bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time frequency domain deep CNN for automatic background classification in speech signals
Authors: Yakkati Rakesh Reddy , Yeduri Sreenivasa Reddy , Tripathy Rajesh Kumar , Cenkeramaddi Linga Reddy 
Published: September 2023

AbstractMany application areas, such as background identification, predictive maintenance in industrial applications, smart home applications, assisting deaf people with their daily activities and indexing and retrieval of content-based multimedia, etc., use automatic background classification using speech signals. It is challenging to predict the background environment accurately from speech signal information. Thus, a novel synchrosqueezed wavelet transform (SWT)-based deep learning (DL) approach is proposed in this paper for automatically classifying background information embedded in speech signals. Here, SWT is incorporated to obtain the time-frequency plot from the speech signals. These time-frequency signals are then fed to a deep convolutional neural network (DCNN) to classify background information embedded in speech signals. The proposed DCNN model consists of three convolution layers, one batch-normalization layer, three max-pooling layers, one dropout layer, and one fully connected layer. The proposed method is tested using various background signals embedded in speech signals, such as airport, airplane, drone, street, babble, car, helicopter, exhibition, station, restaurant, and train sounds. According to the results, the proposed SWT-based DCNN approach has an overall classification accuracy of 97.96 (± 0.53)% to classify background information embedded in speech signals. Finally, the performance of the proposed approach is compared to the existing methods.
Effects of a peer advocacy intervention on cervical cancer screening among social network members: results of a randomized controlled trial in Uganda
Authors: Wagner Glenn J. , Matovu Joseph K. B. , Juncker Margrethe , Namisango Eve , Bouskill Kathryn , Nakami Sylvia , Beyeza-Kashesya Jolly , Luyirika Emmanuel , Bogart Laura M. , Green Harold D. , Wanyenze Rhoda K. 
Published: September 2023

AbstractCervical cancer (CC) is the most common cancer among women in Uganda, yet lifetime CC screening is as low as 5%. Training women who have screened for CC to engage in peer advocacy could increase uptake of CC screening in social networks. We conducted a randomized controlled trial of a peer-facilitated, manualized, 7-session group intervention to train women to engage in CC prevention advocacy. Forty women recently screened for CC (index participants) enrolled and were assigned to receive the intervention (n = 20) or wait-list control (n = 20). Each index was asked to recruit up to three female social network members (alters) who had not been screened for CC (n = 103 enrolled alters). All index and alter participants were assessed at baseline and month-6 follow-up. All but one (n = 39; 98%) index and 98 (95%) alter participants completed the month 6 assessment. In multivariate regression models controlling for baseline outcome measures and demographic covariates, intervention alters were more likely to have been screened for CC at month 6 [67% vs. 16%; adjusted OR (95% CI) = 12.13 (4.07, 36.16)], compared to control alters. Data also revealed significant increased engagement in CC prevention advocacy, among both index and alter participants in the intervention group at month 6, compared to the control group. The intervention was highly effective in increasing CC screening uptake among social network members, and engagement in CC prevention advocacy among not only intervention recipients, but also targets of advocacy, suggesting the potential for wide dissemination of CC knowledge.Trial Registration. NIH Clinical Trial Registry NCT04960748 (clinicaltrials.gov).
Effects of Reaction Temperature and Time on Structural, Morphological and Optical Properties of Magnetite Quantum Dots Prepared by Solvothermal Method
Authors: Ajibade Peter A. , Mona Lijo P. , Songca Sandile P. 
Published: September 2023

AbstractMagnetite quantum dots were synthesized by solvothermal method in a conventional reflux setup, in an inert environment, at different reaction temperatures and times. Powder X-ray diffraction patterns of the as-prepared iron oxide confirmed magnetite crystalline phases irrespective of the reaction conditions. TEM micrographs revealed that the iron oxides obtained at 200, 180 and 165 °C are spherical in shapes with mean particle sizes of 3.25, 3.3 and 3.90 nm respectively, showing an increase in particle size with decreasing temperature. TEM micrographs revealed that particles obtained at 6 and 3 h have a mean particle size of 3.25 nm while particles obtained at 4.5-h has a bigger mean particle size of 7.5 nm. The results show that irrespective of the synthetic parameters, the average particle sizes of the magnetite are in the range 3–8 nm and could be described as quantum dots. Optical absorption spectra of the as-prepared magnetite quantum dots exhibited maximum absorption in the range of 200–210 nm. Fe3O4-1, Fe3O4-2 and Fe3O4-3 synthesized at 200, 180 and 165 °C have energy band gaps of 5.49, 5.13 and 5.01 eV respectively, showing a decrease in band gap with decreasing temperature. Fe3O4-4 and Fe3O4-6 synthesized at 6 and 3 h have optical band gap of 5.44 eV while Fe3O4-5 synthesized at 4.5 h has a band gap of 3.75 eV. FTIR spectra confirmed the passivation of the surface of the as-prepared magnetite quantum dots by oleate.
Targeted adversarial attacks on wind power forecasts
Authors: Heinrich René , Scholz Christoph , Vogt Stephan , Lehna Malte 
Published: September 2023

AbstractIn recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a long short-term memory (LSTM) network for predicting the power generation of individual wind farms and a convolutional neural network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model’s performance, as well as the extent to which the attacker’s goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.
Weakly supervised human skin segmentation using guidance attention mechanisms
Authors: Hashemifard Kooshan , Climent-Perez Pau , Florez-Revuelta Francisco 
Published: September 2023

AbstractHuman skin segmentation is a crucial task in computer vision and biometric systems, yet it poses several challenges such as variability in skin colour, pose, and illumination. This paper presents a robust data-driven skin segmentation method for a single image that addresses these challenges through the integration of contextual information and efficient network design. In addition to robustness and accuracy, the integration into real-time systems requires a careful balance between computational power, speed, and performance. The proposed method incorporates two attention modules, Body Attention and Skin Attention, that utilize contextual information to improve segmentation results. These modules draw attention to the desired areas, focusing on the body boundaries and skin pixels, respectively. Additionally, an efficient network architecture is employed in the encoder part to minimize computational power while retaining high performance. To handle the issue of noisy labels in skin datasets, the proposed method uses a weakly supervised training strategy, relying on the Skin Attention module. The results of this study demonstrate that the proposed method is comparable to, or outperforms, state-of-the-art methods on benchmark datasets.
Optimal control of combined heat and power station operation
Authors: Kůdela J. , Suja J. , Šomplák R. , Pluskal J. , Hrabec D. 
Published: September 2023

AbstractCombined heat and power stations have become one of the most utilized units of district heating systems. These stations usually contain several boilers for burning fossil fuels and renewable resources used for heating up steam, which can be used either for residential and commercial heating or electricity generation. To ensure efficiency, a boiler should either run continuously (for at least a given period) on a power output higher than a given threshold or switch off. The optimal control of the plant operations should combine an efficient setup for the turbine and boilers in operation, reflecting the demand for steam and the price of electricity, and a schedule that describes which boilers should be in operation at a given time. This paper proposes a method for optimal control of combined heat and power station operation for a given time horizon. The method is based on a two-level approach. The lower-level models correspond to finding the optimal setup of the combined heat and power station parameters for an hourly demand for different kinds of steam. The upper-level model corresponds to the optimal schedule of the operations of the individual boilers, which is planned for the entire time horizon. The lower-level model is modeled as a mixed-integer linear programming problem and is solved using parametric programming. A dynamic programming algorithm solves the upper-level model with a rolling horizon. The validity of the proposed method and its computational complexity for different granularity of the time horizon, different ranges of the parameters, varying demand for various kinds of steam, and varying electricity prices are investigated in a case study. The presented approach can be readily applied to other control problems with a similar structure.
Approximation of intra-particle reaction/diffusion effects of immobilized enzyme system following reverse Michaelis–Menten (rMM) mechanism: third degree polynomial and Akbari–Ganji methods
Authors: Abu-Reesh Ibrahim M. 
Published: September 2023

AbstractTwo approximate analytical expressions based on third degree polynomial and Akbari–Ganji’s method (AGM) were derived for the reaction/diffusion controlled kinetics of an immobilized enzyme ($$IE$$

IE

) systems. The approximation methods predict substrate concentration profile and effectiveness factor ($$\eta$$
η
) in a porous spherical particle. The reaction is assumed to follow reverse Michaelis–Menten ($$rMM$$

rMM

) kinetics. The approximate methods predictions were comparable to that of numerical solution (using the Matlab finite difference function, $$bvp4c$$

b
v
p
4
c

) at wide range of $${\phi }^{2}$$

ϕ

2

and $${y}_{o}$$

y
o

especially at low $${\phi }^{2}$$

ϕ

2

and high $${y}_{o}$$

y
o

(polynomial equation) and low $${\phi }^{2}$$

ϕ

2

and low $${y}_{o}$$

y
o

(AGM equation). Although the approximate solution was derived for $$rMM$$

rMM

kinetics, the results can be used to describe other important enzymatic reaction kinetics such as simple Michaelis–Menten ($$MM)$$

M
M
)

kinetics and $$MM$$

MM

with competitive product inhibition kinetics. A necessary design equation should be satisfied when using polynomial or AGM approximation for different enzyme kinetic equations. In this work, two examples of enzymatic reactions of industrial interest were studied, namely glucose-fructose isomerization follows $$rMM$$

rMM

kinetics and hydrolysis of lactose follows Michaelis–Menten ($$MM$$

MM

) equation with competitive product (galactose) inhibition. Predictions of the developed third degree polynomial and AGM approximation equations agree with that of numerical solution, the percentage relative error for the effectiveness factor was less than 11 in comparison with the numerical solution. Good agreement between approximate and numerical estimations demonstrates the validity of these approximation methods.
Leniency Inflation, Cartel Damages, and Criminalization
Authors: Marvão Catarina , Spagnolo Giancarlo 
Published: September 2023

AbstractWe revisit the pros and cons of introducing cartel criminalization in the EU. We document the recent EU “leniency inflation”, whereby leniency has been increasingly awarded to many (or all) cartel members, which softens the “courthouse race” effect. Coupled with the insufficient protection of leniency applicants from damages (2014 Damages Directive), it may have led to a decrease in leniency applications and cartel convictions. Given the current level of fines, criminalization may have to be introduced. We then explore US criminal sanctions (1990–2015) to highlight potential areas of concern for EU policymakers, of which recidivism appears to be a significant one.
Prevalence of atrial fibrillation and reasons for undertreatment with oral anticoagulants
Authors: Lilja Johan , Själander Anders , Själander Sara 
Published: September 2023

Abstract
Objectives
To investigate the prevalence of atrial fibrillation (AF), the proportion of AF patients not receiving oral anticoagulation (OAC) and reasons for abstaining from OAC treatment.

Methods
A retrospective cross-sectional study of patients aged 18 years or older with an AF diagnosis on June 1st 2020 in Västernorrland County, Sweden. AF diagnosis was retrieved using the ICD10 code I.48, and medical records were reviewed for comorbidities and documented reasons to abstain OAC treatment.

Results
Of 197 274 residents in Västernorrland County, 4.7% (9 304/197 274) had a documented AF diagnosis. Of these, 19% (1 768/9 304) had no OAC treatment, including 4.2% (393/9 304) with no indication, 2.5% (233/9 304) with a questionable and 2.5% (231/9 304) with a documented clear contraindication for OAC. In total 9.8% (911/9 304) were not treated with OAC despite indication and no reasonable documented contraindication, thus 90.8% (8 447/9 304) of all AF-patients were eligible for OAC treatment. Common reasons for abstaining treatment without reasonable contraindication were present sinus rhythm in 13.7% (125/911), perceived not an OAC candidate in 10.6% (97/911) and anemia in the past in 4.3% (39/911).

Conclusions
In the population of Västernorrland County, a very high AF prevalence of 4.7% was found, of which just over 90% would theoretically benefit from OAC treatment. This is higher than previously reported and stresses the importance of stroke prevention in this large patient group.
Circular economy, bioeconomy, and sustainable development goals: a systematic literature review
Authors: Ferraz Diogo , Pyka Andreas 
Published: September 2023

AbstractThe circular economy (CE) and bioeconomy (BE) are recognized as potential solutions for achieving sustainable development, yet little research has examined their potential contribution to the United Nations’ Sustainable Development Goals (SDGs). In this study, we conducted a bibliometric analysis of 649 articles published between 2007 and 2022, as well as a systematic literature review of 81 articles, to assess the extent to which the CE and BE communities have addressed the SDGs. Our analysis identified 10 research gaps including the limited number of empirical quantitative papers, particularly in the context of BE, and the underrepresentation of developing regions such as Latin America and Africa in the literature. Our main finding reveals that the CE community primarily focuses on SDG 12, Responsible Consumption and Production, followed by SDG 9, Industry, Innovation, and Infrastructure; SDG 7, Affordable and Clean Energy; and SDG 6, Clean Water and Sanitation. The BE community, on the other hand, focuses primarily on SDG 7, followed by SDG 9 and SDG 12. However, both communities lack attention to social SDGs such as quality education, poverty, and gender equality. We propose that a combination of CE and BE, known as circular bioeconomy, could help countries achieve all SDGs. Further research is needed to develop and implement circular bioeconomy policies that address these gaps and promote sustainable development. In this sense, our study identified an important research gap that needs more attention in the future.
Multiscale Asymptotic Analysis Reveals How Cell Growth and Subcellular Compartments Affect Tissue-Scale Hormone Transport
Authors: Kiradjiev K. B. , Band L. R. 
Published: September 2023

AbstractDetermining how cell-scale processes lead to tissue-scale patterns is key to understanding how hormones and morphogens are distributed within biological tissues and control developmental processes. In this article, we use multiscale asymptotic analysis to derive a continuum approximation for hormone transport in a long file of cells to determine how subcellular compartments and cell growth and division affect tissue-scale hormone transport. Focusing our study on plant tissues, we begin by presenting a discrete multicellular ODE model tracking the hormone concentration in each cell’s cytoplasm, subcellular vacuole, and surrounding apoplast, represented by separate compartments in the cell-file geometry. We allow the cells to grow at a rate that can depend both on space and time, accounting for both cytoplasmic and vacuolar expansion. Multiscale asymptotic analysis enables us to systematically derive the corresponding continuum model, obtaining an effective reaction–advection–diffusion equation and revealing how the effective diffusivity, effective advective velocity, and the effective sink term depend on the parameters in the cell-scale model. The continuum approximation reveals how subcellular compartments, such as vacuoles, can act as storage vessels, that significantly alter the effective properties of hormone transport, such as the effective diffusivity and the induced effective velocity. Furthermore, we show how cell growth and spatial variance across cell lengths affect the effective diffusivity and the induced effective velocity, and how these affect the tissue-scale hormone distribution. In particular, we find that cell growth naturally induces an effective velocity in the direction of growth, whereas spatial variance across cell lengths induces effective velocity due to the presence of an extra compartment, such as the apoplast and the vacuole, and variations in the relative sizes between the compartments across the file of cells. It is revealed that hormone transport is faster across cells of decreasing lengths than cells with increasing lengths. We also investigate the effect of cell division on transport dynamics, assuming that each cell divides as soon as it doubles in size, and find that increasing the time between successive cell divisions decreases the growth rate, which enhances the effect of cell division in slowing hormone transport. Motivated by recent experimental discoveries, we discuss particular applications for transport of gibberellic acid (GA), an important growth hormone, within the Arabidopsis root. The model reveals precisely how membrane proteins that mediate facilitated GA transport affect the effective tissue-scale transport. However, the results are general enough to be relevant to other plant hormones, or other substances that are transported in a similar way in any type of cells.
Derivation of Naïve Human Embryonic Stem Cells Using a CHK1 Inhibitor
Authors: Ware Carol B. , Jonlin Erica C. , Anderson Donovan J. , Cavanaugh Christopher , Hesson Jennifer , Sidhu Sonia , Cook Savannah , Villagomez-Olea Guillermo , Horwitz Marshall S. , Wang Yuliang , Mathieu Julie 
Published: September 2023

AbstractEmbryonic development is a continuum in vivo. Transcriptional analysis can separate established human embryonic stem cells (hESC) into at least four distinct developmental pluripotent stages, two naïve and two primed, early and late relative to the intact epiblast. In this study we primarily show that exposure of frozen human blastocysts to an inhibitor of checkpoint kinase 1 (CHK1) upon thaw greatly enhances establishment of karyotypically normal late naïve hESC cultures. These late naïve cells are plastic and can be toggled back to early naïve and forward to early primed pluripotent stages. The early primed cells are transcriptionally equivalent to the post inner cell mass intermediate (PICMI) stage seen one day following transfer of human blastocysts into in vitro culture and are stable at an earlier stage than conventional primed hESC.
Graphical Abstract
Single-Cell RNA Sequencing Transcriptomics Revealed HCMV IE2-Related Microglia Responses in Alzheimer’s-Like Disease in Transgenic Mice
Authors: Liu Fengjun , Wang Zhifei , Niu Delei , Zhang Xianjuan , Nan Fulong , Jiang Shasha , Li Jun , Yu Meng , Yang Xiaoli , Zhang Shuyun , Zhou Xiaoqiong , Wang Hui , Zhang Xueming , Liu Wenxuan , Li Zonghui , Wang Yunyang , Wang Bin 
Published: September 2023

Abstract Although multiple factors are known to concur with Alzheimer’s disease (AD), the relationship between human cytomegalovirus (HCMV) and AD-like disease is unclear. Here, we propose a hypothesis that HCMV immediate-early 2 (IE2) protein promotes microglia activation and thus leads to AD-like disease. We successfully constructed IE2 transgenic mice expressing IE2 in the hippocampus. Single-cell sequencing analysis revealed that IE2 promoted the activation of microglia and upregulated the expression of disease-associated microglia genes. Differentially expressed gene analysis and pathway enrichment revealed that IE2 upregulated immune and nervous system disease-related genes. Immunohistochemical analysis showed that the expressions of both amyloid precursor protein (APP) and p-Tau were significantly upregulated in the brains of IE2 mice and were markers of AD. Taken together, these findings provide useful insights into AD-like disease activated by HCMV IE2.
Inhibiting the NLRP3 Inflammasome with MCC950 Alleviates Neurological Impairment in the Brain of EAE Mice
Authors: Hou Baohua , Yin Jun , Liu Shuyan , Guo Jincheng , Zhang Baobao , Zhang Zhenzhen , Yang Lanping , Tan Xiying , Long Yijiao , Feng Sijie , Zhou Jingchun , Wu Yifan , Wang Xueyang , Han Song , Wang Zhenhui , He Xiaohua 
Published: September 2023

AbstractMultiple sclerosis (MS) is a chronic disease that is characterized by demyelination and neuronal damage. Experimental autoimmune encephalomyelitis (EAE) mice are used to model the disease progression of MS and mirror MS-like pathology. Previous researches have confirmed that inhibition of NLRP3 inflammasome significantly alleviated the severity of EAE mice and the demyelination of spinal cord, but its effect on neuronal damage and oligodendrocyte loss in the brain remains unclear. In this study, female C57BL/6 mice were immunized with MOG35–55 and PTX to establish experimental autoimmune encephalomyelitis (EAE) model. MCC950, a selective NLRP3 inflammasome inhibitor, was used to investigate the effect of NLRP3 inflammasome on the pathological changes and glial cell activation in the brain of EAE mice by immunohistochemistry. Our results demonstrated that MCC950 ameliorated the neuronal damage, demyelination, and oligodendrocyte loss in the brain of EAE mice. This protective effect of MCC950 may be attributed to its ability to suppress the activation of glial cells and prevents microglia polarization to M1 phenotype. Our work indicates that inhibition of NLRP3 inflammasome has the therapeutic effects of neuroprotection through immunomodulation and is a promising therapeutic strategy for MS.