Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Review and Attestation of Software Attribute Claims

Software can be described, like human users and other objects, through attributes. For this work, we define software attributes as humanly verifiable, falsifiable, or judgeable statements regarding characteristics of said software. Much like attributes in general, software attributes require robust identities for their source but also for their target, meaning a software in general or a binary in particular. As software can be of critical importance, performing an independent review of attribute claims appears beneficial. We posit that decentralized platforms that were developed and refined over the past decade can bridge the gap between existing tools and methods for software review and their open, transparent, and accountable use for the benefit of users. In this work, we explore the feasibility and implications of decentralizing an independent review of software attribute claims. We envision the decentralization of a review process from initialization and execution to the persistent recording of results. We sketch the available design space by decomposing the overall process into a modular design and describe how each component covers overarching objectives. To illustrate practical implications and tradeoffs, we present ETHDPR, a proof of concept implementation based on Ethereum and IPFS. Through a quantitative and qualitative evaluation, we show that a decentralized software review is practically feasible. We illustrate the flexibility of the proposed approach using a toy example of a software component in automotive systems. Lastly, we provide a discussion on fundamental limits and open issues of facilitating independent reviews via technological means.
Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model

The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment.
Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network

Terahertz (THz) spectroscopy is the de facto method to study the vibration modes and rotational energy levels of molecules and is a widely used molecular sensor for non-destructive inspection. Here, based on the THz spectra of 20 amino acids, a method that extracts high-dimensional features from a hybrid spectrum combined with absorption rate and refractive index is proposed. A convolutional neural network (CNN) calibrated by efficient channel attention (ECA) is designed to learn from the high-dimensional features and make classifications. The proposed method achieves an accuracy of 99.9% and 99.2% on two testing datasets, which are 12.5% and 23% higher than the method solely classifying the absorption spectrum. The proposed method also realizes a processing speed of 3782.46 frames per second (fps), which is the highest among all the methods in comparison. Due to the compact size, high accuracy, and high speed, the proposed method is viable for future applications in THz chemical sensors.
Robust Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference

Inspired by passivity-based control (PBC) techniques, we propose a data-driven approach in order to learn a neural net parameterized storage function of underactuated mechanical systems. First, the PBC problem is cast as an optimization problem that searches for point estimates of the neural net parameters. Then, we improve the robustness properties of this deterministic framework against system parameter uncertainties and measurement error by injecting techniques from Bayesian inference. In the Bayesian framework, the neural net parameters are samples drawn from a posterior distribution learned via Variational Inference. We demonstrate the performance of the deterministic and Bayesian trainings on the swing-up task of an inertia wheel pendulum in simulation and real-world experiments.
Analysis of AM Hub Locations for Hybrid Manufacturing in the United States

iii ACKNOWLEDGEMENTS . v LIST OF FIGURES . viii LIST OF TABLES . x LIST OF ABBREVIATIONS . xi CHAPTER 1: INTRODUCTION . 1 SUMMARY . 3 PRELIMINARY RESEARCH . 4 REFERENCES . 6 CURRENT STATE AND POTENTIAL OF ADDITIVE HYBRID MANUFACTURING FOR METAL PARTS . 8
Replacing the Concentrate Feed Mixture with Moringa oleifera Leaves Silage and Chlorella vulgaris Microalgae Mixture in Diets of Damascus Goats: Lactation Performance, Nutrient Utilization, and Ruminal Fermentation

Simple Summary Moringa oleifera and Chlorella vulgaris microalgae have a good balance of amino acids with high protein contents; however, complementary and synergic effects between proteins can improve their nutritive value compared to the individual additive. Replacing a concentrate mixture at 20% to 40% levels with a mixture of M. oleifera and microalgae improved nutrient digestibility, ruminal fermentation characteristics, milk production, composition and the fatty acid profile of goats. Inclusion of M. oleifera and microalgae mixture to partially replace concentrates may enhance milk production performance and milk quality without affecting animal health. Abstract Exploring suitable alternatives for high-cost concentrate feeds is a critical factor for successful livestock production. The present experiment aimed to evaluate the dietary inclusion of Moringa oleifera silage and Chlorella vulgaris microalgae (at 1% of total diet, DM basis) in a quintuplicate 3 × 3 Latin square design for milk production performance, nutrient utilization and ruminal fermentation in Damascus goats. Fifteen lactating Damascus goats were divided into three groups to be fed a diet composed of a concentrate mixture and rice straw at 60:40 (DM basis) in the control group and fed for 30 days in each period. The concentrate mixture in the control treatment was replaced with M. oleifera silage and C. vulgaris microalgae at 20% (MA20 treatment) or 40% (MA40 treatment). Treatments did not affect total feed intake but increased (p < 0.01) crude protein (CP) and fiber intakes while decreasing nonstructural carbohydrates intake. The digestibility of CP and acid detergent fiber increased due to silage supplementation compared to the control treatment. Treatments increased (p < 0.05) ruminal pH and the concentrations of total volatile fatty acids, acetate, and propionate; however, they decreased (p < 0.05) the concentrations of ammonia-N. Treatments increased (p < 0.05) the concentrations of serum glucose and antioxidant capacity. Both MA20 and MA40 treatments increased the daily milk production, the concentrations of milk fat and lactose, and feed efficiency compared to the control treatment. Additionally, MA20 and MA40 treatments increased the proportions of total polyunsaturated fatty acids and total conjugated linoleic acids. It is concluded that the concentrate feed mixture in the diet of lactating goats can be replaced up to 40% (equals to 24% of total diet) with M. oleifera silage to improve feed intake and nutrient utilization, and milk production performance.
The Impact of Prognostic Uncertainty on Condition-Based Maintenance Scheduling: an Integrated Approach

One of the challenges of Condition-Based Maintenance (CBM) is to combine health monitoring and predictions with efficient scheduling tools. However, the majority of literature is focusing on the assessment of prognostics algorithms performance. In fact, the added value of these algorithms can only be assessed when considering their impact on maintenance decision process. Furthermore, in practice, when considering the scenario of an aircraft fleet with multiple monitored components, it is hard for a human decision-maker to translate and identify the effect of probabilistic results from all prognostics models from all systems on the maintenance schedule. Therefore, to support the implementation of CBM, the prognostics algorithms have to be integrated within a scheduling framework. Our paper proposes this integration in order to evaluate the impact of different level of prognostics accuracy and uncertainty on the aircraft fleet maintenance scheduling level. First, a Support Vector Regression (SVR) model is used to predict the Remaining Useful Life (RUL) distributions of the monitored components. Second, the maintenance scheduling problem is solved within a Reinforcement Learning (RL) approach incorporating a state-of-the-art Partially Observable Monte Carlo algorithm. Implementing a rolling horizon approach, our proposed framework is applied to a fleet of 10 aircraft, each equipped with multiple monitored systems. A case study with multiple different prediction accuracy and uncertainty scenarios is performed to assess the impact of prognostics uncertainty on optimal maintenance scheduling. The performed analysis aims to guide the development and assessment of prognostic models in terms of accuracy and uncertainty in the context of CBM.
An actor-based approach to understanding radical right viral tweets in the UK

ABSTRACT Radical right actors routinely use social media to spread highly divisive, disruptive, and anti-democratic messages. Assessing and countering such content is crucial for ensuring that online spaces can be open, accessible, and constructive. However, previous work has paid little attention to understanding factors associated with radical right content that goes viral. We investigate this issue with a new dataset (the ‘ROT' dataset) which provides insight into the content, engagement, and followership of a set of 35 radical right actors who are active in the UK. ROT contains over 50,000 original entries and over 40 million retweets, quotes, replies and mentions, as well as detailed information about followership. We use a multilevel model to assess engagement with tweets and show the importance of both actor- and content-level factors, including the number of followers each actor has, the toxicity of their content, the presence of media and explicit requests for retweets. We argue that it is crucial to account for role of actors in radical right viral tweets, and therefore, moderation efforts should be taken not only on a post-to-post level but also on an account level.
Roasted Wheat Germ: A Natural Plant Product in Development of Nutritious Milk Pudding; Physicochemical and Nutritional Properties

Wheat germ has been recognized as an economical source of high-quality plant proteins and bioactive compounds for food fortification. Thus, it can be used for valorization of food products as a feasible strategy to enhance the nutritional quality and reduce wheat milling waste. In this research roasted wheat germ (RG) was added in formulation of egg-free milk pudding to enhance its nutritional value and the effects of RG particle size (125, 210 and 354 μm) and quantity (0.0, 2.5, 5.0, 7.5 and 10%) on the quality, nutritional and sensory properties of the resulting pudding were investigated. Reducing the particle size of RG significantly altered its chemical composition but had no significant effect on its antioxidant activity. Increasing the level of RG in the pudding, reduced pH and syneresis while increased dry matter content, hardness, cohesiveness and gumminess of the product. The quantity of RG had more effects on physicochemical properties of the puddings than changing the particle size. Based on the sensory evaluation results, the most acceptable sample was obtained by addition of 7.5% RG with a particle size of 125 μm.
UAV Trajectory Optimization for Joint Relay Communication and Image Surveillance

This work examines the use of image surveillance UAVs for relay communication between ground users and a remote base station (BS). UAVs take aerial images of the surveillance region and forward them to the BS while serving the uplink transmission demands of ground users. We first consider the single-UAV scenario and jointly determine the UAV’s trajectory, task assignment, user association, and rate allocation by maximizing the sum-log-throughput of the users subject to constraints on the surveillance coverage, image transmission requirements, and relay capacity. The resulting mixed-integer nonlinear programming problem is solved by an inexact block coordinate descent (BCD) algorithm where we inherit ideas from the exact penalty method for mathematical programming with equilibrium constraints to relax the integer constraints and the successive convex approximation approach to address the non-convexity of the trajectory optimization problem. Then, we extend the proposed framework to the case with multiple UAVs that are dispatched to cover a wide surveillance region. The UAVs may complete both relay and surveillance tasks more efficiently through cooperation and proper task allocation for UAVs. A similar BCD algorithm is adopted to solve the problem. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme over several baseline methods.
Bio-Assisted Synthesis and Characterization of Zinc Oxide Nanoparticles from Lepidium sativum and Their Potent Antioxidant, Antibacterial and Anticancer Activities

Nanotechnology is an emerging area of research that deals with the production, manipulation, and application of nanoscale materials. Bio-assisted synthesis is of particular interest nowadays, to overcome the limitations associated with the physical and chemical means. The aim of this study was to synthesize ZnO nanoparticles (NPs) for the first time, utilizing the seed extract of Lepidium sativum. The synthesized NPs were confirmed through various spectroscopy and imagining techniques, such as XRD, FTIR, HPLC, and SEM. The characterized NPs were then examined for various in vitro biological assays. Crystalline, hexagonal-structured NPs with an average particle size of 25.6 nm were obtained. Biosynthesized ZnO NPs exhibited potent antioxidant activities, effective α-amylase inhibition, moderate urease inhibition (56%), high lipase-inhibition (71%) activities, moderate cytotoxic potential, and significant antibacterial activity. Gene expression of caspase in HepG2 cells was enhanced along with elevated production of ROS/RNS, while membrane integrity was disturbed upon the exposure of NPs. Overall results indicated that bio-assisted ZnO NPs exhibit excellent biological potential and could be exploited for future biomedical applications. particularly in antimicrobial and cancer therapeutics. Moreover, this is the first comprehensive study on Lepidium sativum-mediated synthesis of ZnO nanoparticles and evaluation of their biological activities.
Association between Timing of Epinephrine Administration and Outcomes of Traumatic Out-of-Hospital Cardiac Arrest following Traffic Collisions

The effects of epinephrine administration timing on patients with out-of-hospital cardiac arrest (OHCA) following traffic collisions are unknown. We analyzed the 2013–2019 All-Japan Utstein Registry data of 2024 such patients aged ≥18 years who were resuscitated by emergency medical service (EMS) personnel or bystanders and then transported to medical institutions. Time from 119 call to epinephrine administration was classified into quartiles: Q1 (6–21 min), Q2 (22–26 min), Q3 (27–34 min), and Q4 (35–60 min). Multivariable logistic regression analysis was used to assess the effects of epinephrine administration timing on one-month survival after OHCA. Overall, the one-month survival rates were 3.2% (15/466) in Q1, 1.1% (5/472) in Q2, 1.9% (11/577) in Q3, and 0.2% (1/509) in Q4. Additionally, the one-month survival rate decreased significantly in the Q4 group (adjusted odds ratio, 0.07; 95% confidence interval, 0.01–0.57) compared with the Q1 group, and the probability of one-month survival decreased as the time from the EMS call to epinephrine administration increased (p-value for trend = 0.009). Only four patients (0.9% [4/466]) with the earliest epinephrine administration showed a good neurological outcome.
Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Coptidis Rhizoma for the Treatment of Alzheimer's Disease

Background Alzheimer's disease (AD) is becoming a more prevalent public health issue in today's culture. The experimental study of Coptidis Rhizoma (CR) and its chemical components in AD treatment has been widely reported, but the principle of multi-level and multi-mechanism treatment of AD urgently needs to be clarified. Objective This study focuses on network pharmacology to clarify the mechanism of CR's multi-target impact on Alzheimer's disease. Methods The Phytochemical-compounds of CR have been accessed from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and Symmap database or HPLC determination. The values of Oral Bioavailability (OB) ≥ 30% and Drug Like (DL) ≥ 0.18 or blood ingredient were used to screen the active components of CR; the interactive network of targets and compounds were constructed by STRING and Cytoscape platform, and the network was analyzed by Molecular Complex Detection (MCODE); Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) and metabolic pathway enrichment of targets were carried out with Metascape, the Database for Annotation, Visualization and Integrated Discovery (DAVID) and MetaboAnalyst platform; Based on CytoHubba, the potential efficient targets were screened by Maximal Clique Centrality (MCC) and Degree, the correlation between potential efficient targets and amyloid β-protein (Aβ), Tau pathology was analyzed by Alzdata database, and the genes related to aging were analyzed by Aging Altas database, and finally, the core targets were obtained; the binding ability between ingredients and core targets evaluated by molecular docking, and the clinical significance of core targets was assessed with Gene Expression Omnibus (GEO) database. Results 19 active components correspond to 267 therapeutic targets for AD, of which 69 is potentially effective; in module analysis, RELA, TRAF2, STAT3, and so on are the critical targets of each module; among the six core targets, RELA, MAPK8, STAT3, and TGFB1 have clinical therapeutic significance; GO function, including 3050 biological processes (BP), 257 molecular functions (MF), 184 cellular components (CC), whose functions are mainly related to antioxidation, regulation of apoptosis and cell composition; the HIF-1 signaling pathway, glutathione metabolism is the most significant result of 134 KEGG signal pathways and four metabolic pathways, respectively; most of the active components have an excellent affinity in docking with critical targets. Conclusion The pharmacological target prediction of CR based on molecular network pharmacology paves the way for a multi-level networking strategy. The study of CR in AD treatment shows a bright prospect for curing neurodegenerative diseases.
Patient-Derived Extracellular Vesicles Proteins as New Biomarkers in Multiple Myeloma - A Real-World Study

Multiple myeloma (MM) is a hematological malignancy of clonal antibody–secreting plasma cells (PCs). MM diagnosis and risk stratification rely on bone marrow (BM) biopsy, an invasive procedure prone to sample bias. Liquid biopsies, such as extracellular vesicles (EV) in peripheral blood (PB), hold promise as new minimally invasive tools. Real-world studies analyzing patient-derived EV proteome are rare. Here, we characterized a small EV protein content from PB and BM samples in a cohort of 102 monoclonal gammopathies patients routinely followed in the clinic and 223 PB and 111 BM samples were included. We investigated whether EV protein and particle concentration could predict an MM patient prognosis. We found that a high EV protein/particle ratio, or EV cargo >0.6 µg/108 particles, is related to poorer survival and immune dysfunction. These results were supported at the protein level by mass spectrometry. We report a set of PB EV-proteins (PDIA3, C4BPA, BTN1A1, and TNFSF13) with a new biomarker potential for myeloma patient outcomes. The high proteomic similarity between PB and BM matched pairs supports the use of circulating EV as a counterpart of the BM EV proteome. Overall, we found that the EV protein content is related to patient outcomes, such as survival, immune dysfunction, and possibly treatment response.
FOLLOW ME ROBOT USING INFRARED BEACONS

It is desirable in many applications for a mobile robot to track and follow a person [1][3]. There have been various efforts in literature to create person-tracking robots. However, current person-tracking robots are not capable of operating in unstructured environments. The problem of creating a person-tracking mobile robot has been studied by many researchers in literature. There are two main issues associated with this problem. The first issue is to equip a robot with proper sensory devices so that it is able to identify and locate the target person in a crowd in real time. Various approaches have been investigated, including vision, infrared sensors, ultrasonic sensors, and other approaches. The second issue is to control and navigate the robot so that it follows the target person within a certain distance. This seems simple, but in reality it is a fairly difficult task. For example, if the target person is in a busy corridor with many people standing and walking, the robot has to constantly avoid other people while following the target. There is still no reported evidence that a person-tracking robot has been implemented that is able to track a person in arbitrary environmental conditions. By using an ultrasonic sensor system and infrared sensor, an intelligent persontracking mobile robot is to be implemented that is able to follow the target person in unstructured, practical environments. The main focus of the thesis is development and implementation of control algorithms. We have used Ultrasonic sensor for Obstacle avoidance and IR sensor for target identification.