Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network

This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1–5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts.
Amorphous Layer Formation on Nickel Alloy Surface by Intense Pulsed Ion Beam Irradiation

The formation of an amorphous layer due to irradiation of intense pulsed ion beams (PIBs) has been studied experimentally. A mixed carbon and fluorine PIB with energy of 180 keV, ion beam current density of 180 A/cm2 and pulse duration of 30 ns is irradiated on a Ni65Cr15P16B4 alloy, resulting in the formation of an amorphous layer on the substrate surface to a depth of 0.66 µ m which is consistent with the thermal diffusion length. Repeated irradiations of PIB lead to the formation of an amorphous phase at depths exceeding 0.66 µ m from the surface. PIB irradiation leads to rapid heating of the substrate surface to its boiling point (2732° C), and the cooling rate from the melting point to the glass transition point is estimated to be 2.2×109° C/s which is larger than the critical cooling rate for amorphous layer formation of nickel alloys.
The Maximum Reduction in Transmission Losses Possible with Reactive Power Allocation

Currently several methods are available in the literature which determine the optimum locations and sizes of additional reactive power injections required in a power system which results in a reduction in transmission line losses. These methods consider only two or three locations for the injections and obviously don't determine the maximum reduction in losses possible by reactive power injections. This paper presents the conditions which result in maximum reduction in losses possible by reactive power injections and outlines the method for allocating those injections. The method is tested on IEEE14 IEEE24 and IEEE57 bus networks and the results are presented.
Gravitational waves in general relativity VIII. Waves in asymptotically flat space-time

Gravitational fields containing bounded sources and gravitational radiation are examined by analyzing their properties at spatial infinity. A convenient way of splitting the metric tensor and the Einstein field equations, applicable in any space-time, is first introduced. Then suitable boundary conditions are set. The group of co-ordinate transformations that preserves the boundary conditions is analyzed. Different possible gravitational fields are characterized intrinsically by a combination of (i) characteristic initial data, and (ii) Dirichlet data at spatial infinity. To determine a particular solution one must specify four functions of three variables and three functions of two variables; these functions are not subject to constraints. A method for integrating the field equations is given; the asymptotic behaviour of the metric and Riemann tensors for large spatial distances is analyzed in detail; the dynamical variables of the radiation modes are exhibited; and a superposition principle for the radiation modes of the gravitational field is suggested. Among the results are: (i) the group of allowed co-ordinate transformations contains the inhomogeneous orthochronous Lorentz group as a subgroup; (ii) each of the five leading terms in an asymptotic expansion of the Riemann tensor has the algebraic structure previously predicted from analyzing the Petrov classification; (iii) gravitational waves appear to carry mass away from the interior; (iv) time-dependent periodic solutions of the field equations which obey the stated boundary conditions do not exist. It was found that the general fields studied in the present work are in many ways very similar to the axially symmetric fields recently studied by Bondi, van der Burg & Metzner.
Inverse optimal neural control of a class of nonlinear systems with constrained inputs for trajectory tracking

This paper focusses on recurrent adaptive neural control applied to unknown nonlinear systems with input constraints. A recurrent high‐order neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then a control law, which stabilizes the reference tracking error dynamics, is developed using the inverse optimal control approach. Tracking error stability is established as a function of design parameters. Chaotic systems are considered to demonstrate the applicability of the approach, via simulations, as for their stabilization and synchronization. The obtained simulation results illustrates the proposed approach capabilities for trajectory tracking of unknown nonlinear systems, whose inputs are constrained. Copyright © 2011 John Wiley & Sons, Ltd.
Compact Fully-Differential CMOS Current Driver for Bioimpedance Measurements

A fully-differential CMOS current driver, suitable to provide the excitation signal in an on-chip bioimpedance measurement system, is presented. The circuit, able to convert the voltage waveform produced by a sinusoidal signal generator into a highly-linear sinusoidal current, consists of a linearized voltage-to-current (V-to-I) converter and a common-mode feedback (CMFB) network. The transconductance, maximum output current, and DC level of the output voltage can be adjusted in a straightforward manner, whereas the maximum output current can be scaled if required by only modifying the gain of two current mirrors. The circuit has been designed in 180 nm CMOS technology to operate with a 1.8-V supply. The simulated output resistance of the current driver has been found to be $62.9\ \mathrm{M}\Omega$ at low frequencies and it is kept above $250\ \mathrm{k}\Omega$ for a frequency equal to 1 MHz. A maximum output current of $3.7\ \mu\mathrm{A}$ and 1 MHz with a THD of −40 dB is obtained when a $150-\text{mV}_{pp}$ input voltage is applied.
Circularly polarized single-fed microstrip patch for wireless communications

For more than 25 years the circularly polarized single-fed microstrip patch have been considered to be extremely narrow-band. The performances reported are almost exclusively related to single patches printed on a dielectric substrate, which partially explains the frequency characteristics. The antenna proposed is a variation of the 'almost-square' patch and exhibits a theoretical axial ratio of less than 2.5 dB over a 10% bandwidth. The antenna was designed for PCN and PCS applications.
How do interparticle contact friction, packing density and degree of polydispersity affect force propagation in particulate assemblies?

Interparticle contact friction, packing density and polydispersity are known to be major contributors to the macroscopic strength of particulate assemblies, that is, their bulk resistance to deformation. For example, when a solid object penetrates a particulate material, the penetration resistance (i.e. the force opposing the object) increases concomitantly with an increase in the degree of polydispersity, packing density and interparticle friction. To establish the underlying mechanisms by which these properties govern the macroscopic response, we characterize quantitatively force propagation at length scales beyond that of the interparticle contact region. Using data derived from discrete element simulations of a two-dimensional granular assembly subject to indentation by a rigid flat punch, we examine the properties of force chains and the force chain network as they evolve during the course of the deformation. Findings indicate that increasing interparticle friction, packing density and degree of polydispersity promotes the formation of straighter chains and a greater degree of branching in the force chain network. Although the force chain length appears to be independent of friction and polydispersity, on average, denser systems tend to favour shorter chains. Thus, straighter and shorter force chains, combined with a greater degree of branching in the force chain network, result in a macroscopically stronger granular material.
Mitigating the Impact of Variability in NCFET-based Coupled-Oscillator Networks Applications

Coupled oscillators are attracting increasing interest because of their potential to perform computation efficiently, enabling new applications in computing and information processing. Coupled nano-oscillator implementations using emerging devices have arisen, but the immaturity of these technologies has allowed only simple experimental demonstrations. The potential of Negative Capacitance FET (NCFET) for such applications has recently been recognized, which is a step towards the physical realization given their ease of co-integration with commercial CMOS technologies. However, the design of circuits using these devices can be seriously compromised by the variability inherent in them. In this paper, we will highlight this problem through the design of an oscillatory neural network for pattern recognition applications. We propose the application of subharmonic injection mechanisms to mitigate the impact of NCFET transistor variability and present results showing that the performance of these circuits improves significantly.
Jelly Candy Hydroxyapatite from Mackerel Fish Bone

Background: Calcium is a mineral that is needed by bones and teeth. Calcium needs have not been achieved evenly among children, about 1000mg/day. Hydroxyapatite contains Ca and apatite which is good for the maintenance of bones and teeth. Hydroxyapatite is made from the bones of mackerel, where the bones of mackerel have a high source of calcium. For easy being consumed for children, the hydroxyapatite made in the form of jelly candy. Objective: The aim of this research was Hydroxyapatite Mackerel Fish Bone can be made as Jelly Candy. Methods: Hydroxyapatite from mackerel bones is made by the precipitation method. Then, hydroxyapatite was formulated into jelly candy with concentrations of hydroxyapatite was 18%, 19% and 20% respectively for Formula I, II and III. Jelly candy evaluation includes organoleptic test, gel strength, khamir and ALT ochre, weight uniformity, pH, hedonics, homogeneity and storage. Results: The evaluation showed that all jelly candy formulas are safe for consumption according to SNI 3547.2-2008. Conclusion: In conclusion, hydroxyapatite mackerel fish bone can be made as Jelly Candy with the best concentration in Formula 1.
Neuroprotective Properties of Cardoon Leaves Extracts against Neurodevelopmental Deficits in an In Vitro Model of Rett Syndrome Depend on the Extraction Method and Harvest Time

This study investigates the bioactive properties of different extracts of cardoon leaves in rescuing neuronal development arrest in an in vitro model of Rett syndrome (RTT). Samples were obtained from plants harvested at different maturity stages and extracted with two different methodologies, namely Naviglio® and supercritical carbon dioxide (scCO2). While scCO2 extracts more hydrophobic fractions, the Naviglio® method extracts phenolic compounds and less hydrophobic components. Only the scCO2 cardoon leaves extract obtained from plants harvested in spring induced a significant rescue of neuronal atrophy in RTT neurons, while the scCO2 extract from the autumn harvest stimulated dendrite outgrowth in Wild-Type (WT) neurons. The scCO2 extracts were the richest in squalene, 3ß-taraxerol and lupeol, with concentrations in autumn harvest doubling those in spring harvest. The Naviglio® extract was rich in cynaropicrin and exerted a toxic effect at 20 µM on both WT and RTT neurons. When cynaropicrin, squalene, lupeol and 3ß-taraxerol were tested individually, no positive effect was observed, whereas a significant neurotoxicity of cynaropicrin and lupeol was evident. In conclusion, cardoon leaves extracts with high content of hydrophobic bioactive molecules and low cynaropicrin and lupeol concentrations have pharmacological potential to stimulate neuronal development in RTT and WT neurons in vitro.
The Relationship between Bullshit Receptivity and Willingness to Share Misinformation about Climate Change: The Moderating Role of Pregnancy

Widespread dissemination of misinformation about climate change has seriously harmed the health of future generations and the world. Moreover, misinformation-sharing behaviors exhibit strong individual characteristics. However, research is limited on the antecedents of and mechanism underlying the willingness to share misinformation about climate change in terms of individual personalities and physiological states. Accordingly, we surveyed 582 women (224 pregnant) using a questionnaire and constructed a moderated mediation model to explore the relationships among individuals’ bullshit receptivity, belief in misinformation about climate change, willingness to share misinformation about climate change, and pregnancy. The results showed that: (1) bullshit receptivity is positively related to the willingness to share misinformation about climate change; (2) belief in misinformation about climate change mediates the relationship between bullshit receptivity and willingness to share misinformation about climate change; and (3) for individuals with higher bullshit receptivity, pregnancy exacerbates the detrimental effects of bullshit receptivity on belief in misinformation about climate change.
Risk Factors and Outcomes of Extended Length of Stay in Older Adults with Intertrochanteric Fracture Surgery: A Retrospective Cohort Study of 2132 Patients

We aimed to identify the risk factors associated with an extended length of hospital stay (eLOS) in older hip-fracture patients and to explore the relationships between eLOS and mortality and functional outcomes. In this retrospective analysis of surgically treated intertrochanteric fracture (IF) patients, all variables were obtained and compared between the eLOS group and the normal LOS group. All participants were followed-up for a minimum of two years and the relation between the eLOS and all-cause mortality and functional outcomes were compared. After adjustment for potential confounders, we identified that patients with high modified Elixhauser’s Comorbidity Measure (mECM) had the highest likelihood of eLOS, followed by obesity, admission in winter, living in urban, pulmonary complications, admission in autumn, and time from injury to surgery. In addition, our results showed no significant difference in the mortality and functional outcomes between the two groups during follow-up. By identifying these risk factors in the Chinese geriatric population, it may be possible to risk-stratify IF patients and subsequently streamline inpatient resource utilization. However, the differences between health care systems must be taken into consideration. Future studies are needed to preemptively target the modifiable risk factors to demonstrate benefits in diminishing eLOS.
An integrated non-parametric sequential approach to multi-class pattern classification

A now method of pattern classification, evolved by integrating in a sequential mode a non-parametric feature selection criterion with an explicit learning scheme, is presented. This feature selection criterion is based on the well-known concept of inter-class and intra-class Euclidean distances as a measure of the separability of the pattern classes in a given feature space. An ‘ effective figure of merit’ is denned and the feature subset in which this figure of merit attains the maximum value is construed as the best feature subset. Usually in most of the existing techniques, a single feature subset is chosen as the hest for the multi-class problem as a whole. A distinctive departure from this practice has been made here in that an individual best feature subset is determined for each of the pattern classes. The values of the effective figure of merit for the best feature subsets of the different pattern classes are sorted to determine the best separable pattern class. The learning scheme developed here .
RF-Sensing: A New Way to Observe Surroundings

Radio frequency sensing (RF-sensing) is an emerging field assisting vision technology for object detection, tracking, and various such use cases. RF-Sensing technology uses radio signals and their reflections in order to capture the surrounding details and then applies artificial intelligence algorithms to sense the objects. RF-sensing has advantage over the existing vision technology when it comes to low light conditions, privacy concerns, and far-range scenarios. In this paper, we propose different RF-sensing methods for person identification (PI), human activity identification (HAI), and surrounding-location identification (SLI). Here, we make use of data generated from millimeter-Wave sensors and pre-process it using signal processing techniques before feeding it to deep learning (DL) based classifiers. For PI and SLI, we propose a DL network consisting of convolutional neural network (CNN) and long short term memory (LSTM) blocks. Here, first the input is passed through the CNN layers and then followed by the LSTM. For HAI, we propose a hierarchical classifier consisting of two stages. First, similar activities are clubbed together and the coarse level classifier is trained to distinguish among the non-similar activities. This is followed by fine level classifiers, which are trained to distinguish between those activities which are deemed to be similar while training the coarse level classifier. While testing, we employ the coarse level classifier unconditionally. The fine level classifier is employed only when the coarse level classifier’s output class corresponds to that of similar activity. We use the output of the hierarchical classifier to determine the activity. Further, we show that the proposed algorithms for the PI and SLI are able to predict accurately among 5 persons and among 5 different surrounding-locations respectively and the proposed hierarchical classifier for HAI, which is used to classify among 11 different activities, shows an improvement of $\approx 4 \%$ compared to other DL based architectures in the literature.
A New Adaptive Image Denoising Method Based on Wavelet Packet Transform and Neighbor Dependency

In image processing, removal of noise without blurring the image edges is a difficult problem. Aiming at orthogonal wavelet transform and traditional thresholds shortage, a new adaptive threshold image de-noising method which is based on wavelet packet transform and neighbor dependency is proposed. Low frequency part and high frequency part can be decomposed at the same time in wavelet packet transform and the information contained in wavelet coefficients is redundant. Using this kind of relativity in wavelet packet coefficients, we use a new variance neighbor estimation method and then neighbor dependency adaptive threshold is produced. From the experiment result, we see that compared with traditional methods, this method can not only effectively eliminate noise, but can also well keep original images information and the quality after image de-noising is very well.
Digital management on zakat institutions: Mapping using Biblioshiny R

Purpose – This study aims to identify and map the research most relevant to developing digital technology research trends published by leading journals on digital management in zakat institutions. Methodology – The analysis focused on describing the characteristics and trends of keywords, authors, citations, and journals. The data analyzed were 25 journal publications for the last four years starting from 2019-2022, accessed online by Dimensions on June 09 2022. The methods used were descriptive statistics and bibliometric analysis with Biblioshiny, an R-based application, to generate bibliometric maps. Findings – The study found that Indonesia's covid pandemic and zakat management blockchain is the most common words in this topic. Implications – This paper focuses on the development of the Islamic world in building and contributing to the digital ecosystem in zakat institutions. Originality – This research is expected to help carry out a comprehensive study of the center, identify research gaps, and help researchers determine research development by exploring findings that can still be developed related to digital management in zakat institutions are still limited
Comment on ‘The Polarizability of a confined atomic system: an application of the Dalgarno–Lewis method’

In a recent paper (2021 Eur. J. Phys. 42 045407), Antão and Peres study both the static and dynamic polarizability of a bound system. The binding potential they consider is the one-dimensional Dirac delta. The authors use the Dalgarno and Lewis method to calculate the polarizabilities to demonstrate its effectiveness in perturbation calculations. The authors left out essential steps in applying the time-dependent perturbation method to derive the expression for the dynamic electric polarizability. In addition, references that are useful to the paper are missing. To aid our comment, we first give a summary of the Dalgarno and Lewis method. Second, we present the work of the authors in deriving the dynamic electric polarizability. We follow by presenting the time-dependent perturbation method, which is needed to derive the expression for the dynamic electric polarizability. In the process, we will provide some essential references, which will be of value to the subject of the paper.
Continuous‐wave differential absorption lidar

This work proves the feasibility of a novel concept of differential absorption lidar based on the Scheimpflug principle. The range‐resolved atmospheric backscattering signal of a laser beam is retrieved by employing a tilted linear sensor with a Newtonian telescope, satisfying the Scheimpflug condition. Infinite focus depth is achieved despite employing a large optical aperture. The concept is demonstrated by measuring the range‐resolved atmospheric oxygen concentration with a tunable continuous‐wave narrow‐band laser diode emitting around 761 nm over a path of one kilometer during night time. Laser power requirements for daytime operation are also investigated and validated with single‐band atmospheric aerosol measurements by employing a broad‐band 3.2‐W laser diode. The results presented in this work show the potential of employing the continuous‐wave differential absorption lidar (CW‐DIAL) technique for remote profiling of atmospheric gases in daytime if high‐power narrow‐band continuous‐wave light sources were to be employed.