bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surgical repair of post‐cesarean vesicouterine fistula: A systematic review and a plea for prevention
Authors: Bonavina Giulia , Busnelli Andrea , Acerboni Stefano , Martini Alberto , Candiani Massimo , Bulfoni Alessandro 
Published: December 2023

AbstractBackgroundVesicouterine fistula (VUF) is a iatrogenic injury in the vast majority of cases. The worldwide increase of cesarean delivery rates is expected to lead to increased complications.ObjectivesTo assess current evidence on VUF pathogenesis and surgical management.Search StrategyPubmed and Embase databases were searched from January 2000 to January 2023 using relevant key words.Selection CriteriaOnly original articles including either transabdominal or transvaginal surgical routes for post‐cesarean VUF repair, in English language, were included.Data Collection and AnalysisTwo authors independently screened the references for eligibility, data extraction, and assessment of methodologic quality. All available surgical outcomes were recorded.Main ResultsOf the 1160 studies retrieved, 67 were selected for analysis. Most of these were case reports, case series, or observational cohort studies including a total of 284 patients. The majority (78.6%) of patients had more than one cesarean section, and approximately 10% of them experienced an overt bladder injury and/or uterine rupture at the time of cesarean delivery. The supratrigonal part of the bladder was most commonly involved (92.5%). The majority of patients (88.8%) underwent delayed VUF repair through laparotomy. Length of stay and blood loss were significantly less in patients treated via a minimally invasive approach (P < 0.001 and P = 0.02, respectively). Most patients had double‐layer bladder repair and single‐layer uterine repair. The overall success rate was 100% on first attempt for each independent combination of different surgical approaches and techniques. Live birth following VUF repair was reported in 23 patients.ConclusionsPaying close attention to surgical details is crucial to reduce the incidence of this complication and recurrence rates. Double‐layer bladder closure and delayed timing of repair of VUF are recommended.
The effect of ablation settings on lesion characteristics with DiamondTemp ablation system: An ex vivo experiment
Authors: Nomura Takehiro , Maeda Manabu , Kumazawa Daiki , Mizuno Yosuke , Onodera Kosuke , Toyoda Shigeru , Yamashita Kennosuke 
Published: December 2023

AbstractIntroductionCreating large lesion in ablations using the DiamondTemp (DTA) ablation system may reduce the frequency of arrhythmia recurrence and allow the treatment of ventricular arrhythmias. Therefore, this study aimed to investigate whether power, application time, contact force (CF), and contact angle affect lesion formation in the ventricles.MethodsAblations were delivered to porcine myocardial preps to evaluate the lesion characteristics. Ablations were conducted with a maximum power of 50 W, target temperature of 58°C, CF of 10, 20, or 30 g, and contact angle between the catheter tip and tissue. The ablation durations were 15, 30, 60 s, 15 s × 2, or 30 s × 2.ResultsSteam pops occurred only in cases with perpendicular contact. The lesion depth was larger in all settings in the perpendicular orientation than in the parallel orientation. The temperatures were lower in all settings in the perpendicular orientation than in the parallel orientation. The lesions became larger as CF increased with perpendicular contact and duration of ≥30 s. The longer application time resulted in larger surface area, depth, and volume of the lesion. Lesion depth was greater with single application of 30 and 60 s than with 15 s × 2 and 30 s × 2, respectively.ConclusionIt is important to perform a single prolonged application as much as possible to create deeper lesions. Parallel contact with the tissue should be maintained to take advantage of the temperature sensor's capabilities to avoid pop phenomenon.
Assessment of extreme rainfall events over the Indian subcontinent during the historical and future projection periods based on CMIP6 simulations
Authors: Suthinkumar P. S. , Varikoden Hamza , Babu C. A. 
Published: December 2023

AbstractThe impact of climatic change on the summer monsoon season is studied to understand the rainfall pattern towards the end of the century utilizing the Coupled Model Intercomparison Project, Phase 6 (CMIP6) released by the World Climate Research Programme (WCRP). The analysis of model simulations from CMIP6 was carried out using 64 years of the historical period (1951–2014) and future projections till the end of the century (2015–2100). The models are compared with observed daily rainfall data from the APHRODITE (Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation). The analysis revealed that most of the models show an overestimation in the annual cycle of rainfall in the historical period; however, a few of them underestimate the values. The majority of them capture the onset signal of the summer monsoon in early June, along with a good seasonality in the daily rainfall climatology. The simulations that are coherent with the observational data sets are selected on the basis of the Taylor diagram for future projections in four scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). The projections of the aforementioned scenarios are taken from the model outputs of EC‐Earth3‐Veg‐LR, INM‐CM4‐8, INM‐CM5‐0, MIROC‐ES2L and MPI‐ESM1‐2‐HR. The selected models exhibit far greater agreement among the 30 models when it comes to the features of rainfall during the summer monsoon. We have given more emphasis on summer monsoon rainfall in the historical and future projection periods since the trends are becoming more chaotic, as reported in observational studies. Over the Indian subcontinent, all of the chosen scenarios show an increased frequency of intense rainfall events with varying decadal and multidecadal features. Central India and west coastal belts are showing positive trends in extreme rainfall events towards the end of the century. At the turn of the century, the southern peninsular region experienced a decline in monsoon precipitation, whereas central India experienced an increase. The severity of rainfall variations during the monsoon season and trends in extremes are increasing as we move from the low‐emission scenario to the high‐emission scenario.
Lead exposure aggravates glucose metabolism disorders through gut microbiota dysbiosis and intestinal barrier damage in high‐fat diet‐fed mice
Authors: Wang Nana , Gao Xue , Huo Yuan , Li Yuting , Cheng Fangru , Zhang Zengli 
Published: December 2023

AbstractBACKGROUNDLead (Pb) is an ancient toxic metal and is still a major public health issue. Our previous study found that Pb exposure promotes metabolic disorders in obese mice, but the molecular mechanisms remain unclear. The present study explored the effects of Pb exposure on glucose homeostasis in mice fed a normal diet (ND) and high‐fat diet (HFD) from the perspective of gut microbiota.RESULTSPb exposure had little effect on glucose metabolism in ND mice, but exacerbated hyperglycemia and insulin resistance, and impaired glucose tolerance in HFD mice. Pb exposure impaired intestinal tight junctions and mucin expression in HFD mice, increasing intestinal permeability and inflammation. Moreover, Pb exposure altered the composition and structure of the gut microbiota and decreased short‐chain fatty acids (SCFAs) levels in HFD mice. Correlation analysis revealed that the gut microbiota and SCFAs were significantly correlated with the gut barrier and glucose homeostasis. Furthermore, the fecal microbiota transplantation from Pb‐exposed HFD mice resulted in glucose homeostasis imbalance, intestinal mucosal structural damage and inflammation in recipient mice. However, Pb did not exacerbate the metabolic toxicity in HFD mice under depleted gut microbiota.CONCLUSIONThe findings of the present study suggest that Pb induces impairment of glucose metabolism in HFD mice by perturbing the gut microbiota. Our study offers new perspectives on the mechanisms of metabolic toxicity of heavy metals and demonstrates that the gut microbiota may be a target of action for heavy metal exposure. © 2023 Society of Chemical Industry.
Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network
Authors: Karthik V. , Lakshmi R. , Abraham Salini , Ramkumar M. 
Published: December 2023

AbstractSoftware defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN‐integrated VANET (SDN‐int‐VANET) has numerous benefits, but it is susceptible to threats like distributed denial of service (DDoS). Several methods were suggested for DDoS attack detection (AD), but the existing approaches to optimization have given a base for enhancing the parameters. An incorrect selection of parameters results in a poor performance and poor fit to the data. To overcome these issues, residual‐based temporal attention red fox‐convolutional neural network (RTARF‐CNN) for detecting DDoS attacks in SDN‐int‐VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre‐processed data with the help of stacked contractive autoencoders (St‐CAE), which reduces the processing time of the introduced method. The selected features are classified by residual‐based temporal attention‐convolutional neural network (RTA‐CNN). The weight parameter of RTA‐CNN is optimized with the help of red fox optimization (RFO) for better classification. The introduced method is implemented in the PYTHON platform. The RTARF‐CNN attains 99.8% accuracy, 99.5% sensitivity, 99.80% precision, and 99.8% specificity. The effectiveness of the introduced technique is compared with the existing approaches.
An observational study of clinical outcome measures in patients treated with cannabis‐based medicinal products on the UK Medical Cannabis Registry
Authors: Erridge Simon , Leung Ophilia , Holvey Carl , Coomber Ross , Beri Sushil , Khan Shaheen , Weatherall Mark W. , Rucker James J. , Platt Michael W. , Sodergren Mikael H. 
Published: December 2023

AbstractIntroductionWhile there is increasing evidence of the effects of cannabis‐based medicinal products (CBMPs) on health‐related quality of life (HRQoL), a major limitation of the current literature is the heterogeneity of studied CBMPs. This study aims to analyze changes in HRQoL in patients prescribed a homogenous selection of CBMPs.MethodsPrimary outcomes were changes in patient‐reported outcomes (PROMs) at 1, 3, 6, and 12 months from baseline. The secondary outcome was an adverse events analysis. Statistical significance was defined as p < 0.050.Results1378 patients prescribed Adven® CBMPs (Curaleaf International, Guernsey, UK) were included in the final analysis. 581 (42.16%) participants were current users of cannabis at baseline. 641 (46.51%), 235 (17.05%), and 502 (36.43%) patients were treated with oils, dried flowers, or a combination of the two, respectively. Improvements were found in all PROMs in each route of administration at 1, 3, 6, and 12 months from baseline (p < 0.010). Those prescribed dried flower only or both oils and dried flower experienced greater improvements in GAD‐7, SQS, and EQ‐5D‐5L index values at 12 months (p < 0.050). There was no difference in outcomes between those prescribed dried flower only or dried flower with oils (p > 0.050). 3663 (265.82%) adverse events were reported by 297 (21.55%) patients.ConclusionThere was an associated improvement in self‐reported anxiety, sleep quality, and HRQoL in patients treated with the CBMPs. Those prescribed treatment formulations including dried flower were most likely to show a clinical improvement. However, these results must be interpreted with caution given the limitations of study design.
Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space
Authors: Dobša Jasminka , Kiers Henk A. L. 
Published: December 2023

AbstractIn the paper we propose a method for representation of documents in a semantic lower-dimensional space based on the modified Reduced k-means method which penalizes clusterings that are distant from classification of training documents given by experts. Reduced k-means (RKM) enables simultaneously clustering of documents and extraction of factors. By projection of documents represented in the vector space model on extracted factors, documents are clustered in the semantic space in a semi-supervised way (using penalization) because clustering is guided by classification given by experts, which enables improvement of classification performance of test documents. Classification performance is tested for classification by logistic regression and support vector machines (SVMs) for classes of Reuters-21578 data set. It is shown that representation of documents by the RKM method with penalization improves the average precision of classification by SVMs for the 25 largest classes of Reuters collection for about 5,5% with the same level of average recall in comparison to the basic representation in the vector space model. In the case of classification by logistic regression, representation by the RKM with penalization improves average recall for about 1% in comparison to the basic representation.
Old and New Constraints in Model Based Clustering
Authors: García-Escudero Luis A. , Mayo-Iscar Agustín , Morelli Gianluca , Riani Marco 
Published: December 2023

AbstractModel-based approaches to cluster analysis and mixture modeling often involve maximizing classification and mixture likelihoods. Without appropriate constrains on the scatter matrices of the components, these maximizations result in ill-posed problems. Moreover, without constrains, non-interesting or “spurious” clusters are often detected by the EM and CEM algorithms traditionally used for the maximization of the likelihood criteria. A useful approach to avoid spurious solutions is to restrict relative components scatter by a prespecified tuning constant. Recently new methodologies for constrained parsimonious model-based clustering have been introduced which include the 14 parsimonious models that are often applied in model-based clustering when assuming normal components as limit cases. In this paper we initially review the traditional approaches and illustrate through an example the benefits of the adoption of the new constraints.
Data Clustering and Representation Learning Based on Networked Data
Authors: Labiod Lazhar , Nadif Mohamed 
Published: December 2023

AbstractTo deal simultaneously with both, the attributed network embedding and clustering, we propose a new model exploiting both content and structure information. The proposed model relies on the approximation of the relaxed continuous embedding solution by the true discrete clustering. Thereby, we show that incorporating an embedding representation provides simpler and easier interpretable solutions. Experiment results demonstrate that the proposed algorithm performs better, in terms of clustering, than the state-of-art algorithms, including deep learning methods devoted to similar tasks.
Towards a Bi-Stochastic Matrix Approximation of k-means and Some Variants
Authors: Labiod Lazhar , Nadif Mohamed 
Published: December 2023

AbstractThe k-means algorithm and some k-means variants have been shown to be useful and effective to tackle the clustering problem. In this paper we embed k-means variants in a bi-stochastic matrix approximation (BMA) framework. Then we derive from the k-means objective function a new formulation of the criterion. In particular, we show that some k-means variants are equivalent to algebraic problem of bi-stochastic matrix approximation under some suitable constraints. For optimizing the derived objective function, we develop two algorithms; the first one consists in learning a bi-stochastic similarity matrix while the second seeks for the optimal partition which is the equilibrium state of a Markov chain process. Numerical experiments on real data-sets demonstrate the interest of our approach.
Unsupervised Classification of Categorical Time Series Through Innovative Distances
Authors: López-Oriona Ángel , Vilar José A. , D’Urso Pierpaolo 
Published: December 2023

AbstractIn this paper, two novel distances for nominal time series are introduced. Both of them are based on features describing the serial dependence patterns between each pair of categories. The first dissimilarity employs the so-called association measures, whereas the second computes correlation quantities between indicator processes whose uniqueness is guaranteed from standard stationary conditions. The metrics are used to construct crisp algorithms for clustering categorical series. The approaches are able to group series generated from similar underlying stochastic processes, achieve accurate results with series coming from a broad range of models and are computationally efficient. An extensive simulation study shows that the devised clustering algorithms outperform several alternative procedures proposed in the literature. Specifically, they achieve better results than approaches based on maximum likelihood estimation, which take advantage of knowing the real underlying procedures. Both innovative dissimilarities could be useful for practitioners in the field of time series clustering.
Gender Equality, Climate Action, and Technological Innovation for Sustainable Development in Africa: Conclusion and Recommendations
Authors: Adeola Ogechi , Evans Olaniyi , Ngare Innocent 
Published: December 2023

AbstractExisting policies in developing countries are gender-neutral and do not effectively address gender-related issues and women’s rights protections. Of the 17 United Nations’ Sustainable Development Goals (SDGs), Goal 13, climate action, specifically calls for urgent efforts to combat climate change. There are complex and intricate links between the SDGs, and the attainment of one goal is inevitably connected to the achievement of the other. Gender, represented by SDG 5, which aims to achieve gender equality and empower all women and girls, presents one of the crucial interlinkages due to the gendered effect of climate change. Particularly in Africa, women and children are disproportionately affected by the adverse impacts of climate change. This concluding chapter presents a cohesive theme and argument based on the findings and discussions in the study, providing policy directions for Africa followed by the appropriate recommendations. The recommendations focus on how Africa can address the impacts of climate change, particularly regarding vulnerable communities, the need for response and protection plans by stakeholders, and the crucial role of technological innovation in mitigating climate effects. Finally, the recommendations in this book provide evidence-based discussions that can lead to more gender-inclusive climate change policymaking, gender integration in climate change planning, and technological innovation for climate change mitigation in Africa. By implementing these recommendations, Africa can achieve sustainable development and ensure that women and girls are not left behind.
Gender and Climate Issues in Africa
Authors: Adeola Ogechi , Evans Olaniyi , Ngare Innocent 
Published: December 2023

AbstractThe disproportionate impact of climate change on African communities, particularly those heavily reliant on agriculture, exacerbates the feminisation of poverty and environmental degradation. Patriarchy and gender inequality impede women’s ability to adapt to climate change, but addressing gender issues can enhance climate change efforts. This chapter provides insights into the intersection of gender, climate change, and sustainability in Africa, highlighting the need for inclusive policies that address gender disparities and the value of incorporating women’s perspectives for successful climate change initiatives in West, East, North, and Southern Africa. By incorporating gender equality and social inclusion into climate change mitigation efforts, the impact of climate change can be reduced, and sustainable solutions can be achieved.
Leveraging ICT for Climate Change Adaptation in Africa: A Focus on Women
Authors: Adeola Ogechi , Evans Olaniyi , Ngare Innocent 
Published: December 2023

AbstractInformation and communication technology (ICT) offers significant support for data gathering, making informed decisions, execution, and assessment in support of climate change adaptation for African women. The development and implementation of adaptive processes for the mitigation of climate change, which disproportionately affects women, can significantly benefit from the adoption of specialised technology. Emerging technologies, which could be particularly beneficial for African women, include integrated soil management practices, biotechnology, renewable energy, plant breeding, and synthetic biology. Africa must take action towards implementing effective scientific and technological measures to address the short- and long-term effects of climate change, especially as they affect women. This chapter introduces the section on technological innovation and climate change, discusses ICT-enabled energy transition for climate change mitigation, and presents recommendations on how Africa, particularly African women, can mitigate the impact of climate change through technology.
Thinking About Disability: Implications for Practice
Authors: Bigby Christine 
Published: December 2023

AbstractThis chapter considers the diversity of people with disabilities, highlighting their common experiences of significant social disadvantage on all social indicators. It introduces different theoretical models for understanding disability, comparing and contrasting the orientation of each and implications for providing support services for people with disabilities. It reviews the relative merits of each approach for informing practice that furthers quality of life and highlights the drawbacks of reliance on any one model. It provides an overview of the rights-based aims of contemporary disability policy and the broad contours of individualised funding models such as the NDIS, the social demographics of people with disability and the eight domains of quality of life which form the framework for the book.It proposes the need for Both/And thinking in disability practice and the dangers of using As If as a device for obscuring difference.
Building Strong Foundations: Listening to and Learning from People with Intellectual Disabilities and Their Families
Authors: Jackson Aaron J. , Bigby Christine 
Published: December 2023

AbstractThis chapter explores the importance of amplifying the voices and perspectives of people with intellectual disabilities in disability policy, research and service provision. It highlights what support workers and organisations might learn from actively listening to individuals with intellectual disabilities and their families. The chapter emphasises the critical role that family members play in the lives of some service users, particularly those with severe or profound intellectual disabilities. Drawing on both published literature and unpublished data from a longitudinal study of group homes, the chapter identifies key themes and recommendations for enhancing the quality of support provided in group homes and other forms of supported accommodation services. These include prioritising effective communication, promoting a culture of accountability and teamwork, maintaining strong family relationships and fostering community engagement and social connections. By implementing these strategies, service providers can better meet the diverse support needs of people with intellectual disabilities while honouring their voices and individuality.