Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of the non-Gaussian covariance of the weak lensing power spectrum and bispectrum on cosmological parameter estimation

We study how well the Gaussian approximation is valid for computing the covariance matrices of the convergence power and bispectrum in weak gravitational lensing analyses. We focus on its impact on the cosmological parameter estimations by comparing the results with and without non-Gaussian error contribution in the covariance matrix. We numerically derive the covariance matrix as well as the cosmology dependence of the spectra from a large set of N-body simulations performed for various cosmologies and carry out Fisher matrix forecasts for tomographic weak lensing surveys with three source redshifts. After showing the consistency of the power and bispectra measured from our simulations with the state-of-the-art fitting formulas, we investigate the covariance matrix assuming a typical ongoing survey across 1500 deg^2 with the mean source number density of 30 arcmin^{-2} at the mean redshift z_s=1.0. Although the shape noise contributes a significant fraction to the total error budget and it mitigates the impact of the non-Gaussian error for this source number density, we find that the non-Gaussian error degrades the cumulative signal-to-noise ratio up to the maximum multipole of 2000 by a factor of about 2 (3) in the power (bi-) spectrum analysis. Its impact on the final cosmological parameter forecast with 6 parameters can be as large as 15% in the size of the one-dimensional statistical error. This can be a problem in future wide and deep weak lensing surveys for precision cosmology. We also show how much the dark energy figure of merit is affected by the non-Gaussian error contribution and demonstrate an optimal survey design with a fixed observational time.
‘The school of our dreams’: engaging with children’s experiences and hopes at a remote school in India

ABSTRACT Schools are one of the most important social-geographic sites where children’s lives play out. Although researchers have sought to understand children’s school experiences in India, very few have considered marginalised children’s opinions regarding what they value and the alternatives to their current school experiences. Consequently, this article draws on the voices of 10 marginalised children (11-13 years) as co-researchers from a remote public school in Uttarakhand, India. The co-researchers generated qualitative data with the help of participatory tools based on the Mosaic Approach to identify ways in which their experiences at school could be improved. Children expressed three critical aspects where their school experiences can be enriched: 1) better school and classroom conditions to enhance learning spaces, 2) enhancing school accessibility, classroom pedagogy, and curriculum, and 3) space to act and effect change by learning and becoming. Last, we provide implications for policymakers, educators, and researchers with a call to reimagine schooling and children’s agency within the rural and remote school context.
Resistance to Thyroid Hormones: A Case-Series Study

The aim of the study is to describe the clinical features of two unrelated patients with resistance to thyroid hormones (RTH), the first, a total thyroidectomized patient, and the second, a pregnant woman. We report the features found in her newborn who also showed RTH. Patient 1 is a 38-year-old man with total thyroidectomy managed for excessive thyroid stimulating hormone (TSH) production, which poorly responded to the replacement therapy. He was found with a THRβ c.1378G>A p.(Glu460Lys) heterozygous mutation, which was also present in other members of his family (son, brother, and father). Interestingly, Patient 1 had hypertension, dyslipidemia, and hepatic steatosis, which have been recently suggested as RTH-related comorbidities. Patient 2 is a 32-year-old pregnant woman with multinodular goiter, and the THRβ heterozygous variant c.959G>C, that, to the best of our knowledge, has been reported in literature only once. Her newborn had tachycardia and increased thyroid hormone levels, and showed the same mutation. After delivery, high parathyroid hormone (PTH) and calcium serum levels were found in Patient 2 and the scintigraphy showed the presence of adenoma of a parathyroid gland. This case-series study provides a practical example of the management of RTH in a thyroidectomized patient, a pregnant woman, and a newborn. A novel RTH pathogenic mutation is described for the second time in literature. Furthermore, the importance of metabolic assessment in patients with RTHβ has been highlighted and the possible correlation between RTH and primary hyperparathyroidism is discussed.
DIVERSITY ANALYSIS OF CHICKPEA GERMPLASM FOR THE YIELD RELATED TRAITS

Chickpea is one of the major food legumes cultivated on a large area in the world and is famous for highly proteinaceous seeds. Germplasm diversity plays a key role in chickpea improvement programs. In this regard, the experiment was conducted for the assessment of phenotypic diversity present in chickpea germplasm for yield-related traits. The 74 genotypes and 14 modern cultivars of chickpea were sown following the randomized complete block design at the farm area of MNS University of Agriculture, Multan, in November 2018. The evaluation was based on the morphological parameters including plant height (cm), number of pods per 5 plants, number of seeds per pod and 100 seed weight (g). The analysis of variance represented that significant differences were present for these traits among germplasm lines. Based on recorded results, the plant height ranged from 52.58 cm to 82.60 cm in genotypes and from 46.73 cm to 72.86 cm in cultivars. In the case of 100 seeds weight, the range was 13.99 g to 26.51 g in genotypes while 16.67 g to 22.58 g in cultivars. The number of pods in five plants ranged from 23.33 to 211.33 in genotypes and that of 98.33 to 210 in cultivars. Finally, the minimum and the maximum number of seeds per pod in genotypes were 1.53 and 2.93, respectively, while those in the case of the cultivars were 1.6 and 2.5, respectively. Further, based on yield-related traits viz. No. of pods per plant, No. of seeds per pod and 100 seed weight, the TGK1508 was the highest yielding genotype and the D072-11 remained the lowest yielding genotype while Fakhar-e-Thal performed best and was the highest yielding cultivar.
From Specification Models to Explanation Models: An Extraction and Refinement Process for Timed Automata

Autonomous systems control many tasks in our daily lives. To increase trust in those systems and safety of the interaction between humans and autonomous systems, the system behaviour and reasons for autonomous decision should be explained to users, experts and public authorities. One way to provide such explanations is to use behavioural models to generate context- and user-speciﬁc explanations at run-time. However, this comes at the cost of higher modelling effort as additional models need to be constructed. In this paper, we propose a high-level process to extract such explanation models from system models, and to subsequently reﬁne these towards speciﬁc users, explanation purposes and situations. By this, we enable the reuse of speciﬁcation models for integrating self-explanation capabilities into systems. We showcase our approach using a running example from the autonomous driving domain.
Friedel sum rule in the presence of topological defects for graphene

The Friedel sum rule is extended to deal with topological defects for the case of a graphene cone in the presence of an external Coulomb charge. The dependence in the way the number of states change due to both the topological defect as well as the Coulomb charge are studied. Our analysis addresses both the cases of a subcritical as well as a supercritical value of the Coulomb charge. We also discuss the experimental implications of introducing a self-adjoint extension of the system Hamiltonian. We argue that the boundary conditions following from the self-adjoint extension encode the effect of short range interactions present in the system.
Online Model-Free Reinforcement Learning for Output Feedback Tracking Control of a Class of Discrete-Time Systems With Input Saturation

In this paper, a new model-free Model-Actor (MA) reinforcement learning controller is developed for output feedback control of a class of discrete-time systems with input saturation constraints. The proposed controller is composed of two neural networks, namely a model-network and an actor network. The model-network is utilized to predict the output of the plant when a certain control action is applied to it. The actor network is utilized to estimate the optimal control action that is required to drive the output to the desired trajectory. The main advantages of the proposed controller over the previously proposed controllers are its ability to control systems in the absence of explicit knowledge of these systems’ dynamics and its ability to start learning from scratch without any offline training. Also, it can explicitly handle the control constraints in the controller design. Comparison results with a previously published reinforcement learning output feedback controller and other controllers confirm the superiority of the proposed controller.
Infection by the Parasite Myxobolus bejeranoi (Cnidaria: Myxozoa) Suppresses the Immune System of Hybrid Tilapia

Myxozoa (Cnidaria) is a large group of microscopic obligate endoparasites that can cause emerging diseases, affecting wild fish populations and fisheries. Recently, the myxozoan Myxobolus bejeranoi was found to infect the gills of hybrid tilapia (Nile tilapia (Oreochromis niloticus) × Jordan/blue tilapia (O. aureus)), causing high morbidity and mortality. Here, we used comparative transcriptomics to elucidate the molecular processes occurring in the fish host following infection by M. bejeranoi. Fish were exposed to pond water containing actinospores for 24 h and the effects of minor, intermediate, and severe infections on the sporulation site, the gills, and on the hematopoietic organs, head kidney and spleen, were compared. Enrichment analysis for GO and KEGG pathways indicated immune system activation in gills at severe infection, whereas in the head kidney a broad immune suppression included deactivation of cytokines and GATA3 transcription factor responsible for T helper cell differentiation. In the spleen, the cytotoxic effector proteins perforin and granzyme B were downregulated and insulin, which may function as an immunomodulatory hormone inducing systemic immune suppression, was upregulated. These findings suggest that M. bejeranoi is a highly efficient parasite that disables the defense mechanisms of its fish host hybrid tilapia.
Nuclear Molecular Imaging of Disease Burden and Response to Treatment for Cardiac Amyloidosis

Simple Summary Cardiac amyloidosis (CA) is characterized by extracellular infiltration and deposition of amyloid fibrils primarily derived from the circulating transthyretin protein (TTR) or immunoglobulin light chain (AL). With the development of non-invasive diagnostic approaches and the emergence of new pharmacotherapeutic treatments for CA, the transformative effects of bone scintigraphy have been important in diagnosing TTR-CA. Positron emission tomography (PET) imaging is another promising, non-invasive option for the diagnosis of CA and may help differentiate between ATTR and AL amyloidosis. Bone-seeking single-photon emission tomography/computed tomography (SPECT/CT) quantification and amyloid-targeting PET imaging could be useful as a new strategy for disease burden and therapy monitoring to provide more insights into therapy response assessed by quantifying the amyloid burden in CA. Abstract Cardiac amyloidosis (CA) is a heterogeneous group of diseases in which extracellular insoluble amyloid proteins are deposited in specific organs and tissues locally or systemically, thereby interfering with physiological function. Transthyretin protein (TTR) and light chain (AL) amyloidosis are the most common types of cardiac amyloidosis. Radionuclide bone scintigraphy has recently become the most common non-invasive test for the diagnosis of TTR-CA but is of limited value for the diagnosis of AL-CA. PET has proved promising for the diagnosis of CA and its applications are expected to expand in the future. This review summarizes the current bone scintigraphy and amyloid-targeting Positron emission tomography (PET) imaging, the binding imaging properties of radiotracers, and the values of diagnosis, prognosis, and monitoring therapy response in CA.
Enhancing Account Privacy in Blockchain-Based IoT Access Control via Zero Knowledge Proof

Blockchain-enabled IoT Access Control (BIAC) is a promising paradigm to achieve reliable and automatic access control management for IoT systems. Due to the involvement of blockchain which is transparent, account privacy is easily violated in BIAC. Motivated by the recent progress of utilizing Zero Knowledge Proof (ZKP) to protect account privacy in cryptocurrencies, this article systematically investigates a ZKP-based privacy-enhancing BIAC architecture. We summarize the design principles as authenticating transactions with ZKP proofs instead of digital signatures so that permissions are linked to off-chain secrets rather than on-chain accounts. However, since the ZKP proofs are stored on the blockchain, an adversary may launch replay attacks to access resources illegally. To tackle this challenge, we carefully design two account-hiding transactions as well as validation rules for the classical capability-based AC (CBAC) model, where all ZKP proofs expire once used. The enhanced system preserves all necessary functions of CBAC while achieving permission invisibility and requester anonymity. We implement the designed ZKP operations, where the evaluation results demonstrate our solution incurs low overheads.
New Insights into Dietary Pterostilbene: Sources, Metabolism, and Health Promotion Effects

Pterostilbene (PTS), a compound most abundantly found in blueberries, is a natural analog of resveratrol. Several plant species, such as peanuts and grapes, produce PTS. While resveratrol has been extensively studied for its antioxidant properties, recent evidence also points out the diverse therapeutic potential of PTS. Several studies have identified the robust pharmacodynamic features of PTS, including better intestinal absorption and elevated hepatic stability than resveratrol. Indeed, due to its higher bioavailability paired with reduced toxicity compared to other stilbenes, PTS has become an attractive drug candidate for the treatment of several disease conditions, including diabetes, cancer, cardiovascular disease, neurodegenerative disorders, and aging. This review article provides an extensive summary of the nutraceutical potential of PTS in various disease conditions while discussing the crucial mechanistic pathways implicated. In particular, we share insights from our studies about the Nrf2-mediated effect of PTS in diabetes and associated complications. Moreover, we elucidate the important sources of PTS and discuss in detail its pharmacokinetics and the range of formulations and routes of administration used across experimental studies and human clinical trials. Furthermore, this review also summarizes the strategies successfully used to improve dietary availability and the bio-accessibility of PTS.
Ultra-Low Crosstalk and Fabrication-Tolerant Silicon-Nitride O-Band (de)Multiplexer Using Bragg Grating-Assisted Contra-Directional Coupler

A wavelength division (de)multiplexing (WDM) filter with ultra-low channel crosstalk (XT) and high tolerance was proposed for a 1 × 4 O-band coarse-WDM (CWDM) system on a silicon-on-insulator (SOI). The filter consists of four waveguide Bragg grating (WBG) structures, each one having a multi-mode waveguide and corrugations at both waveguide side walls. To relax the critical dimension, low-material-index silicon nitride (SiN<inline-formula><tex-math notation="LaTeX">$_{x}$</tex-math></inline-formula>) was utilized instead of silicon. For efficient design of each SiN<inline-formula><tex-math notation="LaTeX">$_{x}$</tex-math></inline-formula> multi-mode WBG (MMWBG), the core and corrugation widths were engineered over the perturbed-permittivity coupled-mode theory, leading to an ultra-high side-lobe suppression ratio <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>25.3 dB. The overall CWDM filter offers flat-top responses with ultra-low excess losses (ELs) <inline-formula><tex-math notation="LaTeX">$< $</tex-math></inline-formula>0.6 dB, a high channel uniformity <inline-formula><tex-math notation="LaTeX">$>-$</tex-math></inline-formula>0.45 dB, broad 1-dB bandwidths (BWs) <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>13.45 nm, ultra-low XTs <inline-formula><tex-math notation="LaTeX">$<$</tex-math></inline-formula><inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>28 dB, and ultra-broad available bandwidths of 14.35 nm for XTs <inline-formula><tex-math notation="LaTeX">$< $</tex-math></inline-formula><inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>28 dB (ABW<inline-formula><tex-math notation="LaTeX">$_{28-}$</tex-math></inline-formula><sub>dB</sub> <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>14.35 nm). Analysis of the tolerance showed that XTs of the overall filter remained <−25 dB even for extreme cases with <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>18-nm over-etching errors without compromising the ELs or BWs. Given its ultra-low XT, ultra-broad ABW, and high tolerance, the proposed MMWBG shows the great potential and high attractiveness for the O-band CWDM telecommunication systems.
Virtual Reality as a Surgical Care Package for Patients Undergoing Weight Loss Surgery: A Narrative Review of the Impact of an Emerging Technology

While bariatric surgery is regarded as the most effective treatment for people with severe and morbid obesity, its pathway is regarded as a complex one due to the multidisciplinary approaches required from pre-surgery education until long-term management. This is essential to maintain weight loss and improve the quality of life after bariatric surgery. Although these approaches are broadened, patient education, pre-operative preparation, behavioural therapy, rehabilitation, and dietary changes are regarded as the main domains in such complex care. With the increase in technological adaptation in medical services, virtual reality (VR) has shown many benefits that can be utilized in the care of bariatric patients undergoing surgery. However, VR has not been innovated to be a multidomain care package in which bariatric patients could benefit throughout their journey from the pre-operative optimization, recovery, and long-term follow-up. This review aims to give a brief description of some of the applications of VR technology and question whether it has the potential to be considered as a virtual ecosystem to improve the bariatric patients’ experience and pathway throughout surgery and follow-up.
Statistics of the Stokes Parameters for the Signal Scattered by Layered Structures With an Arbitrary Number of Slightly Rough Interfaces

Horizontally stratified structures are commonly used to represent naturally occurring structures, such as soils. The electromagnetic signature of such a medium illuminated by radar and the polarization state of the scattered wave are fully determined by the knowledge of the four Stokes parameters. In this article, we determine the statistics of the four Stokes parameters for the signal scattered by layered structures with an arbitrary number of slightly rough interfaces. The rough interfaces are realizations of second-order stationary centered Gaussian stochastic processes and the layered structure is illuminated by an elliptically polarized monochromatic wave. The zenithal and azimuthal components of the far scattered electric field are derived from the first-order small perturbation method. The derivation leads to a multivariate Gaussian model for the underlying complex scattered amplitudes, and we establish the closed-form expressions of the probability density function, the cumulative density function, and the first- and second-order moments for the four Stokes parameters. For an observation direction outside the incidence plane, we establish the condition on the incidence wave parameters for which the zenithal and azimuthal components are uncorrelated. For an air/snow cover/frozen soil/unfrozen soil structure, we analyze the marginal probabilities and validate the theory by comparison with Monte Carlo simulations. More generally, when the two complex components of the field scattered by the illuminated zone are Gaussian random variables, these statistics offer possibilities for in-depth investigating the polarization of scattering processes from random media.
An Efficient 3-D Point Cloud Place Recognition Approach Based on Feature Point Extraction and Transformer

In dynamic environments, sensor occlusions and viewpoint changes occur frequently, leading to challenges for point-based place recognition retrieval. Existing deep learning-based methods are impossible to possess the strengths of high detection accuracy, small network model, and rapid detection simultaneously, making them inapplicable to real-life situations. In this article, we propose an efficient 3-D point cloud place recognition approach based on feature point extraction and transformer (FPET-Net) to improve the detection effect of place recognition and reduce the model computation. We first introduce a feature point extraction module, which can greatly reduce the size of the point cloud and preserve the data features, further reducing the impact of environmental changes on data acquisition. Then, a point transformer module is developed to control the computational effort while extracting the global descriptors by discriminative properties. Finally, a feature similarity network module computes the global descriptor similarity using a bilinear tensor layer with lower parameters correlated across latitudes. Experiments show that the parameters of our algorithm are 2.7 times smaller than the previous lightest efficient 3D point cloud feature learning for large-scale place recognition (EPC-Net), and the computation speed of one frame point cloud is 4.3 times faster. The network also achieves excellent results with a maximum $F_{1}$ score of 0.975 in place recognition experiments based on the KITTI dataset.
Intelligent Traffic Management System for Smart Cities Utilizing Reinforcement Learning Algorithm

Traffic congestion on roadways or at intersections can occur for a variety of reasons, including increased vehicle queues, slow driving, and so on. When multiple emergencies occur at the same time, traffic gridlock may occur. An adaptive traffic control system is a traffic management method that allows signals to adapt to the current traffic demand in real time. Adaptive traffic signals work by synchronizing software and hardware. Q-learning necessitates the creation of accurate natural environment forms prior to taking action. Instead, a dynamic communication system was developed to explore the interplay between state, action, and rewards in that particular setting. The current traffic signal functions are based on predetermined traffic flow information to extract short time expectations, making it easier to calculate the signal management system's conclusion. If a certain model is used, then customizable traffic light management agents will have to collect photos of the current traffic situation and generate control signals at regular intervals. To improve the algorithm's stability, replay, occurrence, and ideal techniques have been implemented. The primary goal of the algorithm is to regulate congested traffic, which is accomplished by combining the dynamic network with the linear signal layout.