bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inflammatory landscape in Xeroderma pigmentosum patients with cutaneous melanoma
Authors: Chikhaoui Asma , Jones Meriem , Režen Tadeja , Ben Ahmed Melika , Naouali Chokri , Komel Radovan , Zghal Mohamed , Boubaker Samir , Abdelhak Sonia , Yacoub-Youssef Houda 
Published: August 2022

AbstractXeroderma pigmentosum (XP) is a DNA repair disease that predisposes to early skin cancers as cutaneous melanoma. Melanoma microenvironment contains inflammatory mediators, which would be interesting biomarkers for the prognosis or for the identification of novel therapeutic targets. We used a PCR array to evaluate the transcriptional pattern of 84 inflammatory genes in melanoma tumors obtained from XP patients (XP-Mel) and in sporadic melanoma (SP-Mel) compared to healthy skin. Commonly expressed inflammatory genes were further explored via GTEx and GEPIA databases. The differentially expressed inflammatory genes in XP were compared to their expression in skin exposed to UVs, and evaluated on the basis of the overall survival outcomes of patients with melanoma. Monocyte subsets of patients with SP-Mel, XP and healthy donors were also assessed. PCR array data revealed that 34 inflammatory genes were under-expressed in XP-Mel compared to SP-Mel. Differentially expressed genes that were common in XP-Mel and SP-Mel were correlated with the transcriptomic datasets from GEPIA and GTEx and highlighted the implication of KLK1 and IL8 in the tumorigenesis. We showed also that in XP-Mel tumors, there was an overexpression of KLK6 and KLK10 genes, which seems to be associated with a bad survival rate. As for the innate immunity, we observed a decrease of intermediate monocytes in patients with SP-Mel and in XP. We highlight an alteration in the immune response in XP patients. We identified candidate biomarkers involved in the tumorigenesis, and in the survival of patients with melanoma. Intermediate monocyte’s in patients at risk could be a prognostic biomarker for melanoma outcome.
An automated 3D-printed perfusion bioreactor combinable with pulsed electromagnetic field stimulators for bone tissue investigations
Authors: Gabetti Stefano , Masante Beatrice , Cochis Andrea , Putame Giovanni , Sanginario Alessandro , Armando Ileana , Fiume Elisa , Scalia Alessandro Calogero , Daou Farah , Baino Francesco , Salati Simona , Morbiducci Umberto , Rimondini Lia , Bignardi Cristina , Massai Diana 
Published: August 2022

AbstractIn bone tissue engineering research, bioreactors designed for replicating the main features of the complex native environment represent powerful investigation tools. Moreover, when equipped with automation, their use allows reducing user intervention and dependence, increasing reproducibility and the overall quality of the culture process. In this study, an automated uni-/bi-directional perfusion bioreactor combinable with pulsed electromagnetic field (PEMF) stimulation for culturing 3D bone tissue models is proposed. A user-friendly control unit automates the perfusion, minimizing the user dependency. Computational fluid dynamics simulations supported the culture chamber design and allowed the estimation of the shear stress values within the construct. Electromagnetic field simulations demonstrated that, in case of combination with a PEMF stimulator, the construct can be exposed to uniform magnetic fields. Preliminary biological tests on 3D bone tissue models showed that perfusion promotes the release of the early differentiation marker alkaline phosphatase. The histological analysis confirmed that perfusion favors cells to deposit more extracellular matrix (ECM) with respect to the static culture and revealed that bi-directional perfusion better promotes ECM deposition across the construct with respect to uni-directional perfusion. Lastly, the Real-time PCR results of 3D bone tissue models cultured under bi-directional perfusion without and with PEMF stimulation revealed that the only perfusion induced a ~ 40-fold up-regulation of the expression of the osteogenic gene collagen type I with respect to the static control, while a ~ 80-fold up-regulation was measured when perfusion was combined with PEMF stimulation, indicating a positive synergic pro-osteogenic effect of combined physical stimulations.
Liquid–liquid equilibrium measurements and computational study of salt–polymer aqueous two phase system for extraction of analgesic drugs
Authors: Ghaffari Fariba , Khorsandi Mohammad , Shekaari Hemayat , Zafarani-Moattar Mohammed Taghi 
Published: August 2022

AbstractIn recent decades, aqueous two phase systems have gained a lot of attention for extraction of different materials. In this work, an aqueous two phase system was made by polyethylene glycol 600 and potassium hydroxide and phase diagram were determined for this system. The experimental binodal data were described using two empirical nonlinear three parameter expressions developed by Merchuk and Zafarani-Moattar. The consistency of the experimental tie-line data was determined by utilizing the Othmer-Tobias, Bancraft, and Setschenow correlations. Also, the extraction of two analgesic drugs, namely ibuprofen and acetaminophen were investigated by the mentioned ATPS. For this purpose, partition coefficients and extraction efficiencies of each drug were calculated. The trend of extraction efficiencies indicated that the responsibility of extraction of the mentioned drugs into the polymer-rich top phase is related to their hydrophobicity. The Diamond-Hsu equation and its modified version were used to correlate the drugs experimental partition coefficients. Furthermore, the interaction of mentioned drugs with polyethylene glycol was investigated employing quantum computing techniques based upon density functional theory (DFT). These results were in good agreement with the trend of extraction efficiencies of studied drugs.
Hearing Aids Mitigate Tinnitus, But Does It Matter if the Patient Receives Amplification in Accordance With Their Hearing Impairment or Not? A Meta-Analysis
Authors: Waechter Sebastian , Jönsson Anders 
Published: August 2022

Purpose:
The purpose of the present meta-analysis is to explore the potential effects of objective verification of hearing aid amplification on tinnitus-related outcomes.

Method:
Twenty-seven studies reporting tinnitus outcomes pre and post hearing aid fitting were identified through a systematic literature search. From these studies, data from 1,400 participants were included in the present meta-analysis. Studies were divided into subgroups based on whether they had reported performing objective verification of the participants' hearing aid amplification or not. Outcome measures were tinnitus distress and tinnitus loudness.

Results:

Meta-analyses of all included studies indicated verified amplification to result in significantly enhanced reduction of tinnitus loudness (
p
< .00001), while the enhanced reduction of tinnitus distress only approached statistical significance (
p
= .07). However, when excluding an outlier from the subgroup of studies using unverified amplification, individuals receiving verified amplification showed significantly greater reduction of tinnitus distress (
p
= .02). In addition, analyses of longitudinal effects revealed that the reductions of tinnitus distress decreased over time among individuals receiving unverified amplification but increased over time among individuals receiving verified amplification.

Conclusions:
The present meta-analysis indicates verified hearing aid amplification to be superior to unverified amplification in terms of reduction of tinnitus loudness and distress. The longitudinal increase of mitigation of tinnitus distress with verified amplification only may reflect improved neural reorganization and/or better adherence to hearing aid use, with verified compared to unverified amplification. Due to the low cost of hearing aid verification compared to the high societal cost of tinnitus, objective verification of hearing aid amplification for tinnitus patients is recommended.
Internet of Things‐based healthcare system on patient demographic data in Health 4.0
Authors: Abdullayeva Fargana J. 
Published: August 2022

AbstractRecently, the spread of COVID‐19 virus infection and the increase of people number with chronic diseases have attracted great attention all over the world. The detection and control of such diseases based on patient demographic data are considered to be a major problem. The key issue in the solution to these problems is the development of methods and algorithms to forecast wellness and categorise patients according to their healthy and unhealthy states. In this paper, a comprehensive analysis of machine learning approaches in the field of diagnosing COVID‐19 has been conducted, and for the detection of chronic diseases in patients, to identify symptoms of COVID‐19 virus infection in advance, and control the situation a healthcare system has been proposed. The constructed system provides real‐time monitoring of chronic diseases and COVID‐19 virus infection in patients. The proposed system consists of five layers: IoT sensor layer, Data transmission layer, Fog layer, Cloud layer, the Application layer. The system architecture in the Fog layer uses machine learning and deep learning algorithms to diagnose patients' diseases, to generate and send diagnostic and emergency alerts to users. The classification module of the system's Fog layer categorises the patient's health status into healthy and unhealthy classes. In this module, to classify medical data the Decision Tree, Random Forest, SVM, Gradient Boosting, Logistic Regression algorithms are used. The COVID‐19 dataset is used to test the effectiveness of the methods. The best results from the comparative analysis of the methods are obtained from the Decision Tree, Random Forest, and Gradient Boosting algorithms, which are recognised data points with high accuracy and on the accuracy metric reached 1.0, 0.99, 1.0 values, respectively. The classification of the other two SVM and Logistic Regression algorithms provided the worst results, and the accuracy score of both classifiers obtained a 0.89 value.
A novel output power determination and power distribution of hybrid energy storage system for wind turbine power smoothing
Authors: Eydi Mohammad , Alishahi Mohammad , Zarif Mahdi 
Published: August 2022

AbstractThis paper deals with the power smoothing of the wind power plants connected to a microgrid using a hybrid energy storage system (HESS). In a HESS, the power should be distributed between the battery and capacitor such that the capacitor supplies the peaks of power and its high‐frequency fluctuations, and the battery compensates for the rest. Besides, due to the relatively low lifetime of the batteries compared to the capacitors, it is preferred to transfer power fluctuations to the capacitor as much as possible. In this paper, methods for calculating the output, battery, and capacitor powers are presented. The output power is determined based on the grid restrictions and the battery SOC. The battery and capacitor powers are decided via an adaptive low‐pass filter. The cut‐off frequency of the low‐pass filter is specified by a fuzzy controller such that not only the power spikes and high‐frequency fluctuations are transferred to the capacitor but also the battery SOC variations are reduced. The simulation results show that for the presented wind speed profile, the output power determination reduces 13.7% of the HESS exchanged energy compared to the ramp limiting method. Besides, the proposed power distribution method reduces 92.6% of the unbeneficial charges and discharges of HESS and 8% of the battery exchanged energy compared to the condition that the constant cut‐off frequency filter is utilised. The experimental results confirm the effectiveness of the proposed output power determination and HESS power distribution methods. Analysing the methods’ cost proves that although the utilisation of the capacitor bank increases the system initial investment cost, it will return after a while relying on the units’ capacity, power fluctuations, control method etc.
An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters
Authors: Yang Mao , Yan Qi , Dai Bozhi , Chen Xinxin , Ma Miaomiao , Su Xin 
Published: August 2022

AbstractLarge‐scale day‐ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarely consider spatial correlation factors. This necessitates further improvements to forecasting systems. In this study, to increase the day‐ahead WPF accuracy for WFCs, fractal transform theory is introduced to optimize the process of WPF for WFCs through spatial upscaling and establish a day‐ahead WPF model for WFCs based on an improved spatial upscaling method. First, a WFC is partitioned into subclusters. Then, using fractal transform theory, an affine relation is established between the local output of each subcluster and the overall output of the WFC. Finally, a day‐ahead power forecast is produced for the WFC through spatial upscaling of the forecast for the subcluster with the highest grey relational grade with the output of the WFC. The applicability of the proposed forecasting model is examined using historical measured data for a large WFC in north‐eastern China. The case study results show that the proposed forecasting model outperforms the summation method and the statistical upscaling method in terms of forecasting accuracy.
Deep side group sparse coding network for image denoising
Authors: Yin Haitao , Wang Tianyou 
Published: August 2022

AbstractRecently, deep learning has made significant progress in image denoising. However, most of existing deep learning based methods are purely data‐driven, without considering the knowledge of image denoising. Moreover, the parameters of deep denoising network are not explainable. According to these issues, this paper proposes a deep side group sparse coding network for image denoising, named a side group sparse coding (SGSC)‐Net. First, SGSC model for image denoising by exploiting prior information regarding the group sparse coefficients consistency is developed. Specifically, the side information is constructed as the weighted combination of intermediate estimations, and updated iteratively. Then, the optimisation solution of SGSC model is turned into a deep neural network using deep unfolding, that is, SGSC‐Net. The computational path of SGSC‐Net fully follows the iterations of optimisation solution, and consequently the network parameters are interpretable. Furthermore, the design of SGSC‐Net employs the insight of SGSC denoising model. The experimental results on well‐known datasets quantitatively and qualitatively demonstrate that SGSC‐Net is competitive to existing deep unfolding‐based and typical deep neural network‐based methods.
Multiple‐model Gaussian mixture probability hypothesis density filter based on jump Markov system with state‐dependent probabilities
Authors: Sun Yi‐Chieh , Kim Dohyeung , Hwang Inseok 
Published: August 2022

AbstractThe Gaussian mixture probability density (GM‐PHD) filter has become a popular approach to solve the multiple‐target tracking (MTT) problem because it can effectively and efficiently estimate the number of targets and target states that change over time from noisy measurements. In the GM‐PHD filter, the target detection and survival probabilities, and the birth rate are assumed to be constant, irrespective of the target state. However, in some applications, for example, when the target follows the planned trajectory, the detection, survival and birth of the target depend on its state. Besides, when the target reaches a waypoint along the planned trajectory, it will take a manoeuvre to go to the next waypoint, but the GM‐PHD filter does not accommodate manoeuvring targets that switch between several motion models. To address this, we propose a multiple‐model GM‐PHD filter with state‐dependent probabilities, which can explicitly consider the mode transition probabilities, the probabilities of detection and survival, and the birth rate as a function of the target state. The performance of the proposed algorithm is demonstrated by illustrative MTT scenarios which include target manoeuvres during occlusion.
Anti‐cracking performance test of thick steel structure fireproof coating under vibration fatigue load for critical health infrastructure
Authors: Zhang Jie , Liu Tingting , Hu Xinmin , Raffik Rasheed , Bhatt Mohammed Wasim , Ofori Isaac 
Published: August 2022

AbstractThe infrastructure of health and biomedical facilities caters to a variety of purposes that frequently vary over time, flexibility and adaptability are crucial traits for future re‐configuration. This paper chooses the relevant cement‐based solid metal structures fire‐retardant coatings available on the market as the test object and performs high‐cycle fatigue stress experiments on steel structural elements painted with cement‐based thick steel structure fire‐retardant varnishes. It simulates the deformation of the steel structure under the daily variable load of high‐rise steel structure buildings, investigates the cracking and adhesion properties of the fireproof coating on the surface of the steel component under the repeated action of this load, and evaluates the service life of the fireproof coating for the steel structure under normal use conditions. When the best dosage of modified epoxy resin E51 is 10%, the best ratio of emulsifier is m(SDS): m(OP‐10 = 2:1, the best dosage is 1.25% of the monomer quality, and the best dosage of G04 is 3% of the monomer mass, and the particle size distribution of the latex particles is concentrated. The average particle size of the emulsion is 138.3 nm, and the hardness and water resistance of the paint film are the best.
Prediction of battery charging process based on aggregation neural network
Authors: Sun Shuo , Sun Jun‐zhong , Yang Zhan‐lu , Zhou Zhi‐yong , Ding Yi‐yang 
Published: August 2022

AbstractAn online sequence extreme learning machine neural network prediction model based on sample aggregation method (AOSELM) is proposed, which is used to solve the prediction of the charging process of lead‐acid batteries. The model is based on the online sequence extreme learning machine (OSELM) neural network and finds the similar samples of the test samples based on the mean influence value (MIV) algorithm. Similar samples are used for temporary incremental learning of the neural network, then the updated neural network makes predictions on the test samples. Taking the terminal voltage prediction of the battery charging process as an example, the BP neural network, the OSELM neural network and the AOSELM neural network are used to compare the predictions, and the charging process of the battery is predicted based on the AOSELM neural network. The comparison results show that the AOSELM neural network has higher prediction accuracy and adaptive ability than the BP neural network, and reduces the maximum absolute percentage error and the maximum absolute error of the prediction result when compared with the OSELM neural network. The prediction shows that the charging process of lead‐acid batteries can be accurately predicted by the AOSELM neural network.
Calibration of image plate and back illuminated charge coupled device detectors at the thermal emission band of high Z target laser produced plasmas (80–800 eV)
Authors: Ehrlich Y. , Levy I. , Fraenkel M. 
Published: August 2022

We present a systematic method to absolutely calibrate detector efficiency vs photon energy using a laser produced plasma broadband x-ray source, a gold standard calibrated detector, and transmission gratings (TGs) as dispersive elements. Calibration uses one calibrated TG and a calibrated gold standard detector on one channel and a second calibrated TG and a detector to be calibrated on the other channel. Both channels simultaneously view the laser-produced plasma x-ray source from the same angle with respect to the laser beam and the planar target normal. Image plate detectors are calibrated for the first time at photon energies below 700 eV. Single shot simultaneous calibration of several detectors is possible, making this method an efficient and practical way to periodically calibrate detectors, using in-house capabilities of laser laboratories.
Visual and multimodal interaction of metaphor and metonymy*
Authors: Kashanizadeh Zahra , Forceville Charles 
Published: August 2022

Abstract
Conceptual Metaphor Theory’s central idea that metaphor is a figure of thought rather than a figure of language has led to the examination of nonverbal and multimodal manifestations of metaphor. Over the past twenty years, the verbal trope of metonymy has similarly been theorized from a conceptual point of view, but the implications of this work for visual studies have only begun to be examined. Investigating visual manifestations of metonymy will moreover also improve our understanding of visual metaphor, as often these latter depend on, and interact with, metonymies. In this chapter we propose to explore the interaction of metaphor and metonymy in the visual/multimodal realm of print advertising, using Francisco Ruiz de Mendoza and Olga Díez’ (2002) typology, and building on Paula Peréz-Sobrino’s (2017) applications of this typology. Our twofold aim is (1) to see if, and if so, how, all patterns of this typology appear in ads; and (2) to investigate a number of Iranian and Dutch print advertisements in which metaphor and metonymy interact. Analyzing ads from two cultures will enable us to demonstrate how cultural background knowledge is essential for understanding metaphor-metonymy interactions.
Visual metaphor and its narrative function*
Authors: Hetmański Marek 
Published: August 2022

Abstract
The chapter aims at analyzing visual metaphors and allegories in the fine arts – particularly in the symbolic paintings by J. Malczewski – as creative tools of both artistic expression and discourse storytelling in which they play a vital role. Visual metaphors are suggestive and effective in artistic performance and, therefore, in communicating abstract ideas to the individual viewer and the public. Such paintings (symbolic and surrealistic), whilst encompassing concrete (source) and universal (target) domains in their depicted metaphoric structures, can be powerful enough to create possible and alternative courses of events. Based on the analysis of R. Arnheim’s concept of openness of fine art works, J. Bruner’s theory of narrative mind, storytelling and possible worlds, and Ch. Forceville’s analyses of visual metaphors, the chapter will attempt to answer the following two philosophical and epistemological questions: (1) how universal themes are depicted, perceived, conveyed, and comprehended in metaphorical paintings; and (2) what is the difference between the structures of the visual metaphors characteristic for these paintings and merely literary parabolic means. Both conceptual metaphors and blending theories are used in the analyses of selected symbolic and metaphoric paintings by Malczewski to explain in what scope his painting methods and their narrative structures are entangled in Polish national-cultural history, and how important they are in cognitive studies as well as in the history and theory of fine arts.