Formula: see text] states, operating on graphs with [Formula: see text] labels of edge end-points, an automaton without pebbles with [Formula: see text] states accepting the same set of graphs is constructed. The results apply to two-way finite automata (with [Formula: see text]) and to tree-walking automata as special cases.
The Strong Diagnosability of Multiprocessor Systems Under the PMC Model
Authors: Wu Xuemin , Xu Liqiong , Zheng Chuanye
Published: January 2025

The diagnosability is a prominent parameter to estimate the fault diagnosis capability of multiprocessor systems, which is the maximum number of faulty processors that can be identified by the system. Compared to traditional diagnosability, strong diagnosability proposed by Lai et al. [16] is a more pinpoint term for gauging multiprocessor systems’ reliability. A system is strongly [Formula: see text]-diagnosable if it is [Formula: see text]-diagnosable and can attain [Formula: see text]-diagnosability, excepting certain circumstance in which all neighbors of some processor are faulty. In this paper, we obtain the strong diagnosability of regular networks as well as connected networks subject to certain circumstances under the PMC model, respectively. The previous result in Hsieh et al. [7] is extended.
Design of an IoT-Based Intelligent Platform for Ideological and Political Education Management
Authors: Hong Niande , Liu Xiaoqin
Published: January 2025

The social and ideology education, focus instruction, hands-on learning, and a variety of means, students are cultivated in their political quality, moral character, and sense of social responsibility while it is being guided in understanding the nation’s important policies and social current affairs. Ideological and political disciplines are implementing the problem of transformation within the framework of the major criteria of the new curriculum reform. In the conventional teaching process, students face several challenges in achieving the essential quality of ideological and political education. The artificial intelligence-assisted interactive modeling on the Internet of Things platform (AI-IM-IoT) is able to generate a smart system for ideological and political education, addressing various problems in the current situation. Performance in conventional ideological and political learning platforms, an investigation on the construction of intelligent media ideological and political learning platforms are based on AI technology. Hence AI-IM-IoT has been the Ideology of ideas and beliefs that influence social behavior and can possess an effect on educational achievements. Ideological viewpoints on education can influence educational institutions’ aims, values, and priorities, as well as how resources and opportunities are allocated. Students are encouraged to develop an understanding of social, economic, and political issues via ideological and political education programs.
Optimizing Deep Learning-Based Natural Language Processing for Sentiment Analysis
Authors: Bao Dianqing , Su Wen
Published: January 2025

Deep learning (DL)-based natural language processing (NLP) technologies are applied and optimized for the analysis of emotional implications in text data. Understanding emotional intricacies in text has become necessary for a variety of industries, like social media analysis, customer feedback interpretation, and mental health monitoring, due to the exponential rise of digital material. The study emphasizes the difficulties associated with the automation of emotion analysis, including the diversities of the text formats and the NLP complexities. This research gathers that text-based emotion analysis data and pre-processing techniques are utilized for the analysis of emotion, including normalizing methods such as tokenization and lemmatization, along with the removal of stop words and punctuation. The model incorporates pre-trained embeddings and attention mechanisms to capture contextual information and emotional subtleties for any language. The study proposes the hybrid model using tuned crow search optimized dynamic graph neural network (TCSO-DGNN) on an automatically optimized process to enhance the accuracy of emotion detection. The proposed model results on emotion analysis data and superior results are obtained, indicating better performance in precision (94.72%), accuracy (96.43%), recall (94.36%), and F1-score (93.02%). The findings indicate that the inclusion of TCSO-DGNN significantly enhances the identification of complex emotional expressions concerning traditional NLP techniques. This study explores the potential of integrated DL approaches to refine the capabilities of emotion analysis tools, particularly in contexts requiring refined interpretations of human emotions expressed online. Additionally, it contributes to academic discourse in the field of emotion analysis while providing practical insights for developers of NLP applications across business and public service sectors.
Multi-block Directed Radiation Routing Algorithm for Optimized Wireless Sensor Network
Authors: Feng Luyao , Guo Yewei , Guo Ying
Published: January 2025

In recent years, the axial radiation model has emerged as a pivotal framework for Wireless Sensor Networks (WSN), particularly in enhancing intelligent sensing platforms. This study delves into the WSN structured around the axial radiation model, encompassing critical aspects like model reconstruction, node deployment, and routing optimization. Our focus is on evaluating the performance metrics that influence the responsiveness of these intelligent platforms. We introduce the Multi-block Directed Radiation (MBDR) routing algorithm, designed to extend system operational time and boost data transmission efficiency. Comprehensive experimental analyses demonstrate the significant advancements of MBDR in survivability, data transmission rates, and regional balance within central axis radiation model environments.
Analytical simulation of mixed convective Williamson nanofluid flow above a stretched Riga plate considering viscous dissipation effects
Authors: Rehman Ali , Khan Dolat , Inc Mustafa , Rezapour Shahram
Published: January 2025

This work examines, accounting for viscous dissipation, an analytical simulation of mixed convection heat transfers in a Williamson blood base nanofluid flow on a stretched Riga plate (RP). The Lorentz force, which influences fluid dynamics, is produced by the alternating magnetic fields that comprise the RP. The flow characteristics are further complicated by the blood base Williamson nanofluid’s (WN) non-Newtonian behavior. The governing partial differential equations (PDEs) for momentum and energy are transformed into a set of nonlinear ordinary differential equations (NODEs) by similarity transformations. We solve these equations analytically using the homotopy analysis method (HAM). We investigate in detail how the temperature and velocity profiles (VPs) are affected by significant parameters as the Williamson parameter (WP), viscous dissipation, volume friction of nanoparticles, modified Hartmann number (MHN), heat generation parameter and Biot number (BN). The findings show that the thermal conductivity (TC) of nanoparticles is increased, improving the efficiency of heat transfer. Moreover, a Lorentz force generated by the RP considerably alters the temperature and flow fields. With regard to the design and optimization of thermal systems utilizing complex boundary conditions (BCs) and non-Newtonian nanofluids, this work offers significant new insights.
Significance of Fe3O4/MnZnFe2O4 nanoparticles for thermal efficiency of ethylene glycol: Analysis for single-phase nanofluid flow using Cattaneo Christov theory
Authors: Bani-Fwaz Mutasem Z. , Karim Sadia , Ullah Khan Sami , Bilal Muhammad , Khan Yasir , Tlili Iskander
Published: January 2025

Thermal conductivity of nanoparticles is a vibrant parameter in the heat transfer applications applied in thermal, mechanical and chemical engineering. The use of nanoparticles in the conventional fluids augments the thermal conductance which directly affects the heat transfer mechanism. It greatly depends on the nature of nanoparticles. Hence, the excellent conductivity of hybrid Fe3O4/MnZnFe2O4 allows to select for catalytic agent with EG as primary solvent for heat transfer applications. Besides nanoparticles, the physical phenomena like heating species, Cattaneo Christov thermal flux and variable temperature of the surface potentially alter the performance of single phase nanofluids. Therefore, the current effort aims to formulate a single phase problem through slanted elongating surface having acute angle with the ground level and influenced parametric ranges. The final problem was examined numerically (shooting scheme coupled with RK scheme) and then analyzed and predicted the ranges for better performance. The parallel results reveal that the nanofluid movement is slower than common fluid over the domain because of dominant denser effects. The heat generative parameter ([Formula: see text] and radiation number ([Formula: see text] are observed to be excellent catalytic parameters to boost the model efficiency. However, thermally radiative nanofluids are more efficient which augment the performance remarkably. The shear drag improved from [Formula: see text] to [Formula: see text] (for [Formula: see text], [Formula: see text] to [Formula: see text] (for [Formula: see text] against [Formula: see text] to [Formula: see text]. Further, the heating source Q enhances the heat transport rate at the slanted surface from [Formula: see text] to [Formula: see text] (for [Formula: see text], and [Formula: see text] to [Formula: see text] (for [Formula: see text]. However, it is prevailing for [Formula: see text] for specified values of Q.
Mathematical modeling of electroosmotic flow and thermal transport in stenotic tapered arteries using finite difference method
Authors: Haider Jamil Abbas , Ahmad Shahbaz , Nadeem Sohail
Published: January 2025

This study investigates the electroosmotic flow and thermal transport of nanofluids, specifically aluminum oxide and titanium dioxide, within tapered arteries with stenosis. Using the finite difference method (FDM), we modeled the flow dynamics and heat transfer mechanisms under the assumption of low Reynolds numbers and mild stenosis. The study focuses on the effects of nanoparticle volume fraction, heat absorption parameters, and electroosmotic forces on blood flow, velocity profiles, and temperature distribution. Results show that increasing nanoparticle concentration and heat absorption significantly reduce flow velocity, particularly in divergent artery geometries. Additionally, the inclusion of electroosmotic effects enhances heat transfer, while the Grashof number influences central velocity. These findings offer valuable insights into optimizing nanoparticle-based therapeutic interventions for cardiovascular treatments, providing a framework for enhancing drug delivery systems and improving the efficacy of heat-based therapies. The study’s outcomes could lead to improved diagnostic tools and therapeutic strategies for managing cardiovascular diseases.
A Cyber-War Between Bots: Cognitive Attackers are More Challenging for Defenders than Strategic Attackers
Authors: Du Yinuo , Prebot Baptiste , Malloy Tyler , Gonzalez Cleotilde
Published: January 2025

Adversary emulation is commonly used to test cyber-defense performance against known threats to organizations. However, many adversary emulation methods often rely on automated planning and underplay the role of human cognition. Consequently, defenders are often underprepared for human attackers who can think creatively and adapt their strategies. In this paper, we propose the design of adversarial cognitive agents that are dynamic, adaptable, and able to learn from experience. These cognitive agents are built based on the theoretical principles of Instance-Based Learning Theory (IBLT) of experiential choice in dynamic tasks, making them more challenging than strategically optimal adversaries for human defenders. Our research offers three main contributions. First, in a simulation experiment, we demonstrate how IBL attacker agents can learn from experience and become as efficient as optimal strategic algorithms against a strategic defender. In a second simulation experiment, the IBL attackers are pitted against an IBL defender, showing that the IBL attacker can be a more challenging adversary for the IBL defender, while the IBL defender can learn to counter carefully crafted optimal attack strategies. To test these observations, we conducted a third experiment, where humans played the role of defenders against both strategic and IBL attackers in an interactive task. The results confirm the predictions of the second simulation experiment: a cognitive attackers are more challenging for human defenders than strategic attackers. These insights contribute to informing future adversary emulation efforts and training of cyber defenders.
Introducing Phylogenetics in Search-based Software Engineering: Phylogenetics-aware SBSE
Authors: Blasco Daniel , Iglesias Antonio , Echeverría Jorge , Pérez Francisca , Cetina Carlos
Published: January 2025

Phylogenetics studies the relationships, in terms of biological history and kinship, of a set of taxa (e.g., species). We argue that in Search-based Software Engineering (SBSE), the individuals of an evolutionary computation-driven population could be considered as taxa for which the leverage of Phylogenetic Inference might be beneficial. In this work, we present our Phylogenetics-aware SBSE approach. Our approach introduces a novel Phylogenetic Operation to promote results which are sufficiently aligned (in terms of lineage) with a certain reference given by the domain expert. Our approach is evaluated in two heterogeneous industrial case studies: Procedural Content Generation from Game Software Engineering, and Feature Location from Software Maintenance. The results are analyzed using quality-of-the-solution and acceptance-by-developers measurements. We performed a statistical analysis to determine whether the impact on the results is significant compared to baselines that do not leverage Phylogenetics. The results show that our approach significantly outperforms two baselines in both case studies. Furthermore, two focus groups confirmed the acceptance of our approach and stressed that solution acceptance may make the difference in industrial environments. Our work has the potential to motivate a new breed of research work on Phylogenetics awareness to produce better results in Software Engineering.
Test Oracle Automation in the Era of LLMs
Authors: Molina Facundo , Gorla Alessandra , d’Amorim Marcelo
Published: January 2025

The effectiveness of a test suite in detecting faults highly depends on the quality of its test oracles. Large Language Models (LLMs) have demonstrated remarkable proficiency in tackling diverse software testing tasks. This paper aims to present a roadmap for future research on the use of LLMs for test oracle automation. We discuss the progress made in the field of test oracle automation before the introduction of LLMs, identifying the main limitations and weaknesses of existing techniques. Additionally, we discuss recent studies on the use of LLMs for this task, highlighting the main challenges that arise from their use, e.g., how to assess quality and usefulness of the generated oracles. We conclude with a discussion about the directions and opportunities for future research on LLM-based oracle automation.
RA-MOSAIC
: Resource Adaptive Edge AI Optimization over Spatially Multiplexed Video Streams
Authors: Gokarn Ila , Hu Yigong , Abdelzaher Tarek , Misra Archan
Published: January 2025

Sustaining real-time, high fidelity AI-based vision perception on edge devices is challenging due to both the high computational overhead of increasingly “deeper” Deep Neural Networks (DNNs) and the increasing resolution/quality of camera sensors. Such high-throughput vision perception is even more challenging in multi-tenancy systems, where video streams from multiple such high-quality cameras need to share the same GPU resource on a single edge device. Criticality-aware
canvas-based processing
is a promising paradigm that decomposes
multiple concurrent
video streams into Regions of Interest (RoI) and spatially channels the limited computational resources to selected RoI with higher “resolution”, thereby moderating the trade-off between computational load, task fidelity, and processing throughput.
RA-MOSAIC (Resource Adaptive MOSAIC)
employs such canvas-based processing, while further tuning the incoming video streams and available resources on-demand to allow the system to adapt to dynamic changes in workload (often arising from variations in the number or size of relevant objects observed by individual cameras).
RA-MOSAIC
utilizes two distinct and synergistic concepts. First, at the camera sensor, a
bandwidth-adaptive
and lightweight Bandwidth Aware Camera Transmission (BACT) method applies differential down-sampling to create mixed-resolution individual frames that preferentially preserve resolution for critical ROIs, before being transmitted to the edge node. Second, at the edge, BACT video streams received from multiple cameras are decomposed into multi-scale RoI tiles and spatially packed using a novel
workload-adaptive
bin-packing strategy into a single ‘canvas frame’. Notably, the canvas frame itself is dynamically sized such that the edge device can opportunistically provide higher processing throughput for selected high-priority tiles during periods of lower aggregate workloads. To demonstrate
RA-MOSAIC’s 
gains in processing throughput and perception fidelity, we evaluate
RA-MOSAIC
on a single NVIDIA Jetson TX2 edge device for two benchmark tasks: Drone-based Pedestrian Detection and Automatic License Plate Recognition. In a bandwidth-constrained wireless environment,
RA-MOSAIC
employs a batch size of 1 to pack up to 6 concurrent video streams on a dynamically sized canvas frame to provide (i) 14.3% gain in object detection accuracy and (ii) 11.11% gain in throughput on average (up to 20 FPS per camera, cumulatively 120 FPS), over our previous work MOSAIC, a naïve canvas-based baseline. Compared to prior state of the art baselines such as batched inference over extracted RoI,
RA-MOSAIC
provides a very-significant, 29.6% gain in accuracy for a comparable throughput. Similarly,
RA-MOSAIC
dramatically outperforms bandwidth adaptive baselines, such as FCFS (

\(\leq 1\%\)

accuracy gain but

\(5.6\)

x or 566.67% throughput gain) and uniform grid packing (17% accuracy improvement and 5% throughput gain).
DISA: Disentangled Dual-Branch Framework for Affordance-Aware Human Insertion
Authors: Cao Xuanqing , Zhou Wengang , Sun Qi , Wang Weilun , Li Li , Li Houqiang
Published: January 2025

Affordance-aware human insertion is a controllable human synthesis task aimed at seamlessly integrating a person into a scene while aligning human pose with contextual scene affordance and preserving human visual identity. Previous methods, typically reliant on a general framework of inpainting that injects all conditional information into a single branch, often struggle with the complexities of real-world contexts and the nuanced attributes of human figures. To this end, we present a novel
DIS
entangled dual-branch framework for
A
ffordance-aware human insertion task, termed as DISA, which focuses on both scene context comprehension and precise person attribute extraction. Specifically, our dual-branch design facilitates diffusion models to ensure disentangled and precise manipulations: one branch utilizes an additional network for deep scene context comprehension and control, while the other branch employs a parallel encoder to extract the feature of the reference person and injects this information through cross-attention mechanism. Furthermore, to comprehensively evaluate affordance-aware human insertion task, we introduce a new metric to assess the preservation of visual identity. We conduct a broad variety of evaluation experiments and validate the diversity and robustness of our method in different settings and downstream applications. Both qualitative and quantitative experimental analysis demonstrates that our approach outperforms previous methods in terms of image quality, pose accuracy and visual identity preservation.
Translating Natural Language questions into CIDOC-CRM SPARQL queries to access Cultural Heritage knowledge bases
Authors: Varagnolo Davide , Melo Dora , Pimenta Rodrigues Irene
Published: January 2025

To explore information on the Semantic Web, SPARQL queries or DL-queries are suitable tools. However, users interested in exploring the content of such knowledge bases often find it challenging to employ formal query languages, as this requires familiarity with the target domain’s representation model. To address these challenges, a Question-Answering System that automatically translates natural language questions into SPARQL queries, over the Smithsonian American Art Museum CIDOC-CRM representation is presented. The proposed approach uses an ontology, named Query Ontology, defined to represent the natural language concepts and relations specific to the question’s domain. This system’s architecture uses a traditional natural language processing symbolic approach, with a pipeline of modules for the syntactic, semantic, and pragmatic analysis. An evaluation of the proposed system is presented and shows very promising results.
Long-Term Risks of Living Kidney Donation: State of the Evidence and Strategies to Resolve Knowledge Gaps
Authors: Fleetwood Vidya A. , Lam Ngan N. , Lentine Krista L.
Published: January 2025

Living-donor kidney transplantation is the preferred treatment for kidney failure. In the United States, rates of living kidney donation have been stagnant, which is partly related to concerns over medical and financial risks. Recent research has better characterized the risks of living kidney donation, although the field is limited by a lack of robust registries. Available evidence supports small increases in the risks of end-stage kidney disease and hypertensive disorders of pregnancy in living donors. For most donors, the 15-year risk of kidney failure is less than 1%, but for certain populations this risk may be higher. New tools such as genetic kidney disease panels may assist with risk stratification. Living kidney donors generally have similar or improved psychosocial health following donation compared to prior to donation and nondonor experience. Postdonation care allows for preventative care measures to mitigate risk as well as ongoing surveillance of donor outcomes. Continuing efforts to capture and report outcomes of living donation are necessary to safely expand living donation worldwide.
New Oral Selective Estrogen Receptor Degraders Redefine Management of Estrogen Receptor–Positive Breast Cancer
Authors: Pathak Neha , Oliveira Mafalda
Published: January 2025

Oral selective estrogen receptor degraders (SERDs) are pure estrogen receptor antagonists that have the potential to overcome common resistance mechanisms to endocrine therapy in estrogen receptor–positive breast cancer. There are currently five oral SERDs in published and ongoing clinical trials—elacestrant, camizestrant, giredestrant, imlunestrant, and amcenestrant—with more in development. They offer a reasonably well-tolerated oral therapy option with low discontinuation rates in studies. This review summarizes the currently available literature on this new class of drugs.
Damage Identification in Large‐Scale Structures Using Time Series Analysis and Improved Sparse Regularization
Authors: Chen Huihui , Yuan Xiaojing
Published: January 2025

The academic community has paid much attention to the research topic of structural health monitoring (SHM) and damage identification for several decades, which flourishes the development of diverse damage identification approaches. The sensitivity‐based damage identification enjoys popularity due to its clear physical meaning and long development history. However, this type of method faces challenges of fewer applications on the large‐scale structure, slow convergence rate, and poor performance based on the sensitivity of the modal parameters. Aiming at the existing obstacles, this study enables to propose a novel method based on time series analysis model and improved sparse regularization technique for damage identification of the large‐scale structure. Firstly, the model reduction technique is introduced to condense the unconcerned degrees of freedom (DOFs) of the finite element model (FEM) of the large‐scale structure, and then the sensitivity of the autoregression (AR) coefficient with respect to the damage coefficient for the large‐scale structure has been deduced, which can reduce the uncertainty, which comes from modal identification, in modal parameter sensitivity. Furthermore, the mean‐value normalization strategy is incorporated into the sparse regularization solving process to improve the computational efficiency. The proposed method has been validated based on a numerical continuous rigid frame bridge and an experimental steel truss bridge. Compared to the moth‐flame optimization (MFO) algorithm and traditional regularization methods, for both noise‐free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. Furthermore, regarding the numerical example, the damage identification results show that the max identification errors of the proposed method are 6% among the three cases under noise‐free and noise‐polluted data, while the max identification errors for MFO and the regularization method are 15% and 10%, respectively. And for the experimental example, based on the limited sensors, the proposed method can accurately detect the damage location and quantify damage severity with a good accuracy, which means this method has good potential for actual engineering structure. To summarize, the findings highlight that the AR coefficient sensitivity combined with improve sparse regularization can effectively detect damage of large‐scale structure, providing a feasible way to expand the application of the sensitivity‐based method.
Utilizing Fuzzy Logic Control and Neural Networks Based on Artificial Intelligence Techniques to Improve Power Quality in Doubly Fed Induction Generator‐Based Wind Turbine System
Authors: Sayeh Karim Fathi , Tamalouzt Salah , Ziane Djamel , Bekhiti Abdellah , Belkhier Youcef
Published: January 2025

This article presents novel artificial intelligence (AI)‐based techniques for controlling wind energy conversion systems, specifically fuzzy logic control and neural networks, known as fuzzy hysteresis‐direct power control (FH‐DPC) and neural hysteresis‐DPC (NH‐DPC), respectively. The primary purpose is to overcome conventional DPC (C‐DPC) limitations in doubly fed induction generator wind turbines (WT‐DFIG), focusing on power quality improvement and enhanced system efficiency. The techniques aim to reduce power ripples and improve the quality of alternating current (AC) grid energy by improving current signal quality in all WTs’ operation modes with WT‐DFIG and all compensation power modes. The suggested techniques are thoroughly examined using the MATLAB/Simulink environment under various wind scenarios, demonstrating a reduction in active power ripples by over 70%, a reduction in reactive power ripples of around 77% on average, a decrease in generated current total harmonic distortions (THDs) by over 70% compared to C‐DPC. The performances of FH‐DPC and NH‐DPC are contrasted with C‐DPC and other previously suggested methods, and it is concluded that the proposed control approaches perform more effectively than them regarding ripples in local reactive compensation and generated active powers as well as THD currents, with FH‐DPC slightly outperforming NH‐DPC. The research indicates that AI can enhance the effectiveness and quality of power generated by wind‐power systems.
A Modified Approach to Distributed Bregman ADMM for a Class of Nonconvex Consensus Problems
Authors: Xue Zhonghui , Ma Qianfeng , Dang Yazheng
Published: January 2025

This article presents a refined iteration of the distributed Bregman alternating direction method of multipliers (ADMM) tailored to tackle nonconvex consensus issues, especially those with multiple blocks. The reliability of this novel approach is established through demonstrating its robust convergence under specific conditions. These conditions entail the requirement that the potential function satisfies the Kurdyka–Łojasiewicz property and that the penalty parameter exceeds a predefined constant. Initial numerical trials have shown encouraging outcomes, suggesting notable efficiency enhancements in the refined distributed Bregman ADMM algorithm.