bluuebunny/crossref_metadata_2025_split huggingface dataset:
An aseptic approach towards skin lesion localization and grading using deep learning and harris hawks optimization
Authors: Balaha Hossam Magdy , Hassan Asmaa El-Sayed , El-Gendy Eman M. , ZainEldin Hanaa , Saafan Mahmoud M. 
Published: July 2023

AbstractSkin cancer is the most common form of cancer. It is predicted that the total number of cases of cancer will double in the next fifty years. It is an expensive procedure to discover skin cancer types in the early stages. Additionally, the survival rate reduces as cancer progresses. The current study proposes an aseptic approach toward skin lesion detection, classification, and segmentation using deep learning and Harris Hawks Optimization Algorithm (HHO). The current study utilizes the manual and automatic segmentation approaches. The manual segmentation is used when the dataset has no masks to use while the automatic segmentation approach is used, using U-Net models, to build an adaptive segmentation model. Additionally, the meta-heuristic HHO optimizer is utilized to achieve the optimization of the hyperparameters of 5 pre-trained CNN models, namely VGG16, VGG19, DenseNet169, DenseNet201, and MobileNet. Two datasets are used, namely "Melanoma Skin Cancer Dataset of 10000 Images" and "Skin Cancer ISIC" dataset from two publicly available sources for variety purpose. For the segmentation, the best-reported scores are 0.15908, 91.95%, 0.08864, 0.04313, 0.02072, 0.20767 in terms of loss, accuracy, Mean Absolute Error, Mean Squared Error, Mean Squared Logarithmic Error, and Root Mean Squared Error, respectively. For the "Melanoma Skin Cancer Dataset of 10000 Images" dataset, from the applied experiments, the best reported scores are 97.08%, 98.50%, 95.38%, 98.65%, 96.92% in terms of overall accuracy, precision, sensitivity, specificity, and F1-score, respectively by the DenseNet169 pre-trained model. For the "Skin Cancer ISIC" dataset, the best reported scores are 96.06%, 83.05%, 81.05%, 97.93%, 82.03% in terms of overall accuracy, precision, sensitivity, specificity, and F1-score, respectively by the MobileNet pre-trained model. After computing the results, the suggested approach is compared with 9 related studies. The results of comparison proves the efficiency of the proposed framework.
SUShe: simple unsupervised shadow removal
Authors: Koutsiou Dimitra-Christina C. , Savelonas Michalis A. , Iakovidis Dimitris K. 
Published: July 2023

AbstractShadow removal is an important problem in computer vision, since the presence of shadows complicates core computer vision tasks, including image segmentation and object recognition. Most state-of-the-art shadow removal methods are based on complex deep learning architectures, which require training on a large amount of data. In this paper a novel and efficient methodology is proposed aiming to provide a simple solution to shadow removal, both in terms of implementation and computational cost. The proposed methodology is fully unsupervised, based solely on color image features. Initially, the shadow region is automatically extracted by a segmentation algorithm based on Electromagnetic-Like Optimization. Superpixel-based segmentation is performed and pairs of shadowed and non-shadowed regions, which are nearest neighbors in terms of their color content, are identified as parts of the same object. The shadowed part of each pair is relighted by means of histogram matching, using the content of its non-shadowed counterpart. Quantitative and qualitative experiments on well-recognized publicly available benchmark datasets are conducted to evaluate the performance of proposed methodology in comparison to state-of-the-art methods. The results validate both its efficiency and effectiveness, making evident that solving the shadow removal problem does not necessarily require complex deep learning-based solutions.
On the analysis of a heterogeneous coupled network of memristive Chialvo neurons
Authors: Ghosh Indranil , Muni Sishu Shankar , Fatoyinbo Hammed Olawale 
Published: July 2023

AbstractWe perform a numerical study on the application of electromagnetic flux on a heterogeneous network of Chialvo neurons represented by a ring-star topology. Heterogeneities are realized by introducing additive noise modulations on both the central–peripheral and the peripheral–peripheral coupling links in the topology not only varying in space but also in time. The variation in time is understood by two coupling probabilities, one for the central–peripheral connections and the other for the peripheral–peripheral connections, respectively, that update the network topology with each iteration in time. We have further reported various rich spatiotemporal patterns like two-cluster states, chimera states, coherent, and asynchronized states that arise throughout the network dynamics. We have also investigated the appearance of a special kind of asynchronization behavior called “solitary nodes” that have a wide range of applications pertaining to real-world nervous systems. In order to characterize the behavior of the nodes under the influence of these heterogeneities, we have studied two different metrics called the “cross-correlation coefficient” and the “synchronization error.” Additionally, to capture the statistical property of the network, for example, how complex the system behaves, we have also studied a measure called “sample entropy.” Various two-dimensional color-coded plots are presented in the study to exhibit how these metrics/measures behave with the variation of parameters.
Paul Cohen’s philosophy of mathematics and its reflection in his mathematical practice
Authors: Wagner Roy 
Published: July 2023

AbstractThis paper studies Paul Cohen’s philosophy of mathematics and mathematical practice as expressed in his writing on set-theoretic consistency proofs using his method of forcing. Since Cohen did not consider himself a philosopher and was somewhat reluctant about philosophy, the analysis uses semiotic and literary textual methodologies rather than mainstream philosophical ones. Specifically, I follow some ideas of Lévi-Strauss’s structural semiotics and some literary narratological methodologies. I show how Cohen’s reflections and rhetoric attempt to bridge what he experiences as an uncomfortable tension between reality and the formal by means of his notion of intuition.
Genotyping of Trypanosoma brucei evansi in Egyptian camels: detection of a different non-RoTat 1.2 Trypanosoma brucei evansi in Egyptian camels
Authors: Behour Tahani Salama , Abd EL Fattah Eman Mohamed 
Published: July 2023

AbstractTrypanosoma brucei evansi (T. b. evansi) is an enzootic organism found in Egyptian camels, which genetically classified into types A and B. To detect the parasite genotype circulating in Egyptian camels, we collected 94 blood samples from three distant districts and subjected them to different PCR assays; T. brucei repeat (TBR), internal transcribed spacer-1 (ITS-1), and variable surface glycoproteins (VSG) (RoTat 1. 2, JN 2118Hu) and EVAB PCRs. The highest prevalence was obtained with TBR (80/91; 87.9%), followed by ITS-1 (52/91; 57.1%), VSG JN 2118Hu (42/91; 46.2%), and VSG RoTat 1. 2 (34/91; 37.4%). We reported a different non-RoTat 1. 2 T. b. evansi for the first time in Egyptian camels. Results showed that 47 (58.7%) out of 80 samples were classified as T. b. evansi. Of these, 14 (29.8%) were RoTat 1. 2 type, 13 (27.6%) were non-RoTat 1. 2 type, and 20 (42.6%) samples were from mixed infection with both types. All samples were tested negative with EVAB PCR. RoTat 1. 2 T. b. evansi was the most prevalent in Giza and El Nubariyah, whereas, in Aswan, the only type detected was non-RoTat 1. 2 T. b. evansi. The nucleotide sequences of the VSG RoTat 1.2 and JN 2118Hu PCR products were submitted to DNA Data Bank of Japan (DDBJ) and GenBank under the accession numbers LC738852, and (OP800400-OP800403). Further research is required to increase the sample size and verify the new sequences to corroborate the prevalence of a new variant of non-RoTat 1.2 T. b. evansi in Egypt.
Mental Health and Substance Use Co-Occurrence Among Indigenous Peoples: a Scoping Review
Authors: Hobden Breanne , Freund Megan , Rumbel Jennifer , Heard Todd , Davis Robert , Ooi Jia Ying , Newman Jamie , Rose Bronwyn , Sanson-Fisher Rob , Bryant Jamie 
Published: July 2023

AbstractThis scoping review examined the literature on co-occurring mental health conditions and substance use among Indigenous peoples globally across (i) time, (ii) types of conditions examined, (iii) countries, (iv) research designs, and (v) participants and settings. Medline, Embase, PsycInfo, and Web of Science were searched across all years up until October 2022 for relevant studies. Ninety-four studies were included, with publications demonstrating a slight and gradual increase over time. Depressive disorder and alcohol were the most examined co-occurring conditions. Most studies included Indigenous people from the United States (71%). Ninety-seven percent of the studies used quantitative descriptive designs, and most studies were conducted in Indigenous communities/reservations (35%). This review provides the first comprehensive exploration of research on co-occurring mental health and substance use conditions among Indigenous peoples. The information should be used to guide the development of strategies to improve treatment and prevention.
Betriebliches Gesundheitsmanagement (BGM) vital: Entwicklung und Evaluation einer adressatenorientierten und diversitätssensiblen BGM-Servicestelle
Authors: Friedrich Julian , Alam Naima , Bilgic Leman , Feng You-Shan , Lehrke Liliann , Marschall Susanne , Martus Peter , Münch Anne-Kristin , Münster Patrick , Niebuhr Fiona , Rupp Maylin , Strauß Jasper , Schneider Kurt , Sudeck Gorden , Thiel Ansgar , Zimmermann Gottfried , Voelter-Mahlknecht Susanne 
Published: July 2023

Zusammenfassung
Hintergrund
Eine Herausforderung bei der Umsetzung von Betrieblichem Gesundheitsmanagement (BGM) ist ein bedarfsorientiertes und evidenzbasiertes Vorgehen. In dem vorliegenden Beitrag wird eine Studie vorgestellt, in der kleine und mittlere Unternehmen anhand einer Online-Servicestelle mit Gesundheitsinformationen und spezifischen Tools bei der Implementierung von BGM unterstützt und angeregt werden, dieses bedarfsorientiert sowie diversitätssensibel auszurichten.

Methode
In qualitativen Interviews mit Expert:innen und Arbeitgebenden sowie Fokusgruppen mit Beschäftigten und in einer quantitativen Hauptbefragung (n = 828) wurden Bedarfe und eine Datenbasis bezüglich gesundheitsrelevanter Faktoren wie beispielsweise Gesundheitskompetenz oder Partizipationsmöglichkeiten bei der Arbeit für die Gestaltung der zu entwickelnden Servicestelle generiert. In einer Mixed-Methods-Evaluationsstudie wurden die Praxistauglichkeit qualitativ mit Arbeitgebenden und Interventionseffekte quantitativ mit Beschäftigten (n = 104) auf Basis einer zehntägigen Nutzung überprüft.

Ergebnisse
Es zeigte sich in den qualitativen Daten ein Hinweis auf eine digitale Bereitstellung von Gesundheitsinformationen sowie der Bedarf nach niedrigschwelligen Informationsmaterialien. In der quantitativen Hauptbefragung wurden die arbeitsbezogene Gesundheitskompetenz sowie Partizipationsmöglichkeiten zu Gesundheit als Datengrundlage erfasst, wobei sich ein inadäquater oder problematischer wissens- und fähigkeitsbasierter Umgang mit Gesundheitsinformationen bei 47 % der Befragten zeigte. Die Ergebnisse der Evaluationsstudie gaben Hinweise auf eine positiv bewertete Gestaltung der Online-Servicestelle bzgl. Diversität und Barrierefreiheit sowie einen kleinen Interventionseffekt für das Finden, Verstehen und Beurteilen von Gesundheitsinformationen nach kurzzeitiger Nutzung der Servicestelle.

Schlussfolgerungen
Das BGM sollte adressatenorientiert und diversitätssensibel ausgerichtet sein, um die Beschäftigten optimal zu unterstützen und die Rahmenbedingungen der Unternehmen zukunftsfähig zu gestalten. Eine Datengrundlage zu Bedarfen kann dabei helfen, Unternehmen adressatenorientiert und diversitätssensibel für passende Maßnahmen und Ausrichtungen zu unterstützen.
Quaternion algebra approach to nonlinear Schrödinger equations with nonvanishing boundary conditions
Authors: Demontis Francesco , van der Mee Cornelis 
Published: July 2023

AbstractIn this article we apply quaternionic linear algebra and quaternionic linear system theory to develop the inverse scattering transform theory for the nonlinear Schrödinger equation with nonvanishing boundary conditions. We also determine its soliton solutions by using triplets of quaternionic matrices.
Reproducible clustering with non-Euclidean distances: a simulation and case study
Authors: Staples Lauren , Ring Janelle , Fontana Scott , Stradwick Christina , DeMaio Joe , Ray Herman , Zhang Yifan , Zhang Xinyan 
Published: July 2023

AbstractCertain categorical sequence clustering applications require path connectivity, such as the clustering of DNA, click-paths through web-user sessions, or paths of care clustering with sequences of patient medical billing codes.$$K$$K-means and$$k$$k-medoids clustering with non-Euclidean distance metrics such as the Jaccard or edit distances maintains such path connectivity. Although$$k$$k-means and$$k$$k-medoids clustering with the Jaccard and edit distances have enjoyed success in these domains, the limits of accurate cluster recovery in these conditions have not yet been defined. As a first step in approaching this goal, we performed a simulated study using$$k$$k-means and$$k$$k-medoids clustering with non-Euclidean distances and show the performance deteriorates at a certain level of noise and when the number of clusters increases. However, we identify initialization strategies that improve upon cluster recovery in the presence of noise. We employ the use of the Tibshirani and Guenther (J Comput Graph Stat 14(3):511–528, 2005) Prediction Strength method, which creates a hypothesis testing scenario that determines if there is clustering structure to the data (if the clusters are reproducible), with the null hypothesis being there is none. We then applied the framework to perinatal episodes of care and the clusters reproducibly and organically split between Cesarean and vaginal deliveries, which itself is not a clinical finding but sensibly validates the approach. Further visualizations of the clusters did bring insights into subclusters that split along groups of physicians, cost and risk scores, warranting the outlined future work into ways of improving this framework for better resolution.
Guiding Deep Learning with Expert Knowledge for Dense Stereo Matching
Authors: Iqbal Waseem , Paffenholz Jens-André , Mehltretter Max 
Published: July 2023

AbstractDense depth information can be reconstructed from stereo images using conventional hand-crafted as well as deep learning-based approaches. While deep-learning methods often show superior results compared to hand-crafted ones, they commonly learn geometric principles underlying the matching task from scratch and neglect that these principles have already been intensively studied and were considered explicitly in various models with great success in the past. In consequence, a broad range of principles and associated features need to be learned, limiting the possibility to focus on important details to also succeed in challenging image regions, such as close to depth discontinuities, thin objects and in weakly textured areas. To overcome this limitation, in this work, a hybrid technique, i.e., a combination of conventional hand-crafted and deep learning-based methods, is presented, addressing the task of dense stereo matching. More precisely, the input RGB stereo images are supplemented by a fourth image channel containing feature information obtained with a method based on expert knowledge. In addition, the assumption that edges in an image and discontinuities in the corresponding depth map coincide is modeled explicitly, allowing to predict the probability of being located next to a depth discontinuity per pixel. This information is used to guide the matching process and helps to sharpen correct depth discontinuities and to avoid the false prediction of such discontinuities, especially in weakly textured areas. The performance of the proposed method is investigated on three different data sets, including studies on the influence of the two methodological components as well as on the generalization capability. The results demonstrate that the presented hybrid approach can help to mitigate common limitations of deep learning-based methods and improves the quality of the estimated depth maps.
Forecasting heating and cooling loads in residential buildings using machine learning: a comparative study of techniques and influential indicators
Authors: Mehdizadeh Khorrami Behrouz , Soleimani Alireza , Pinnarelli Anna , Brusco Giovanni , Vizza Pasquale 
Published: July 2023

AbstractResidential buildings are a significant source of energy consumption and greenhouse gas emissions, making it crucial to accurately predict their energy demand for reducing their environmental impact. In this study, machine-learning techniques such as linear regression, decision tree classification, logistic regression, and neural networks were applied to forecast the heating and cooling loads of 12 different building types using their area and height attributes. The correlation coefficient was utilized to assign weights to the predictors in linear regression, and the models’ performance was evaluated using metrics such as equations of R2, MAE, and RMSE. The decision tree technique demonstrated the highest accuracy of 98.96% and 93.24% for predicting heating and cooling loads, respectively, among the classification methods. Notably, the cooling load prediction was more accurate than the heating load prediction. The height and area of the roof and floor, along with the relative compactness of the building, were identified as the most influential factors in the heating and cooling loads. These findings have significant implications for optimizing energy efficiency in residential buildings and mitigating their impact on climate change.
A hybrid data mining framework for variable annuity portfolio valuation
Authors: Gweon Hyukjun , Li Shu 
Published: July 2023

AbstractA variable annuity is a modern life insurance product that offers its policyholders participation in investment with various guarantees. To address the computational challenge of valuing large portfolios of variable annuity contracts, several data mining frameworks based on statistical learning have been proposed in the past decade. Existing methods utilize regression modeling to predict the market value of most contracts. Despite the efficiency of those methods, a regression model fitted to a small amount of data produces substantial prediction errors, and thus, it is challenging to rely on existing frameworks when highly accurate valuation results are desired or required. In this paper, we propose a novel hybrid framework that effectively chooses and assesses easy-to-predict contracts using the random forest model while leaving hard-to-predict contracts for the Monte Carlo simulation. The effectiveness of the hybrid approach is illustrated with an experimental study.
ASYMMETRIC CUT AND CHOOSE GAMES
Authors: HENNEY-TURNER CHRISTOPHER , HOLY PETER , SCHLICHT PHILIPP , WELCH PHILIP 
Published: July 2023

AbstractWe investigate a variety of cut and choose games, their relationship with (generic) large cardinals, and show that they can be used to characterize a number of properties of ideals and of partial orders: certain notions of distributivity, strategic closure, and precipitousness.
The Contents of Imagination
Authors: Fernández Jordi 
Published: July 2023

AbstractOur imaginings seem to be similar to our perceiving and remembering episodes in that they all represent something. They all seem to have content. But what exactly is the structure and the source of the content of our imaginings? In this paper, I put forward an account of imaginative content. The main tenet of this account is that, when a subject tries to imagine a state of affairs by having some experience, their imagining has a counterfactual content. What the subject imagines is that perceiving the state of affairs would be, for them, like having that experience. I discuss three alternative views of imaginative content, and argue that none of them can account for two types of error in imagination. The proposed view, I suggest, can account for both types of error while, at the same time, preserving some intuitions which seem to motivate the alternative views.
Factors associated with stigma and manifestations experienced by Indian health care workers involved in COVID-19 management in India: A qualitative study
Authors: Grover Ashoo , Venkatesh U , Kishore Jugal , Chakma Tapas , Thomas Beena , Menon Geetha , Periyasamy Murugesan , Kulkarni Ragini , Prusty Ranjan K , Venkateswaran Chitra , Mishra Bijaya , Balu Vinoth , Viray Maribon , Mathew Geetu , Ketharam Asha , Balachandar Rakesh , Singh Prashant , Jakhar Kiran , Devi Rekha , Saha Kalyan , Barde Pradeep , Moral Rony , Singh Ravinder , John Denny , Yadav Jeetendra , Kohli Simran , Aggarwal Sumit , Rao Vishnu , Panda Samiran 
Published: July 2023

Abstract
Healthcare personnel who deal with COVID-19 experience stigma. There is a lack of national-level representative qualitative data to study COVID-19-related stigma among healthcare workers in India. The present study explores factors associated with stigma and manifestations experienced by Indian healthcare workers involved in COVID-19 management. We conducted in-depth interviews across 10 centres in India, which were analysed using NVivo software version 12. Thematic and sentiment analysis was performed to gain deep insights into the complex phenomenon by categorising the qualitative data into meaningful and related categories. Healthcare workers (HCW) usually addressed the stigma they encountered when doing their COVID duties under the superordinate theme of stigma. Among them, 77.42% said they had been stigmatised in some way. Analyses revealed seven interrelated themes surrounding stigma among healthcare workers. It can be seen that the majority of the stigma and coping sentiments fall into the mixed category, followed by the negative sentiment category. This study contributes to our understanding of stigma and discrimination in low- and middle-income settings. Our data show that the emergence of fear of the virus has quickly turned into a stigma against healthcare workers.
Turbulent drag reduction by spanwise wall forcing. Part 2. High-Reynolds-number experiments
Authors: Chandran D. , Zampiron A. , Rouhi A. , Fu M.K. , Wine D. , Holloway B. , Smits A.J. , Marusic I. 
Published: July 2023

We present measurements of turbulent drag reduction (DR) in boundary layers at high friction Reynolds numbers in the range of$4500 \le Re_\tau \le 15\ 000$. The efficacy of the approach, using streamwise travelling waves of spanwise wall oscillations, is studied for two actuation regimes: (i) inner-scaled actuation (ISA), as investigated in Part 1 of this study, which targets the relatively high-frequency structures of the near-wall cycle, and (ii) outer-scaled actuation (OSA), which was recently presented by Marusicet al.(Nat. Commun., vol. 12, 2021) for high-$Re_\tau$flows, targeting the lower-frequency, outer-scale motions. Multiple experimental techniques were used, including a floating-element balance to directly measure the skin-friction drag force, hot-wire anemometry to acquire long-time fluctuating velocity and wall-shear stress, and stereoscopic particle image velocimetry to measure the turbulence statistics of all three velocity components across the boundary layer. Under the ISA pathway, DR of up to 25 % was achieved, but mostly with net power saving (NPS) losses due to the high-input power cost associated with the high-frequency actuation. The low-frequency OSA pathway, however, with its lower input power requirements, was found to consistently result in positive NPS of 5–10 % for moderate DRs of 5–15 %. The results suggest that OSA is an attractive pathway for energy-efficient DR in high-Reynolds-number applications.