Graphene quantum dots from chemistry to applications

Graphene quantum dots (GQDs) have been widely studied in recent years due to its unique structure-related properties, such as optical, electrical and optoelectrical properties. GQDs are considered new kind of quantum dots (QDs), as they are chemically and physically stable because of its intrinsic inert carbon property. Furthermore, GQDs are environmentally friendly due to its non-toxic and biologically inert properties, which have attracted worldwide interests from academic and industry. In this review, a number of GQDs preparation methods, such as hydrothermal method, microwave-assisted hydrothermal method, soft-template method, liquid exfoliation method, metal-catalyzed method and electron beam lithography method etc., are summarized. Their structural, morphological, chemical composition, optical, electrical and optoelectrical properties have been characterized and studied. A variety of elemental dopant, such as nitrogen, sulphur, chlorine, fluorine and potassium etc., have been doped into GQDs to diversify the functions of the material. The control of its size and shape has been realized by means of preparation parameters, such as synthesis temperature, growth time, source concentration and catalyst etc. As far as energy level engineering is concerned, the elemental doping has shown an introduction of energy level in GQDs which may tune the optical, electrical and optoelectrical properties of the GQDs. The applications of GQDs in biological imaging, optoelectrical detectors, solar cells, light emitting diodes, fluorescent agent, photocatalysis, and lithium ion battery are described. GQD composites, having optimized contents and properties, are also discussed to extend the applications of GQDs. Basic physical and chemical parameters of GQDs are summarized by tables in this review, which will provide readers useful information

Arkhitetonicheskata forma na sofiĭskata t͡sŭrkva sv. Sofii͡a : khuozhestvo-istorichesko izsli͡edvane.

"Prilozhenie. Materiali kŭm istorii͡ata na sofiĭskata Sv. Sofii͡a pri͡ez XIV-XVII stoli͡etie."Mode of access: Internet

Transvenous lead extraction – summary of the experience of a single Bulgarian center – a retrospective study

Броят на имплантациите на електронни устройства за кардиостимулация постоянно се повишава в световен мащаб. С това респективно се увеличава и броят на пациентите, които имат абсолютни индикации за екстракция на електроди (пейсмейкърни и ICD). Целта на настоящото проучване е да се представят непосредствените и дългосрочните резултати при пациенти след трансвенозна екстракция на електроди, проведена в Клиниката по кардиология на УМБАЛ „Света Анна“ – София. Материали и методи: Проведено е ретроспективно проучване при пациенти с имплантирано устройство за електрокардиостимулация с давност повече от 1 година, при които е направена екстракция на трансвенозните електроди. Проучени са клиничните и процедурните характеристики, успеваемостта и усложненията с проследяване честотата на реинфекция след процедурата. Резултати: В периода август 2016–май 2021 г. при нас са постъпили 54 пациенти с абсолютна индикация за екстракция на системата за електрокардиостимулация. При проведените 54 процедури са екстрахирани 114 пейсмейкърни и ICD електрода. При 47 процедури (87%) е постигнат пълен технически успех с премахване на всички електроди, а при 53 (98,1%) – клиничен успех на процедурата. Регистрирани са 2 големи перипроцедурни усложнения (3,7%), изискващи спешна кардиохирургична интервенция. При направеното проследяване се установява много ниска честота на повторна инфекция – само при 1 пациент (1.9%). Изводи: Екстракцията на трансвенозни електроди е ефективна и безопасна процедура. Изграждането на специализиран център с организирана програма за екстракция на електроди е от ключово значение за постигането на добри непосредствени и дългосрочни резултати.  There has been increased rate of cardiac implantable electronic devices (CIED) implanted worldwide. Respectively the numbers of patients with absolute indications for lead extraction (pacemaker and ICD) grow up exponentially. The aim of our study is to present the initial experience and long-term results of pacemaker and ICD lead extraction in patients treated in Cardiology department of University hospital “Sveta Anna” Sofi a. Material and Methods: Retrospective study was performed of patients with CIED implanted greater than 1 year and indications for lead extraction. Clinical and procedural characteristics, success rate, complications rate, and reinfection rate data were collected and analyzed. Results: In the period August 2016 to May 2021, a total of 54 patients were admitted to our department with an absolute indication for lead extraction. A total of 114 pacemakers and ICD electrodes were extracted during 54 procedures. In 47 patients (87%) was shown complete technical success with removal of all hardware and in 53 patients (98.1%) clinical success of the procedure was found. The incidence of major periprocedural complications requiring urgent cardiac surgery in our series was 2 (3.7%). Follow-up revealed a very low recurrence infection rate – only 1 patient (1.9%). Conclusion: Lead extraction is a safe and effective procedure. The establishment of a specialized center with an organized system for lead extraction is key in achieving excellent short- and long-term results

Benchmarking Deep Learning for On-Board Space Applications

Benchmarking deep learning algorithms before deploying them in hardware-constrained execution environments, such as imaging satellites, is pivotal in real-life applications. Although a thorough and consistent benchmarking procedure can allow us to estimate the expected operational abilities of the underlying deep model, this topic remains under-researched. This paper tackles this issue and presents an end-to-end benchmarking approach for quantifying the abilities of deep learning algorithms in virtually any kind of on-board space applications. The experimental validation, performed over several state-of-the-art deep models and benchmark datasets, showed that different deep learning techniques may be effectively benchmarked using the standardized approach, which delivers quantifiable performance measures and is highly configurable. We believe that such benchmarking is crucial in delivering ready-to-use on-board artificial intelligence in emerging space applications and should become a standard tool in the deployment chain