bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation of extended kalman filter for localization of ambulance robot
Authors: Yang Chan-Yun , Samani Hooman , Tang Zirong , Li Chunxu 
Published: June 2024

AbstractThis paper focuses on the implementation of the Extended Kalman Filter for indoor localization of a semi-autonomous Ambulance Robot system named Ambubot. The system is designed to reduce the response time for lay rescuers to locate an Automated External Defibrillator (AED) during sudden cardiac arrest events. To achieve this objective, the robot is equipped with an AED, and the Extended Kalman Filter is utilized for optimal indoor localization. The filter is implemented using data from the robot’s Inertial Measurement Unit, which comprises 9 Degrees of Freedom. The paper provides an explicit description of the performance of the Extended Kalman Filter in estimating the position of Ambubot, and demonstrates that the proposed approach is effective in accurately determining and estimating the robot’s position in unknown indoor environments. The results suggest that the proposed method is a promising solution for improving survival rates in cardiac arrest cases, and may have potential applications in other fields where accurate indoor localization is required.
On the cost of risk misspecification in insurance pricing
Authors: Finger D. , Albrecher H. , Wilhelmy L. 
Published: June 2024

AbstractIn the non-life insurance industry, pricing is often done relative to individual criteria of policyholders. Various classification algorithms are in use to categorize policyholders into risk classes defined by the insurer, but classification errors may result from this process. In the light of recent automatic classification practices, it becomes important to assess the risks caused by such errors. In this paper we examine the impact of risk class misspecifications for a simple situation with two risk types. We provide a mean-variance framework for quantitatively studying the insurer’s optimization problem of specifying premiums and we analyze the tradeoff of costs and benefits when classification error probabilities are known.
Sustainable energy efficient workflow classification and scheduling in geo distributed cloud datacenter
Authors: Sharma Anu Priya , Singh Jaspreet , Gulzar Yonis , Gupta Deepali , Kumar Mukesh 
Published: June 2024

AbstractData centers are a major source of carbon emissions and are subsequently contributing to global carbon footprints. Keeping in view of providing a sustainable solution to society, we have analyzed various factors that can help to achieve carbon neutrality and maximum sustainability. Our study pointed towards the need to follow a sustainable approach for incoming workflow throughout the life-cycle of Data centers. We analyzed that workloads need to be segregated before assigning them to the data centers so that energy-efficient resource allocation could be done. This paper demonstrates unsupervised learning techniques to cluster the incoming cloud workloads. The heterogeneous workloads were characterized using machine learning approaches and appropriate clusters were crafted. For analysis, Google Cluster Dataset is used. In order to improve the accuracy, data were normalized, and random samples of data were selected for clustering. The machine learning algorithms applied were able to successfully determine the appropriate clusters that can further be used for energy-efficient resource scheduling.
Development of a simultaneous LC–MS/MS analytical method for plasma: 16 antipsychotics approved in Japan and 4 drug metabolites
Authors: Maekawa Masamitsu , Yokota Maki , Sato Toshihiro , Sato Yu , Kumondai Masaki , Sato Yuji , Suzuka Masato , Kobayashi Daisuke , Sakamoto Kotaro , Matsuura Masaki , Kikuchi Masafumi , Komatsu Hiroshi , Fujii Kumiko , Ozeki Yuji , Tomita Hiroaki , Mano Nariyasu 
Published: June 2024

AbstractThe increased risk of adverse drug reactions due to the concomitant use of antipsychotics is problematic in the treatment of schizophrenia. Therefore, the simultaneous analysis of their plasma concentrations is required. In this study, we developed a simultaneous liquid chromatography/tandem mass spectrometry (LC–MS/MS) method for analyzing plasma antipsychotics approved in Japan for therapeutic drug monitoring (TDM) applications. First, we counted the prescriptions for 16 antipsychotics and concomitant drugs used at the Tohoku University Hospital. LC–MS/MS was used for the simultaneous analysis of 16 antipsychotics and four drug metabolites. This analysis was conducted using a combination of selected reaction monitoring mode and reversed-phase chromatography. Following the examination of the MS/MS and LC conditions, an analytical method validation test was conducted. The developed method was used to analyze plasma antipsychotic levels in patients with schizophrenia. One-third of the patients received treatment with multiple antipsychotics. Under LC–MS/MS conditions, LC separation was performed using a combination of a C18 column and ammonium formate-based mobile phases with a gradient flow. The calibration curves were optimized by adjusting the ion abundance, and 11 compounds met the criteria for intra- and inter-day reproducibility tests. Some stability test results did not meet these criteria; therefore, further investigation is required. The developed method permitted the measurement of all the plasma parameters, including concentrations above the therapeutic range. Therefore, this method may be useful in the daily TDM practice of antipsychotics.
Graphical abstract
Topologically optimised facade brackets: an embodied carbon, structural and residual stress analysis
Authors: Grigoriadis Kostas , Bouchard John , Herrmann Michael 
Published: June 2024

AbstractThe research investigates the topological optimisation of the metal brackets that connect curtain wall panelling to the floor slabs of a building. As is typically the case with standard building components, the brackets are overdesigned with higher load margins than real applied loads. Optimising them results in reduced mass and a more evenly spread stress distribution. Correspondingly, the question that the project asks is whether the optimised designs have a comparable structural performance to the standard bracketry used in construction, and a lower embodied carbon. To answer this, several optimisations of a standard facade bracket are performed, resulting in a total of six converged design options, with three of them progressed for fabrication. The manufactured designs are then horizontal and vertical load and residual stress tested to assess their performance, and an embodied carbon analysis is performed to calculate the corresponding emissions for raw material extraction, processing, and component fabrication. The results indicate the presence of compressive yield magnitude residual stresses, and that structural performance is comparable to a standard bracket, but embodied carbon is in most cases higher. The paper concludes with a discussion of the findings, and possible next steps in the optimisation, structural testing, and embodied carbon analysis workflow.
A survey on deep clustering: from the prior perspective
Authors: Lu Yiding , Li Haobin , Li Yunfan , Lin Yijie , Peng Xi 
Published: June 2024

AbstractFacilitated by the powerful feature extraction ability of neural networks, deep clustering has achieved great success in analyzing high-dimensional and complex real-world data. The performance of deep clustering methods is affected by various factors such as network structures and learning objectives. However, as pointed out in this survey, the essence of deep clustering lies in the incorporation and utilization of prior knowledge, which is largely ignored by existing works. From pioneering deep clustering methods based on data structure assumptions to recent contrastive clustering methods based on data augmentation invariances, the development of deep clustering intrinsically corresponds to the evolution of prior knowledge. In this survey, we provide a comprehensive review of deep clustering methods by categorizing them into six types of prior knowledge. We find that in general the prior innovation follows two trends, namely, i) from mining to constructing, and ii) from internal to external. Besides, we provide a benchmark on five widely-used datasets and analyze the performance of methods with diverse priors. By providing a novel prior knowledge perspective, we hope this survey could provide some novel insights and inspire future research in the deep clustering community.
Multi-dimensional classification: paradigm, algorithms and beyond
Authors: Jia Bin-Bin , Zhang Min-Ling 
Published: June 2024

AbstractMulti-dimensional classification (MDC) aims at learning from objects where each of them is represented by a single instance while associated with multiple class variables. In recent years, this practical learning paradigm has attracted increasing attentions in machine learning community. In this paper, a timely review on this topic is provided with emphasis on representative algorithms. Firstly, the MDC learning framework, commonly used evaluation metrics and publicly available MDC datasets are given. Then, eight state-of-the-art MDC algorithms are scrutinized as the representatives of three categories. After that, several related learning settings are briefly summarized. Finally, this paper is concluded with discussing some open problems to be studied in the future.
Traditional health care systems and immunity boosting: exploring plant based indigenous knowledge systems amidst the COVID-19 pandemic
Authors: Maikhuri Rakesh Kumar , Maletha Ajay , Singh Ravindra , Bhatt Girish , Agarwal Sunil , Dhyani Shalini , Nautiyal Sunil , Phondani Prakash Chandra , Rawat Lakhpat Singh , Badoni Priyanka , Kandari Laxman Singh , Srivastava Abhishek , Maikhuri Rama , Chamola Bhagwati Prasad , Semwal Rajeev Lochan , Purohit Vijay Kant , Singh Shankar 
Published: June 2024

AbstractThe COVID-19 outbreak has highlighted the importance of utilizing traditional Ecological knowledge (TEK) to address the health risks linked to the pandemic. Mountain communities have traditionally relied on wild plant species and indigenous crops to enhance their immune system and maintain nutritional security. Recognizing the significance of indigenous knowledge systems this study was undertaken to explore plant-based traditional healthcare systems to revitalize immunity against COVID-19. A comprehensive survey and interviews of 195 local healers (Vaidyas) and residents were carried out using structured and semi-structured questionnaires following the convenience sampling method. The survey identified 40 plant species traditionally used for treating various ailments and augmenting immune functions. This study revealed a strong interest (over 80%) among the local population in plant-based remedies to enhance immunity for COVID-19. This study underscores the necessity of revitalizing and integrating diminishing traditional knowledge into mainstream healthcare practices, emphasizing the significance of region-specific approaches aligned with local resources and requirements. Therefore, there is an urgent need for exploration and recognition of the value of traditional knowledge and indigenous foods, alongside rigorous clinical trials to ascertain their role in fortifying health resilience against COVID-19.
The Transfer of Solar Wind Energy to the Venusian Ionosphere Through Magnetic Pumping: Evidence From Pioneer Venus Orbiter Observations
Authors: Fowler C. M. , Frost A. , Agapitov O. , Frahm R. , Xu S. 
Published: June 2024

AbstractThe collisionless nature of planetary magnetospheres means that electromagnetic forces are fundamental in controlling the flow of energy and momentum through these systems. We use Pioneer Venus Orbiter (PVO) observations to demonstrate that the magnetic pumping process can be active at Venus, in analogy to its recent discovery at Mars. The presented case study demonstrates the framework for how the process can work at Venus, and the results of a statistical analysis show that the ambient plasma conditions support the process being active. Magnetic pumping enables low frequency magnetosonic waves to heat ambient ionospheric electrons and provides a mechanism that couples the solar wind to the Venusian ionosphere. This is the first time the magnetic pumping process has been discussed at Venus.
Nonlinear and Non‐Gaussian Ensemble Assimilation of MOPITT CO
Authors: Gaubert Benjamin , Anderson Jeffrey L. , Trudeau Michael , Smith Nadia , McKain Kathryn , Pétron Gabrielle , Raeder Kevin , Arellano Avelino F. , Granier Claire , Emmons Louisa K. , Ortega Ivan , Hannigan James W. , Tang Wenfu , Worden Helen M. , Ziskin Daniel , Edwards David P. 
Published: June 2024

AbstractSatellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets.
Pioneer Venus Orbiter Observations of Solar Wind Driven Magnetosonic Waves Interacting With the Dayside Venusian Ionosphere
Authors: Fowler C. M. , Ledvina S. , Chaston C. C. , Persson M. , Ramstad R. , Luhmann J. 
Published: June 2024

AbstractWe use in situ plasma observations made by the Pioneer Venus Orbiter spacecraft to show for the first time that magnetosonic waves can couple the solar wind to the upper ionosphere and deposit energy there. The waves are generated upstream of Venus, are advected into the shock and propagate across the draped magnetic field, through the magnetosheath and into the dayside upper ionosphere. The magnetosonic waves damp in the upper ionosphere in a region where physical collisions are rare, and electromagnetic forces must control this damping. The waves damp when the ionospheric heavy ion density is a few thousand cm−3 and wave‐particle interactions with the dominant O+ ions are postulated as the damping mechanism. Estimates of ion heating rates show that 1%–5% of the O+ ion distribution function could be heated to escape energy in 10–40 s.
Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes
Authors: Kojima Kaname , Tadaka Shu , Okamura Yasunobu , Kinoshita Kengo 
Published: June 2024

AbstractWidely used genotype imputation methods are based on the Li and Stephens model, which assumes that new haplotypes can be represented by modifying existing haplotypes in a reference panel through mutations and recombinations. These methods use genotypes from SNP arrays as inputs to estimate haplotypes that align with the input genotypes by analyzing recombination patterns within a reference panel, and then infer unobserved variants. While these methods require reference panels in an identifiable form, their public use is limited due to privacy and consent concerns. One strategy to overcome these limitations is to use de-identified haplotype information, such as summary statistics or model parameters. Advances in deep learning (DL) offer the potential to develop imputation methods that use haplotype information in a reference-free manner by handling it as model parameters, while maintaining comparable imputation accuracy to methods based on the Li and Stephens model. Here, we provide a brief introduction to DL-based reference-free genotype imputation methods, including RNN-IMP, developed by our research group. We then evaluate the performance of RNN-IMP against widely-used Li and Stephens model-based imputation methods in terms of accuracy (R2), using the 1000 Genomes Project Phase 3 dataset and corresponding simulated Omni2.5 SNP genotype data. Although RNN-IMP is sensitive to missing values in input genotypes, we propose a two-stage imputation strategy: missing genotypes are first imputed using denoising autoencoders; RNN-IMP then processes these imputed genotypes. This approach restores the imputation accuracy that is degraded by missing values, enhancing the practical use of RNN-IMP.
How risky is a second allogeneic stem cell transplantation?
Authors: Penack Olaf , Abouqateb Mouad , Peczynski Christophe , Boreland William , Kröger Nicolaus , Zeiser Robert , Ciceri Fabio , Schroeder Thomas , Dreger Peter , Passweg Jakob , Schetelig Johannes , Stelljes Matthias , Blau Igor Wolfgang , Franke Georg-Nikolaus , Riesner Katarina , Schoemans Hélène , Moiseev Ivan , Peric Zinaida 
Published: June 2024

AbstractThere is no consensus on second allogeneic stem cell transplantation (alloSCT) indications in patients with hematologic malignancies relapsing after a first alloSCT. In historic publications, a very high non-relapse mortality (NRM) has been described, arguing against performing a second alloSCT. We analysed the outcome of 3356 second alloSCTs performed 2011–21 following a hematologic malignancy relapse. Outcomes at two years after second alloSCT were: NRM 22%, relapse incidence 50%, overall survival 38%, and progression-free survival 28%. Key risk factors for increased NRM were: older age, low performance score, high disease-risk-index, early relapse after the first alloSCT, unrelated/haploidentical donor, and GVHD before second alloSCT. Any type of GVHD after first alloSCT was also important risk factor for acute GVHD and chronic GVHD after second alloSCT. There was a preferential use of a different donor (80%) at second alloSCT from first alloSCT. However, in multivariate analysis, the use of the same alloSCT donor for second alloSCT vs. a different donor was not associated with any of the survival or GVHD endpoints. We show considerably improved outcome as compared to historic reports. These current data support a wider use of second alloSCT and provide risk factors for NRM that need to be considered.
Elasto-inertial focusing and particle migration in high aspect ratio microchannels for high-throughput separation
Authors: Tanriverdi Selim , Cruz Javier , Habibi Shahriar , Amini Kasra , Costa Martim , Lundell Fredrik , Mårtensson Gustaf , Brandt Luca , Tammisola Outi , Russom Aman 
Published: June 2024

AbstractThe combination of flow elasticity and inertia has emerged as a viable tool for focusing and manipulating particles using microfluidics. Although there is considerable interest in the field of elasto-inertial microfluidics owing to its potential applications, research on particle focusing has been mostly limited to low Reynolds numbers (Re<1), and particle migration toward equilibrium positions has not been extensively examined. In this work, we thoroughly studied particle focusing on the dynamic range of flow rates and particle migration using straight microchannels with a single inlet high aspect ratio. We initially explored several parameters that had an impact on particle focusing, such as the particle size, channel dimensions, concentration of viscoelastic fluid, and flow rate. Our experimental work covered a wide range of dimensionless numbers (0.05 < Reynolds number < 85, 1.5 < Weissenberg number < 3800, 5 < Elasticity number < 470) using 3, 5, 7, and 10 µm particles. Our results showed that the particle size played a dominant role, and by tuning the parameters, particle focusing could be achieved at Reynolds numbers ranging from 0.2 (1 µL/min) to 85 (250 µL/min). Furthermore, we numerically and experimentally studied particle migration and reported differential particle migration for high-resolution separations of 5 µm, 7 µm and 10 µm particles in a sheathless flow at a throughput of 150 µL/min. Our work elucidates the complex particle transport in elasto-inertial flows and has great potential for the development of high-throughput and high-resolution particle separation for biomedical and environmental applications.
Dexmedetomidine improves the circulatory dysfunction of the glymphatic system induced by sevoflurane through the PI3K/AKT/ΔFosB/AQP4 pathway in young mice
Authors: Wang Shuying , Yu Xiaojin , Cheng Lili , Ren Weishu , Wen Gehua , Wu Xue , Lou Haoyang , Ren Xinghua , Lu Lei , Hermenean Anca , Yao Jun , Li Baoman , Lu Yan , Wu Xu 
Published: June 2024

AbstractMultiple sevoflurane exposures may damage the developing brain. The neuroprotective function of dexmedetomidine has been widely confirmed in animal experiments and human studies. However, the effect of dexmedetomidine on the glymphatic system has not been clearly studied. We hypothesized that dexmedetomidine could alleviate sevoflurane-induced circulatory dysfunction of the glymphatic system in young mice. Six-day-old C57BL/6 mice were exposed to 3% sevoflurane for 2 h daily, continuously for 3 days. Intraperitoneal injection of either normal saline or dexmedetomidine was administered before every anaesthesia. Meanwhile the circulatory function of glymphatic system was detected by tracer injection at P8 and P32. On P30-P32, behavior tests including open field test, novel object recognition test, and Y-maze test were conducted. Primary astrocyte cultures were established and treated with the PI3K activator 740Y-P, dexmedetomidine, and small interfering RNA (siRNA) to silence ΔFosB. We propose for the first time that multiple exposure to sevoflurane induces circulatory dysfunction of the glymphatic system in young mice. Dexmedetomidine improves the circulatory capacity of the glymphatic system in young mice following repeated exposure to sevoflurane through the PI3K/AKT/ΔFosB/AQP4 signaling pathway, and enhances their long-term learning and working memory abilities.