bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phasor-based analysis of a neuromorphic architecture for microwave sensing
Authors: Soleimani Ashkan , Forooraghi Keyvan , Atlasbaf Zahra 
Published: July 2024

AbstractThis article presents a design procedure for implementing artificial neural networks (ANNs) using conventional microwave components at the hardware level with potential applications in radar and remote sensing. The main objective is to develop structured hardware design methods for implementing artificial neurons, utilizing microwave devices to create neuromorphic devices compatible with high-frequency electromagnetic waves. The research aims to address the challenge of encoding and modulating information in electromagnetic waves into a format suitable for the neuromorphic device by using frequency-modulated information instead of intensity-modulated information. It also proposes a method for integrating principal component analysis as a dimensionality reduction technique with the implementation of ANNs on a single hardware. As a dummy task, the process outlined here is used to implement an artificial neural network at the hardware level, with a specific emphasis on creating hardware that is capable of performing matrix multiplications in the form of dot products while also being able to extract the resulting data in an interpretable manner. The proposed implementation involves the use of directional couplers to implement weights and sample the resulting signal at specific intervals to obtain the multiplication result.
Effect of low blood pressure on prognosis of acute heart failure
Authors: Kim Hyun-Jin , Jo Sang-Ho 
Published: July 2024

AbstractLow blood pressure (BP) is associated with poor outcomes in patients with heart failure (HF). We investigated the influence of initial BP on the prognosis of HF patients at admission, and prescribing patterns of HF medications, such as angiotensin-converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARB), and beta-blockers (BB). Data were sourced from a multicentre cohort of patients admitted for acute HF. Patients were grouped into heart failure reduced ejection fraction (HFrEF) and HF mildly reduced/preserved ejection fraction (HFmrEF/HFpEF) groups. Initial systolic and diastolic BPs were categorized into specific ranges. Among 2778 patients, those with HFrEF were prescribed ACEi, ARB, or BB at discharge, regardless of their initial BP. However, medication use in HFmrEF/HFpEF patients tended to decrease as BP decreased. Lower initial BP in HFrEF patients correlated with an increased incidence of all-cause death and composite clinical events, including HF readmission or all-cause death. However, no significant differences in clinical outcomes were observed in HFmrEF/HFpEF patients according to BP. Initial systolic (< 120 mmHg) and diastolic (< 80 mmHg) BPs were independently associated with a 1.81-fold (odds ratio [OR] 1.81, 95% confidence interval [CI] 1.349–2.417, p < 0.001) and 2.24-fold (OR 2.24, 95% CI 1.645–3.053, p < 0.001) increased risk of long-term mortality in HFrEF patients, respectively. In conclusion, low initial BP in HFrEF patients correlated with adverse clinical outcomes, and BP < 120/80 mmHg independently increased mortality. However, this relationship was not observed in HFmrEF/HFpEF patients.
Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality
Authors: Zhao Bu , Nguyen Vy Kim , Xu Ming , Colacino Justin A. , Jolliet Olivier 
Published: July 2024

AbstractUnderstanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based machine learning models as more flexible nonparametric techniques to examine the combined effects of multiple physiological risk factors on mortality. More specifically, we (1) study the combined effects between multiple physiological factors and all-cause mortality, (2) identify the five most influential factors and visualize their combined influence on all-cause mortality, and (3) compare the mortality cut-offs with the current clinical thresholds. Data from the 1999–2014 NHANES Survey were linked to National Death Index data with follow-up through 2015 for 17,790 adults. We observed that the five most influential factors affecting mortality are the tobacco smoking biomarker cotinine, glomerular filtration rate (GFR), plasma glucose, sex, and white blood cell count. Specifically, high mortality risk is associated with being male, active smoking, low GFR, elevated plasma glucose levels, and high white blood cell count. The identified mortality-based cutoffs for these factors are mostly consistent with relevant studies and current clinical thresholds. This approach enabled us to identify important cutoffs and provide enhanced risk prediction as an important basis to inform clinical practice and develop new strategies for precision medicine.
Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies
Authors: Khodadadzadeh Massoud , Sloan Aliza T. , Jones Nancy Aaron , Coyle Damien , Kelso J. A. Scott 
Published: July 2024

AbstractA recent experiment probed how purposeful action emerges in early life by manipulating infants’ functional connection to an object in the environment (i.e., tethering an infant’s foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism~world connection.
Utilizing triethylenetetramine-functionalized MIP-206 for highly efficient removal of Pb(II) from wastewater
Authors: Shilani Bizhan , Mehdipour Reza , Mousazadeh Behnam , Noruzi Yasin , Hosseini Seyednooroldin , Al-Saedi Hasan N. , Mohealdeen Sura Mohammad 
Published: July 2024

AbstractThe global concern over heavy metal pollution necessitates urgent measures to safeguard human health and the environment. This study focuses on employing triethylenetetramine (TETA)-functionalized MIP-206-OH (TMIP-206) as an effective adsorbent for removing Pb(II) from wastewater. TMIP-206 was synthesized via a hydrothermal method followed by functionalization with TETA. Kinetic studies demonstrate that lead removal on TMIP-206 conforms to the pseudo-second-order model, indicating an efficient removal process. Experimental results reveal that TMIP-206 aligns with the Langmuir isotherm, exhibiting a maximum removal capacity of 267.15 mg/g for lead ions. The sorption efficiency of TMIP-206 for Pb ions remains stable across six cycles, with a reduction of less than 15%. Optimal adsorption performance is observed at a pH of 6. These findings underscore the potential of TMIP-206 as an alternative for adsorbing Pb(II) from aqueous environments, addressing the global challenge of heavy metal pollution. Future research should explore the scalability and long-term stability of TMIP-206-based adsorbents to enhance their practical applicability in diverse environmental contexts and contribute to broader strategies for mitigating heavy metal contamination.
Development and validation of a nomogram for breast cancer-related lymphedema
Authors: Jiang Qihua , Hu Hai , Liao Jing , Li Zhi-hua , Tan Juntao 
Published: July 2024

AbstractTo establish and validate a predictive model for breast cancer-related lymphedema (BCRL) among Chinese patients to facilitate individualized risk assessment. We retrospectively analyzed data from breast cancer patients treated at a major single-center breast hospital in China. From 2020 to 2022, we identified risk factors for BCRL through logistic regression and developed and validated a nomogram using R software (version 4.1.2). Model validation was achieved through the application of receiver operating characteristic curve (ROC), a calibration plot, and decision curve analysis (DCA), with further evaluated by internal validation. Among 1485 patients analyzed, 360 developed lymphedema (24.2%). The nomogram incorporated body mass index, operative time, lymph node count, axillary dissection level, surgical site infection, and radiotherapy as predictors. The AUCs for training (N = 1038) and validation (N = 447) cohorts were 0.779 and 0.724, respectively, indicating good discriminative ability. Calibration and decision curve analysis confirmed the model’s clinical utility. Our nomogram provides an accurate tool for predicting BCRL risk, with potential to enhance personalized management in breast cancer survivors. Further prospective validation across multiple centers is warranted.
Probiotic evaluation, adherence capability and safety assessment of Lactococcus lactis strain isolated from an important herb “Murraya koenigii”
Authors: Shivani Tholla Madana , Sathiavelu Mythili 
Published: July 2024

AbstractLactic acid bacteria (LAB) isolated from medicinal herb Murraya koenigii, commonly known as curry leaf, which promotes the growth and maintenance of gut microbiota, were studied for their probiotic potential. The key objective of this research was to isolate and evaluate probiotic characteristics, test adherence capabilities, and confirm their safety. Lactococcus lactis (MKL8), isolated from Murraya koenigii, was subjected to in vitro analysis to assess its resistance to the gastric environment, ability to adhere Caco-2 cells, anti-microbial activity, hydrophobicity, auto-aggregation, and safety profiling through MTT assay and hemolytic. MKL8 exhibited growth at 0.5% phenol concentrations (> 80%) and was able to survive in conditions with high bile concentrations (> 79%) and a relatively low pH (72%-91%). It shows high tolerance to high osmotic conditions (> 73%) and simulated gastric juice (> 72%). Additionally, MKL8 demonstrated strong hydrophobicity (85%), auto-aggregation (87.3%-91.7%), and adherence to Caco-2 cells. Moreover, it had an inhibitory effect against pathogens too. By performing the hemolytic and MTT assays, the non-toxicity of MKL8 isolate was examined, and it exhibited no harmful characteristics. Considering MKL8's resistance to gastrointestinal tract conditions, high surface hydrophobicity, non-toxicity, and ability to inhibit the tested pathogens, it can be concluded that MKL8 demonstrated promising probiotic properties and has potential for use in the food industry.
Modulation of the Meisenheimer complex metabolism of nitro-benzothiazinones by targeted C-6 substitution
Authors: Keiff François , Bernal Freddy A. , Joch Melanie , Jacques dit Lapierre Thibault J. W. , Li Yan , Liebing Phil , Dahse Hans-Martin , Vilotijevic Ivan , Kloss Florian 
Published: July 2024

AbstractTuberculosis, caused by Mycobacterium tuberculosis, remains a major public health concern, demanding new antibiotics with innovative therapeutic principles due to the emergence of resistant strains. Benzothiazinones (BTZs) have been developed to address this problem. However, an unprecedented in vivo biotransformation of BTZs to hydride-Meisenheimer complexes has recently been discovered. Herein, we present a study of the influence of electron-withdrawing groups on the propensity of HMC formation in whole cells for a series of C-6-substituted BTZs obtained through reductive fluorocarbonylation as a late-stage functionalization key step. Gibbs free energy of reaction and Mulliken charges and Fukui indices on C-5 at quantum mechanics level were found as good indicators of in vitro HMC formation propensity. These results provide a first blueprint for the evaluation of HMC formation in drug development and set the stage for rational pharmacokinetic optimization of BTZs and similar drug candidates.
Hourly step recommendations to achieve daily goals for working and older adults
Authors: Ang Gregory , Tan Chuen Seng , Lim Nicole , Tan Jeremy , Müller-Riemenschneider Falk , Cook Alex R. , Chen Cynthia 
Published: July 2024

Abstract
Background
The widespread use of physical activity trackers enables the collection of high-resolution health data, such as hourly step counts, to evaluate health promotion programmes. We aim to investigate how participants meet their daily step goals.

Methods
We used 24-h steps data from the National Steps ChallengeTM Season 3, wherein participants were rewarded with vouchers when achieving specified goals of 5000, 7500, and 10,000 steps per day. We extracted data from 3075 participants’ including a total of 52,346 participant-days. We modelled the hourly step counts using a two-part model, in which the distribution for step counts was allowed to depend on the sum of step counts up to the previous hour and participant demographics.

Results
Participants have a mean age of 44.2 years (standard deviation = 13.9), and 40.4% are males. We show that on weekdays, the hourly mean step counts among participants aged 60 and above are higher than participants aged 30 to 59 from the start of the day till 6 p.m. We also find that participants who accumulate at least 7000 steps by 7 p.m. are associated with higher success of achieving 10,000 steps.

Conclusions
We provide recommendations on the hourly targets to achieve daily goals, based on different participants’ characteristics. Future studies could experimentally test if prompts and nudges at the recommended times of day could promote reaching step goals.
Increased anti-correlation between the left dorsolateral prefrontal cortex and the default mode network following Stanford Neuromodulation Therapy (SNT): analysis of a double-blinded, randomized, sham-controlled trial
Authors: Gajawelli Niharika , Geoly Andrew D. , Batail Jean-Marie , Xiao Xiaoqian , Maron-Katz Adi , Cole Eleanor , Azeez Azeezat , Kratter Ian H. , Saggar Manish , Williams Nolan R. 
Published: July 2024

AbstractSNT is a high-dose accelerated intermittent theta-burst stimulation (iTBS) protocol coupled with functional-connectivity-guided targeting that is an efficacious and rapid-acting therapy for treatment-resistant depression (TRD). We used resting-state functional MRI (fMRI) data from a double-blinded sham-controlled randomized controlled trial1 to reveal the neural correlates of SNT-based symptom improvement. Neurobehavioral data were acquired at baseline, post-treatment, and 1-month follow-up. Our primary analytic objective was to investigate changes in seed-based functional connectivity (FC) following SNT and hypothesized that FC changes between the treatment target and the sgACC, DMN, and CEN would ensue following active SNT but not sham. We also investigated the durability of post-treatment observed FC changes at a 1-month follow-up. Study participants included transcranial magnetic stimulation (TMS)-naive adults with a primary diagnosis of moderate-to-severe TRD. Fifty-four participants were screened, 32 were randomized, and 29 received active or sham SNT. An additional 5 participants were excluded due to imaging artifacts, resulting in 12 participants per group (Sham: 5F; SNT: 5F). Although we did not observe any significant group × time effects on the FC between the individualized stimulation target (L-DLPFC) and the CEN or sgACC, we report an increased magnitude of negative FC between the target site and the DMN post-treatment in the active as compared to sham SNT group. This change in FC was sustained at the 1-month follow-up. Further, the degree of change in FC was correlated with improvements in depressive symptoms. Our results provide initial evidence for the putative changes in the functional organization of the brain post-SNT.
Asteroid follow-up and precovery problem: Partial banana mapping solution
Authors: Vavilov D. E. , Hestroffer D. 
Published: July 2024

Context. Precovery of asteroids, that is, finding older observations of already discovered asteroids, allows us to refine our knowledge of their orbits, glean information about close encounters and the probability of collisions with Earth, and to determine some dynamical and physical properties, such as the Yarkovsky acceleration. Existing approaches generally look for an observation next to the predicted position from the nominal orbit, and often do not take into account the whole uncertainty distribution of coordinates.
Aims. We aim to develop a computationally fast technique for predicting the possible spherical coordinates of near-Earth asteroids in order to find observations in existing catalogs or archived observations (plates, CCDs, etc.).
Methods. We modified the partial banana mapping method, and used it to estimate impact probabilities of asteroids with the Earth. For a near-Earth asteroid, a Gaussian law for the equinoctial orbital elements well approximates the uncertainty region of the object at the epoch of the observation. We sample virtual asteroids on the main line of the curved uncertainty region at the epoch of observation, project all of them with their small uncertainty vicinity onto the celestial sphere, and evaluate the brightness of the asteroids. We also estimate the probability of finding the asteroids on the image, and the length of the uncertainty region (which shows the quality of the orbit) in order to establish a priority list among the images. The higher the probability and the poorer the quality of the orbit, the more interesting it is to find the object for further improvement of its orbit and to refined its impact probability computation.
Results. We demonstrate the applicability of the developed method. We tested it on the case of precovery observations of asteroid (506074) Svarog (provisional designation 2015 UM67 ) as if it had recently been discovered, meaning the orbit is obtained with only 3 months of observations. In this case, we estimated a probability of precovery of about 10%, predicted the possible positions, and actually found the object close to the constructed uncertainty region. The nominal position is outside of the image’s field of view, meaning that conventional methods would fail. The uncertainty region is curved and asymmetric, which shows that using only the covariance matrix of celestial coordinates for the nominal orbit would poorly approximate the actual uncertainty region in the place of the sky, preventing the asteroid from being found.
Conclusions. The developed method selects interesting images and guides us in our search for asteroids on them, even if the position predicted for the nominal orbit is out of the image window.
Instruments measuring motivation to learn Arabic as a second language: evidence of validity and reliability
Authors: Alshammari Alya , Shaalan Danya , Al-onazi Badriyya 
Published: July 2024

AbstractAs studies of second language (L2) learning and motivation typically focus on English as a foreign language (EFL), L2 motivation for Arabic has been less studied. Instruments measuring two sources of L2 Arabic motivation, identity-related motivation, and motivation arising from the L2 learning environment (L2LE), were developed and piloted among L2 Arabic learners. Pilot study results showed strong motivation due to Islamic identity and desire for cultural exposure, but also revealed speaking anxiety in the L2LE. In this study, the objectives were to determine: a) What evidence exists for or against the validity of the subscale measurements from these instruments? and b) how this evidence can be used to explain what appear to be contradictions in the pilot study results. In this mixed-methods study, instruments were administered twice to learners at an L2 Arabic learning institute in the Kingdom of Saudi Arabia (KSA), and a sample was interviewed. Quantitative analysis showed evidence of instrument reliability, and analysis of interview data revealed several findings. First, consistent with pilot data, we found that learners, who were all Muslim, were motivated to learn L2 Arabic as part of desiring cultural and social exposure in KSA and to become closer to Islam. Interview data revealed that these learners aimed to adopt the customs and lifestyle of those in KSA as part of becoming closer to Islam, some of which they did through social interaction. Second, L2 Arabic learning of Classical Arabic (CA) at the institute was impeded by the learners’ lack of knowledge of the conversational Modern Standard Arabic (MSA) dialect, which made practical communication with those in KSA difficult. Recommendations are made for instrument improvement, as well as a strategy to incorporate some MSA instruction into the CA curriculum.
Faking the war: fake posts on Turkish social media during the Russia–Ukraine war
Authors: Uluşan Oshan , Özejder İbrahim 
Published: July 2024

AbstractThis study aims to analyze the fake posts circulated on Turkish social media during the Russia–Ukraine war. With advancing technology, social media platforms have a profound impact on the way we perceive and interpret events and make us question the accuracy of information generated about international events such as wars. While the Russia–Ukraine war constitutes an important turning point in international relations, the reflection of these events on social media is also seen in fake posts. In this context, the main purpose of this study is to identify the common themes of fake social media posts and to reveal the general context of these posts on social media. In addition, the study aims to analyze the fake content circulating on Turkish social media and to reveal the emerging polarized discourses through the identified themes. The research revolves around five main themes that feed polarization: war reporting, ideological misrepresentation, humor, hate speech, and conspiracy theories. The findings show that fake content is particularly concentrated around ideological polarization and antagonisms. It was also found that misinformation and decontextualized humor blurred the true context of the war and that fake content combined with hate speech and conspiracy theories distorted the context of the war.
First results of turbulence investigation on Globus-M2 using radial correlation Doppler reflectometry
Authors: PONOMARENKO A. , YASHIN A. , GUSEV V. , KISELEV E. , KURSKIEV G. , MINAEV V. , PETROV Y. , SAKHAROV N. , SHCHEGOLEV P. , TKACHENKO E. , ZHILTSOV N. 
Published: July 2024

Abstract
The first results of investigation of the turbulence structure using Doppler backscattering (DBS) on the Globus-M2 tokamak are presented. A one-channel DBS system with a variable probing frequency within the 18–26 GHz range was installed to investigate the edge plasma at normalized minor radii

0.9–1.1. Radial correlation Doppler reflectometry was used to study the changes in turbulence eddies after the LH transition. Correlation analysis was applied to the phase derivative of complex in-phase and quadrature (IQ) signals of the DBS diagnostic as it contains information about the poloidal plasma rotation velocity. In L-mode, the radial correlation length L

r
is estimated to be 3 cm and after transition to H-mode reduces to approximately 2 cm. Gyrokinetic modelling in a linear local approximation using code GENE indicates that the instability with positive growth rate at the normalized minor radius

0.75 in L-mode and H-mode on Globus-M2 was the ion temperature gradient (ITG) mode.