Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Sourcing and the Exposition of Fake Financial News at Kenya’s Standard Newspaper

Purpose: This study investigated the sway that data sourcing has on the exposition of fake financial news at Standard Newspaper, Kenya’s oldest print media. Methods: The research, which was of concurrent mixed design, was conducted between October 2022 and December 2022. It involved financial news editors, reporters and graphic designers drawn from The Standard, The Saturday Standard and The Sunday Standard. Participants were enlisted through criterion-i purposeful sampling in the research whose structured and semi-structured data were gathered concurrently through face-to-face, telephone and electronic mail. Responses were organised systematically informed by the objective of the research. Thereafter, the quantitative and qualitative data were analysed separately before the pair of data were compared and integrated, and later interpreted. Findings: The study established that data sourcing has a huge sway in the generation of fake financial news at the Standard Newspaper. Unique Contribution to Theory, Policy and Practice: This study showed that how the media arranges, structures and packages and delivers content has a sway on the public, hence validating the Framing Theory. The outcome will guide media actors to pay for more focus on content sourcing for it informs the final output of the media.
PyThia: A Python package for Uncertainty Quantification based on non-intrusive polynomial chaos expansions

PyThia is a Python package for quantifying uncertainties by computing polynomial chaos surrogates for computationally expensive parametric models (e.g., parametric partial differential equations) and for performing a local and global sensitivity analysis of those parameters. At its core, PyThia aims to be close to mathematical foundations, general in its applications, and easy to use by non-experts. To achive broad applicability in many areas, PyThia relies on a non-intrusive regression approach (Farchmin et al., 2019; Sudret, 2008). This allows it to compute global polynomial approximations of complex physical models by accessing samples of inputs and outputs alone. In particular, this means that PyThia can be used without any adaptation of legacy software, independent of operating system, programming language, involved proprietary software, or computing device.
RNA-seq provides insights into potato deubiquitinase responses to drought stress in seedling stage

Ubiquitination is a specific protein degradation and reversible post-translational modification process that can be reversed by deubiquitinase (DUBs). DUBs can hydrolyze and release ubiquitin in the substrate protein so that the substrate can avoid degradation or change its activity, and it has an impact on plant growth and development, cell cycle, abiotic stress response, and other biological processes. Transcript sequences of potato varieties “DM1-3”, “Atlantic” and “Cooperation-88” downloaded from Potato Genome Resources were used for genome-wide identification of the DUB gene family using Hidden Markov Models and verified in the NCBI CD-Search tool. The characteristics of DUB genes from different potato varieties were analyzed including subcellular localization, gene structural motifs, phylogenetic tree, and sequence homology. Polyethylene glycol 6000 (PEG6000) induced drought stress transcriptome analysis was performed on the “Atlantic”, and differentially expressed genes were screened, with emphasis on the characterization of deubiquitinase. DUB genes have a complex gene structure, often with a large number of exons and alternative splicing. Their promoters contain abundant abiotic stress-responsive elements, such as 425 MYC, 325 ABRE, and 320 MYB. There are also a large number of orthologous genes in the DUBs of the three potato varieties, and these genes are often clustered in similar regions on the genome. We performed transcriptome sequencing of the potato under PEG-induced drought stress and analyzed it for the first time using the Atlantic as a reference genome. We identified a total of 6067 down-regulated differentially expressed genes (DEGs) and 4950 up-regulated DEGs under PEG-induced drought stress. We screened the expression of DUBs and observed that 120 DUBs were up-regulated where most of them functioned in the nucleus, and the interacting proteins of DUBs were also localized in the nucleus. We have comprehensively identified and analyzed potato DUBs, and the accurately aligned transcriptome data which will further deepen the understanding of DUBs involved in the regulation of osmotic stress.
AI-Powered Vision Inspection System for Object Classification Application

Human operators are often susceptible to eye fatigue due to sleep deprivation and excessive workload, which may negatively impact their consistency and efficiency in performing repetitive and challenging inspection tasks. This paper presents the development of an AI-powered vision inspection system for object sorting applications, utilizing a You-Only-Look-Once (YOLO) version 3 pre-trained model based on Deep Convolutional Neural Network's (DCNN) transfer learning technique. Feature extraction for each data point is performed using Darknet53, which subsequently trains the YOLO v3 model. The dataset is partitioned into a training set and test set at a 90:10 ratio. The trained model achieves a mean average precision (mAP) of 99.146%. Enhancing the precision and recall values of the model can be accomplished by increasing the number of dataset instances used for training.
A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain’s Cognitive Mechanism

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat’s brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain’s cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.
Ameliorative Effect of Thymoquinone and Thymoquinone Nanoparticles against Diazinon-Induced Hepatic Injury in Rats: A Possible Protection Mechanism

The health benefits of thymoquinone (TQ) have been a significant focus of numerous studies. However, more research is needed to ascertain whether its nano-form can effectively treat or prevent chronic diseases. In this study, we investigated how thymoquinone and its nanoparticles can mitigate liver damage induced by diazinon in male Wistar rats and explored the intracellular mechanisms involved. Forty-two Wistar male rats (n = 42) were randomly allotted into seven groups. Group 1 served as the control. Group 2 (vehicle) consisted of rats that received corn oil via a gastric tube daily. In Group 3 (TQ), rats were given a daily oral administration of TQ (40 mg/kg bw). Group 4 (thymoquinone nanoparticles, NTQ) included rats that received NTQ (0.5 mg/kg bw) orally for 21 days. Group 5 (DZN) involved rats that were administered diazinon (DZN, 15 mg/kg bw) orally. In Group 6 (TQ + DZN), rats first received TQ orally, followed by DZN. Group 7 (NTQ + DZN) consisted of rats receiving NTQ orally, then DZN. After 21 days of treatment, the rats were euthanized. After oral administration of DZN, liver enzymes were significantly elevated (p < 0.05). Additionally, there were noticeable increases in oxidative injury markers, such as nitric oxide, malondialdehyde, redox oxygen radicals, and overall increases in hydrogen peroxide and liver protein carbonyl concentrations. This was accompanied by the upregulation of apoptotic markers (Bax, caspase9, caspase 3, bax/Bcl2 ratio), inflammatory cytokines (TNF-α, IL-6), and DNA damage. There was also a noteworthy decrease (p < 0.05) in the activities of antioxidant enzymes and anti-apoptotic markers. However, the oral administration of thymoquinone or its nanoparticle form mitigated these diazinon complications; our histopathological findings corroborated our biochemical and molecular observations. In conclusion, the significant antioxidant properties of thymoquinone, or its nanoparticle form, in tandem with the downregulation of apoptotic markers and inflammatory cytokines, provided a protective effect against hepatic dysfunction caused by diazinon.
Impact of Oil Viscosity on Dispersion in the Aqueous Phase of an Immiscible Two‐Phase Flow in Porous Media: An X‐Ray Tomography Study

In this study, dispersion and mixing were studied in a steady two‐phase flow generated using a co‐injection method. The impact of oil viscosity was investigated over a large range of fluid viscosity ratios. The results indicate that highly heterogeneous flow fields are generated by a wide distribution of oil clusters with varied volumes. Variation in the velocity distribution enhanced the deformation and spreading of a tracer plume, resulting in large dispersion scales and accelerated spreading rates. The dispersion coefficients vary with time and exhibit a non‐Fickian dispersion during co‐injection. Consequently, anomalous mixing behaviors can be observed when the viscosity ratio exceeds 10. The mixing strength, characterized by the scalar dissipation rate, is first enhanced by distortion on the surface of the solute. Therefore, diffusion contributes to mixing, resulting in a faster decrease in the mixing strength in the late time regime. These results can be attributed to the fact that the non‐wetting fluid becomes disconnected, and the size of each cluster decreases as the oil viscosity increases. The formation of an oil film narrows pore spaces, and a lubrication effect of the oil film may contribute to the enhanced dispersion and mixing state, even with the low relative permeability of the wetting phase. This study provides insights into dispersion in partially saturated porous media with varied oil viscosities at both the macro and pore scales, which can further improve CO2 storage capacity and safety.
On the distribution of norm groups in the intervals corresponding to odd degree extensions of algebraic number fields

Abstract Let X be a subgroup of a group Y. The interval (X, Y) is the set of subgroups of Y that contain X including X and Y. Let K/k be a finite extension of a -adic number field k. One of the fundamental theorems local class field theory establishes a correspondence between the finite number of norm groups contained in the interval and finite extensions of k. In our earlier work, we proved that iff for any finite Galois extensions of an algebraic number field k. It is natural to determine the norm groups contained in the interval for a given finite extension K/k of algebraic number fields. In our earlier work, we showed that there are extensions K/k such that the corresponding interval contains only a finite number of norm groups, and there are extensions with the corresponding interval containing infinitely many norm groups. The extensions that we considered in our earlier work were primarily of even degrees. In the present work, we investigate the distribution of norm groups in the intervals corresponding to extensions of algebraic number fields of primarily odd degrees divisible by two primes.
Direct oral anticoagulants for the treatment of cerebral venous thrombosis – a protocol of an international phase IV study

Introduction Current guidelines recommend that patients with cerebral venous thrombosis (CVT) should be treated with vitamin K antagonists (VKAs) for 3–12 months. Direct oral anticoagulants (DOACs), however, are increasingly used in clinical practice. An exploratory randomized controlled trial including 120 patients with CVT suggested that the efficacy and safety profile of dabigatran (a DOAC) is similar to VKAs for the treatment of CVT, but large-scale prospective studies from a real-world setting are lacking. Methods DOAC-CVT is an international, prospective, observational cohort study comparing DOACs to VKAs for the prevention of recurrent venous thrombotic events after acute CVT. Patients are eligible if they are 18 years or older, have a radiologically confirmed CVT, and have started oral anticoagulant treatment (DOAC or VKA) within 30 days of CVT diagnosis. Patients with an absolute contra-indication for DOACs, such as pregnancy or severe renal insufficiency, are excluded from the study. We aim to recruit at least 500 patients within a three-year recruitment period. The primary endpoint is a composite of recurrent venous thrombosis and major bleeding at 6 months of follow-up. We will calculate an adjusted odds ratio for the primary endpoint using propensity score inverse probability treatment weighting. Discussion DOAC-CVT will provide real-world data on the comparative efficacy and safety of DOACs versus VKAs for the treatment of CVT. Clinical trial registration ClinicalTrials.gov, NCT04660747.
A Further Step toward Sustainable Development: The Case of the Edible Mushroom Supply Chain

This study provides an accurate economic characterization of the supply of edible mushrooms throughout Italy within the European context to fill the relevant research gap and highlight barriers and opportunities that are consistent with the Sustainable Development Goals. Italian companies operating in this field were identified and economically characterized using the Chamber of Commerce’s Register of Companies. A qualitative web content analysis was then conducted to extract information about the marketed products, mushroom species, and retail channels, as well as the adopted certifications. The obtained data were quantitatively analyzed through descriptive statistics and multiple correspondence analysis. The Italian market is concentrated in northern areas of the country, and the limited company size indicates fragmentation at the production level, which led to Italy not being competitive enough and, thus, heavily rely on imports. Production is limited to less than 10 species, and innovative mushroom-based products, such as burgers, have shown a limited presence on the market, although they are gaining market share online. The novelty of growing kits highlights the potential to use food production waste to create fungal substrates. Investments in training new mushroom growers and studying new formulations and new fungal species are needed; these investments could allow greater market differentiation and be a good opportunity to promote local economies and create new job opportunities, thus meeting the requirements for sustainable development.
Design and Characterization of a <4-mW/Qubit 28-nm Cryo-CMOS Integrated Circuit for Full Control of a Superconducting Quantum Processor Unit Cell

A universal fault-tolerant quantum computer will require large-scale control systems that can realize all the waveforms required to implement a gateset that is universal for quantum computing. Optimization of such a system, which must be precise and extensible, is an open research challenge. Here, we present a cryogenic quantum control integrated circuit (IC) that is able to control all the necessary degrees of freedom of a two-qubit subcircuit of a superconducting quantum processor. Specifically, the IC contains a pair of 4–8-GHz RF pulse generators for <inline-formula> <tex-math notation="LaTeX">$XY$ </tex-math></inline-formula> control, three baseband current generators for qubit and coupler frequency control, and a digital controller that includes a sequencer for gate sequence playback. After motivating the architecture, we describe the circuit-level implementation details and present experimental results. Using standard benchmarking techniques, we show that the cryogenic CMOS (cryo-CMOS) IC is able to execute the components of a gateset that is universal for quantum computing while achieving single-qubit <inline-formula> <tex-math notation="LaTeX">$XY$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula> average gate error rates of 0.17%–0.36% and 0.14%–0.17%, respectively, as well as two-qubit average cross-entropy benchmarking (XEB) cycle error rates of 1.2%. These error rates, which were achieved while dissipating just 4 mW/qubit, are comparable to the measured error rates obtained using baseline room-temperature electronics.
T-SSD: A Transformer-based Single-Stage Multi-Scale Sampling Object Detector

The object detection algorithms are the cornerstones of autonomous driving systems, they are mostly based on convolutional neural networks (CNNs) with one or two stages. Since its strong correlation with the life safety of the driver, the accuracy of object detectors is crucial and limited by its foundation, CNN, which is hard to improve nowadays. But at the same time, the basic transformer shows its better performance compared with the advanced CNN. To improve the accuracy, using transformers seems to be a better choice. However, most transformer-based detectors are only backbone replacements, ViT concept extension, or a fusion with CNN, cannot give a full play to the performance referring to the characteristics of the transformer. We proposed a single-stage object detector T-SSD (Transformer-based Single-Stage Detector) that comes with a multi-scale feature modeling ability. The transformer backbone extracts feature in different scales and aggregates them into an intermediate representation. The transformer neck then directly queries the semantic information from the aggregated representation and feed them to heads to make prediction once and for all. After training on COCO2017, by combining the construction philosophy of the object detector and the characteristics of transformers, our T-SSD-Tiny gives an AP (Average Precision) up to 9.0 higher than the CNN-based detectors with 100 fewer epochs, better than YOLOv3-Base and SSD-300. Also, the AP given by our T-SSD-Small is up to 4.7 higher than the transformer-based detector with the same epoch, indicating a comparable performance with DETR-ResNet-101 and YOLOS-Small.
Broadband simultaneously RHCP‐LHCP radiating microstrip fed rectangular slot antenna for polarization diversity applications

In this article, for the first time, an antenna that can radiate LHCP and RHCP waves simultaneously is presented. The antenna enables simultaneous transmission of both right‐handed (RH) and left‐handed (LH) circular polarized (CP) waves separately over an elevation range from −45 ° to −5 ° and 5 ° to 45 ° from the zenith. The simultaneous radiation of dual sense CP in the different spatial directions enables the antenna to act as polarization diversity transmitter. The mechanism of virtual sequential rotation of magnetic currents inside the different parts of the slot, excited with uniform phase fields results in dual CP generation. The uniform phase orthogonal fields are generated in the different parts of the slot essentially by exciting the full wavelength rectangular slot loaded with grounded stubs, symmetrically, with a shorted microstrip line. The final design of the slot antenna arrived with a rigorous parametric study on different dimensional parameters of slot and grounded stub. The measured impedance bandwidth of 22.5% centered around 7 GHz and axial ratio bandwidth of 19% is achieved. An overlapping bandwidth of 17% is achieved where both matching and AR are very good. The measured isolation between the RHCP and LHCP in the above‐mentioned elevation ranges is maintained above 10 dB. The simulated and experimental results are matching very well.
From Words to Flight: Integrating OpenAI ChatGPT with PX4/Gazebo for Natural Language-Based Drone Control

The rapid progress of large language models in recent years has opened new opportunities for human-robot interaction. In this paper, we propose a novel approach to control drones using natural language commands by integrating OpenAI ChatGPT with the PX4/Gazebo simulator. The proposed system enables users to interact with the simulator using everyday language, allowing them to control the drone's actions with ease, eliminating the need for extensive training in drone piloting. We discuss the implementation details, including the validation of the ChatGPT-generated commands and their translation into executable actions in the simulator. To the best of our knowledge, this is the first proposal of a verification and validation system for commands generated by ChatGPT and LLMs in general. Furthermore, we discuss the crafting of effective prompts and the essential criteria for doing so. Our approach demonstrates promising results in terms of both usability and reliability, paving the way for further research on natural language-based control systems for robotics applications.
A comprehensive modeling approach for the task mapping problem in heterogeneous systems with dataflow processing units

We introduce a new model for the task mapping problem to aid in the systematic design of algorithms for heterogeneous systems including, but not limited to, CPUs, GPUs, and FPGAs. A special focus is set on the communication between the devices, its influence on parallel execution, as well as on device‐specific differences regarding parallelizability and streamability. We give a comprehensive description on how a given task mapping can be abstractly evaluated including mappings to dataflow‐based hardware accelerators. We show how this model can be utilized in different system design phases and present two novel mixed‐integer linear programs to demonstrate the usage of the model, showing significant improvements compared to pure CPU mapping for randomly generated task graphs. To the best of our knowledge, we present the first ILP for task mapping that considers pipelining effects when streaming tasks on an FPGA.
Participation in the absolute gravity comparison with a compact cold atom gravimeter

The first Asia-Pacific Comparison of Absolute Gravimeters (APMP.M.G-K1) was organized by the National Institute of Metrology (NIM) of China from December 21, 2015 to March 25, 2016 in Changping, Beijing. Our compact cold atom gravimeter (CCAG) was transported from Hangzhou to Beijing with a long distance of about 1200 km to participate in this comparison. The CCAG is the only one, to the best of our knowledge, that is based on the principle of atom interferometry among all the instruments. Absolute gravity in the indicated three test sites has been measured as requested by the organizer. The sensitivity of our CCAG is estimated to be 90 μGal/Hz, even when the measurements are carried out without any vibration isolation. Besides, the accuracy of this gravimeter has been evaluated to be about 19 μGal by considering the significant system errors. Our results show a good agreement with the given reference value.