bluuebunny/crossref_metadata_2025_split huggingface dataset:
A medical image segmentation method for rectal tumors based on multi‐scale feature retention and multiple attention mechanisms
Authors: Zhao Jumin , Liu Linjun , Yang Xiaotang , Cui Yanfen , Li Dengao , Zhang Huiting , Zhang Kenan 
Published: April 2024

AbstractBackgroundWith the continuous development of deep learning algorithms in the field of medical images, models for medical image processing based on convolutional neural networks have made great progress. Since medical images of rectal tumors are characterized by specific morphological features and complex edges that differ from natural images, achieving good segmentation results often requires a higher level of enrichment through the utilization of semantic features.PurposeThe efficiency of feature extraction and utilization has been improved to some extent through enhanced hardware arithmetic and deeper networks in most models. However, problems still exist with detail loss and difficulty in feature extraction, arising from the extraction of high‐level semantic features in deep networks.MethodsIn this work, a novel medical image segmentation model has been proposed for Magnetic Resonance Imaging (MRI) image segmentation of rectal tumors. The model constructs a backbone architecture based on the idea of jump‐connected feature fusion and solves the problems of detail feature loss and low segmentation accuracy using three novel modules: Multi‐scale Feature Retention (MFR), Multi‐branch Cross‐channel Attention (MCA), and Coordinate Attention (CA).ResultsCompared with existing methods, our proposed model is able to segment the tumor region more effectively, achieving 97.4% and 94.9% in Dice and mIoU metrics, respectively, exhibiting excellent segmentation performance and computational speed.ConclusionsOur proposed model has improved the accuracy of both lesion region and tumor edge segmentation. In particular, the determination of the lesion region can help doctors identify the tumor location in clinical diagnosis, and the accurate segmentation of the tumor edge can assist doctors in judging the necessity and feasibility of surgery.
Estimate and compensate head motion in non‐contrast head CT scans using partial angle reconstruction and deep learning
Authors: Chen Zhennong , Li Quanzheng , Wu Dufan 
Published: April 2024

AbstractBackgroundPatient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored.PurposeTo develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion.MethodsHead motion is considered as a rigid transformation described by six time‐variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B‐spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion‐compensated image. The performance of our DL model is evaluated through both simulation and phantom studies.ResultsThe DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45 mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48 mm or degree). The resulting motion‐corrected image, , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back‐projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 33HU to 37 9HU, structural similarity index (SSIM) increases from 0.60 0.06 to 0.98 0.01) and the phantom study (image MAE drops from 117 17HU to 42 19HU, SSIM increases from 0.83 0.04 to 0.98 0.02).ConclusionsWe demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.
Effect of digital technology interventions for cognitive function improvement in mild cognitive impairment and dementia: A systematic review and meta‐analysis
Authors: Park Hyojin , Ha Juyoung 
Published: April 2024

AbstractThe development and commercialization of digital therapeutics are increasing. The aim of this study was to determine the effects of digital technology interventions on cognitive function, thereby providing evidence for the development and practical application of interventions to manage cognitive function in patients with mild cognitive impairment and dementia. We conducted a systematic review and meta‐analysis of randomized controlled trials according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analysis) guidelines. Randomized controlled trials on digital technology interventions published until April 2023 were searched in PubMed, Embase, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases without a period limit. Articles that identified the effects of digital technology interventions on cognitive function improvement in dementia and mild cognitive impairment were integrated and analyzed. RevMan software 5.4 was used for quality assessment and meta‐analysis. Twelve out of 708 studies were included in the review and meta‐analysis. Digital technology interventions had significant effects on global cognitive function (standardized mean difference [SMD] = 0.31; 95% confidence interval [CI] = 0.04–0.57; p = 0.02; I² = 60%). In addition, these interventions had significant effects on neuropsychological characteristics, including attention (SMD = 1.17; 95% CI = 0.36–1.97; p = 0.004; I² = 84%), visuospatial perception (SMD = 0.68; 95% CI = 0.19–1.17; p = 0.006; I² = 57%), and memory (SMD = 0.45; 95% CI = 0.19–0.71; p = 0.0007; I² = 22%). The results suggest that digital technology interventions help improve cognitive function in patients with dementia and mild cognitive impairment.
Relationship Between National Residency Matching Program (NRMP) Rank Order and Otolaryngology Residency Performance
Authors: Ezeh Uche C. , Svirsky Mario A. , April Max M. 
Published: April 2024

AbstractObjectiveThe process of resident recruitment is costly, and our surgical residency program expends significant time on the resident selection process while balancing general duties and responsibilities. The aim of our study was to explore the relationship between otolaryngology–head and surgery (OHNS) residents' National Residency Matching Program (NRMP) rank‐list position at our institution and their subsequent residency performance.Study DesignRetrospective cohort study.SettingSingle site institution.MethodsWe retrospectively reviewed 7 consecutive resident classes (2011‐2017) at a single tertiary OHNS residency program. We reviewed each resident's absolute rank order in the NRMP matches. Measures of residency performance included overall faculty evaluation during postgraduate year 5 (PGY5), annual in‐service examination scores (scaled score), and the number of manuscripts published in peer‐reviewed journals. Correlations between NRMP rank order and subsequent residency performance were assessed using Spearman's rho correlation coefficients (ρ).ResultsTwenty‐eight residents entered residency training between 2011 and 2017. The average rank position of the trainees during this study was 9.7 (range: 1‐22). We found no significant correlation between rank order and faculty evaluation during PGY5 (ρ = 0.097, P = .625) or number of publications (ρ = −0.256, P = .189). Additionally, when assessing the association between rank order and annual Otolaryngology Training Examination‐scaled scores, no statistically significant relationship was found between the 2 (P > .05).ConclusionOur results showed that there were no significant correlations between OHNS rank order and various measures of success in residency training, which aligns with existing literature. Further investigation of this relationship should be conducted to ensure the applicability of our findings.
Synthesis and characterization of cellulose nanofibers from waste paper and their utilization in wood adhesion
Authors: Joshi Garima , Chauhan Shakti Singh 
Published: April 2024

AbstractNanocellulose extraction from lignocellulosic materials is a highly chemical and energy‐intensive process as it requires the removal of lignin and hemicellulose. Writing paper is one of the processed materials that could be used as a raw material for the extraction of nanocellulose. In this work, cellulose nanofibers (CNFs) were synthesized from paper waste using TEMPO‐oxidation followed by high‐shear microfluidization. Scanning electron microscopy and atomic force microscopy confirmed the diameter of fibers in the nano‐range and a consistent zeta potential confirmed the stability of CNF suspension in water over a long time frame. Characterization of the CNFs using Fourier transform infrared spectroscopy indicated the presence of a carbonyl group due to the oxidation process. Thermogravimetric analysis and x‐ray diffraction revealed lower thermal stability and reduction in the crystallinity index of CNFs, as compared to pulp fibers. The obtained CNFs were used successfully as the sole binding agent in the preparation of fiberboards and also utilized as a reinforcing agent for polyvinyl acetate (PVAc) adhesive in the preparation of laminated veneer lumber (LVLs). The addition of CNFs in PVAc improved the glue shear strength indicating superior bonding characteristics and also increased the water resistance of the LVLs.Highlights
This work focused on cellulose nanofibers (CNFs) extraction from waste paper
Obtained CNFs form stable water suspension and have a diameter of less than 15 nm
Thermal stability and crystallinity index reduced after conversion to nanofibers
CNFs form a complex network and act as a sole binder to make fiberboards
CNFs were utilized as a reinforcing agent for PVAc in preparation of LVLs
Dynamical study of three African Easterly Waves in September 2021
Authors: Jonville Tanguy , Flamant Cyrille , Lavaysse Christophe 
Published: April 2024

AbstractThree convectively active African easterly waves (AEWs) that propagated south of the African easterly jet were observed over the northeast Atlantic Ocean in September 2021. Their evolution is studied using a suite of theoretical frameworks, as well as the European Centre for Medium‐range Weather Forecast reanalyses and satellite‐derived brightness temperature observations. The environment of these AEWs was sampled during the Cloud–Atmospheric Dynamics–Dust Interactions in West Africa campaign near Cape Verde with the goal to assess their potential for developing into tropical cyclones. We highlight the processes that inhibited the development of the first AEW (which evolved into tropical disturbance Pierre‐Henri) and that played a role in the development of the later two into tropical storms Rose and Peter on September 19, 2021. The three AEWs developed a so‐called “marsupial protective” pouch. For Peter and Rose, the pouch was associated with a vertically aligned vortex at low levels and efficiently protected the convective systems inside from dry and dusty air intrusion. The development of this low‐level vortex is associated with an interaction with the monsoon trough for Rose and with a vorticity center associated with a wave propagating north of the African easterly jet (AEJ) in the case of Peter. The presence of a dust flux toward the convective core near the surface is highlighted for Rose and Peter in spite of the presence of the protective marsupial pouch. On the other hand, Pierre‐Henri interacted positively with both the monsoon trough and an AEW north of the AEJ but failed to develop into a tropical cyclone. The wave north of the AEJ brought Saharan air layer air masses inside the pouch that led to a drying of the circulation that may explain the decrease in convective activity.
Iterative distributed model predictive control for nonlinear systems with coupled non‐convex constraints and costs
Authors: Wu Jinxian , Dai Li , Xia Yuanqing 
Published: April 2024

AbstractThis paper proposes a distributed model predictive control (DMPC) algorithm for dynamic decoupled discrete‐time nonlinear systems subject to nonlinear (maybe non‐convex) coupled constraints and costs. Solving the resulting nonlinear optimal control problem (OCP) using a DMPC algorithm that is fully distributed, termination‐flexible, and recursively feasible for nonlinear systems with coupled constraints and costs remains an open problem. To address this, we propose a fully distributed and globally convergence‐guaranteed framework called inexact distributed sequential quadratic programming (IDSQP) for solving the OCP at each time step. Specifically, the proposed IDSQP framework has the following advantages: (i) it uses a distributed dual fast gradient approach for solving inner quadratic programming problems, enabling fully distributed execution; (ii) it can handle the adverse effects of inexact (insufficient) calculation of each internal quadratic programming problem caused by early termination of iterations, thereby saving computational time; and (iii) it employs distributed globalization techniques to eliminate the need for an initial guess of the solution. Under reasonable assumptions, the proposed DMPC algorithm ensures the recursive feasibility and stability of the entire closed‐loop system. We conduct simulation experiments on multi‐agent formation control with non‐convex collision avoidance constraints and compare the results against several benchmarks to verify the performance of the proposed DMPC method.
Not the only novice in the room: Partnerships and belongingness in a research immersion program
Authors: Ayers Katherine Ann , Pennella Robyn Ann , Mohorn‐Mintah Olayinka , Jasper Summer Jane , Nordstrom Susan Naomi 
Published: April 2024

AbstractLack of access to STEMM mentors has been identified as a critical barrier to biomedical research careers, leading to a lack of diversity in this field. To address such a barrier, the National Institutes of Health invested funds to support institutions in developing research immersion programs to provide “underrepresented” students with mentored research experiences. While providing access and opportunity for research experiences is an important equity endeavor, a focus solely on broadening participation neglects the role of institutions in perpetuating hegemonic views of science. Institutions often fail to recognize how entanglements of affect and emotion shape youth experiences in these programs and work to (de)legitimize their sense of belonging in science and perpetuate the notion of science as for an exclusive few. In this paper, we describe findings from a project aimed at understanding the entanglement of emotion and affect in a research immersion program and how these entanglements shaped participants' sense of belongingness in the program and research more broadly. Drawing on a poststructural feminist framework, we come to understand how individual histories and emotional experiences with racial and gender stereotypes work at the meta‐affective level to contract feelings of belongingness in science for students.
Self‐Powered Water Splitting of Ni3FeN@Fe24N10 Bifunctional Catalyst Improved Catalytic Activity and Durability by Forming Fe24N10 on Catalyst Surface via the Kirkendall Effect
Authors: Jeong Dong In , Kang Donghyeon , Kang Bong Kyun , Lee Ui Young , Suh In‐Yong , Kim Yeseul , Weon Byung Mook , Kim Sang‐Woo , Yoon Dae Ho 
Published: April 2024

AbstractHighly efficient water splitting electrocatalyst for producing hydrogen as a renewable energy source offers potential to achieve net‐zero. However, it has significant challenges in using transition metal electrocatalysts as alternatives to noble metals due to their low efficiency and durability, furthermore, the reliance on electricity generation for electrocatalysts from fossil fuels leads to unavoidable carbon emissions. Here, a highly efficient self‐powered water splitting system integrated is designed with triboelectric nanogenerator (TENG) and Ni3FeN@Fe24N10 catalyst with improved catalytic activity and durability. First, the durability of the Ni3FeN catalyst is improved by forming N, P carbon shell using melamine, polyetherimide, and phytic acid. The catalyst activity is improved by generating Fe24N10 in the carbon shell through the Kirkendall effect. The synthesized Ni3FeN@Fe24N10 catalyst exhibited excellent bifunctional catalytic activity (ηOER = 261.8 mV and ηHER = 151.8 mV) and remarkable stability (91.7% in OER and 90.5% in HER) in 1 m KOH. Furthermore, to achieve ecofriendly electricity generation, a rotation‐mode TENG that sustainably generate high‐performance is realized using butylated melamine formaldehyde. As a result, H2 is successfully generated using the integrated system composed of the designed TENG and catalyst. The finding provides a promising approach for energy generation to achieve net‐zero.
Pushing the Limits of Heat Conduction in Covalent Organic Frameworks Through High‐Throughput Screening of Their Thermal Conductivity
Authors: Thakur Sandip , Giri Ashutosh 
Published: April 2024

AbstractTailor‐made materials featuring large tunability in their thermal transport properties are highly sought‐after for diverse applications. However, achieving `user‐defined’ thermal transport in a single class of material system with tunability across a wide range of thermal conductivity values requires a thorough understanding of the structure‐property relationships, which has proven to be challenging. Herein, large‐scale computational screening of covalent organic frameworks (COFs) for thermal conductivity is performed, providing a comprehensive understanding of their structure‐property relationships by leveraging systematic atomistic simulations of 10,750 COFs with 651 distinct organic linkers. Through the data‐driven approach, it is shown that by strategic modulation of their chemical and structural features, the thermal conductivity can be tuned from ultralow (≈0.02 W m−1 K−1) to exceptionally high (≈50 W m−1 K−1) values. It is revealed that achieving high thermal conductivity in COFs requires their assembly through carbon–carbon linkages with densities greater than 500 kg m−3, nominal void fractions (in the range of ≈0.6–0.9) and highly aligned polymeric chains along the heat flow direction. Following these criteria, it is shown that these flexible polymeric materials can possess exceptionally high thermal conductivities, on par with several fully dense inorganic materials. As such, the work reveals that COFs mark a new regime of materials design that combines high thermal conductivities with low densities.
Product and service quality risks: A survey about evolution and application in different business domains to facilitate quality engineering
Authors: Poth Alexander 
Published: April 2024

AbstractProducts and services are developed to realize chances on the market. Often, development teams are focusing primarily on the potential chances rather than on the corresponding risks of new products and services. However, it is possible to integrate the product and service quality risks early into the development procedures for a balanced and holistic view. The integration enables systematic mitigation and quality assurance actions to handle the risks. The article analyzes the literature related to quality risk approaches, identifies mature and generally applicable product and service risk approaches, and gives selected approach examples about its methodical evolution and how it can be integrated into the development procedures including soft‐skills development such as employee awareness and systematical training within organizations. A case study in the Group IT of the Volkswagen AG is discussed.
Assessing the viability of virtual structured democratic dialogue
Authors: Laouris Yiannis , Metcalf Gary 
Published: April 2024

AbstractThe paper examines the impact of virtual rendering of the Structured Democratic Dialogue (SDD) process. The authors discuss the implications of implementing each process stage virtually and suggest necessary adaptations to the relevant practices. They provide recommendations for improving the overall quality of online sessions and propose qualitative and quantitative indicators that can be used to compare and evaluate face‐to‐face and hybrid or virtual implementations. Pertinent indicators include financial cost, completion time, cost of required resources, number of factors generated, Spreadthink, extent, depth and quality of discussions during clustering and mapping stages, number of dimensions identified, number of factors included in the influence map, number of connections in the influence tree, situational complexity indices and degree of agreement on how to move forward. The authors propose that further research and additional case studies can help systems scientists and practitioners converge on reliable and validated criteria and metrics to allow solid assessment of possible quality loss associated with rendering each stage of the SDD process virtual.
Answering Temporal Conjunctive Queries over Description Logic Ontologies for Situation Recognition in Complex Operational Domains
Authors: Westhofen Lukas , Neurohr Christian , Jung Jean Christoph , Neider Daniel 
Published: April 2024

AbstractFor developing safe automated systems, recognizing safety-critical situations in data from their complex operational domain is imperative. This capability is, for example, essential when evaluating the system’s conformance to specified requirements in test run data. The requirements involve a temporal dimension, as the system operates over time. Moreover, the generated data are usually relational and require additional background knowledge about the domain for correctly recognizing the situation. This fact makes propositional temporal logics, an established tool, unsuitable for the task. We address this issue by developing a tailored temporal logic to query for situations in relational data over complex domains. Our language combines mission-time linear temporal logic with conjunctive queries to access time-stamped data with background knowledge formulated in an expressive description logic. Currently, however, no tools exist for answering queries in such settings. We hence also contribute an implementation in the logic reasoner Openllet, leveraging the efficacy of well-established conjunctive query answering. Moreover, we present a benchmark generator in the setting of automated driving and demonstrate that our tool performs well when tasked with recognizing safety-critical situations in road traffic.
SootUp: A Redesign of the Soot Static Analysis Framework
Authors: Karakaya Kadiray , Schott Stefan , Klauke Jonas , Bodden Eric , Schmidt Markus , Luo Linghui , He Dongjie 
Published: April 2024

AbstractSince its inception two decades ago, Soot has become one of the most widely used open-source static analysis frameworks. Over time it has been extended with the contributions of countless researchers. Yet, at the same time, the requirements for Soot have changed over the years and become increasingly at odds with some of the major design decisions that underlie it. In this work, we thus present SootUp, a complete reimplementation of Soot that seeks to fulfill these requirements with a novel design, while at the same time keeping elements that Soot users have grown accustomed to.