bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Distributional Impact of Fatigue on Performance
Authors: Bavafa Hessam , Jónasson Jónas Oddur 
Published: July 2023

Little is known about how people-centric factors affect the shape of service time distributions despite distributional statistics (variance or quantiles) being key drivers of system performance in many service industries. We investigate the impact of one people-centric factor—worker fatigue—on the average, variance, and quantiles of service times in paramedic operations. Our analysis uses data on the performance of 368,634 paramedic teams in the London Ambulance Service over 10 years. We measure fatigue by the number of prior jobs a paramedic crew has completed during a shift and estimate its impact on the time it takes the crew to respond to incidents and bring patients to hospitals. Using a recentered influence function regression approach with multiple fixed effects, we find that the average time to hospital increases by 5% throughout the course of an average shift. In addition, the workers become less consistent with fatigue; service time variance increases by 39% during a normal shift. Furthermore, we find that in addition to an upward shift in mean service times, both the upper and lower tails of the distribution have more weight for fatigued paramedics. These effects are driven mostly by the performance of paramedics at the scene, rather than their driving to or from the incident. The distributional effects of fatigue are only slightly mitigated by increased experience or reduced system workload. Our work demonstrates that the impact of people-centric factors can be highly nonuniform across the service time distribution. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: The authors are grateful for financial research support from the Wisconsin Alumni Research Foundation at the University of Wisconsin–Madison. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4855 .
Staging Dystopian Communities: Reimagining Shakespeare in Selected English Plays
Authors: Lachman Michał 
Published: July 2023

Among the countless afterlives of William Shakespeare’s playwriting there is a strong presence of his visions of state and political powers. In universal, philosophical ways Shakespeare was addressing issues concerning the state power, social organization, hierarchy, and rank in what inevitably were the origins of modern, capitalistic societies. Therefore, many of his powerful images resonate today in the works of contemporary writers who intend to compose stories of utopian or dystopian character which diagnose the condition of modern society. This article aims to present three plays by post-war English dramatists (Edward Bond’s Bingo, Frank McGuinness’s Mutabilitie, and David Greig’s Dunsinane) which reuse Shakespearian themes, motifs, or characters to build politically contentious and subversive plots within a narrower context of their specific cultures, societies, and historical periods. It is assumed that the Shakespearean legacy the writers engage with is not merely a dramatic text, but a complex cultural structure of accumulated narratives, interpretations, and myths which contemporary dramatists rewrite and recycle. The aim of the article is to show how this multifaceted legacy of Shakespeare’s life and work helps build dystopian visions of contemporary communities or images of state and political justice. In other words, the article intends to analyse ways of visualizing modern societies through the palimpsestic presence of the Renaissance master.
Deep learning and Magnet Resonance Imaging for Prostate Cancer Detection and Determination of the clinical Significance
Authors: Eminaga Okyaz , Leyh-Bannurah Sami-Ramzi , Al-Itaibi Rakan , Semjonow Axel , Witt Jorn , Boegemann Martin , Breil Bernhard 
Published: July 2023

Abstract

Background
A human perception-based assessment of multi-parametric magnetic resonance imaging (mpMRI) of the prostate does not necessarily tap the full potential in determining prostate cancer (PCa) and identifying significant prostate cancer (sPCa).
Methods
Our multi-institutional international study includes 6,448 mpMRI prostate images from 1,830 patients (PCa diagnosis in 69.7% of patients). MR Images from a single institution were utilized for the model development and in-house validation, and from two international institutions for external validation. We utilized volumetric data, PlexusNET architecture, and attention algorithms to develop deep learning models. Performance was measured using the area under receiving characteristic operating curve (AUROC) and compared to the PI-RADS score system (version 2) at the case level for PCa diagnosis and sPCa identification. The reduction rate of biopsy settings without missing any PCa cases measured the clinical utility.
Results
Our compact models were internally and externally validated for a significant improvement in PCa detection by 7.25% compared to the PI-RADS score system. Following the model recommendation would avoid at least 11.3% of unnecessary biopsies. Moreover, the DL model correctly predicted PCa presence in 22.5% of cases, which were misclassified according to the PI-RADS score system. The identification accuracy of sPCa for the model was statistically significantly higher than PI-RADS scores (AUROC: 0.769 vs. 0.726; p < 0.021) on a PCa cohort with 79% sPCa.
Conclusions
Our solution facilitates mpMRI assessment of the prostate for PCa diagnosis and the determination of sPCa; we demonstrated a great potential of AI for clinical utility and improved mpMRI assessment.
Incorporation of stoichiometric CNPS inputs in soils with variable clay content
Authors: Amin Nazish , Panthi Sharan , Kabiri Shayan , O'Rourke Sharon Mary 
Published: July 2023

Abstract
Purpose: Evidence exists for a constant C, N, P, and S content in the stabilized fine fraction pool of organic carbon in soils globally. This study aimed to test C:N:P:S stoichiometry to increase the rate of organic matter mineralization and soil organic carbon (SOC) stabilisation following straw incorporation in soils with varying clay content. Objectives were to (i) determine whether stoichiometric CNPS input affected mineralisation by increasing CO2 production, (ii) quantify the effect of nutrient limitation (N, P or S) on CO2-C and (iii) determine if enhanced mineralisation could be detected in the stable C fraction (FF-C) after one incubation cycle.
Methods: Straw was incorporated in soil at a rate of 8 t ha-1 with or without stoichiometrically balanced nutrients to convert a target 30% fresh C-input to SOC.
Results: Both treatment and soil type had a statistically significant effect on the total amount of CO2-C production (p<0.001). A positive significant relationship was observed during peak concentration of CO2-C between C2 (C-input) and N1 (balanced stoichiometric CNPS input) in two soils with higher clay and silt content (loam soils), with nutrient-limiting treatments responding accordingly. No significant treatment effect was observed for FF-C, as an indicative response of SOC stabilisation in heavier textured soils. This suggests that lighter soils may take a longer time to respond to stoichiometric CNPS inputs to achieve stabilised SOC.
Conclusion: In conclusion, Multiple C addition cycle may be required to show both the straw mineralisation and SOC stabilisation response to stoichiometrically balanced inputs of CNPS.
Pilocarpine aiding the management of early post-operative silicon oil migration into the anterior chamber after vitrectomy in an aphakic child: case report
Authors: Alrajhi Furat , Alobudi Abdulrahman , AlQahtani Faisal , Alzendi Nouf 
Published: July 2023

Abstract
Background:
Silicon oil has been used in retinal surgeries for decades. One of the observed complications after its use is a high intraocular pressure. Herein, the management of early high intraocular pressure secondary to silicon oil migration into the anterior chamber in an aphakic child post vitrectomy is described.
Case presentation:
An eight-year-old boy presented to the emergency department just five days post lensectomy; pars plana vitrectomy and silicon oil tamponade was done due to traumatic cataract in the left eye. He had a high intraocular pressure with massive silicon oil infiltration into the anterior chamber. He was managed conservatively with face-down position, mydriatics, and antiglaucoma drugs. Afterwards, the silicon oil retracted into the posterior pole, and the patient was given pilocarpine to constrict the pupil and prevent re-migration of silicon oil into the anterior chamber. His intraocular pressure remained controlled till it was safe to remove the silicon oil by the vitreoretinal surgeon.
Conclusion:
This case highlights the value of pilocarpine in the prevention of silicon oil remigration into the anterior chamber; thus creating time for safe silicon oil removal. By utilizing these conservative measures, invasive interventions can be deferred, minimizing risks and optimizing patient outcomes, particularly in the pediatric population.
Deep learning-based prediction of post-pancreaticoduodenectomy pancreatic fistula
Authors: Lee Woohyung , Park Hyo Jung , Lee Hack-Jin , Song Ki Byung , Hwang Dae Wook , Lee Jae Hoon , Lim Kyongmook , Ko Yousun , Kim Hyoung Jung , Won Kim Kyung , Kim Song Cheol 
Published: July 2023

Abstract
Postoperative pancreatic fistula is a life-threatening complication with an unmet need for accurate prediction. This study was aimed to develop preoperative artificial intelligence-based prediction models. Patients who underwent pancreaticoduodenectomy were enrolled and stratified into model development and validation sets by surgery between 2016 and 2017 or in 2018, respectively. Machine learning models based on clinical and body composition data, and deep learning models based on computed tomographic data, were developed, combined by ensemble voting, and final models were selected comparison with earlier model. Among the 1333 participants (training, n = 881; test, n = 452), postoperative pancreatic fistula occurred in 421 (47.8%) and 134 (31.8%) and clinically relevant postoperative pancreatic fistula occurred in 59 (6.7%) and 27 (6.0%) participants in the training and test datasets, respectively. In the test dataset, the area under the receiver operating curve [AUC (95% confidence interval)] of the selected preoperative model for predicting all and clinically relevant postoperative pancreatic fistula was 0.75 (0.71–0.80) and 0.68 (0.58–0.78). Furthermore, these models achieved better predictive performance than earlier models. The deep learning-based models developed based on preoperative variables achieved good performance for predicting pancreatic fistula, and outperformed earlier model.
Adult-type Anomalous Origin of the Left Coronary Artery from the Pulmonary Artery and Right Coronary-Right Atrial Fistula：A Case Report
Authors: Luo Hao , Ofe Eugene , Yue Rongchuan 
Published: July 2023

Abstract
Background；Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is a rare congenital cardiac anomaly，mortality rates in infancy reach approximately 90%, with only a small number of patients surviving into adulthood，therefore，most of the literature reports mainly focus on infantile type.
Case presentation：A 55-year-old female was admitted due to persistent repeated chest pain experienced and had worsened in for unknown reason. Color doppler echocardiography, coronary computed tomographic angiography, and coronary angiography confirmed the diagnosis of ALCAPA and concurrent right coronary artery-right atrial fistula. The symptoms of chest pain exhibited notable improvement subsequent to corrective surgery for the anomalous origin of the coronary artery.
Conclusions：This report shows an unique case of ALCAPA in an adult patient, characterizing the condition's combination with a right coronary-right atrial fistula, and it is prone to misdiagnosis and misdiagnosis. We aims to provide valuable insights for clinical diagnosis and treatment of ALCAPA.
Meta-Analysis of Consumer Willingness to Pay for Organic Food
Authors: Ribeiro Jose Eduardo 
Published: July 2023

Abstract
This meta-analysis provides a consensus on the willingness to pay (WTP) for organic food, and the factors influencing its premium. It includes 56 studies which satisfy the criteria established by its protocol, contributing to 108 observations. Results show that, on average, consumers worldwide are willing to pay a 60\% premium for the organic attribute. Premiums have increased over the years and are larger for relatively cheaper products in Asia and Africa. Education level has an inverse relationship with the extra amount consumers would pay for organic and is a pivotal variable in WTP estimates. However, premia are also highly influenced by the methodology applied in the study, indicating that stated preference approaches on average increase estimates by 55\%, though the treatment of hypothetical bias mitigates most of this, with an effect of -43\%. Face-to-face surveys and information given during the survey also positively affect premiums. Thus, these should be considered when eliciting WTP estimates.
Conformational Landscape and Effect of Microhydration on the Secondary Structure of Capped Phenylalanine: A Computational Study
Authors: Alauddin Md. , Mamun Abdullah-Al , Barai Hasi Rani , Hasan Zubair , Joo Sang W. 
Published: July 2023

Abstract
The N-acetyl-phenylalaninylamide (NAPA) and microhydrated NAPA, [NAPA-A(H2O)n (n = 1, 2, 3, 4)] have been studied theoretically in the gas phase to explore the conformational landscape and the effect of hydration on the peptide backbone of NAPA. The most stable conformer of NAPA has an extended backbone form with the structural motif of βL(a) and assigned NAPA-A. After the addition of a single water molecule, the βL(a) structure of NAPA folded to γ-turn with a structural motif of γL(a). The sequential addition of water on the NAPA-A conformer shows moderate intermolecular hydrogen bonding with the structural arrangement of PPII. Calculations for all the most stable conformers of NAPA-A(H2O)n (n = 1, 2, 3, 4) clusters have been done using B3LYP, B3PW91, PBE0, M06-2X, wB97XD, CAM-B3LYP density functionals with cc-pVTZ basis set in the gas phase to assess their ability to describe the conformational landscape and hydration effect. In terms of interaction energy and the strength of the hydrogen bond, the performance is obtained in the order of M06-2X > wB97XD > CAM-B3LYP > PBE0 > B3LYP > B3PW91 with cc-pVTZ basis set. We also calculated interaction energy using the MP2 method with 6-31G basis set to see the trends of interaction energy from the bare NAPA-A to [NAPA-A(H2O)n (n = 1, 2, 3, 4)] clusters. Moreover, natural bond orbital (NBO) analysis has been carried out to clarify the nature of hydrogen bonding in NAPA-A.
Deep Learning-Based Noninvasive Auxiliary Pulmonary TumorDiagnosis System on Multi-modality Radio-mics Data
Authors: Jiang Hao Jiang Hao , Chao Liu Chao Liu , Chen Wenyang Chen Wenyang , Zhang Jun Zhang Jun , Tai Yonghang Tai Yonghang 
Published: July 2023

Abstract
The increasing prevalence of lung cancer necessitates the development of non-invasive preliminaryscreening systems to support clinical diagnosis scenarios. Currently, the core challenges in noduledetection involve misalignment and data sparsity. Moreover, post-cancer screening, single modalityinformation is inadequate for comprehensive analysis of lung cancer by medical professionals. In thisarticle, we propose two methods for preliminary cancer screening and medical analysis, respectively.Firstly, we introduce a hybrid training strategy to optimize the TOOD architecture, addressing thechallenges encountered during the preliminary screening process. Secondly, we extract radiomicsfeatures from nodule images, adhering to the image biomarker standardization initiative (IBSI). Byconsidering radiomics characteristics as independent variables and effective survival prognosis asthe dependent variable, we explore the relationship between tumor images and survival prognosis.The proposed method demonstrates a reduction in TOOD detection speed from 4.4s to 3.3s, andimproved mAP50 and Recall values for the hybrid neural network (TOOD+DCNV) from 0.4871to 0.7123 and 0.6181 to 0.8325, respectively. Comprehensive validation supports the effectivenessof the proposed method for preliminary nodule detection. Furthermore, the proposed non-invasivepreliminary screening system yields 107 quantitative radiomics features from detected nodules, witha text classification accuracy of 72%. Overall, the integration of deep learning and multi-model dataforms the foundation of the preliminary screening system, offering potential to alleviate the burden oftumor diagnosis and achieve intelligent assistive medical capabilities.
Anoikis-related long non-coding RNA signatures to predict prognosis and small molecular drug response in glioma: based on multi-database analysis
Authors: Chen Desheng , Li Yang , Yu Shan , Han Ke , Ma Xinqi , Yang Qingsong , He Ke , Hu Bowen , Kuang Liangwen , Yao Penglei , Xia Songsong , Yao Jiawei , Zhao Yan , Gu Shuqing , Guo Mian , Wang Guangzhi 
Published: July 2023

Abstract
Background

Gliomas are intrinsic brain tumors that originate from neuroglial progenitor cells. Traditional treatments, including surgery, chemotherapy, and radiotherapy, have limited improvement in prognosis for patients with gliomas, and recurrence rates remain high.The construction of prognostic model can predict the development and treatment effect of glioma, which is of great clinical significance.
Methods
Anoikis play a key role in the critical stages of tumor development, metastasis, and cancer cell dissemination. Based on TCGA database and CGGA database, the LASSO model is constructed with Anoikis-related lncRNAs as the core. Combined with clinicopathological features, univariate- and multivariate COX analysis were used to verify the effectiveness of the model. Despite the existence of various prognostic models, none of them are truly suitable for clinical application. The model we have constructed provides an option for clinical application.
Results
We constructed a risk model with 8 ARlncRNAs(LINC00519, AC140481.1, LINC00928, HOXA-AS2, CRNDE, ACAP2-IT1, USP30-AS1, TMPO-AS1) at its core and validated their high accuracy in predicting overall survival. We also confirmed their association with clinicopathological features. Studies of drug sensitivity and immunological associations suggest that it could provide more precise guidance to clinicians.
Conclusion
Our study elucidated a prognostic prediction model of glioma by 8 Anoikis-related long non-coding RNAs.High-risk patients have a short survival time and a pro-tumor immune microenvironment.
A Novel Approach for Customer Churn Prediction in Telecom using Machine Learning Models
Authors: Chaudhary Aditi , Rizvi Ali , Kumar Navneet , Mishra Ashish Kumar 
Published: July 2023

Abstract

For organizations across a range of industries, customer churn is a serious problem.Businesses must identify consumers who are in danger of leaving in order totake actions to keep them. By examining previous data and seeing trends indicatingclient behavior, machine learning provides a potent remedy for forecastingcustomer attrition. In this article, the investigation of the way machine learningtechniques can be utilized to forecast consumer attrition in the telecommunicationindustry has been done. The machine learning models decision trees and randomforests have been trained and assessed using IBM’s dataset of customer behaviorand demographics. These models are compared using performance parameterslike recall, accuracy, F1-score, and precision. According to chosen data, the randomforest model exceeds the competition with a remarkable accuracy of 96%.This study demonstrates the potential of machine learning in predicting customerturnover and offers insightful information for creating strategies that effectivelyretain customers.
ViT with diamond patches for lane line detection under occlusion
Authors: Shi Xianrang , Wang Rong , Zhang Hengyu , Li Zezhi , Su Yang , Ti Yan , Song Tinglun 
Published: July 2023

Abstract
Lane line detection based on deep learning has achieved good results in common scenarios. However, it is challenging to detect lane lines in extreme occlusion scenes where the visual clues are severely missing. To address this problem, we propose a novel method that leverages Vision Transformer (ViT) for de-occlusion and feature fusion. Specifically, we first design a ViT-based model to reconstruct the occluded lane lines from the input image. Then, we extract the feature map of the model and fuse it with the original image feature map. Finally, we use the fused feature map to detect the lane lines in a robust manner. Moreover, we introduce a sensitivity loss function that measures the error of each pixel and considers the coordinate difference between pixels. Experiments show that our sensitivity loss function can improve the performance of lane line detection. We evaluate our method on three benchmark datasets: TuSimple, CULane and CurveLanes. The results demonstrate that our method outperforms the existing methods in terms of accuracy and F1-score on all these datasets.
Effects of Antimicrobial Photosensitizers of Photodynamic Therapy (PDT)
to Treat Periodontitis
Authors: Bourbour Samaneh , Darbandi Atieh , Bostanghadiri Narjess , Ghanavati Roya , Taheri Behrouz , Bahador Abbas 
Published: July 2023

Abstract:
Antimicrobial photodynamic therapy or aPDT is an alternative therapeutic approach in
which lasers and different photosensitizing agents are used to eradicate periodontopathic bacteria
in periodontitis. Periodontitis is a localized infectious disease caused by periodontopathic bacteria
and can destroy bones and tissues surrounding and supporting the teeth. The aPDT system has
been shown by in vitro studies to have high bactericidal efficacy. It was demonstrated that aPDT
has low local toxicity, can speed up dental therapy, and is cost-effective. Several photosensitizers
(PSs) are available for each type of light source which did not induce any damage to the patient
and are safe. In recent years, significant advances have been made in aPDT as a non-invasive
treatment method, especially in treating infections and cancers. Besides, aPDT can be perfectly
combined with other treatments. Hence, this survey focused on the effectiveness and mechanism
of aPDT of periodontitis by using lasers and the most frequently used antimicrobial PSs such as
methylene blue (MB), toluidine blue ortho (TBO), indocyanine green (ICG), malachite green
(MG) (Triarylmethanes), erythrosine dyes (ERY) (Xanthenes dyes), rose bengal (RB) (Xanthenes
dyes), eosin-Y (Xanthenes dyes), radachlorin group and curcumin. The aPDT with these PSs can
reduce pathogenic bacterial loads in periodontitis. Therefore, it is clear that there is a bright future
for using aPDT to fight microorganisms causing periodontitis.