Adaptive λ-[lambda]-tracking control of activated sludge processes

An adaptive controller for activated sludge processes is introduced. The control objective is to keep, in the presence of input constraints, the concentration of the biomass proportional to the influent flow rate, where a prespecified small tracking error of size lambda is tolerated. This is achieved by the so called lambda-tracker which is simple in its design, relies only on structural properties of the process and weak feasibility properties, and does not invoke any estimation or identification mechanism or probing signals. lambda-Tracking is proved for a model of an activated sludge process with unknown reaction kinetics and including unknown time-varying process parameters. It is illustrated by simulations that the lambda-tracker works successfully, and even under practical circumstances which go beyond what we can prove mathematically, it can cope with 'white noise' corrupting the measurement and periodically acting disturbances

Promotional and prophylactic problems of children during the school age

Опазването на детското здраве е система от мерки, която обхваща планирането на бременността, самата бременност, раждането, следродовия период и развитието на детето до 18-годишна възраст. Управлението на детското здраве изисква специфични мерки и усилия за обединение в една интегрална съвкупност на различни области на интервенции, които изискват различна компетентност, промотивни, профилактични, социални и психологически мерки за подобряване на диагностиката и лечението, обучение и квалификация на медицинските специалисти, педагози, психолози, социални работници и цялото население на страната.Реализирането на промоция на детското здраве, профилактиката на болестите и целенасочена здравна политика е осъществима с активното участие на всички обществени сектори като здравеопазване, образование, икономика, финанси, социални грижи, спорт и други. Дейностите по осигуряване на здравните грижи, насочени към различни проблеми във възрастовите периоди на детето от раждането до 18-годишна възраст, излизат от тесните рамки на здравната политика и са насочени главно към прилагане на европейските стандарти към редките болести, генетични заболявания и предразположения, хронични заболявания в детската възраст, деца с увреждания, деца със специфични потребности и други.В този смисъл са и препоръките на ЕС, изразени в изготвената през 2005 г. Европейска стратегия „Здраве и развитие на децата и подрастващите`. Настоящата политика е синхронизирана с Европейската стратегия за детско здраве и интегрира седемте приоритетни направления за действие в условията на Европа - здраве на майката и новороденото, хранене, инфекциозни болести, травми и насилие, физическа околна среда, здраве на подрастващите, психосоциално развитие и психично здраве. Политиката излиза от тесните рамки на дейностите по осигуряване главно на медицински грижи за задоволяване потребностите от диагностика и лечение на нежеланата бременност, пренаталните грижи за майките, медицинските грижи, насочени към различните проблеми във възрастовите периоди от 0-18 г. Политиката е насочена и към прилагане на европейските стандарти към редки болести, генетични заболявания и предразположения, хронични заболявания в детската възраст, деца с увреждания, деца със специфични потребности и др.От съществена важност е осигуряването на информационни, образователни и здравноконсултативни услуги за здравословен начин на живот, на превенцията на злоупотребата с наркотици, тютюн и алкохол, на социокултурната и материалната околна среда, в която децата живеят и се социализират.Protection of children`s health is a system of measures, which covers the planning of pregnancy, pregnancy itself, childbirth, postpartum and child development up to 18 years of age. The management of child health requires specific measures and efforts to be united in a single integrated set of different areas of interventions that require different expertise, promotive, preventive, social and psychological measures to improve diagnosis and treatment, education and training of medical professionals, educators, psychologists, social workers and the entire population of the country.The implementation of promotion of child health, disease prevention and targeted health policy is feasible with the active participation of all social sectors such as health, education, economics, finance, social welfare, sports and others.The activities of providing health care to meet the needs of diagnosis and treatment of unwanted pregnancy, prenatal care for mothers, health care aimed at various issues in the age periods of the child from birth to 18 years of age are directed mainly towards the application of European standards for rare, genetic and chronic diseases in childhood, children with disabilities and children with specific needs.It is essential to provide information, education and healthcare consulting services for a healthy lifestyle, prevent of drug abuse, tobacco and alcohol, as well as to guarantee a socio-cultural and physical environment in which children live and socialize

A deep learning-based dirt detection computer vision system for floor-cleaning robots with improved data collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.publishe

Metabolomic and Proteomic Analysis of the Mesenchymal Stem Cells’ Secretome

Mesenchymal stem cells (MSCs) are multipotent stromal cells with a strong potential in human regenerative medicine due to their ability to renew themselves and differentiate into various specialized cell types under certain physiological or experimental conditions. MSCs secrete a broad spectrum of autocrine and paracrine factors (MSCs’ secretome) that could exert significant effects on cells in their vicinity. MSCs have been clinically tested and have displayed a great potential in the treatment of bone/cartilage fractures and disorders, diabetes, cardiovascular diseases and immune, neurodegenerative and inflammatory diseases. The therapeutic efficacy of MSCs was initially attributed to their multipotent character and ability to engraft and differentiate at the site of injury. However, in recent years, it has been revealed that either undifferentiated or differentiated MSCs’ secretome plays an important role in the therapeutic potential of MSCs. The deciphering of the composition of MSCs’ secretome through proteomic and metabolic analyses and implementation of certain advanced analytical (nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), chromatography, etc.) and immunological methods could contribute to the understanding of the mechanisms underlying the therapeutic effects of MSCs

Mathematical Modeling of the Relation between Basic Chemical Elements and Soil Properties

This paper presents mathematical modeling of the relation between basic chemical elements and soil properties. An overview of the basic chemical elements and properties of the soil is presented. An approach is proposed to conduct an experimental study of the impact of basic chemical elements and soil properties. Statistical methods are used for data processing. Mathematical models for relation between basic chemical elements and soil quality indicators are developed. Mathematical models for indirect determining the content of basic chemical elements by measuring the main soil indicators are analyzed

Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence

Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.This work was supported by the Project Odyssey: Platform for Automated Sensing in Archaeology Co-Financed by COMPETE 2020 and Regional Operational Program Lisboa 2020 through Portugal 2020 and FEDER under Grant ALG-01-0247-FEDER-070150.info:eu-repo/semantics/publishedVersio

Dried Blood Spots as a Clinical Samples for Laboratory Diagnosis and Surveillance of Vaccine-Preventable Diseases in Bulgaria

In recent years the dried blood spots (DBS) had new and innovative applications in medicine, neonatology, virology and microbiology. This study aimed to evaluation of the frequency of detection of viral IgM/IgG markers in dried blood spots and introducing an easy-to-implement protocol for serum extraction in measles, mumps and rubella surveillance. The total 204 clinical samples (102 serum samples and 102 dried blood spots) collected from 102 patients were included. All specimens were tested for presence of specific viral markers (IgM and IgG antibodies) by a commercial indirect enzyme-linked immunosorbent assay (ELISA). Of all tested patients, three (3/102, 2.94%, 95% CI: 0 ÷ 6.22) were confirmed for acute measles infection and two (2/102, 1.96%, 95% CI: 0 ÷ 4.65) for mumps. Double positive ELISA-IgM results were found in their serum samples and DBS. No acute rubella infection and rubella IgM marker were detected in both clinical samples. By immunoassay analysis of all 102 patients, measles, mumps and rubella IgG were found in 83/102 (81%, 95% CI: 73.40 ÷ 88.60), 76/102 (75%, 95% CI: 66.60 ÷ 83.40) and 79/102 (77%, 95% CI: 68.83 ÷ 85.17) serum samples.  Comparative results were obtained in the adequately obtained DBS. Viral IgG seroprevalence in DBS were obtained in 79/102 (77%, 95% CI: 68.83 ÷ 85.17) for measles, 69/102 (68%, 95% CI: 58.67 ÷ 77.33) for mumps and 73/102 (72%, 95% CI: 63 ÷ 81) for rubella, respectively. Double negative results for each screened viral markers were proven in six tested patients.The study shown higher extinction value (Ratio and NovaTec units) in DBS compared to serum samples of same persons were calculated. Our studies show over 90% coincidence in combined ELISA assay of viral markers against measles, mumps, and rubella in serum samples and DBS. DBS clinical approach is non-aggressive and more acceptable to the public (including young children, pregnant women, etc.). It has a variety of new and innovative applications in medicine and in particular in the laboratory diagnosis of acute and past (presence of protective immunity) measles, mumps and rubella infection in the phase of elimination

A Review on Dried Blood Spots (DBS) as Alternative, Archival Material for Detection of Viral Agents (Measles, Mumps, Rubella, Hepatitis B Virus)

In recent years there appears a variety of new and innovative applications of the dried blood spots. The areas of their range of application are medicine, neonatology, virology, microbiology, toxicology and pharmacokinetics, metabolic exchange, therapeutic drug monitoring, toxicology, and control of environmental pollution. The advantages of DBS technology can be combined into four main groups: (1) compared to conventional venipuncture, requires less blood volume, which is especially important in pediatrics and neonatology; (2) the procedure for blood collection is easy, inexpensive and noninvasive; (3) the risk of bacterial contamination or hemolysis is minimal; and (4) DBS can be maintained for a long time with almost no impact on the quality of the analysis. In recent years is increasing the application of DBS as method for seroepidemiological survey with focus viral infections: measles, mumps, rubella and hepatitis B virus. The DBS technique is optimized as an alternative approach (non-invasive, inexpensive, not requiring trained staff and cold chain for transport and storage) of venipuncture collection of clinical material in virology.This method facilitates the scientific researches about the concentration of virus specific antibodies in peripheral blood taken from a finger or heel; determining the percentage susceptibility / protection of the studied group of patients againt vaccine-preventable infectious - measles, mumps, rubella and hepatitis B; social benefits - non-invasive technique for testing of small children and infants and applications in regions in the countries with not well developed logistics infrastructure