РАСПРЕДЕЛЕНИЕ ЦЕНТРОВ ДИСЛОКАЦИОННОЙ ЛЮМИНЕСЦЕНЦИИ D1 В КРЕМНИИ, ПОДВЕРГНУТОМ ИМПЛАНТАЦИИ ИОНОВ Si+, И МОДЕЛЬ ФОТОЛЮМИНЕСЦЕНЦИИ

Using step−by−step removal of silicon layers, in which dislocation−related photoluminescence is observed after Si+ (100 keV, 1 ·1015 cm−2) ion implantation followed by high−temperature annealing in a chlorine−containing atmosphere, it has been found that a majority of dislocation−related centers of luminescence at ~ 1,5 μm (D1 line) is localized at the depths of Si+ ion ranges. Cross−sectional electron microscopy shows that the dislocations introduced by the implantation treatment (implantation plus annealing) penetrate to depths of ~ 1μm. A phenomenological model of the D1−line dislocation−related luminescence is developed based on the assumption that the K−centers and modified A−centers located in the atmospheres of dislocations are responsible for this luminescence line. The temperature dependence of luminescence intensity calculated on the basis of the model fits well the experimental data for the D1 line.Путем последовательного удаления слоев с кремния, в котором наблюдается дислокационная фотолюминесценция после ионной имплантации Si+ (100 кэВ, 1 · 1015 см−2) с последующим высокотемпературным отжигом в хлорсодержащей атмосфере, установлено, что основная доля центров дислокационной люминесценции при ~1,5 мкм (линия D1) сосредоточена в области пробегов ионов Si+. Методом электронной микроскопии поперечного среза показано, что введенные имплантационной обработкой (имплантация и последующий отжиг) дислокации проникают до глубин ~1 мкм. Предложена феноменологическая модель дислокационной фотолюминесценции для линии D1, базирующаяся на предположении, что за эту линию ответственны расположенные в атмосферах дислокаций К−центры и модифицированные А−центры. Температурная зависимость интенсивности линии D1, рассчитанная на основе модели, описывает экспериментальные данные.

COVID-19: Is There Evidence for the Use of Herbal Medicines as Adjuvant Symptomatic Therapy?

Background: Current recommendations for the self-management of SARS-Cov-2 disease (COVID-19) include self-isolation, rest, hydration, and the use of NSAID in case of high fever only. It is expected that many patients will add other symptomatic/adjuvant treatments, such as herbal medicines. Aims: To provide a benefits/risks assessment of selected herbal medicines traditionally indicated for “respiratory diseases” within the current frame of the COVID-19 pandemic as an adjuvant treatment. Method: The plant selection was primarily based on species listed by the WHO and EMA, but some other herbal remedies were considered due to their widespread use in respiratory conditions. Preclinical and clinical data on their efficacy and safety were collected from authoritative sources. The target population were adults with early and mild flu symptoms without underlying conditions. These were evaluated according to a modified PrOACT-URL method with paracetamol, ibuprofen, and codeine as reference drugs. The benefits/risks balance of the treatments was classified as positive, promising, negative, and unknown. Results: A total of 39 herbal medicines were identified as very likely to appeal to the COVID-19 patient. According to our method, the benefits/risks assessment of the herbal medicines was found to be positive in 5 cases (Althaea officinalis, Commiphora molmol, Glycyrrhiza glabra, Hedera helix, and Sambucus nigra), promising in 12 cases (Allium sativum, Andrographis paniculata, Echinacea angustifolia, Echinacea purpurea, Eucalyptus globulus essential oil, Justicia pectoralis, Magnolia officinalis, Mikania glomerata, Pelargonium sidoides, Pimpinella anisum, Salix sp, Zingiber officinale), and unknown for the rest. On the same grounds, only ibuprofen resulted promising, but we could not find compelling evidence to endorse the use of paracetamol and/or codeine. Conclusions: Our work suggests that several herbal medicines have safety margins superior to those of reference drugs and enough levels of evidence to start a clinical discussion about their potential use as adjuvants in the treatment of early/mild common flu in otherwise healthy adults within the context of COVID-19. While these herbal medicines will not cure or prevent the flu, they may both improve general patient well-being and offer them an opportunity to personalize the therapeutic approaches

Applying Machine Learning Methods and Models to Explore the Structure of Traffic Accident Data

The problem of reducing the increasing number of road traffic accidents has become more relevant in recent years. According to the United Nations plan this number has to be halved by 2030. A very effective way to handle it is to apply the machine learning paradigm to retrospective road traffic accident datasets. This case study applies machine learning techniques to form typical “portraits” of drivers violating road traffic rules by clustering available data into seven homogeneous groups. The obtained results can be used in forming effective marketing campaigns for different target groups. Another relevant problem under consideration is to use available retrospective statistics on mechanical road traffic accidents without victims to estimate the probable type of road traffic accident for the driver taking into account her/his personal features (such as social characteristics, typical road traffic rule violations, driving experience, and age group). For this purpose several machine learning models were applied and the results were discussed