Miracle-dz/UniFAR-FTUs huggingface dataset:
ℓ1-to-ℓ1 interval observation design for discrete-time switched linear systems under dwell time constraint

ABSTRACT In this brief article, sufficient conditions characterising the -gain of discrete-time switched positive linear systems (DSPLSs) under dwell time constraint are obtained. The dwell time refers to range, minimum and constant dwell time. These conditions are presented in terms of linear programming. Then, convex sufficient conditions on the existence of -to- interval observers for discrete-time switched linear systems (DSLSs) are derived based on the -gain stability analysis results of DSPLSs. By adding some parameter matrices, the proposed interval observer has a new structure that relaxes the design conditions. Suitable observer gains can be extracted from the solution of the dimensional optimisation problem where the -gain of the system mapping the disturbances to the weighted observation errors is minimised. At last, some numerical examples of DSLSs are given for illustration.
Synthesized Spoken Names: Biases Impacting Perception

Utilizing a existing neural text-to-speech synthesis architecture to generate person names and comparing them to reference names read aloud in a formal context, we explore how bias resulting from training data impacts the synthesis of person names, focusing on frequency and origin of names. Long-term, we aim to apply voice conversion of person names to aid the effective reading aloud of such names in celebratory ceremonies.
How Long is Enough? Predicting Student Outcomes with Same-Day Gameplay Data in an Educational Math Game

Curriculum-integrated games can provide teachers with data to help them decide when and how to intervene with individual students. Based on our prior work observing teachers using ST Math, teachers may not be able to attend to a dashboard or student screens to determine who might need intervention. We therefore set out to determine how much data we need from the current ST Math gameplay session to predict performance. Based on the available log data that tracks student performance over SETS of puzzles, we performed two experiments to predict performance. The ﬁrst uses data from one game level, which is about 3 minutes long, to predict the performance on the next level, and the second uses the ﬁrst 6 minutes of gameplay to predict how many levels a student can complete in 20 minutes, a typical class length. Our results show that our data are not ﬁne-grained enough to allow for paired level prediction, but that 6 minutes of gameplay can be used to rank students in order of performance for a class session. These results can be used as a basis for an alert system that could help teachers prioritize their time in the classroom.
Acceleration of Hydro Poro-Elastic Damage Simulation in a Shared-Memory Environment

Hydro-PED [1] is a numerical simulation software which models nucleation and propagation of damage zones and seismicity patterns induced by wellbore fluid injection. While most of the studies in geo-physical simulation acceleration and parallelization usually focus on exascale scenarios which are translated into vast meshes, encouraging a distributed fashion of parallelization, the nature of the current simulations of Hydro-PED dictates amount of data that can conveniently fit on a single compute node NUMA and accelerator memory alike. Thus shared-memory parallelization (such as OpenMP) can be fully implemented. In order to utilize this insight, Hydro-PED was interfaced with Trilinos [2] linear algebra solvers package, which enabled an evolution to iterative methods such as CG and GMRES. Additionally, several code sectors were parallelized and offloaded to an accelerator using OpenMP in a fine grained manner. The changes implemented in Hydro-PED gained a total speedup of x5-x12, which will enable Hydro-PED to calculate long-term simulation scenarios of hundreds of years in a feasible time a few weeks rather than a year.
Bit rate conditions to stabilise a scalar linear system with measurement noise based on event triggering

ABSTRACT This paper investigates the input-to-state stability (ISS) problem for a scalar continuous-time linear system with measurement noise under event-triggered sampling. The feedback information is transmitted through a digital communication network with bounded delay. Due to measurement noise, it is difficult to perfectly capture the system's state, which may harm the performance, and even destabilise the system. In order to deal with the measurement noise, a state observer is incorporated into the event generator. By designing the event-triggering strategy based on the observed state, the input-to-state stability of the concerned system can be ensured. We quantitatively analyse the required stabilising bit rate, which is mainly determined by the network delay and the system's dynamics. By selecting appropriate parameters of the event-triggered sampling strategy, the required stabilising bit rate is lower than the one by the conventional periodic sampling strategies. Due to the existence of measurement noise, extra bit rate is required compared with the case with perfect state. The bit rate increase of measurement noise can be arbitrarily reduced at the cost of the degraded control performance, which is measured by the ultimate upper bounds on the state. Simulations are done to illustrate the obtained stabilising bit rate conditions
Distributed observer-based control law with better dynamic performance based on distributed high-gain observer

ABSTRACT The design of distributed observers is a hot topic in the research of multi-agent system. It can estimate the whole states of the plant with only portion of output information and the information communicated in the network. This paper introduces the high-gain matrix into the classical distributed observers to improve its convergence speed. In this scene, the performance of distributed observers-based control law can be improved. Since high-gain matrix change the structure of classical distributed observers, a group of sufficient conditions for the stability of the distributed high-gain observers are proved. Moreover, we prove the distributed high-gain observers-based control law satisfies certainty equivalency principle and the designed observer has performance recovery capability. A numerical simulation demonstrates the correction and superior property of our method.
Potential Application of Gold Nanospheres as a Surface Plasmon Resonance Based Sensor for In-Situ Detection of Residual Fungicides

It is essential to develop a simple and sensitive method to rapidly detect residual fungicides in agricultural products to protect human health. So far, little studies have been reported on potential application of gold nanospheres (AuNSps) as a surface plasmon resonance based sensor for in-situ detection of residual fungicides. Therefore, in this study, we investigated the potential application of AuNSps as a surface plasmon resonance based sensor for in-situ detection of fungicides. AuNSps were successfully synthesized via a seed-mediated method with some modifications. Firstly, gold nanoseeds were made during the reduction of chloroauric acid by trisodium citrate dihydrate (TSC). Then, AuNSps were grown from the seeds by using HAuCl4, TSC and EDTA. AuNSps were subsequently dropped on a glass substrate before covered by thiophanate methyl, a broad-spectrum systemic fungicide. The AuNSps coated glass substrate was subsequently dried in the air for further surface-enhanced Raman spectroscopy (SERS) measurements. Optical properties, shape and size of AuNSps were confirmed by UV-vis spectroscopy, XRD, SEM-EDX and TEM. The results showed that AuNSps were successfully synthesized with the size of 53 nm, and their resonance peak was located at 560 nm. The Raman signal intensity of thiophanate methyl covered on AuNSps is higher than that without AuNSps, indicating SERS effects of AuNSps deposited glass substrate.
Cognizant Multitasking in Multiobjective Multifactorial Evolution: MO-MFEA-II

Humans have the ability to identify recurring patterns in diverse situations encountered over a lifetime, constantly understanding relationships between tasks and efficiently solving them through knowledge reuse. The capacity of artificial intelligence systems to mimic such cognitive behaviors for effective problem solving is deemed invaluable, particularly when tackling real-world problems where speed and accuracy are critical. Recently, the notion of evolutionary multitasking has been explored as a means of solving multiple optimization tasks simultaneously using a single population of evolving individuals. In the presence of similarities (or even partial overlaps) between high-quality solutions of related optimization problems, the resulting scope for intertask genetic transfer often leads to significant performance speedup—as the cost of re-exploring overlapping regions of the search space is reduced. While multitasking solvers have led to recent success stories, a known shortcoming of existing methods is their inability to adapt the extent of transfer in a principled manner. Thus, in the absence of any prior knowledge about the relationships between optimization functions, a threat of predominantly negative (harmful) transfer prevails. With this in mind, this article presents a realization of a cognizant evolutionary multitasking engine within the domain of multiobjective optimization. Our proposed algorithm learns intertask relationships based on overlaps in the probabilistic search distributions derived from data generated during the course of multitasking—and accordingly adapts the extent of genetic transfers online. The efficacy of the method is substantiated on multiobjective benchmark problems as well as a practical case study of knowledge transfers from low-fidelity optimization tasks to substantially reduce the cost of high-fidelity optimization.
Tobacco Smoking Increases the Lung Gene Expression of ACE2, the Receptor of SARS-CoV-2

On March 11, 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak a pandemic. As of May 28, 2020, laboratories had confirmed 5,701,337 COVID-19 cases, and 357,668 deaths had been reported in 216 countries, areas, or territories (1). COVID-19 is caused by a new type of pathogenic coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is phylogenetically similar to SARS-CoV, with approximately 80% identity between the genomes (2). SARS viruses affect the respiratory tract and cause an acute respiratory response through the same cell-entry receptor, ACE2 (angiotensin-converting enzyme 2), which is the only experimentally confirmed SARS-CoV-2 receptor. SARSCoV-2 infection also uses activation of the spike proteins found on the surface of the virus for cellular entry. The best candidates for priming spike proteins are two host cell enzymes called Furin and TMPRSS2 (2). In the current severe global emergency, to enable effective prevention and care, it is imperative to identify potential risk factors, such as cigarette smoking, which is a substantial risk factor for various important bacterial and viral infections. Some of the results of this study were previously published in preprint form (medRxiv, https://www.medrxiv.org/content/ 10.1101/2020.02.05.20020107v3).
Actor–Critic Learning Control With Regularization and Feature Selection in Policy Gradient Estimation

Actor–critic (AC) learning control architecture has been regarded as an important framework for reinforcement learning (RL) with continuous states and actions. In order to improve learning efficiency and convergence property, previous works have been mainly devoted to solve regularization and feature learning problem in the policy evaluation. In this article, we propose a novel AC learning control method with regularization and feature selection for policy gradient estimation in the actor network. The main contribution is that $\ell _{1}$ -regularization is used on the actor network to achieve the function of feature selection. In each iteration, policy parameters are updated by the regularized dual-averaging (RDA) technique, which solves a minimization problem that involves two terms: one is the running average of the past policy gradients and the other is the $\ell _{1}$ -regularization term of policy parameters. Our algorithm can efficiently calculate the solution of the minimization problem, and we call the new adaptation of policy gradient RDA-policy gradient (RDA-PG). The proposed RDA-PG can learn stochastic and deterministic near-optimal policies. The convergence of the proposed algorithm is established based on the theory of two-timescale stochastic approximation. The simulation and experimental results show that RDA-PG performs feature selection successfully in the actor and learns sparse representations of the actor both in stochastic and deterministic cases. RDA-PG performs better than existing AC algorithms on standard RL benchmark problems with irrelevant features or redundant features.
A 3D Printed Self-Sustainable Cell-Encapsulation Drug Delivery Device for Periocular Transplant-Based Treatment of Retinal Degenerative Diseases

Self-sustainable release of brain-derived neurotrophic factor (BDNF) to the retina using minimally invasive cell-encapsulation devices is a promising approach to treat retinal degenerative diseases (RDD). Herein, we describe such a self-sustainable drug delivery device with human retinal pigment epithelial (ARPE-19) cells (cultured on collagen coated polystyrene (PS) sheets) enclosed inside a 3D printed semi-porous capsule. The capsule was 3D printed with two photo curable polymers: triethylene glycol dimethacrylate (TEGDM) and polyethylene glycol dimethylacrylate (PEGDM). The capsule’s semi-porous membrane (PEGDM) could serve three functions: protecting the cells from body’s immune system by limiting diffusion (5.97 ± 0.11%) of large molecules like immunoglobin G (IgG)(150 kDa); helping the cells to survive inside the capsule by allowing diffusion (43.20 ± 2.16%) of small molecules (40 kDa) like oxygen and necessary nutrients; and helping in the treatment of RDD by allowing diffusion of cell-secreted BDNF to the outside environment. In vitro results showed a continuous BDNF secretion from the device for at least 16 days, demonstrating future potential of the cell-encapsulation device for the treatment of RDD in a minimally invasive and self-sustainable way through a periocular transplant.
Polymorphism of CLOCK Gene rs3749474 as a Modulator of the Circadian Evening Carbohydrate Intake Impact on Nutritional Status in an Adult Sample

The aim of this study was to evaluate the distribution of energy intake and macronutrients consumption throughout the day, and how its effect on nutritional status can be modulated by the presence of the rs3749474 polymorphism of the CLOCK gene in the Cantoblanco Platform for Nutritional Genomics (“GENYAL Platform”). This cross-sectional study was carried out on 898 volunteers between 18 and 69 years old (65.5% women). Anthropometric measurements, social issues and health, dietary, biochemical, genetic, and physical activity data were collected. Subsequently, 21 statistical interaction models were designed to predict the body mass index (BMI) considering seven dietary variables analyzed by three genetic models (adjusted by age, sex, and physical activity). The average BMI was 26.9 ± 4.65 kg/m2, 62.14% presented an excess weight (BMI > 25 kg/m2). A significant interaction was observed between the presence of the rs3749474 polymorphism and the evening carbohydrate intake (% of the total daily energy intake [%TEI]) (adjusted p = 0.046), when predicting the BMI. Participants carrying TT/CT genotype showed a positive association between the evening carbohydrate intake (%TEI) and BMI (β = 0.3379, 95% CI = (0.1689,0.5080)) and (β = 0.1529, 95% CI = (−0.0164,0.3227)), respectively, whereas the wild type allele (CC) showed a negative association (β = −0.0321, 95% CI = (−0.1505,0.0862)). No significant interaction with the remaining model variables was identified. New dietary strategies may be implemented to schedule the circadian distribution of macronutrients according to the genotype. Clinical Trial number: NCT04067921.
End-to-End QoS “Smart Queue” Management Algorithms and Traffic Prioritization Mechanisms for Narrow-Band Internet of Things Services in 4G/5G Networks

This paper proposes a modified architecture of the Long-Term Evolution (LTE) mobile network to provide services for the Internet of Things (IoT). This is achieved by allocating a narrow bandwidth and transferring the scheduling functions from the eNodeB base station to an NB-IoT controller. A method for allocating uplink and downlink resources of the LTE/NB-IoT hybrid technology is applied to ensure the Quality of Service (QoS) from end-to-end. This method considers scheduling traffic/resources on the NB-IoT controller, which allows eNodeB planning to remain unchanged. This paper also proposes a prioritization approach within the IoT traffic to provide End-to-End (E2E) QoS in the integrated LTE/NB-IoT network. Further, we develop “smart queue” management algorithms for the IoT traffic prioritization. To demonstrate the feasibility of our approach, we performed a number of experiments using simulations. We concluded that our proposed approach ensures high end-to-end QoS of the real-time traffic by reducing the average end-to-end transmission delay.
Molecular modelling and dynamic simulations of sequestosome 1 (SQSTM1) missense mutations linked to Paget disease of bone

Abstract The Paget disease (PDB; OMIM is 167250) is a chronic bone disease caused by pathogenic mutations in Sequestome1/p62 (SQSTM1) gene. This study has aimed to interpret the relationship of PDB linked SQSTM1 mutations with protein structure and its molecular dynamic features. The disease causative missense mutations were initially collected, and then analyzed for their, exonic and domain distribution, impact on secondary and tertiary structures, and their ability on protein-ligand interactions, using a combination of systems biology approaches. Our results show that most PDB linked SQSTM1 missense mutations affect amino acid residues clustered within or near the UBA domain (aa 389–434), which participates in the ubiquitination of substrates. We also report that the majority mutations occurred in α-helices over β-strands but their effects on the secondary structure were mostly neutral. Global tertiary structure deviations were minimal; however, at amino acid residue level minor structural changes were evident. The molecular dynamics simulation analysis showed that both PB1 and UBA domains were under constant structural fluctuations resulting in closed form conformation of SQSMT1 protein structure, when it is bound to PRKCI ligand. We also found salt bridge conformation changes in the UBA domain of SQSTM1 mutants when they bound to the PRKCI interactor protein. This finding suggests the possibility that mutations in SQSTM1 could impair its ability to ubiquitinate the substrates, eventually affecting autophagy and apoptosis, especially in mature osteoclasts. This study presents the additional insight into structure and function relationship between SQSTM1 mutations and PDB pathogenesis. Communicated by Ramaswamy H. Sarma.
Development and Psychometric Properties of a Questionnaire Assessing Self-Reported Generic Health Literacy in Adolescence

Health literacy is a promising approach to promoting health and preventing disease among children and adolescents. Promoting health literacy in early stages of life could contribute to reducing health inequalities. However, it is difficult to identify concrete needs for action as there are few age-adjusted measures to assess generic health literacy in young people. Our aim was to develop a multidimensional measure of health literacy in German to assess generic health literacy among 14- to 17-year-old adolescents, namely, the “Measurement of Health Literacy Among Adolescents Questionnaire” (MOHLAA-Q). The development process included two stages. Stage 1 comprised the development and validation using a literature review, two rounds of cognitive interviews, two focus groups and two rounds of expert assessments by health literacy experts. Stage 2 included a standard pretest (n = 625) of the questionnaire draft to examine the psychometric properties, reliability and different validity aspects. The MOHLAA-Q consists of 29 items in four scales: (A) “Dealing with health-related information (HLS-EU-Q12-adolescents-DE)”; (B) “Communication and interaction skills”, (C) “Attitudes toward one’s own health and health information”, and (D) “Health-related knowledge”. The confirmatory factor analysis indicated a multidimensional structure of the MOHLAA-Q. The internal consistency coefficients (Cronbach’s α) of the scales varied from 0.54 to 0.77. The development of the MOHLAA-Q constitutes a significant step towards the comprehensive measurement of adolescents’ health literacy. However, further research is necessary to re-examine its structural validity and to improve the internal consistency of two scales.
Structural and functional insights into CWC27/CWC22 heterodimer linking the exon junction complex to spliceosomes

Abstract Human CWC27 is an uncharacterized splicing factor and mutations in its gene are linked to retinal degeneration and other developmental defects. We identify the splicing factor CWC22 as the major CWC27 partner. Both CWC27 and CWC22 are present in published Bact spliceosome structures, but no interacting domains are visible. Here, the structure of a CWC27/CWC22 heterodimer bound to the exon junction complex (EJC) core component eIF4A3 is solved at 3Å-resolution. According to spliceosomal structures, the EJC is recruited in the C complex, once CWC27 has left. Our 3D structure of the eIF4A3/CWC22/CWC27 complex is compatible with the Bact spliceosome structure but not with that of the C complex, where a CWC27 loop would clash with the EJC core subunit Y14. A CWC27/CWC22 building block might thus form an intermediate landing platform for eIF4A3 onto the Bact complex prior to its conversion into C complex. Knock-down of either CWC27 or CWC22 in immortalized retinal pigment epithelial cells affects numerous common genes, indicating that these proteins cooperate, targeting the same pathways. As the most up-regulated genes encode factors involved in inflammation, our findings suggest a possible link to the retinal degeneration associated with CWC27 deficiencies.
Microbial gas vesicles as nanotechnology tools: exploiting intracellular organelles for translational utility in biotechnology, medicine and the environment

A range of bacteria and archaea produce gas vesicles as a means to facilitate flotation. These gas vesicles have been purified from a number of species and their applications in biotechnology and medicine are reviewed here. Halobacterium sp. NRC-1 gas vesicles have been engineered to display antigens from eukaryotic, bacterial and viral pathogens. The ability of these recombinant nanoparticles to generate an immune response has been quantified both in vitro and in vivo. These gas vesicles, along with those purified from Anabaena flos-aquae and Bacillus megaterium , have been developed as an acoustic reporter system. This system utilizes the ability of gas vesicles to retain gas within a stable, rigid structure to produce contrast upon exposure to ultrasound. The susceptibility of gas vesicles to collapse when exposed to excess pressure has also been proposed as a biocontrol mechanism to disperse cyanobacterial blooms, providing an environmental function for these structures.