Miracle-dz/UniFAR-FTUs huggingface dataset:
Adverse Events of Non-Invasive Body Contouring: Analysis of the Food and Drug Administration Manufacturer and User Facility Device Experience Database

BACKGROUND Multiple non-invasive modalities have become popular alternatives to surgical procedures for body contouring. OBJECTIVE To analyze adverse events (AEs) associated with non-invasive body contouring devices reported through the Manufacturer and User Facility Device Experience (MAUDE) database. METHODS AND MATERIALS The MAUDE database were queried for AEs associated with non-invasive body contouring devices between January 2011 and June 2021. An extensive list of keywords and brand and manufacturer names was used. RESULTS A total of 1,325 reports with 1,590 AEs were identified among 6 modalities. More than 70% were reported in the past 5 years. Cryolipolysis made up 38.3% reports, which mostly pertained to paradoxical hyperplasia and hernias. Radiofrequency had the most reports (41.9%). Like laser devices, most of their reports described burns. Focused ultrasound was commonly associated with unintentional fat loss and surface irregularities. Focused electromagnetic field resulted in only 7 reports. CONCLUSION The analysis of present study supports previous studies concerning common local symptoms caused by these devices, but it also reveals complications not reported in previous device studies. This study highlights the importance of proper technique and adherence to device guidelines. Practitioners should be knowledgeable of potential complications from each device to both prevent and manage them accordingly.
Cache Valley Wetland Mapping: Supplemental Report

This report summarizes a recently completed mapping project in Cache County that had three components: mapping wetland and riparian areas to update National Wetland Inventory (NWI) mapping; applying additional Landscape Position, Landform, Water Flow path, and Waterbody Type (LLWW) attributes to enhance utility and information provided by the new mapping; and developing landscape-scale models identifying likely functions provided by wetlands across the entire project area. The project area and extent of the mapping effort covers 533,000 acres of the county and includes the entire Cache Valley as well as parts of the Blacksmith Fork, Logan River, and High Creek watersheds in the Bear River Range. The project area includes most wetland areas in Cache County and the wetlands most likely affected by agricultural and urban development.
Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning

Abstract Transposable elements are mobile sequences that can move and insert themselves into chromosomes, activating under internal or external stimuli, giving the organism the ability to adapt to the environment. Annotating transposable elements in genomic data is currently considered a crucial task to understand key aspects of organisms such as phenotype variability, species evolution, and genome size, among others. Because of the way they replicate, LTR retrotransposons are the most common transposable elements in plants, accounting in some cases for up to 80% of all DNA information. To annotate these elements, a reference library is usually created, a curation process is performed, eliminating TE fragments and false positives and then annotated in the genome using the homology method. However, the curation process can take weeks, requires extensive manual work and the execution of multiple time-consuming bioinformatics software. Here, we propose a machine learning-based approach to perform this process automatically on plant genomes, obtaining up to 91.18% F1-score. This approach was tested with four plant species, obtaining up to 93.6% F1-score (Oryza granulata) in only 22.61 s, where bioinformatics methods took approximately 6 h. This acceleration demonstrates that the ML-based approach is efficient and could be used in massive sequencing projects.
Posttranslational Chemical Mutagenesis Methods to Insert Posttranslational Modifications into Recombinant Proteins

Posttranslational modifications (PTMs) dramatically expand the functional diversity of the proteome. The precise addition and removal of PTMs appears to modulate protein structure and function and control key regulatory processes in living systems. Deciphering how particular PTMs affect protein activity is a current frontier in biology and medicine. The large number of PTMs which can appear in several distinct positions, states, and combinations makes preparing such complex analogs using conventional biological and chemical tools challenging. Strategies to access homogeneous and precisely modified proteins with desired PTMs at selected sites and in feasible quantities are critical to interpreting their molecular code. Here, we summarize recent advances in posttranslational chemical mutagenesis and late-stage functionalization chemistry to transfer novel PTM mimicry into recombinant proteins with emphasis on novel transformations.
Leader‐following consensus of stochastic dynamical multi‐agent systems with fixed and switching topologies under proportional‐integral protocols

With the rapid development of swarm intelligence, consensus problem for multi‐agent systems (MASs) has attracted substantial attention. To deal with the leader‐following consensus problems in stochastic dynamical MASs with fixed and switching topologies, this article designed proportional‐integral (PI) control protocols. On the basis of algebraic graph theory and stochastic analysis techniques, by selecting appropriate Lyapunov functions, it is theoretically shown that leader‐following consensus of MASs with stochastic dynamics underlying fixed and switching topologies can be achieved in mean square, respectively. Sufficient criteria are derived for selecting the PI control gains. Finally, the theoretical results are illustrated through several numerical simulations.
Modeling and Control: Applications to a Double Inverted Pendulum and Radio Frequency Interference.

BERNSTEIN, AMANDA SUE. Modeling and Control: Applications to a Double Inverted Pendulum and Radio Frequency Interference. (Under the direction of Hien Tran). In this work we consider methods for the modeling and control of physical systems. In particular, we examine a double inverted pendulum (DIP) system and the impact of radio frequency interference (RFI) on satellite operations (SATOPS) systems. The DIP is a classic example of a nonlinear, multivariable, unstable system. For the stabilization problem, a controller is designed that stabilizes the pendulum system in the unstable equilibrium position. Physically, this is the position where the two pendulum rods are aligned in a vertical position. The mathematical model of the DIP is derived using Lagrange’s energy method, and parameter estimation is performed to optimize the friction parameters. The resulting nonlinear system is linearized about its unstable equilibrium state, and a linear quadratic regulator (LQR) control and a power series based nonlinear control are each implemented in real-time in order to stabilize the DIP system. Since the real-time system only measures the three position states of the system, the three velocity states must also be estimated in order to apply the state feedback controls. Thus, three estimation methods are presented: a low pass derivative filter, a Kalman observer, and a nonlinear observer. Only the low pass derivative filter is successful in estimating the velocity states and is thus used to obtain the real-time results. A series of analytic models to detect and predict RFI within a SATOPS system is presented which include the behavior of the carrier synchronization loops when evaluating the performance of the system. Models for the carrier acquisition time, the carrier tracking errors, and the bit error rate performance for a phase locked loop in the absence of RFI as well as in the presence of continuous wave or wideband RFI are presented. These models are then implemented in MATLAB and tested under actual operating conditions for purposes of validation and verification. © Copyright 2018 by Amanda Sue Bernstein
Anticoccidial Vaccination Is Associated with Improved Intestinal Health in Organic Chickens

Simple Summary In recent years, the number of organic chicken farms has increased. Chickens can be infected by single-cell parasites, coccidia, which cause lesions in the lining of the intestine leading to poor growth and sometimes death (coccidiosis). This infection can also lead to overgrowth in the intestine of a bacterium, Clostridium perfringens, that may cause further damage (necrotic enteritis). Prevention is often achieved by adding substances in the feed that will slow down the development of parasites and bacteria, but this is not allowed in organic farming. The aim of this study was to investigate if vaccination against coccidia can prevent these diseases in organic chickens. Vaccinated chickens developed milder gut lesions, had fewer and less damaging C. perfringens, and had similar or higher body weight compared to unvaccinated chickens six weeks after vaccination. No deaths from coccidiosis or necrotic enteritis occurred among vaccinated chickens while some unvaccinated chickens died from these diseases. We conclude that vaccination against coccidia benefits organic chickens. This study provides knowledge supporting further development of the organic chicken industry. The results are also of relevance to the management of coccidiosis and necrotic enteritis in conventional broilers. Abstract Eimeria spp. and Clostridium perfringens (CP) are pathogens associated with coccidiosis and necrotic enteritis (NE) in broiler chickens. In this study we evaluated the effect of anticoccidial vaccination on intestinal health in clinically healthy organic Ross 308 chickens. On each of two farms, one unvaccinated flock (A1 and B1) was compared to one vaccinated flock (A2 and B2) until ten weeks of age (WOA). Faecal oocysts were counted weekly, and species were identified by PCR (ITS-1 gene). Lesion scoring, CP quantification and PCR targeting the CP NetB toxin gene were performed at three, four, and six WOA and chickens were weighed. Necropsies were performed on randomly selected chickens to identify coccidiosis/NE. Oocyst shedding peaked at three WOA in all flocks. Later oocyst shedding (E. tenella/E. maxima) in unvaccinated flocks at 5–7 WOA coincided with coccidiosis/NE. Although results differed somewhat between farms, vaccination was associated with lower intestinal lesion scores, reduced caecal CP counts, lower proportions of netB-positive CP, lower body weight at three–four WOA, and similar or slightly increased body weight at six WOA. In conclusion, the intestinal health of organic broilers can benefit from anticoccidial vaccination when oocyst exposure levels are high.
Do Red-Flagged Clients Exit Social Assistance Earlier than Others? The Case of the Finnish Social Assistance System

We examine an early ‘red flag system’ in the Finnish social assistance system and its efficiency in reducing the length of social assistance spells. We utilise the age-threshold in the policy that requires notifying social services on beneficiaries under twenty-five years of age after four months of social assistance receipt. Using monthly administrative data covering all twenty-three to twenty-six-year-old social assistance beneficiaries in Finland in 2018-2019, we compare the social assistance receipt of those below and above the age-threshold. Our findings show that those who are ‘red flagged’ do not exit social assistance earlier than others. On the contrary, four months after the notification is sent to social services, they are more likely to still have social assistance as their primary source of income compared to the twenty-five to twenty-six-year-old individuals. Our results shed light on the little discussed question on how to combine social work with digitalised and automatised welfare.
Spatial–temporal interaction module for action recognition

Abstract. Video action recognition methods based on deep learning can be divided into two types: two-dimensional convolutional networks (2D-ConvNets) relied and three-dimensional convolutional networks (3D-ConvNets) relied. 2D-ConvNets are more efficient to learn spatial features, but cannot capture temporal relationships directly. 3D-ConvNets can jointly learn spatial–temporal features, but their learning is time-consuming because of a large number of networks’ parameters. We therefore propose an effective spatial–temporal interaction (STI) module. The 2D spatial convolution and the one-dimensional temporal convolution are combined through attention mechanism in STI to learn the spatial–temporal information effectively and efficiently. The computation cost of the proposed method is far less than 3D convolution. The proposed STI module can be combined with 2D-ConvNets to obtain the effect of 3D-ConvNets with far fewer parameters, and it can also be inserted into 3D-ConvNets to improve their ability to learn spatial–temporal features, so as to improve the recognition accuracy. Experimental results show that the proposed method outperforms the existing counterparts on benchmark datasets.
Exploring the Reliability of a Cross-Cultural Model for Digital Games: A Systematic Review

In recent years, the game industry has become one of the most popular and competitive industries. To quickly expand game markets and attract more game players and consumers, a variety of types of games are developed by the companies and developers. Cross-region games are also common in the current game markets. Consequently, a multi-national competition across different cultures or countries is inevitable. For successful expansion of game market, the existence of cultural differences of game players with various cultural backgrounds is one of the notable issues we cannot ignore. Even though there are studies focusing on the relevant cultural differences, there are no study summarizing the past findings. Additionally, no powerful norm has yet been defined, therefore this paper will investigate whether it is feasible to apply the Hofstedes’ Cultural Dimensions Theory, often utilized in Management, to game industries and to be a reliable guidance for game design and development for cross-culture game players.
The Effect of Skepticism, Time Pressure, and Remote Audit During the COVID-19 Pandemic on Audit Quality: A Study of Auditors’ Perception

This study aimed to determine the impact of professional skepticism, audit time pressure, and remote audit during the COVID-19 pandemic on audit quality and the effect of remote audits on the relationship between professional skepticism and audit quality. The study was conducted through a questionnaire survey to The Audit Board of The Republic of Indonesia (BPK RI) auditors and analyzed using Smart PLS quantitative analysis methods. The results showed that professional skepticism, audit time pressure, and remote audit affected audit quality. Meanwhile, the remote audit does not moderate the relationship between professional skepticism and audit quality, classified as predictor moderation.
Estimation of silver nanoparticles effect on the reproductive health of female Wistar rats

ABSTRACT The present study aimed to study the effect of Silver nanoparticles (AgNPs) on the reproductive health of female Wistar rats. Twenty-one adult female rats in the estrus phase were divided into three groups. Seven rats each, Group (I) was the control group, Group (II) received a low dose of 2 mg/kg of AgNPs, and Group (III) received a high dose of 4 mg/kg of AgNPs. Rats were intraperitoneally (I.P.) injected three days per week for 30 days. After 24 hours from the last treatment, the rats were sacrificed under anesthesia. The results indicated no change in body weight or relative and absolute weight of reproductive organs. The biochemical results showed decreased estrogen hormone levels and no change in progesterone hormone levels. High dose group revealed a significant increase in follicle-stimulating and luteinizing hormone. AgNPs caused a significant decrease in total antioxidant capacity and a significant increase in malondialdehyde levels. AgNPs induced ovarian follicles degeneration. The uterine epithelial cells showed a high level of hyperplasia, losing their columnar shape showing apoptosis and inflammation. Immunohistochemistry studies showed a significant increase in activated caspase-3. This study showed the harmful effect of AgNPs on reproductive tissues, which may increase the chance of infertility.
A Self-stabilizing Network Size Estimation Gossip Algorithm for Peer-to-Peer Systems

We present a self-stabilizing network size estimation gossip algorithm which determines the number of nodes in a structured peer-to-peer system. The algorithm can handle joins, leaves, and failures and is applicable to most structured peer-to-peer systems providing a distributed hash table abstraction. Furthermore, the algorithm is self-stabilizing with respect to the local estimates of any node, which might be arbitrary at any given time. Once state corruption ceases, the algorithm eventually adjusts all estimates to the correct value even in presence of joins and leaves. The algorithm only assumes that the system is weakly fair, and does hence not require the nodes to make the same number of exchanges, to be correct.
Drug discovery: a multifactorial ecosystem

*Correspondence: Fernando Albericio, Peptide Science Laboratory, School of Chemistry and Physics, University of KwaZulu-Natal, Westville, Durban 4000, South Africa; CIBER-BBN, Networking Centre on Bioengineering, Biomaterials and Nanomedicine, Department of Organic Chemistry, University of Barcelona, 08028 Barcelona, Spain. albericio@ub.edu; Albericio@ukzn.ac. za Academic Editor: Fernando Albericio, University of KwaZulu-Natal, South Africa; University of Barcelona, Spain Received: June 6, 2022 Accepted: June 10, 2022 Published: January 1, 2023
Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning - A Review

Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.
Performance investigation of multi-level inverter for DFIG during grid autoreclosure operation

This paper aims to study the multi-level inverter (MLI) fed the widely used doubly-fed induction generators (DFIG). The new DFIG generator connected to the electrical grid is evaluated during single-pole autoreclosure operation to continue the generator support to the system. The effect of using MLI on the performance of the DFIG system during the dynamic operation of the autoreclosure is studied for the first time and integrating a new MLI during the fault and clearing periods. A dynamic arc model is represented depending on Kizilcay model. Furthermore, the dynamics performance of the DFIG during integration with MLI to improve the performance is studied. The MLI is preferred because of processing high power with high resolution of the voltage waveforms and lower stresses on switches. The MLI enhances the performance of DFIG-based generating system associated with electrical grid system in case of single-pole autoreclosure. The proposed cases of studies are simulated using Matlab/Simulink to evaluate the proposed system performance during the autoreclosure operation for faults in double-circuit transmission systems.
SWAPP: A New Programmable Playground for Web Application Security

Client-side web attacks are one of the major battleﬁelds for cybercriminals today. To mitigate such attacks, researchers have proposed numerous defenses that can be deployed on a server or client. Server-side defenses can be easily deployed and modiﬁed by web developers, but it lacks the context of client-side attacks such as DOM-XSS attacks. On the other hand, client-side defenses, especially in the form of modi-ﬁed browsers or browser extensions, require constant vendor support or user involvement to be up to date. In this work, we explore the feasibility of using a new execution context, the service worker context, as a platform for web security defense development that is programmable, browser agnostic, and runs at the client side without user involvement. To this end, we propose and develop SWAPP (Service Worker APplication Platform), a framework for implementing security mechanisms inside a service worker. As the service worker is supported by most browsers, our framework is compatible with most clients. Furthermore, SWAPP is designed to enable the extensibility and programmability of the apps. We demonstrate the versatility of SWAPP by implementing various apps that can mitigate web attacks including a recent side-channel attack targeting websites that deploy a service worker. SWAPP allows websites to ofﬂoad a part of the security tasks from the server to the client and also enables the possibility to deploy or retroﬁt emerging security features/prototypes before they are ofﬁcially supported by browsers. Finally, we evaluate the performance overhead of our framework and show that deploying defenses on a service worker is a feasible option.
03 03 08 9 v 3 2 0 Fe b 20 04 Anti-deSitter black holes , perfect fluids , and holography

We consider asymptotically anti-de Sitter black holes in d-spacetime dimensions in the thermodynamically stable regime. We show that the Bekenstein-Hawking entropy and its leading order corrections due to thermal fluctuations are reproduced by a weakly interacting fluid of bosons and fermions (‘dual gas’) in ∆ = α(d − 2) + 1 spacetime dimensions, where the energy-momentum dispersion relation for the constituents of the fluid is assumed to be ǫ = κpα. We examine implications of this result for entropy bounds and the holographic hypothesis.
Hierarchical Syntax and Domain-Generality: A Review

This literature review paper will investigate whether the hierarchical fundamentals of generative syntax such as non-adjacent dependencies (NADs) and recursion can be accounted for solely by domain-general processes. In reviewing this, I will start by briefly describing the fundamentals of generative grammar that pertain to NADs and recursion. The bulk of this paper will focus on empirical approaches to investigating the possibility of domain-general learning and processing of these structures. Firstly, I will look at evidence from artificial language (AL) experiments on statistical learning. Subsequently, I will discuss connectionist models such as neural networks, and how they may provide more controlled environments for investigating this question. In tandem with this, I will discuss a model for learning that was introduced in this paradigm: incremental learning. Following this, I will review some of the neurocognitive research on syntactic processing. In my conclusion, I will attempt to synthesise these findings from different areas, and suggest some possible avenues for further research.