Towards a robotic personal trainer for the elderly

The use of robots in the environment of the elderly has grown significantly in recent years. The idea is to try to increase the comfort and well-being of older people through the employment of some kind of automated processes that simplify daily work. In this paper we present a prototype of a personal robotic trainer which, together with a non-invasive sensor, allows caregivers to monitor certain physical activities in order to improve their performance. In addition, the proposed system also takes into account how the person feels during the performance of the physical exercises and thus, determine more precisely if the exercise is appropriate or not for a specific person.This work was partly supported by the Spanish Government (RTI2018-095390-B-C31) and FCT—Fundação para a Ciência e Tecnologia through the Post-Docscholarship SFRH/BPD/102696/2014 (A. Costa) and UID/CEC/00319/2019

Harmonic analysis of iterated function systems with overlap

In this paper we extend previous work on IFSs without overlap. Our method involves systems of operators generalizing the more familiar Cuntz relations from operator algebra theory, and from subband filter operators in signal processing.Comment: 37 page

Aperiodic order and pure point diffraction

We give a leisurely introduction into mathematical diffraction theory with a focus on pure point diffraction. In particular, we discuss various characterisations of pure point diffraction and common models arising from cut and project schemes. We finish with a list of open problems.Comment: 14 page

AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition

A data stream is a flow of unbounded data that arrives continuously at high speed. In a dynamic streaming environment, the data changes over the time while stream evolves. The evolving nature of data causes essentially the appearance of new concepts. This novel concept could be abnormal such as fraud, network intrusion, or a sudden fall. It could also be a new normal concept that the system has not seen/trained on before. In this paper we propose, develop, and evaluate a technique for concept evolution in evolving data streams. The novel approach continuously monitors the movement of the streaming data to detect any emerging changes. The technique is capable of detecting the emergence of any novel concepts whether they are normal or abnormal. It also applies a continuous and active learning for assimilating the detected concepts in real time. We evaluate our approach on activity recognition domain as an application of evolving data streams. The study of the novel technique on benchmarked datasets showed its efficiency in detecting new concepts and continuous adaptation with low computational cost

Rearrangement of the RNA polymerase subunit H and the lower jaw in archaeal elongation complexes

The lower jaws of archaeal RNA polymerase and eukaryotic RNA polymerase II include orthologous subunits H and Rpb5, respectively. The tertiary structure of H is very similar to the structure of the C-terminal domain of Rpb5, and both subunits are proximal to downstream DNA in pre-initiation complexes. Analyses of reconstituted euryarchaeal polymerase lacking subunit H revealed that H is important for open complex formation and initial transcription. Eukaryotic Rpb5 rescues activity of the ΔH enzyme indicating a strong conservation of function for this subunit from archaea to eukaryotes. Photochemical cross-linking in elongation complexes revealed a striking structural rearrangement of RNA polymerase, bringing subunit H near the transcribed DNA strand one helical turn downstream of the active center, in contrast to the positioning observed in preinitiation complexes. The rearrangement of subunits H and A′′ suggest a major conformational change in the archaeal RNAP lower jaw upon formation of the elongation complex

Genome-wide studies of mRNA synthesis and degradation in eukaryotes

In recent years, the use of genome-wide technologies has revolutionized the study of eukaryotic transcription producing results for thousands of genes at every step of mRNA life. The statistical analyses of the results for a single condition, different conditions, different transcription stages, or even between different techniques, is outlining a totally new landscape of the eukaryotic transcription process. Although most studies have been conducted in the yeast Saccharomyces cerevisiae as a model cell, others have also focused on higher eukaryotes, which can also be comparatively analyzed. The picture which emerges is that transcription is a more variable process than initially suspected, with large differences between genes at each stage of the process, from initiation to mRNA degradation, but with striking similarities for functionally related genes, indicating that all steps are coordinately regulated. This article is part of a Special Issue entitled: Nuclear Transport and RNA Processing

Detecting human Activities Based on a multimodal sensor data set using a bidirectional long short-term memory model: a case study

Human falls are one of the leading causes of fatal unintentional injuries worldwide. Falls result in a direct financial cost to health systems, and indirectly, to society’s productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this chapter, we present and evaluate several bidirectional long short-term memory (Bi-LSTM) models using a data set provided by the Challenge UP competition. The main goal of this study is to detect 12 human daily activities (six daily human activities, five falls, and one post-fall activity) derived from multi-modal data sources - wearable sensors, ambient sensors, and vision devices. Our proposed Bi-LSTM model leverages data from accelerometer and gyroscope sensors located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30% and 38.50%, respectivel

Enzymatic bioremediation of xenobiotics

Wciąż wzrastające zanieczyszczenie środowiska związane z częstym przedostawaniem się do środowiska toksycznych substancji jest jednym z największych problemów ekologicznych współczesnego świata. Wzrasta więc zainteresowanie nowymi, ekologicznymi technologiami remediacji, niewymagającymi dużych nakładów finansowych, pozwalającymi na całkowite lub częściowe oczyszczenie środowiska. Technologie bioremediacji, wykorzystujące potencjał mikroorganizmów, mogą być z powodzeniem wykorzystywane do usuwania ze środowiska zanieczyszczeń alifatycznymi bądź aromatycznymi związkami pochodzenia naftowego. Jednakże, w przypadku substancji o charakterze ksenobiotyków efektywność mikrobiologicznego rozkładu może ulec ograniczeniu. Wśród czynników biologicznych enzymy posiadają wysoki potencjał do efektywnego przekształcania i detoksykacji zanieczyszczeń i potencjalnie mogą one zostać wykorzystane do oczyszczania zanieczyszczonego środowiska. Celem pracy było przedstawienie, na podstawie literatury, niektórych grup enzymów zdolnych do przekształcania ksenobiotyków w nieszkodliwe związki. Znaczna uwaga została poświęcona enzymom pochodzącym z grzybów białej zgnilizny, charakteryzujących się wysokim potencjałem do efektywnego rozkładu ksenobiotyków. W artykule zestawiono zarówno zalety, jak i wady stosowania enzymów w bioremediacji zanieczyszczonego środowiska, a także perspektywy aplikacji in situ bioremediacji z wykorzystaniem enzymów.Environmental pollution is growing more and more due to the frequently deliberate release of hazardous, toxic substances into the environment and it has become one of the biggest ecological problems of the world. Therefore, a growing interest is being devoted to develop new, cost-effective and eco-friendly remediation technology capable of partial or total recovery of polluted environment. Bioremediation that uses naturally existing catabolic potential of microorganisms can be efficiently used to clean up certain pollutants such as aliphatic or aromatic hydrocarbons. However, for chemicals exhibiting high xenobiotic character, like polyaromatic hydrocarbons, chlorophenols, dioxines, PCBs, etc., microorganisms can turn out to be ineffective. Among biological agents, enzymes have a great potential to effectively transform and detoxify pollutants and are potentially suitable to restore polluted environments. Moreover, the use of enzymatic proteins may represent a good alternative for overcoming most disadvantages related to the use of microorganisms. They are active in the presence of microbial predators and antagonists, can be used under extreme conditions limiting microbial activity and are effective at low pollutant concentrations. This work will examine the possibility of using enzyme preparations as an element of bioremediation technology. The main terms which must be fulfilled while using this type of technology will be presented. This review will also examine some class of enzymes, mainly oxidoreductases and hydrolases, that are capable of transforming xenobiotics effectively into innocuous products. Particular attention will be devoted to enzymes from white-rot fungi, such as Mn-peroxidase, lignin proxidase and laccase, which have a great potential towards xenobiotic compounds transformation. Also the use of lipase in biodegradation of phtalanes and as an agent for monitoring of bioremediation progress will be discussed. The main advantages as well as disadvantages that are present in the application of enzymes in the bioremediation of polluted environments will be examined in details. The future perspective for the in situ application of enzymatic bioremediation of polluted with xenobiotics environments will be discussed