bluuebunny/crossref_metadata_2025_split huggingface dataset:
Training in robotic-assisted surgery: a systematic review of training modalities and objective and subjective assessment methods
Authors: Rahimi A. Masie , Uluç Ezgi , Hardon Sem F. , Bonjer H. Jaap , van der Peet Donald L. , Daams Freek 
Published: May 2024

Abstract
Introduction
The variety of robotic surgery systems, training modalities, and assessment tools within robotic surgery training is extensive. This systematic review aimed to comprehensively overview different training modalities and assessment methods for teaching and assessing surgical skills in robotic surgery, with a specific focus on comparing objective and subjective assessment methods.

Methods
A systematic review was conducted following the PRISMA guidelines. The electronic databases Pubmed, EMBASE, and Cochrane were searched from inception until February 1, 2022. Included studies consisted of robotic-assisted surgery training (e.g., box training, virtual reality training, cadaver training and animal tissue training) with an assessment method (objective or subjective), such as assessment forms, virtual reality scores, peer-to-peer feedback or time recording.

Results
The search identified 1591 studies. After abstract screening and full-texts examination, 209 studies were identified that focused on robotic surgery training and included an assessment tool. The majority of the studies utilized the da Vinci Surgical System, with dry lab training being the most common approach, followed by the da Vinci Surgical Skills Simulator. The most frequently used assessment methods included simulator scoring system (e.g., dVSS score), and assessment forms (e.g., GEARS and OSATS).

Conclusion
This systematic review provides an overview of training modalities and assessment methods in robotic-assisted surgery. Dry lab training on the da Vinci Surgical System and training on the da Vinci Skills Simulator are the predominant approaches. However, focused training on tissue handling, manipulation, and force interaction is lacking, despite the absence of haptic feedback. Future research should focus on developing universal objective assessment and feedback methods to address these limitations as the field continues to evolve.
Operator-valued multiplier theorems for causal translation-invariant operators with applications to control theoretic input-output stability
Authors: Guiver Chris , Logemann Hartmut , Opmeer Mark R. 
Published: May 2024

AbstractWe prove an operator-valued Laplace multiplier theorem for causal translation-invariant linear operators which provides a characterization of continuity from $$H^\alpha ({\mathbb {R}},U)$$

H
α

(
R
,
U
)

to $$H^\beta ({\mathbb {R}},U)$$

H
β

(
R
,
U
)

(fractional U-valued Sobolev spaces, U a complex Hilbert space) in terms of a certain boundedness property of the transfer function (or symbol), an operator-valued holomorphic function on the right-half of the complex plane. We identify sufficient conditions under which this boundedness property is equivalent to a similar property of the boundary function of the transfer function. Under the assumption that U is separable, the Laplace multiplier theorem is used to derive a Fourier multiplier theorem. We provide an application to mathematical control theory, by developing a novel input-output stability framework for a large class of causal translation-invariant linear operators which refines existing input-output stability theories. Furthermore, we show how our work is linked to the theory of well-posed linear systems and to results on polynomial stability of operator semigroups. Several examples are discussed in some detail.
Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients
Authors: Booker James , Penn Jack , Noor Kawsar , Dobson Richard J. B. , Funnell Jonathan P. , Koh Chan Hee , Khan Danyal Z. , Newall Nicola , Rowland David , Sinha Siddharth , Williams Simon C. , Sayal Parag , Marcus Hani J. 
Published: May 2024

Abstract
Purpose
Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources.

Methods
Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated.

Results
Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92).

Conclusions
NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients’ understanding of surgical procedures.
Precision epileptology: digital biomarkers and cognitive proxies for personalized care
Authors: Jokeit Hennric , Kaufmann Elisabeth , Peltola Jukka , Imbach Lukas 
Published: May 2024

AbstractThe widespread accessibility of smart devices, even if not designated as medical devices, coupled with the critical need for ongoing monitoring of patients taking anti-seizure medication or undergoing new stimulation therapies highlights the urgent need for collaborative research across disciplines. These therapies, while beneficial, may cause sporadic or temporally dynamic side effects affecting cognition and behavior that cannot be adequately assessed by traditional neuropsychological testing methods at certain time points. This scenario highlights the importance of developing innovative approaches to fully monitor and understand these dynamic effects.
Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities
Authors: Ma Zhengjing , Mei Gang , Xu Nengxiong 
Published: May 2024

AbstractData mining and analysis are critical for preventing or mitigating natural hazards. However, data availability in natural hazard analysis is experiencing unprecedented challenges due to economic, technical, and environmental constraints. Recently, generative deep learning has become an increasingly attractive solution to these challenges, which can augment, impute, or synthesize data based on these learned complex, high-dimensional probability distributions of data. Over the last several years, much research has demonstrated the remarkable capabilities of generative deep learning for addressing data-related problems in natural hazards analysis. Data processed by deep generative models can be utilized to describe the evolution or occurrence of natural hazards and contribute to subsequent natural hazard modeling. Here we present a comprehensive review concerning generative deep learning for data generation in natural hazard analysis. (1) We summarized the limitations associated with data availability in natural hazards analysis and identified the fundamental motivations for employing generative deep learning as a critical response to these challenges. (2) We discuss several deep generative models that have been applied to overcome the problems caused by limited data availability in natural hazards analysis. (3) We analyze advances in utilizing generative deep learning for data generation in natural hazard analysis. (4) We discuss challenges associated with leveraging generative deep learning in natural hazard analysis. (5) We explore further opportunities for leveraging generative deep learning in natural hazard analysis. This comprehensive review provides a detailed roadmap for scholars interested in applying generative models for data generation in natural hazard analysis.
Clinical relevance of double heterozygosity revealed by next-generation sequencing of homologous recombination repair pathway genes in South African breast cancer patients
Authors: van der Merwe Nerina C. , Buccimazza Ines , Rossouw Bianca , Araujo Monica , Ntaita Kholiwe S. , Schoeman Mardelle , Vorster Karin , Napo Kgabo , Kotze Maritha J. , Oosthuizen Jaco 
Published: May 2024

Abstract
Purpose
Genetically predisposed breast cancer (BC) patients represent a minor but clinically meaningful subgroup of the disease, with 25% of all cases associated with actionable variants in BRCA1/2. Diagnostic implementation of next-generation sequencing (NGS) resulted in the rare identification of BC patients with double heterozygosity for deleterious variants in genes partaking in homologous recombination repair of DNA. As clinical heterogeneity poses challenges for genetic counseling, this study focused on the occurrence and clinical relevance of double heterozygous BC in South Africa.

Methods
DNA samples were diagnostically screened using the NGS-based Oncomine™ BRCA Expanded Research Assay. Data was generated on the Ion GeneStudio S5 system and analyzed using the Torrent Suite™ and reporter software. The clinical significance of the variants detected was determined using international variant classification guidelines and treatment implications.

Results
Six of 1600 BC patients (0.375%) tested were identified as being bi-allelic for two germline likely pathogenic or pathogenic variants. Most of the variants were present in BRCA1/2, including two founder-related small deletions in three cases, with family-specific variants detected in ATM, BARD1, FANCD2, NBN, and TP53. The scientific interpretation and clinical relevance were based on the clinical and tumor characteristics of each case.

Conclusion
This study increased current knowledge of the risk implications associated with the co-occurrence of more than one pathogenic variant in the BC susceptibility genes, confirmed to be a rare condition in South Africa. Further molecular pathology-based studies are warranted to determine whether clinical decision-making is affected by the detection of a second pathogenic variant in BRCA1/2 and TP53 carriers.
Photocatalytic bioheteronanostructures by integrating multicomponent Cu2O–Au nanoparticles into ZnO-coated butterfly wings colored by photonic nanoarchitectures
Authors: Piszter Gábor , Kertész Krisztián , Nagy Gergely , Kovács Dávid , Zámbó Dániel , Baji Zsófia , Pap József Sándor , Biró László Péter 
Published: May 2024

AbstractComplex biological photocatalytic heteronanostructures were produced by the integration of different multicomponent Au–Cu2O nanoparticles (NPs) into the blue-colored photonic nanoarchitectures occurring in the wings of male Polyommatus icarus butterflies. Both bare wings and wings conformally coated by 15-nm ZnO by atomic layer deposition were used as substrates. The NPs were characterized by UV–visible spectroscopy, focus stacking optical microscopy, and electron microscopy. After the deposition of the different NPs, the photocatalytic performance of the samples under visible light illumination was tested by the photodegradation of methyl orange in aqueous solution monitored continuously by an immersion probe. It was found that the components of the biological hetero-nanoarchitecture: ZnO-coated wings and wings without ZnO with deposited NPs exhibited poor catalytic performance. But the combined system: ZnO-coated wings with NPs deposited onto them exhibited sixfold to eightfold increase in their catalytic performance. This increase is attributed to the extension of the ZnO absorption into the visible range and to the formation of the heterojunction between the n-type ZnO and the p-type Cu2O NPs which resulted in the charge transfer of the photogenerated carriers. As the samples exhibited good stability under the continuous magnetic stirring, they can be used in flow-through systems suitable for wastewater remediation. The biological templates for the hetero-nanoarchitectures were produced by the controlled breeding of herbivorous insects, which does not raise any environmental concerns.
What’s behind pro-poor growth? An investigation of its drivers and dynamics
Authors: Klasen Stephan , Kneib Thomas , Lo Bue Maria C. , Prete Vincenzo 
Published: May 2024

AbstractStandard growth incidence curves describe how growth episodes impact on the overall income distribution. However, measuring the pro-poorness of the growth process is complex due to measurement errors, and to the effect of shocks that may hit the percentiles of the income distribution in different ways. Therefore, standard growth incidence curves may misrepresent the true growth process and its distributive impact. Relying on a non-anonymous approach, we compare actual growth episodes at each percentile of the initial personalized distribution with counterfactual mobility profiles which rule out the presence of shocks. We consider Indonesia in 2000–2007 and 2007–2014, two growth spells in which there was substantial, significant upward mobility among the initially poorer, a sizeable part of which cannot be explained by unobserved individual endowments or standard socio-economic attributes. The difference between actual and expected growth is related, in the early 2000s, to the economy-wide transformations, which characterized the early years of the post-Suharto era. However, in the more recent years, it can be largely attributed to individual recovery from previous negative losses and high vulnerability and reactivity to shocks for the poor.
Thermalization and Hydrodynamics in an Interacting Integrable System: The Case of Hard Rods
Authors: Singh Sahil Kumar , Dhar Abhishek , Spohn Herbert , Kundu Anupam 
Published: May 2024

AbstractWe consider the relaxation of an initial non-equilibrium state in a one-dimensional fluid of hard rods. Since it is an interacting integrable system, we expect it to reach the Generalized Gibbs Ensemble (GGE) at long times for generic initial conditions. Here we show that there exist initial conditions for which the system does not reach GGE even at very long times and in the thermodynamic limit. In particular, we consider an initial condition of uniformly distributed hard-rods in a box with the left half having particles with a singular velocity distribution (all moving with unit velocity) and the right half particles in thermal equilibrium. We find that the density profile for the singular component does not spread to the full extent of the box and keeps moving with a fixed effective speed at long times. We show that such density profiles can be well described by the solution of the Euler equations almost everywhere except at the location of the shocks, where we observe slight discrepancies due to dissipation arising from the initial fluctuations of the thermal background. To demonstrate this effect of dissipation analytically, we consider a second initial condition with a single particle at the origin with unit velocity in a thermal background. We find that the probability distribution of the position of the unit velocity quasi-particle has diffusive spreading which can be understood from the solution of the Navier–Stokes (NS) equation of the hard rods. Finally, we consider an initial condition with a spread in velocity distribution for which we show convergence to GGE. Our conclusions are based on molecular dynamics simulations supported by analytical arguments.
Generalized extended (2+1)-dimensional Kadomtsev-Petviashvili equation in fluid dynamics: analytical solutions, sensitivity and stability analysis
Authors: Demirbilek Ulviye , Nadeem Muhammad , Çelik Furkan Muzaffer , Bulut Hasan , Şenol Mehmet 
Published: May 2024

AbstractThis study discusses a new version of the $$(2+1)$$

(
2
+
1
)

-dimensional Kadomtsev-Petviashvili equation. This equation is used to model the behavior of nonlinear waves in various fields like ferromagnetic media, ion-acoustic waves in plasma physics, and fluid dynamics. It is instrumental in modeling surface and internal waves in straits or channels. The main goal of the research is to determine the exact solutions for this equation and analyze their physical characteristics. We obtain exact solutions using two improved techniques, namely the modified extended tanh-function and the modified generalized Kudryashov methods. These techniques investigate various exact solutions, such as exponential, rational, hyperbolic, and trigonometric. Besides, the sensitivity and the stability analysis of the model are presented. Additionally, three- and two-dimensional contour plots are created to present the physical behavior of the exact solutions.
Were Parents Synchronizing Their Home-Based Working Arrangements During the COVID-19 Pandemic?
Authors: Heydari Barardehi Ilyar , Kurowska Anna 
Published: May 2024

AbstractLittle research has been done on parents’ coordination of their work arrangements during the COVID-19 pandemic. Synchronized work arrangements (e.g., both parents work from home) could enable working parents to maximize their togetherness, whereas desynchronized working arrangements could enable them to address some pandemic-induced challenges, such as an increased need for child care and distance education. We draw upon the Familydemic Harmonized Dataset a cross-county study offering data on work and family outcomes collected in Canada, Germany, Italy, Poland, Sweden, and the US. Using a sample of dual-earner heterosexual parents, this study examines whether working parents harmonized their transitions between home-based work and the workplace during the COVID-19 pandemic and explores factors that potentially contributed to this coordination process. The findings reveal that working couples’ transitions to home-based work from the workplace were synchronized. Parents with similar educational attainment were more likely to experience such coordination than couples with different levels of education. Cohabiting couples in married/registered unions were more likely to transition to home-based work simultaneously than non-registered cohabiting couples. The duration of synchronized home-based work episodes decreased with the number of children, and the length of synchronized workplace attendance increased with the age of the children.
Effect factors for marine invasion impacts on biodiversity
Authors: Gjedde Philip , Carrer Fabio , Pettersen Johan Berg , Verones Francesca 
Published: May 2024

Abstract
Purpose
Marine vertebrate populations have halved in the past decades, and invasive species are a major driver for this loss. While many model the spread of invasive species, a model to assess impacts of marine invasions, after introduction, has hitherto been missing. We present the first regionalized effect factors for marine invasions. These factors gauge differences in biodiversity impacts after invasions, enabling life cycle impact assessments to highlight biodiversity impacts from invasive species.

Methods
Alien species are species that are introduced by humans to ecosystems where they are not native. We combine data from the IUCN red list and the MarINvaders database to identify the potentially disappeared fraction of native species within each marine coastal ecoregion after alien introduction. The effect factors indicate the biodiversity impact from invasions per alien introduction. However, the IUCN red list has a performance bias between taxonomic groups, and both the IUCN and the harmonized citizen science data from MarINvaders have a geographic observer’s bias. We address some of this bias by evaluating the number of threatened species per number of assessed species, as well as including machine-learning derived data for data deficient species.

Results and discussion
The resulting regional effect factors demonstrate high effects of invasions at high latitudes, which is in line with other findings. Our approach is founded on continuously growing citizen science data and so reflects the biases and uncertainties that follow with this uneven way of data sampling. On the other hand, the continuous data collection by citizen scientists will improve data coverage and thus improve the model. Vice versa, the model itself may be motivation for citizens scientists to collect more data.

Conclusion
The effect of marine invasions presented herein reflects current global information on the issue viewed in a perspective relevant for life cycle impact assessments. The developed effect factors can be used for further assessments that will aid decision-making for policies, industries, and consumers to work towards minimizing impacts of marine invasions and are developed to be compatible with different relevant fate factors.
Relative permeability estimation using mercury injection capillary pressure measurements based on deep learning approaches
Authors: Duan Ce , Kang Bo , Deng Rui , Zhang Liang , Wang Lian , Xu Bing , Zhao Xing , Qu Jianhua 
Published: May 2024

AbstractRelative permeability (RP) curves which provide fundamental insights into porous media flow behavior serve as critical parameters in reservoir engineering and numerical simulation studies. However, obtaining accurate RP curves remains a challenge due to expensive experimental costs, core contamination, measurement errors, and other factors. To address this issue, an innovative approach using deep learning strategy is proposed for the prediction of rock sample RP curves directly from mercury injection capillary pressure (MICP) measurements which include the mercury injection curve, mercury withdrawal curve, and pore size distribution. To capture the distinct characteristics of different rock samples' MICP curves effectively, the Gramian Angular Field (GAF) based graph transformation method is introduced for mapping the curves into richly informative image forms. Subsequently, these 2D images are combined into three-channel red, green, blue (RGB) images and fed into a Convolutional Long Short-Term Memory (ConvLSTM) model within our established self-supervised learning framework. Simultaneously the dependencies and evolutionary sequences among image samples are captured through the limited MICP-RP samples and self-supervised learning framework. After that, a highly generalized RP curve calculation proxy framework based on deep learning called RPCDL is constructed by the autonomously generated nearly infinite training samples. The remarkable performance of the proposed method is verified with the experimental data from rock samples in the X oilfield. When applied to 37 small-sample data spaces for the prediction of 10 test samples, the average relative error is 3.6%, which demonstrates the effectiveness of our approach in mapping MICP experimental results to corresponding RP curves. Moreover, the comparison study against traditional CNN and LSTM illustrated the great performance of the RPCDL method in the prediction of both So and Sw lines in oil–water RP curves. To this end, this method offers an intelligent and robust means for efficiently estimating RP curves in various reservoir engineering scenarios without costly experiments.