Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiovascular changes in the NZB/W F1 mouse model of lupus nephritis

Background Patients with systemic lupus erythematosus (SLE), an autoimmune disease, have a higher risk of cardiovascular (CV) disease and death. In addition, up to 40%–50% of SLE patients develop lupus nephritis (LN) and chronic kidney disease, which is an additional CV risk factor. Thus, the individual contributions of LN and other SLE-specific factors to CV events are unclear. Methods In this study, we investigated the effect of LN on the development of CV changes using the female NZBxNZW F1 (NZB/W) mouse model of lupus-like disease, with female NZW mice as controls. Standard serologic, morphologic, immunohistologic, and molecular analyses were performed. In a separate group of NZB/W mice, systolic blood pressure (BP) was measured during the course of the disease using tail plethysmography. Results Our data show marked CV changes in NZB/W mice, i.e., increased heart weight, hypertrophy of the left ventricle (LV) and septum, and increased wall thickness of the intramyocardial arteries and the aorta, which correlated with the progression of renal damage, but not with the age of the mice. In addition, systolic BP was increased in NZB/W mice only when kidney damage progressed and proteinuria was present. Pathway analysis based on gene expression data revealed a significant upregulation of the response to interferon beta in NZB/W mice with moderate kidney injury compared with NZB mice. Furthermore, IFI202b and IL-6 mRNA expression is correlated with CV changes. Multiple linear regression analysis demonstrated serum urea as a surrogate marker of kidney function and IFI202b expression as an independent predictor for LV wall thickness. In addition, deposition of complement factors CFD and C3c in hearts from NZB/W mice was seen, which correlated with the severity of kidney disease. Conclusions Thus, we postulate that the pathogenesis of CV disease in SLE is affected by renal impairment, i.e., LN, but it can also be partly influenced by lupus-specific cardiac expression of pro-inflammatory factors and complement deposition.
Soft Pomeron in a graviton-dilatontachyon AdS/QCD model

In this work we intend to study the graviton Regge trajectory in the context of holographic Quantum Chrmodynamics (QCD) as a model for high energy scattering dominated by soft-Pomeron exchange. In chapter 1, we introduce the AdS/CFT correspondence and summarize some of the features of holographic QCD models (AdS/QCD). In chapter 2, we review important concepts in high energy scattering in QCD, with special interest on the soft Pomeron. In chapter 3, we carry out a detailed study of a particular AdS/QCD model, [1], with emphasis on high energy scattering through the exchange of a spin J field dual to glueball states, very similar to the treatment of [2]. Chapter 4 concludes this work with the main results and further developments.
Voluntary and enforced tax compliance determinants and impact among agrochemical businesses in Ghana

PurposeThis paper examined tax compliance and its impact on agrochemical traders in Ghana.Design/methodology/approachBased on the registered agrochemical lists obtained from the Plant Protection and Regulatory Service Department, 92 agrochemical traders were sampled for data collection. Probit regression was used to estimate determinants of tax compliance, whereas the Inverse Probability Weighted Regression Adjustment Model was employed to evaluate the impact of tax compliance on business performance.FindingsThe results revealed that age and gender relate positively to enforced tax compliance, while education positively impacts voluntary tax compliance. Nonetheless, tax rate, trust and monthly sales positively affect voluntary tax compliance but negatively impact enforced tax compliance. Inversely, while authorities’ power negatively impacted voluntary compliance, it positively influenced enforced tax compliance confirming the Slippery Slope Framework.Originality/valueTo the best knowledge of the authors, this paper is the first to investigate tax compliance determinants and impact among agrochemical traders, despite the tremendous growth of the agrochemical sub-sector in Africa and Ghana. Therefore, this study makes a modest contribution to empirical studies that validate the Slippery Slope Framework in promoting tax compliance in the agricultural and agribusiness sectors of a developing country. Similarly, it also unearths the impact of tax compliance on agribusiness growth which has yet to be highlighted in the extant literature.
Mesh Conflation of Oblique Photogrammetric Models Using Virtual Cameras and Truncated Signed Distance Field

Conflating/stitching 2.5-D raster digital surface models (DSMs) into a large one has been a running practice in geoscience applications; however, conflating full-3-D mesh models, such as those from oblique photogrammetry, is extremely challenging. In this letter, we propose a novel approach to address this challenge by conflating multiple full-3-D oblique photogrammetric models into a single and seamless mesh for high-resolution site modeling. Given two or more individually collected and created photogrammetric meshes, we first propose to create a virtual camera field [with a panoramic field of view (FoV)] to incubate virtual spaces represented by truncated signed distance field (TSDF), an implicit volumetric field friendly for linear 3-D fusion; then, we adaptively leverage the truncated bound of meshes in TSDF to conflate them into a single and accurate full 3-D site model. With drone-based 3-D meshes, we show that our approach significantly improves upon traditional methods for model conflations, to drive new potentials to create excessively large and accurate full 3-D mesh models in support of geoscience and environmental applications.
Channel Capacity Calculation at Large SNR and Small Dispersion within Path-Integral Approach

We consider the optical fiber channel modelled by the nonlinear Shrödinger equation with additive white Gaussian noise. Using Feynman path-integral approach for the model with small dispersion we find the first nonzero corrections to the conditional probability density function and the channel capacity estimations at large signal-to-noise ratio. We demonstrate that the correction to the channel capacity in small dimensionless dispersion parameter is quadratic and positive therefore increasing the earlier calculated capacity for a nondispersive nonlinear optical fiber channel in the intermediate power region. Also for small dispersion case we find the analytical expressions for simple correlators of the output signals in our noisy channel.
An Adaptive Distributed Denial of Service Attack Prevention Technique in a Distributed Environment

Cyberattacks in the modern world are sophisticated and can be undetected in a dispersed setting. In a distributed setting, DoS and DDoS attacks cause resource unavailability. This has motivated the scientific community to suggest effective approaches in distributed contexts as a means of mitigating such attacks. Syn Flood is the most common sort of DDoS assault, up from 76% to 81% in Q2, according to Kaspersky’s Q3 report. Direct and indirect approaches are also available for launching DDoS attacks. While in a DDoS attack, controlled traffic is transmitted indirectly through zombies to reflectors to compromise the target host, in a direct attack, controlled traffic is sent directly to zombies in order to assault the victim host. Reflectors are uncompromised systems that only send replies in response to a request. To mitigate such assaults, traffic shaping and pushback methods are utilised. The SYN Flood Attack Detection and Mitigation Technique (SFaDMT) is an adaptive heuristic-based method we employ to identify DDoS SYN flood assaults. This study suggested an effective strategy to identify and resist the SYN assault. A decision support mechanism served as the foundation for the suggested (SFaDMT) approach. The suggested model was simulated, analysed, and compared to the most recent method using the OMNET simulator. The outcome demonstrates how the suggested fix improved detection.
Experimental Research on the Energy Evolution of Concrete under Impact Loading

This paper presents an experimental study on the dynamic strength of concrete by using a split Hopkinson pressure bar. The stress–strain relationship and fragmentation degree of concrete were analyzed. The change process of the incident energy, reflection energy, transmission energy and consumption energy of concrete was calculated. The corresponding relationship between the variation of each energy and the stress state of concrete was studied. The law of energy evolution during the concrete fracture process was determined and the mechanism of concrete dynamic strength increase was revealed from the perspective of energy. The results show that the higher the strain rate, the higher the fragmentation degree of concrete, the smaller the grain diameter of fragments, the easier cracks are to pass directly through the aggregate, and the more regular the fragment shape. The change process of increasing amplitude of concrete consumption energy can reflect four mechanical states of concrete: stress increase, stress slow releasing, stress rapid releasing, and return-to-zero stress. Since the increase in reflected energy does not increase immediately with the increase in strain rate, it leads to the hysteresis of energy release in concrete, resulting in an increase in the dynamic strength of concrete.
FeFET-Based Synaptic Cross-Bar Arrays for Deep Neural Networks: Impact of Ferroelectric Thickness on Device-Circuit Non-Idealities and System Accuracy

Ferroelectric transistors (FeFETs) are amongst the most promising candidates for synaptic cross-bar array designs used for in-memory computation (IMC) of matrix-vector multiplications (MVMs) (Fig. 1) in deep neural networks (DNNs). However, FeFETs have several non-ideal attributes such as non-linearities, variations and faults. These, along with circuit non-idealities in the cross-bar array [1], produce output currents that may deviate from the expected (ideal) currents (Fig. 1). This can lead to computation errors, impairing system accuracy. Therefore, FeFETs need to be judiciously designed to minimize the impact of the device-circuit non-idealities on DNN accuracy. While several works have explored the implications of FeFETs in DNNs [2]–[3], analysis on the impact of FE thickness ($T_{FE}$) on system accuracy, accounting for the device-circuit non-idealities is lacking. To that end, we analyze the impact of $T_{FE}$ on the characteristics of FeFET-based synaptic devices based on physical models calibrated to experiments (Fig. 2). We present how $T_{FE}$ scaling affects the IMC of MVMs and DNN accuracy, considering device-circuit non-idealities including variations and faults in FeFETs and wire/sink/driver resistances in the cross-bar array.
Synthetic jet generation by high-frequency cavitation

Cavitation bubbles are nucleated at a high repetition rate in water by delivering a pulsed laser through a fibre optic. Continuous high-frequency cavitation drives a stream away from the fibre tip. Using high-speed photography and particle image velocimetry, the stream is characterised as a synthetic jet, generated by trains of vortices induced by non-spherical bubble collapse. At low laser power, the bubbles collapse before the arrival of a subsequent laser pulse. Yet, by increasing the laser power, a system of bubbles is formed which leads to complex bubble–bubble interactions. The synthetic jet is observed regardless of the bubble formation regime, demonstrating the stability of the phenomenon. Synthetic jet generation by repetitive bubble collapse extends the well-studied acoustic streaming from small-amplitude bubble oscillations.
On bounds for harmonic topological index

Let G = (V,E), V = {1, 2, . ,n}, E = {e1, e2, . , em}, be a simple graph with n vertices and m edges. Denote by d1 ≥ d2 ≥ · · · ≥ dn > 0 and d(e1) ≥ d(e2) ≥ · · · ≥ d(em), sequences of vertex and edge degrees, respectively. If i-th and j-th vertices of the graph G are adjacent, it is denoted as i ∼ j. Graph invariant referred to as harmonic index is defined as H(G) = ∑ i∼ j 2 di + d j . Lower and upper bounds for invariant H(G) are obtained.
Effects of Administrative Corruption on Democratic Governance In Nigeria

Administrative corruption has become a major issue sabotaging governance and democratic development in Third World Countries. It is a menace that respects no national boundaries. It distorts socio-political life of the people. In Nigeria, administrative corruption has eroded people’s trust in government because of the persistent increase in inefficiency and ineffective public service delivery. As revealed in the literature, administrative corruption in Nigerian public institutions has made a lot of the nation’s resources mismanaged by the public office holders despite many policy measures put in place to curtail the menace. Sequel to this phenomenon, this paper examined the effect of administrative corruption on democratic governance in Nigeria. Relying on the ex-post approach where literature was surveyed to explain the extent to which administrative corruption impedes the growth of democratic governance in the country. The paper recommends strengthening of the internal control mechanisms to detect cases of administrative corruption in public institutions. It also suggests the creation of pathways to give citizens the relevant tools to engage and participate in governance towards promoting transparency and accountability in the administration of the state.
Disparity estimation by patch matching

In this paper, a stripe-mesh type image representation scheme and a disparity estimation algo-rithm based on the stripe-mesh structure are proposed. The mesh structure consists of stripes that are composed of triangular patches, and lay along the epipolar direction. The borders of the patches coincide with the depth discontinuities on the image plane. Thus, inside each patch the depth of the physical scene can be approximated by planar surfaces such that each surface is modeled by the disparity values of the patch corners. The stereo matching algorithm acquires the structure of the scene by estimating the disparities within the mesh patches, using a modi(cid:12)ed exhaustive search algo-rithm.
Classical Verification and Blind Delegation of Quantum Computations

Author(s): Mahadev, Urmila | Advisor(s): Vazirani, Umesh | Abstract: In this dissertation, we solve two open questions. First, can the output of a quantum computation be verified classically? We give the first protocol for provable classical verifica- tion of efficient quantum computations, depending only on the assumption that the learning with errors problem is post-quantum secure.The second question, which is related to verifiability and is often referred to as blind computation, asks the following: can a classical client delegate a desired quantum compu- tation to a remote quantum server while hiding all data from the server? This is especially relevant to proposals for quantum computing in the cloud. For classical computations, this task is achieved by the celebrated result of fully homomorphic encryption ([21]). We prove an analogous result for quantum computations by showing that certain classical homomorphic encryption schemes, when used in a different manner, are able to homomorphically evaluate quantum circuits.While we use entirely different techniques to construct the verification and homomorphic encryption protocols, they both rely on the same underlying cryptographic primitive of trapdoor claw-free functions.
RaNeRF: Neural 3-D Reconstruction of Space Targets From ISAR Image Sequences

Compared to 2-D inverse synthetic aperture radar (ISAR) images of a space target, its 3-D model can provide adequate details and accurate measurement parameters. However, it is challenging to tackle the problem of feature extraction and correlation during 3-D reconstruction of space targets purely based on radar image sequences, due to their lack of clear evidence in imaging similarity compared to optical images. To address this problem, this article proposes radar neural radiance fields (i.e., RaNeRF), which is a novel 3-D reconstruction method using only observed ISAR image sequences. First, the 3-D structure of a target is represented as a continuous 6-D function of space positions and viewing directions using a fully connected deep network. Second, the relationship between the 3-D structure and 2-D ISAR images of the target is constructed to enable differential rendering of ISAR images. Our overall pipeline can thus be trained using the discrepancy between the modulus of rendered and observed ISAR images in a purely self-supervised manner without 3-D supervision. Finally, the 3-D mesh model of the target can be retrieved from the learned density field via marching cube (MC). As a result, the proposed RaNeRF can directly reconstruct the 3-D structure of targets without explicit feature extraction and correlation of ISAR image sequences. Both quantitative and qualitative results verify the effectiveness of the proposed method. Compared to conventional baseline methods using point clouds, our reconstructed structure is more complete and accurate. In addition, the optimized model can synthesize ISAR images at novel observation direction, which can be used for downstream tasks, including data augmentation and target recognition.
Rasprostranjenost Fusarium vrsta prema korovu domaćinu i geografskoj lokaciji

Endofitne gljive žive unutar biljaka ne uzrokujući očite simptome bolesti. Korovi koji rastu u blizini poljoprivrednih polja mogu poslužiti kao alternativni domaćin ovih gljiva. Izolirali smo endofitne gljive iz 6 vrsta korova, uobičajenih za istočnu Hrvatsku te bez očitih simptoma bolesti: Abutilon theophrasti, Amaranthus retroflexus, Ambrosia arthemisiifolia, Chenopodium album, Sorghum halepense i Xantium strumarium, s osam različitih lokacija. Determinirali smo 11 vrsta roda Fusarium: F. avenaceum, F. acuminatum, F. equiseti, F. graminearum, F. oxysporum, F. proliferatum, F. semitectum, F. solani, F. subglutinans, F. venenatum i F. verticillioides. Cilj ovog istraživanja bio je utvrditi vrstu gljiva i učestalost izolacije za svaki korov te geografsku rasprostranjenost. Pronašli smo značajnu razliku između mikoflore iste vrste korova prisutne na različitim lokacijama. Ovi rezultati sugeriraju da postoji veza između endofitnih gljiva, njihovog korova domaćina i staništa.
A New Approach to Solve Fully Fuzzy Linear Programming with Trapezoidal Numbers Using Conversion Functions

Recently, fuzzy linear programming problems have been considered by many. In the literature of fuzzy linear programming several models are offered and therefore some various methods have been suggested to solve these problems. One of the most important of these problems that recently has been considered; are Fully Fuzzy Linear Programming (FFLP), which all coefficients and variables of the problem are the same kind of fuzzy numbers. One of most common of them is the model in which all fuzzy parameters are discussed by triangle numbers. In this paper, we first define a fully fuzzy linear programming with trapezoidal numbers and then suggest a new method based on reducing the original problem to the problem with triangle number. Specially, a conversion function for converting two trapezoidal and triangular numbers to each other is offered. Finally, the mentioned method is illustrated by a numerical example.
Super-Cutoff Analog Building Blocks for pW/Stage Operation and Demonstration of 78-pW Battery-Less Light-Harvested Wake-Up Receiver down to Moonlight

Techniques to lower power in analog and sensor interfaces well below regular transistor leakage $(\mathrm{V}_{\mathrm{G}\mathrm{S}}=0\mathrm{V})$ are introduced. The proposed circuit techniques enable $\sim$pW power in super-cutoff $(\mathrm{V}_{\mathrm{G}\mathrm{S}}\lt 0\mathrm{V})$ building blocks from current mirrors to OTAs, pseudo-resistors and bias. A LiFi optical wake-up receiver with 78-pW power is demonstrated with continuous operation solely powered by an unregulated 1-mm2 solar cell down to llux (moonlight).
Revolutionary Approaches to Managing Neuropathies: A Review of Innovative Therapies

Neuropathies are a group of disorders that affect the peripheral nervous system and can result in a range of symptoms such as pain, weakness, and numbness. While current treatments for neuropathies include medications, physical therapy, and surgical intervention, there is growing interest in innovative therapies that could provide better outcomes for patients. This review article provides an overview of some of the most promising innovative therapies for neuropathies, including stem cell therapy, gene therapy, neurostimulation, and immunotherapy. While some of these therapies are still in the experimental stage, several clinical trials have shown promising results. Further research is needed to establish the safety and efficacy of these therapies, but their potential benefits could provide hope for patients with neuropathies who have not responded well to current treatments.
Temporal Neural Team Formation with Negative Sampling

Predicting future successful teams of experts who can synergistically work in concert with each other and en masse cover a set of required skills of a degree necessary for the achievement of the desired outcome is challenging due to several reasons, including 1) the magnitude of the pool of plausible expert candidates with diverse backgrounds and skills, and 2) the drift and variability of collaborative ties of experts and their level of expertise in each area in time. Prior works in team formation have neglected the fact that experts’ skill, interests, and collaborative ties change over time. We can categorize previous works in team formation based on their method of optimization: 1) search-based, where the search for the optimum team is carried over all the subgraphs of expert networks or via integer programming, however, these works overlooked the temporal nature of human collaborations. 2) learning-based, where machine learning approaches are used to learn the distributions of experts and skills in the context of successful teams in the history to predict almost surely successful teams in the future. However, they also fail to recognize the possible drift and variability of experts’ skills, interest, and collaborative ties in time and its impact on the prediction of future successful teams. Moreover, neural models are prone to overfitting when training data suffers from the long-tail phenomenon, i.e., few experts have a lot of successful collaborations and the majority have participated sparingly. To overcome the aforementioned problems, i) we propose a streaming scenario training strategy for neural models to help the model in the prediction of future successful teams of experts, where instead of shuffling our datasets, we train the models in an orderly manner, to grasp the changes in experts’ skills, interests, and collaborations, and ii) we propose an optimization objective that leverages both successful and virtually unsuccessful teams via various negative sampling heuristics, and iii) we conduct experiments on four large-scale benchmark datasets with varying distribution of skills and members namely, dblp, imdb, uspt, and github. Finally, we empirically demonstrate how our proposed objective functions and training method, outperform the state-of-the-art approaches in terms of effectiveness and efficiency.