bluuebunny/crossref_metadata_2025_split huggingface dataset:
Finite Element Analysis of Percutaneous Anterograde and Retrograde Screws in the Treatment of Superior Rami Pubis Fracture
Authors: Lu Hui , Han Shuo , Lian Zheng , Liu Rong , Ma Xuexiao 
Published: April 2024

Abstract
Purpose
This study aims to evaluate the biomechanical characteristics of percutaneous antegrade and retrograde screws in treating superior pubic rami fractures in zone II, employing finite element analysis. It provides a theoretical basis for the treatment of the superior rami of pubis zone II fracture.

Methods
The software of Mimics and 3-Matic were utilized for reconstructing a fracture model of the superior pubic rami in zone II. Percutaneous screw implantation was simulated for treatment. Material properties were assigned using Mimics, and Ansys software was employed to simulate forces on the S1 vertebral endplate under a 500 N load in a standing posture. Mechanical stability was assessed by comparing these simulations.

Results
(1) Displacement and stress variations in the pubic bone and screws were consistent across the model and on the fracture site, whether the fracture was in zone I or zone II. However, in zone III, the displacement and stress experienced by the screws were markedly higher than those in zones I and II. (2) When employing an antegrade screw to simulate treatment of a superior pubic rami fracture in zone II, the maximum displacement and stress recorded were 0.88 mm and 15.53 MPa, respectively. Conversely, the use of a retrograde screw for treating a superior pubic rami fracture yielded a maximum displacement of 0.59 mm and stress of 5.51 MPa.

Conclusion
The biomechanical analysis suggests that for zone II superior pubic rami fractures, where the fracture line is no more than 70 mm from the insertion point (encompassing zones I and II fractures), the use of retrograde screw implantation is preferable.
Unraveling the Sequences of Risk Factors Underlying the Development of Criminal Behavior
Authors: Basto-Pereira Miguel , Farrington David P. , Maciel Laura 
Published: April 2024

AbstractThis work aims to investigate the role of sequences of risk factors from childhood to young adulthood in predicting subsequent criminal convictions. This study uses the Cambridge Study in Delinquent Development (CSDD) dataset, a prospective longitudinal research study that followed 411 males from South London from the age of 8 to 61 years. Temporal sequences of risk factors at ages 8–10, 12–14, and 16–18 were analyzed as predictors of subsequent criminal convictions up to the age of 61. Risk factors related to poverty, parenting problems, and children’s risk-taking predisposition at ages 8–10 emerged as prevalent starting points for the most highly predictive developmental sequences leading to convictions. The risk of a criminal conviction significantly increased if these risk factors were followed by low IQ scores or association with delinquent friends at ages 12–14, and by school and professional problems or drug addiction during late adolescence (ages 16–18). At each developmental stage, specific risk factors intricately combine to form chains of risk during development, subsequently predicting criminal convictions. A trajectory-of-risk-need-responsivity approach that identifies and breaks chains of risk factors that generate and enhance favorable conditions for criminal convictions is discussed.
Development and characterization of 3D-printed electroconductive pHEMA-co-MAA NP-laden hydrogels for tissue engineering
Authors: De Nitto Sara , Serafin Aleksandra , Karadimou Alexandra , Schmalenberger Achim , Mulvihill John J. E. , Collins Maurice N. 
Published: April 2024

AbstractTissue engineering (TE) continues to be widely explored as a potential solution to meet critical clinical needs for diseased tissue replacement and tissue regeneration. In this study, we developed a poly(2-hydroxyethyl methacrylate-co-methacrylic acid) (pHEMA-co-MAA) based hydrogel loaded with newly synthesized conductive poly(3,4-ethylene-dioxythiophene) (PEDOT) and polypyrrole (PPy) nanoparticles (NPs), and subsequently processed these hydrogels into tissue engineered constructs via three-dimensional (3D) printing. The presence of the NPs was critical as they altered the rheological properties during printing. However, all samples exhibited suitable shear thinning properties, allowing for the development of an optimized processing window for 3D printing. Samples were 3D printed into pre-determined disk-shaped configurations of 2 and 10 mm in height and diameter, respectively. We observed that the NPs disrupted the gel crosslinking efficiencies, leading to shorter degradation times and compressive mechanical properties ranging between 450 and 550 kPa. The conductivity of the printed hydrogels increased along with the NP concentration to (5.10±0.37)×10−7 S/cm. In vitro studies with cortical astrocyte cell cultures demonstrated that exposure to the pHEMA-co-MAA NP hydrogels yielded high cellular viability and proliferation rates. Finally, hydrogel antimicrobial studies with staphylococcus epidermidis bacteria revealed that the developed hydrogels affected bacterial growth. Taken together, these materials show promise for various TE strategies.
Graphic abstract
Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
Authors: Nagesh O. Sri , Budaraju Raja Rao , Kulkarni Shriram S. , Vinay M. , Ajibade Samuel-Soma M. , Chopra Meenu , Jawarneh Malik , Kaliyaperumal Karthikeyan 
Published: April 2024

AbstractDue to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.
Algorithmic Thinking for the Legal Writing: The Case of Italian Election Law
Authors: Crafa Silvia 
Published: April 2024

AbstractWe examine the Italian election law as a case study to illustrate how the algorithmic thinking can productively interoperate with the legal language to increase the transparency of the legal text, and to enable better reasoning about the procedural content of the law. The effort to rephrase the text of the law in algorithmic terms revealed that the election procedure is under-specified, so that the allocation of seats between constituencies may differ depending on the actual sequence of ballot operations performed by the scrutineers. This may lead to legal uncertainty in a critical section of the election law that one would expect to be fully determined. We then discuss the difference between algorithm and software in the legal context, illustrating how the algorithmic language acts as an interface between the textual description of a legal procedure and its mathematical or computational formalization. Hence we put forward the concept of algorithmic normativity, that is the power of the algorithmic language (different from software’s code) to legally express procedures at an appropriate abstraction level, balancing transparency with scientific precision.
Tracking student progress through graduate programs
Authors: Young Michael E. , Miller Megan , Urban Christopher , Petrescu Claudia 
Published: April 2024

AbstractHigher education is awash with data that, when refined, facilitates data-informed decisions. Such decision-making is much more prevalent in support of undergraduate education given the much larger number of undergraduates pursuing higher education in contrast to the much smaller proportion of graduate students. A simple extension of current undergraduate-focused tools to the population of graduate students risks ignoring large differences in the students, processes, and policies that are unique to graduate education. Graduate education is more decentralized, less grade-focused and more milestone-focused (e.g., passing preliminary exams, defending a thesis), graduate admissions is driven by department-level processes and criteria, expected products vary across programs (e.g., performances, journal articles, books), and curricular specialization is the norm. Consequently, local contextual variables predominate. This article describes the development of a milestones dashboard to support programs in their pursuit of graduate education excellence by creating data transparency at the student level, visualizing student progress through milestones, allowing benchmarking against other programs at the university, and empowering college and university administrators to identify trends and to ask questions when unusual data arise.
A landscape assessment of CTSA evaluators and their work in the CTSA consortium, 2021 survey findings
Authors: Hoyo Verónica , Nehl Eric , Dozier Ann , Harvey Jillian , Kane Cathleen , Perry Anna , Samuels Elias , Schmidt Susanne , Hunt Joe 
Published: April 2024

Abstract
This article presents a landscape assessment of the findings from the 2021 Clinical and Translational Science Award (CTSA) Evaluators Survey. This survey was the most recent iteration of a well established, national, peer-led systematic snapshot of the CTSA evaluators, their skillsets, listed evaluation resources, preferred methods, and identified best practices. Three questions guided our study: who are the CTSA evaluators, what competencies do they share and how is their work used within hubs. We describe our survey process (logistics of development, deployment, and differences in historical context with prior instruments); and present its main findings. We provide specific recommendations for evaluation practice in two main categories (National vs Group-level) including, among others, the need for a national, strategic plan for evaluation as well as enhanced mentoring and training of the next generation of evaluators. Although based on the challenges and opportunities currently within the CTSA Consortium, takeaways from this study constitute important lessons with potential for application in other large evaluation consortia. To our knowledge, this is the first time 2021 survey findings are disseminated widely, to increase transparency of the CTSA evaluators' work and to motivate conversations within hub and beyond, as to how best to leverage existent evaluative capacity.
Roles of TRP and PIEZO receptors in autoimmune diseases
Authors: Yang Baoqi , Ma Dan , Zhu Xueqing , Wu Zewen , An Qi , Zhao Jingwen , Gao Xinnan , Zhang Liyun 
Published: April 2024

Abstract
Autoimmune diseases are pathological autoimmune reactions in the body caused by various factors, which can lead to tissue damage and organ dysfunction. They can be divided into organ-specific and systemic autoimmune diseases. These diseases usually involve various body systems, including the blood, muscles, bones, joints and soft tissues. The transient receptor potential (TRP) and PIEZO receptors, which resulted in David Julius and Ardem Patapoutian winning the Nobel Prize in Physiology or Medicine in 2021, attracted people's attention. Most current studies on TRP and PIEZO receptors in autoimmune diseases have been carried out on animal model, only few clinical studies have been conducted. Therefore, this study aimed to review existing studies on TRP and PIEZO to understand the roles of these receptors in autoimmune diseases, which may help elucidate novel treatment strategies.
Implementation of point-of-care molecular testing for respiratory viruses in congregate living settings
Authors: Tan Charlie , Chan Christina K. , Ofner Marianna , O’Brien Jaclyn , Thomas Neethu R. , Callahan James , Pascual Brigitte , Palmer Shawn J. , Serapion Victoria , Fabro Hannah , Kozak Robert A. , Candon Heather , Chan Adrienne K. , Powis Jeff E. , Leis Jerome A. 
Published: April 2024

AbstractObjective:To implement and evaluate a point-of-care (POC) molecular testing platform for respiratory viruses in congregate living settings (CLS).Design:Prospective quality improvement study.Setting:Seven CLS, including three nursing homes and four independent-living facilities.Participants:Residents of CLS.Methods:A POC platform for COVID-19, influenza A and B, and respiratory syncytial virus was implemented at participating CLS from December 1, 2022 to April 15, 2023. Residents with respiratory symptoms underwent paired testing, with respiratory specimens tested first with the POC platform and then delivered to an off-site laboratory for multiplex respiratory virus panel (MRVP) polymerase chain reaction (PCR) as per standard protocol. Turn-around time and diagnostic accuracy of the POC platform were compared against MRVP PCR. In an exploratory analysis, time to outbreak declaration among participating CLS was compared against a convenience sample of 19 CLS that did not use the POC platform.Results:A total of 290 specimens that underwent paired testing were included. Turn-around time to result was significantly shorter with the POC platform compared to MRVP PCR, with median difference of 36.2 hours (interquartile range 21.8–46.4 hours). The POC platform had excellent diagnostic accuracy compared to MRVP PCR, with area under the curve statistic of .96. Time to outbreak declaration was shorter in CLS that used the POC platform compared to CLS that did not.Conclusion:Rapid POC testing platforms for respiratory viruses can be implemented in CLS, with high diagnostic accuracy, expedited turn-around times, and shorter time to outbreak declaration.
A case series of interventions and outcomes in patients with laryngeal squamous cell cancer
Authors: Townsley Richard , Tan Jiak Ying , Edy Elbert , Lim Alison , McMahon Jeremy 
Published: April 2024

AbstractObjectiveThe larynx is the second most prevalent subsite for head and neck cancer. Over half of head and neck cancer patients present with advanced disease. We report our regional practices for palliative intent laryngeal squamous cell cancer (SCC).MethodsRetrospective analysis of patients with laryngeal SCC treated with palliative intent, discussed at the regional head and neck multidisciplinary team meeting from July 2010 to June 2016.ResultsA total of 65 patients were included, of whom 45 per cent had potentially curable disease but were not fit for curative treatment. Nine patients (14 per cent) underwent tracheostomy, with mean survival and hospital stay of 278 and 48 days, respectively. Four patients (6 per cent) underwent debulking surgery with mean survival and hospital stay of 214 and 1 days, respectively.ConclusionAll palliative treatment measures offered to patients can have an impact on survival and quality of life. Patients should be at the centre of the decision-making process and counselled on the potential impact of interventions.
Modelling of the fluid flow in a thin domain with injection through permeable boundary
Authors: Marušić-Paloka Eduard , Pažanin Igor 
Published: April 2024

AbstractIn this paper, we derive the effective model describing a thin-domain flow with permeable boundary through which the fluid is injected into the domain. We start with incompressible Stokes system and perform the rigorous asymptotic analysis. Choosing the appropriate scaling for the injection leads to a compressible effective model. In this paper, we derive the effective model describing a thin-domain flow with permeable boundary through which the fluid is injected into the domain. We start with incompressible Stokes system and perform the rigorous asymptotic analysis. Choosing the appropriate scaling for the injection leads to a compressible effective model.
Cross-linguistic effects of form overlap in aural recognition of Spanish–English cognates
Authors: Garrido-Pozú Juan J. 
Published: April 2024

Abstract
This study investigated the effect of cross-linguistic overlap in L1 and L2 auditory recognition of Spanish–English cognates. The study examined the correlation between objective and subjective measures of overlap and analyzed how these measures predict patterns in auditory recognition. 62 Spanish-speaking learners of English and 63 English-speaking learners of Spanish completed two auditory lexical decision tasks in Spanish and English and a rating task, where they rated the perceived phonological similarity of cognates. The results revealed moderate correlations between subjective and objective measures of overlap. While orthographic overlap had no effect, increased phonological overlap facilitated recognition in L1 and L2 Spanish and English and had larger effects in L2 recognition. Perceived similarity was the best predictor among the measures of overlap. The findings support models suggesting that cross-linguistic co-activation is facilitated by increased form similarity and studies reporting modality dependent effects of cross-linguistic form overlap in lexical recognition.
RF and microwave metrology for quantum computing – recent developments at the UK’s National Physical Laboratory
Authors: Stanley Manoj , Shang Xiaobang , Celep Murat , Salter Martin , de Graaf Sebastian , Lindstrom Tobias , Shin Sang-Hee , Skinner James , Singh Dilbagh , Stokes Daniel , Acharya Manognya , Ridler Nick 
Published: April 2024

AbstractDevelopment of large-scale quantum computing systems will require radio frequency (RF) and microwave technologies operating reliably at cryogenic temperatures down to tens of milli-Kelvin (mK). The quantum bits in the most promising quantum computing technologies such as the superconducting quantum computing are designed using principles of microwave engineering and operated using microwave signals. The control, readout, and coupling of qubits are implemented using a network of microwave components operating at various temperature stages. To ensure reliable operation of quantum computing systems, it is critical to ensure optimal performance of these microwave components and qubits at their respective operating temperatures, which can be as low as mK temperatures. It is, therefore, critical to understand the microwave characteristics of waveforms, components, circuits, networks, and systems at cryogenic temperatures. The UK’s National Physical Laboratory (NPL) is focussed on developing new microwave measurement capabilities through the UK’s National Quantum Technologies Programme to address various microwave test and measurement challenges in quantum computing. This includes the development of various measurement capabilities to characterize the microwave performance of quantum and microwave devices and substrate materials at cryogenic temperatures. This paper summarizes the roadmap of activities at NPL to address these microwave metrology challenges in quantum computing.
Water‐Wave‐Induced OBS Noise: Theories, Observations, and Potential Applications
Authors: Zhang Chun , An Chao 
Published: April 2024

AbstractThe horizontal records of ocean‐bottom seismometers (OBS) are usually highly noisy, generally due to ocean‐bottom currents tilting the instrument, which greatly limits their practical usage in ocean‐bottom seismology. In shallow water, water waves with energy concentration around 0.07 Hz induce additional noise on OBSs. Such noise is not well understood. In this article, we propose a noise model to explain the horizontal noise around 0.07 Hz. The noise model consists of three types of noise, that is, water‐wave‐induced noise, current noise with a relatively constant orientation, and background random noise. The wave‐induced horizontal acceleration is theoretically shown to be proportional to the time derivative of ocean‐bottom pressure. We validate the noise model and related theories using realistic observations. Results are potentially applicable to determine the propagation direction of water waves nearshore, and also provide constraints on the underlying Earth structure. The results can also be applied to the removal of wave‐induced noise, achieving a typical maximum improvement in the signal‐to‐noise ratio of 10–20 dB for time periods with strong wave noise.
Evaluating Noah‐MP Simulated Runoff and Snowpack in Heavily Burned Pacific‐Northwest Snow‐Dominated Catchments
Authors: Abolafia‐Rosenzweig Ronnie , He Cenlin , Chen Fei , Zhang Yongxin , Dugger Aubrey , Livneh Ben , Gochis David 
Published: April 2024

AbstractTerrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances.