bluuebunny/crossref_metadata_2025_split huggingface dataset:
Provisioning ecotourism does not increase tiger shark site fidelity
Authors: Séguigne Clémentine , Bègue Michel , Meyer Carl , Mourier Johann , Clua Éric 
Published: May 2023

AbstractA perennial criticism of provisioning ecotourism is that it alters the natural behavior and ecology of the target species by providing an artificial food source. Here we evaluate its impact on the long-term site fidelity patterns of tiger sharks in French Polynesia. We hypothesized that a significant impact of provisioning would lead to (1) increases in individual site fidelity over time, and (2) an increase in the number of resident individuals over time. Of 53 individuals photo-identified and monitored during > 500 dives over five years, 10 individuals accounted for > 75% of all sightings, whereas 35 sharks were sighted very infrequently. Even the most frequently observed tiger sharks exhibited overall low fidelity at the site and showed no increase in site fidelity over time. Furthermore, the number of tiger sharks sighted during each dive did not increase. The observed patterns of tiger shark sightings were best explained by natural movements, including general roaming within home ranges along the coastline and seasonal migrations. Despite the apparent lack of impact of provisioning ecotourism on tiger shark ecology in Tahitian waters, it would be prudent to implement a strict code of conduct during any future provisioning activities to maximize the safety of participants and animals involved.
Black electrochromic ink with a straightforward method using copper oxide nanoparticle suspension
Authors: Jeong Chan Yang , Watanabe Hiroshi , Tajima Kazuki 
Published: May 2023

AbstractElectrochromic (EC) materials for smart windows must exhibit a dark colour and block visible light (wavelength = 380–780 nm) to reduce environmental impact. In particular, black tones are also desired, and there are many reports of attempts to create these dark tones using organic materials such as polymers. However, their fabrication methods are complicated, expensive, and may even use hazardous substances; moreover, they are often not sufficiently durable, such as upon exposure to ultraviolet light. There are some reported cases of black materials using the CuO system as an inorganic material, but the synthesis method was complicated and the functionality was not stable. We have found a method to synthesize CuO nanoparticles by simply heating basic copper carbonate and adjusting the pH with citric acid to easily obtain a suspension. The formation and functionality of CuO thin films were also demonstrated using the developed suspension. This research will enable the creation of EC smart windows using existing inorganic materials and methods, such as printing technology, and is the first step towards developing environment-friendly, cost-effective, and functional dark inorganic materials.
Importance analysis of psychosociological variables in frailty syndrome in heart failure patients using machine learning approach
Authors: Pasieczna Aleksandra Helena , Szczepanowski Remigiusz , Sobecki Janusz , Katarzyniak Radosław , Uchmanowicz Izabella , Gobbens Robbert J. J. , Kahsin Aleksander , Dixit Anant 
Published: May 2023

AbstractThe prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the non-physical origins of HF.
A synthetic population-level oscillator in non-microfluidic environments
Authors: Gu Fei , Jiang Wei , Kang Fangbing , Su Tianyuan , Yang Xiaoya , Qi Qingsheng , Liang Quanfeng 
Published: May 2023

AbstractSynthetic oscillators have become a research hotspot because of their complexity and importance. The construction and stable operation of oscillators in large-scale environments are important and challenging. Here, we introduce a synthetic population-level oscillator in Escherichia coli that operates stably during continuous culture in non-microfluidic environments without the addition of inducers or frequent dilution. Specifically, quorum-sensing components and protease regulating elements are employed, which form delayed negative feedback to trigger oscillation and accomplish the reset of signals through transcriptional and post-translational regulation. We test the circuit in devices with 1 mL, 50 mL, 400 mL of medium, and demonstrate that the circuit could maintain stable population-level oscillations. Finally, we explore potential applications of the circuit in regulating cellular morphology and metabolism. Our work contributes to the design and testing of synthetic biological clocks that function in large populations.
New insights into the pathophysiology and therapeutic targets of asthma and comorbid chronic rhinosinusitis with or without nasal polyposis
Authors: Striz Ilja , Golebski Kornel , Strizova Zuzana , Loukides Stelios , Bakakos Petros , Hanania Nicola A. , Jesenak Milos , Diamant Zuzana 
Published: May 2023

Abstract
Asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) or without (CRSsNP) are chronic respiratory diseases. These two disorders often co-exist based on common anatomical, immunological, histopathological, and pathophysiological basis. Usually, asthma with comorbid CRSwNP is driven by type 2 (T2) inflammation which predisposes to more severe, often intractable, disease.
In the past two decades, innovative technologies and detection techniques in combination with newly introduced targeted therapies helped shape our understanding of the immunological pathways underlying inflammatory airway diseases and to further identify several distinct clinical and inflammatory subsets to enhance the development of more effective personalized treatments. Presently, a number of targeted biologics has shown clinical efficacy in patients with refractory T2 airway inflammation, including anti-IgE (omalizumab), anti-IL-5 (mepolizumab, reslizumab)/anti-IL5R (benralizumab), anti-IL-4R-α (anti-IL-4/IL-13, dupilumab), and anti-TSLP (tezepelumab). In non-type-2 endotypes, no targeted biologics have consistently shown clinical efficacy so far. Presently, multiple therapeutical targets are being explored including cytokines, membrane molecules and intracellular signalling pathways to further expand current treatment options for severe asthma with and without comorbid CRSwNP. In this review, we discuss existing biologics, those under development and share some views on new horizons.
Perivascular tumor-associated macrophages and their role in cancer progression
Authors: Bahri Meriem , Anstee Joanne E. , Opzoomer James W. , Arnold James N. 
Published: May 2023

Abstract
Perivascular (Pv) tumor-associated macrophages (TAMs) are a highly specialized stromal subset within the tumor microenvironment (TME) that are defined by their spatial proximity, within one cell thickness, to blood vasculature. PvTAMs have been demonstrated to support a variety of pro-tumoral functions including angiogenesis, metastasis, and modulating the immune and stromal landscape. Furthermore, PvTAMs can also limit the response of anti-cancer and anti-angiogenic therapies and support tumor recurrence post-treatment. However, their role may not exclusively be pro-tumoral as PvTAMs can also have immune-stimulatory capabilities. PvTAMs are derived from a monocyte progenitor that develop and localize to the Pv niche as part of a multistep process which relies on a series of signals from tumor, endothelial and Pv mesenchymal cell populations. These cellular communications and signals create a highly specialized TAM subset that can also form CCR5-dependent multicellular ‘nest’ structures in the Pv niche. This review considers our current understanding of the role of PvTAMs, their markers for identification, development, and function in cancer. The role of PvTAMs in supporting disease progression and modulating the outcome from anti-cancer therapies highlight these cells as a therapeutic target. However, their resistance to pan-TAM targeting therapies, such as those targeting the colony stimulating factor-1 (CSF1)-CSF1 receptor axis, prompts the need for more targeted therapeutic approaches to be considered for this subset. This review highlights potential therapeutic strategies to target and modulate PvTAM development and function in the TME.
Control of UAV quadrotor using reinforcement learning and robust controller
Authors: Zhang Zizuo , Yang Haiyang , Fei Yuanyuan , Sun Changyin , Yu Yao 
Published: May 2023

AbstractThe control of unmanned aerial vehicle quadrotor is challenging because of non‐linearities, coupling and disturbance. Here, a novel control method which includes reinforcement learning (RL) component and robust component is proposed. In this method, the RL component only relies on collected data instead of modelling to handle coupling and disturbance from aerodynamics and model. To ensure safety during training and improve training speed, the robust component is used to reduce the disturbance .The stability of the system with our controller is proven by Lyapunov method. The results of the simulation exhibit advanced performance of our controller.
Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification
Authors: Lin Chia‐Hung , Lai Hsiang‐Yueh , Huang Ping‐Tzan , Chen Pi‐Yun , Li Chien‐Ming 
Published: May 2023

AbstractHuman speech signals may contain specific information regarding a speaker's characteristics, and these signals can be very useful in applications involving interactive voice response (IVR) and automatic speech recognition (ASR). For IVR and ASR applications, speaker classification into different ages and gender groups can be applied in human–machine interaction or computer‐based interaction systems for customised advertisement, translation (text generation), machine dialog systems, or self‐service applications. Hence, an IVR‐based system dictates that ASR should function through users' voices (specific voice‐frequency bands) to identify customers' age and gender and interact with a host system. In the present study, we intended to combine a pitch detection (PD)‐based extractor and a voice classifier for gender identification. The Yet Another Algorithm for Pitch Tracking (YAAPT)‐based PD method was designed to extract the voice fundamental frequency (F0) from non‐stationary speaker's voice signals, allowing us to achieve gender identification, by distinguishing differences in F0 between adult females and males, and classify voices into adult and children groups. Then, in vowel voice signal classification, a one‐dimensional (1D) convolutional neural network (CNN), consisted of a multi‐round 1D kernel convolutional layer, a 1D pooling process, and a vowel classifier that could preliminary divide feature patterns into three level ranges of F0, including adult and children groups. Consequently, a classifier was used in the classification layer to identify the speakers' gender. The proposed PD‐based extractor and voice classifier could reduce complexity and improve classification efficiency. Acoustic datasets were selected from the Hillenbrand database for experimental tests on 12 vowels classifications, and K‐fold cross‐validations were performed. The experimental results demonstrated that our approach is a very promising method to quantify the proposed classifier's performance in terms of recall (%), precision (%), accuracy (%), and F1 score.
Capacity development for knowledge mobilization: a scoping review of the concepts and practices
Authors: Golhasany Hamid , Harvey Blane 
Published: May 2023

AbstractThere is a growing emphasis worldwide on the use of knowledge mobilization (KMb) to improve policies and practices with the latest research evidence. This emphasis calls upon knowledge producers (e.g., university researchers) to produce more relevant evidence, and knowledge users (e.g., practitioners) to access and apply evidence. However, doing KMb can be challenging for these groups without effective support and training. Therefore, individuals and organizations are undertaking capacity development interventions to facilitate the KMb process with more effective support structures, skills, and incentives. Despite its recognized importance, theoretical evidence and practical guidance on capacity development for KMb are scattered across disciplines and practices. To address this, we conducted a scoping review study to review the current practices and concepts and identify significant gaps. One-thousand six-hundred thirty records were gathered, and 105 peer-reviewed and gray literature documents from 2010 to 2020 were reviewed. Two reviewers worked independently in screening the records, and one researcher analyzed the retained documents. The analysis reveals that capacity development for KMb is a multidimensional and multiscalar concept and practice with a diverse range of initiators, initiatives, and beneficiaries. This study also reports on three thematic areas of significance emerging from the literature, namely: (a) individuals’ and organizations’ challenges in doing and supporting KMb, (b) the capacities and supports deemed needed for effective KMb, and (c) the strategies being used for delivering capacity development. Furthermore, this study identifies evidence gaps related to the process aspects of capacity development for KMb (i.e., planning), capacity development initiatives being undertaken in developing country contexts, and results from more formal evaluations of KMb capacity-building effectiveness.
The non-iterates are dense in the space of continuous self-maps
Authors: Bhat B V Rajarama , Gopalakrishna Chaitanya 
Published: May 2023

Abstract
In this paper we first prove a nonexistence result on iterative roots, which presents several sufficient conditions for identifying self-maps on arbitrary sets that have no iterative roots of any order. Then, using this result, we prove that when X is

[
0
,
1

]
m

,

R

m

or

S
1

every non-empty open set of the space

of continuous self-maps on X endowed with the compact-open topology contains a map that does not have even discontinuous iterative roots of order

n
⩾
2

. This, in particular, proves that the complement of

, the set of non-iterates, is dense in

for these X.
An integrated Monte Carlo track-structure simulation framework for modeling inter and intra-track effects on homogenous chemistry
Authors: D-Kondo J Naoki , Garcia-Garcia Omar R , LaVerne Jay A , Faddegon Bruce , Schuemann Jan , Shin Wook-Geun , Ramos-Méndez José 
Published: May 2023

Abstract

Objective. The TOPAS-nBio Monte Carlo track structure simulation code, a wrapper of Geant4-DNA, was extended for its use in pulsed and longtime homogeneous chemistry simulations using the Gillespie algorithm. Approach. Three different tests were used to assess the reliability of the implementation and its ability to accurately reproduce published experimental results: (1) a simple model with a known analytical solution, (2) the temporal evolution of chemical yields during the homogeneous chemistry stage, and (3) radiolysis simulations conducted in pure water with dissolved oxygen at concentrations ranging from 10 μM to 1 mM with [H2O2] yields calculated for 100 MeV protons at conventional and FLASH dose rates of 0.286 Gy s−1 and 500 Gy s−1, respectively. Simulated chemical yield results were compared closely with data calculated using the Kinetiscope software which also employs the Gillespie algorithm. Main results. Validation results in the third test agreed with experimental data of similar dose rates and oxygen concentrations within one standard deviation, with a maximum of 1% difference for both conventional and FLASH dose rates. In conclusion, the new implementation of TOPAS-nBio for the homogeneous long time chemistry simulation was capable of recreating the chemical evolution of the reactive intermediates that follow water radiolysis. Significance. Thus, TOPAS-nBio provides a reliable all-in-one chemistry simulation of the physical, physico-chemical, non-homogeneous, and homogeneous chemistry and could be of use for the study of FLASH dose rate effects on radiation chemistry.
Low tensor train and low multilinear rank approximations of 3D tensors for compression and de-speckling of optical coherence tomography images
Authors: Kopriva Ivica , Shi Fei , Lai Mingyig , Štanfel Marija , Chen Haoyu , Chen Xinijan 
Published: May 2023

Abstract

Objective. Many methods for compression and/or de-speckling of 3D optical coherence tomography (OCT) images operate on a slice-by-slice basis and, consequently, ignore spatial relations between the B-scans. Thus, we develop compression ratio (CR)-constrained low tensor train (TT)—and low multilinear (ML) rank approximations of 3D tensors for compression and de-speckling of 3D OCT images. Due to inherent denoising mechanism of low-rank approximation, compressed image is often even of better quality than the raw image it is based on. Approach. We formulate CR-constrained low rank approximations of 3D tensor as parallel non-convex non-smooth optimization problems implemented by alternating direction method of multipliers of unfolded tensors. In contrast to patch- and sparsity-based OCT image compression methods, proposed approach does not require clean images for dictionary learning, enables CR as high as 60:1, and it is fast. In contrast to deep networks based OCT image compression, proposed approach is training free and does not require any supervised data pre-processing. Main results. Proposed methodology is evaluated on twenty four images of a retina acquired on Topcon 3D OCT-1000 scanner, and twenty images of a retina acquired on Big Vision BV1000 3D OCT scanner. For the first dataset, statistical significance analysis shows that for CR ≤ 35, all low ML rank approximations and Schatten-0 (S
0) norm constrained low TT rank approximation can be useful for machine learning-based diagnostics by using segmented retina layers. Also for CR ≤ 35, S
0-constrained ML rank approximation and S
0-constrained low TT rank approximation can be useful for visual inspection-based diagnostics. For the second dataset, statistical significance analysis shows that for CR ≤ 60 all low ML rank approximations as well as S
0 and S
1/2 low TT ranks approximations can be useful for machine learning-based diagnostics by using segmented retina layers. Also, for CR ≤ 60, low ML rank approximations constrained with S

p
, p ∊ {0, 1/2, 2/3} and one surrogate of S
0 can be useful for visual inspection-based diagnostics. That is also true for low TT rank approximations constrained with S

p
, p ∊ {0, 1/2, 2/3} for CR ≤ 20. Significance. Studies conducted on datasets acquired by two different types of scanners confirmed capabilities of proposed framework that, for a wide range of CRs, yields de-speckled 3D OCT images suitable for clinical data archiving and remote consultation, for visual inspection-based diagnosis and for machine learning-based diagnosis by using segmented retina layers.
Mechanical, thermal, electrical, and corrosion properties of microwave-sintered Ti-0.8Ni-0.3Mo/TiB composites
Authors: Balasundar P , Senthil S , Narayanasamy P , Ramkumar T 
Published: May 2023

Abstract
In this study, Titanium boride (TiB) reinforced Ti-0.8Ni-0.3Mo/XTiB (X = 5, 10, 15, and 20 wt%) composites were successfully fabricated by microwave sintering assisted powder metallurgy process. Scanning electron microscopy (SEM) coupled with energy-dispersive spectroscopy (EDS) and x-ray diffraction (XRD) analyses were used to evaluate the elemental powders individually. The distribution of TiB particles in the sintered Ti-0.8Ni-0.3Mo composites was observed using optical microscopy (OM) and SEM. The Microhardness of the microwave-sintered samples was evaluated through Micro Vicker’s hardness testing machine. Thermal characteristics were estimated for temperatures ranging from 50 to 250 °C. The electrical conductivity of Ti-0.8Ni-0.3Mo/TiB composites was calculated from the measured resistance values using the four-point probe method at room temperature. The immersion method was performed to estimate the corrosion properties by suspending the sintered samples in 3.5% NaCl solution for 60 h. The morphology of the corroded surfaces was examined using SEM. The results revealed that Ti-0.8Ni-0.3Mo/15TiB possessed optimum hardness values from 220 to 260 HV, mechanical properties such as True yield strength from 728 to 814 MPa, ultimate compression strength from 1335 to 1680 MPa, fracture strain of 6.12 to 13.81%. It also revealed less weight loss in a corrosion medium of 0.6 g. The Ti-0.8Ni-0.3Mo/TiB composites had good properties in densification aspects, which is suitable for applications such as marine and airfare components.
4D and 5D phase-space tomography using slowing-down physics regularization
Authors: Schmidt B.S. , Salewski M. , Moseev D. , Baquero-Ruiz M. , Hansen P.C. , Eriksson J. , Ford O. , Gorini G. , Järleblad H. , Kazakov Ye O. , Kulla D. , Lazerson S. , Mencke J.E. , Mykytchuk D. , Nocente M. , Poloskei P. , Rud M. , Snicker A. , Stagner L. , Äkäslompolo S. 
Published: May 2023

Abstract
We compute reconstructions of 4D and 5D fast-ion phase-space distribution functions in fusion plasmas from synthetic projections of these functions. The fast-ion phase-space distribution functions originating from neutral beam injection (NBI) at TCV and Wendelstein 7-X (W7-X) at full, half, and one-third injection energies can be distinguished and particle densities of each component inferred based on 20 synthetic spectra of projected velocities at TCV and 680 at W7-X. Further, we demonstrate that an expansion into a basis of slowing-down distribution functions is equivalent to regularization using slowing-down physics as prior information. Using this technique in a Tikhonov formulation, we infer the particle density fractions for each NBI energy for each NBI beam from synthetic measurements, resulting in six unknowns at TCV and 24 unknowns at W7-X. Additionally, we show that installing 40 LOS in each of 17 ports at W7-X, providing full beam coverage and almost full angle coverage, produces the highest quality reconstructions.