Contribution of microlensing to X-ray variability of distant QSOs

We consider a contribution of microlensing to the X-ray variability of high-redshifted QSOs. Cosmologically distributed gravitational microlenses could be localized in galaxies (or even in bulge or halo of gravitational macrolenses) or could be distributed in a uniform way. We have analyzed both cases of such distributions. We found that the optical depth for gravitational microlensing caused by cosmologically distributed deflectors could be significant and could reach $10^{-2} - 0.1$ at $z\sim 2$. This means that cosmologically distributed deflectors may contribute significantlly to the X-ray variability of high-redshifted QSOs ($z>2$). Considering that the upper limit of the optical depth ($\tau\sim 0.1$) corresponds to the case where dark matter forms cosmologically distributed deflectors, observations of the X-ray variations of unlensed QSOs can be used for the estimation of the dark matter fraction of microlenses.Comment: 6 pages, to appear in "Impact of Gravitational Lensing on Cosmology", IAU Symposium 225, eds. Y. Mellier & G. Meyla

McMini: A Programmable DPOR-based Model Checker for Multithreaded Programs

Model checking has become a key tool for gaining confidence in correctness of multi-threaded programs. Unit tests and functional tests do not suffice because of race conditions that are not discovered by those tests. McMini is an extensible model checker based on DPOR (Dynamic Partial Order Reduction). A mechanism was invented to declare to McMini new, primitive thread operations, typically in 100~lines or less of C~code. The mechanism was extended to also allow the end user to declare alternative thread wakeup policies, including spurious wakeups from condition variables. One declares: (I) under what conditions an operation is enabled; (ii) which thread operations are independent of each other; and (iii) when two operations can be considered as co-enabled. An optional wakeup policy is implemented by defining when a wait operation (on a semaphore, condition variable, etc.) is enabled. A new enqueue thread operation is described, allowing a user to declare alternative wakeup policies. McMini was first confirmed to operate correctly and efficiently as a traditional, but extensible model checker for mutex, semaphore, condition variable, and reader-writer. McMini's extensibility was then tested on novel primitive operations, representing other useful paradigms for multithreaded operations. An example is readers-and-two-writers. The speed of model checking was found to be five times faster and more, as compared to traditional implementations on top of condition variables. Alternative wakeup policies (e.g., FIFO, LIFO, arbitrary, etc.) were then tested using an enqueue operation. Finally, spurious wakeups were tested with a program that exposes a bug only in the presence of a spurious wakeup.Comment: 24 pages, 1 figur

Influence of Microlensing on Spectral Anomaly of Lensed Objects

Here we consider the influence of the microlensing on the spectrum of a lensed object taking into account that composite emission is coming from different regions arranged subsequently around the central source. We assumed that the lensed object has three regions with the black body emission; first the innermost with the highest temperature of $10^4K$, second and third (located around the central) with slightly lower temperatures $7.5\cdot10^3$ and $5\cdot10^3$K, respectively. Than we explore the flux anomaly in lensed object due to microlensing. We compare U,V and B spectra of a such source. This results show that, due to microlensing, in a spectroscopically stratified object a flux anomaly is present.Comment: 4 pages, 2 figures, 1 tabl

THE EFFECTS OF AQUATIC ACTIVITIES ON PHYSICAL FITNESS AND AQUATIC SKILLS IN CHILDREN WITH AUTISM SPECTRUM DISORDERS: A SYSTEMATIC REVIEW

Autism spectrum disorder is a complex brain development disorder characterized by restrictive and repetitive behaviors and a significant impairment of one’s ability to interact with other people and engage in verbal or nonverbal communication, as well as in play. One form of physical activity which can be used with success in people with autism is aquatic activity. The objective of this systematic review study is to collect and analyze studies of the effects of aquatic activity on improving physical fitness and aquatic skills in children with autism. Based on an analysis of electronic databases and the inclusion criteria set, 13 studies were included in the analysis. The following conclusions are proposed based on their analysis: In terms of influence on aquatic skills, aquatic programs at least 10 weeks in duration can effect improvement in aquatic skills in children with autism, by means of learning methods well-used with autistic children, such as the Constant delay procedure, Most to least prompting procedure, and assistance from siblings and peers. Regarding physical fitness improvements following aquatic activity, it is difficult to draw conclusions based on the results obtained in only three studies. Recommendations for future research include the application of aquatic programs with a higher weekly frequency, as well as the use of heart rate monitors during aerobic exercise in order to control heart rate training zones

Between art and engineering: Studies on postwar architecture in Belgrade and Serbia

Publikacija nastaje kao prod višegodišnje saradnje sa gostujućim profesorom Lukom Skansijem u sklopu predmeta "Posebni problemi istraživanja arhitekture i urbanizma" na prvoj godini doktorskih studija. Tema trogodišnjeg ciklusa predavanja i diskusija, koje je vodio prof. Skansi između 2015 i 2017. godine, bio je pojam tektonike u arhitekturi, odnosno razvoj tog teoretskog i analitičnog pojma od sredine devetnaestog veka do danas. Studenti su bili pozvani da za svoj seminarski rad izvedu složenu i iscrpnu tektonsku analizu na jednoj relevantnoij arhitekturi izgrađenoj u Srbiji u konktekstu socijalističke Jugoslavije, u periodu između pedesetih i osamdesetih godina prošlog veka.urednik: Biljana JotićKategorija: M105 Učešće na 45. Salonu arhitekture, 28. mart - 29. april 2023. godine. u Muzeju primenjene umetnosti, Beograd, u kategoriji: Arhitektonska kritika i publikacij

Na međi umetnosti i inženjerstva : studije o posleratnoj arhitekturi u Beogradu i Srbiji [45. Salon arhitekture]

Publikacija je rezultat višegodišnje saradnje sa gostujućim prof. Lukom Skansijem u sklopu predmeta »Posebni problemi istraživanja arhitekture i urbanizma« na prvoj godini doktorskih studija. Tema trogodišnjeg ciklusa predavanja i diskusija, koje je vodio prof. Luka Skansi između 2015. i 2017. godine, bio je pojam tektonike u arhitekturi, odnosno razvoj tog teoretskog i analitičkog pojma od sredine devetnaestog veka do danas. Studenti su bili pozvani da za svoj seminarski rad izvedu složenu i iscrpnu tektonsku analizu na jednoj relevantnoj arhitekturi izgrađenoj u Srbiji u kontekstu socijalističke Jugoslavije, u periodu između pedesetih i osamdesetih godina prošlog veka

Comprehensive genomic profiles of small cell lung cancer

We have sequenced the genomes of 110 small cell lung cancers (SCLC), one of the deadliest human cancers. In nearly all the tumours analysed we found bi-allelic inactivation of TP53 and RB1, sometimes by complex genomic rearrangements. Two tumours with wild-type RB1 had evidence of chromothripsis leading to overexpression of cyclin D1 (encoded by the CCND1 gene), revealing an alternative mechanism of Rb1 deregulation. Thus, loss of the tumour suppressors TP53 and RB1 is obligatory in SCLC. We discovered somatic genomic rearrangements of TP73 that create an oncogenic version of this gene, TP73Dex2/3. In rare cases, SCLC tumours exhibited kinase gene mutations, providing a possible therapeutic opportunity for individual patients. Finally, we observed inactivating mutations in NOTCH family genes in 25% of human SCLC. Accordingly, activation of Notch signalling in a pre-clinical SCLC mouse model strikingly reduced the number of tumours and extended the survival of the mutant mice. Furthermore, neuroendocrine gene expression was abrogated by Notch activity in SCLC cells. This first comprehensive study of somatic genome alterations in SCLC uncovers several key biological processes and identifies candidate therapeutic targets in this highly lethal form of cancer

The Explainable Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing VOCs’ Environmental Fate

In this paper, we explore the computational capabilities of advanced modeling tools to reveal the factors that shape the observed benzene levels and behavior under different environmental conditions. The research was based on two-year hourly data concentrations of inorganic gaseous pollutants, particulate matter, benzene, toluene, m, p-xylenes, total nonmethane hydrocarbons, and meteorological parameters obtained from the Global Data Assimilation System. In order to determine the model that will be capable of achieving a superior level of performance, eight metaheuristics algorithms were tested for eXtreme Gradient Boosting optimization, while the relative SHapley Additive exPlanations values were used to estimate the relative importance of each pollutant level and meteorological parameter for the prediction of benzene concentrations. According to the results, benzene levels are mostly shaped by toluene and the finest aerosol fraction concentrations, in the environment governed by temperature, volumetric soil moisture content, and momentum flux direction, as well as by levels of total nonmethane hydrocarbons and total nitrogen oxide. The types of conditions which provided the environment for the impact of toluene, the finest aerosol, and temperature on benzene dynamics are distinguished and described

Neurodegenerative Condition Detection Using Modified Metaheuristic for Attention Based Recurrent Neural Networks and Extreme Gradient Boosting Tuning

Parkinson’s disease is a neurological disorder, caused by the death of dopaminergic neurons which can cause various movement disorders to appear, recognized as standard Parkinson’s motor symptoms. A drug to stop the progression of the disease is very difficult to find, so current treatment is based on alleviating the symptoms of the disease itself. As no direct treatment exists that would cure the condition, early detection and proper treatment are essential in maintaining the patient’s quality of life. This work explores the potential of merging artificial intelligence and machine learning algorithms for Parkinson’s disease early detection from finger-tapping accelerometer tests. Time series classification is explored through the use of recurrent neural networks augmented with and without attention layers. Additionally, extreme gradient boosting in combination with statistical analysis is explored in order to differentiate Parkinson’s from other developing neurodegenerative disorders. As the performance of algorithms hinges on proper parameter selection, this work applies metaheuristics for performance optimization. A modified version of a recently introduced sinh cosh optimizer algorithm is also proposed. The approach is tested on a publicly available real-world clinical dataset consisting of patients and control group samples and a total of three separate experiments were conducted. The introduced optimizer demonstrated admirable performance in comparative analysis, with the best performing models exceeding 90% accuracy

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution to the ever-increasing energy demands of the world. However, the shift toward renewable energy is not without challenges. While fossil fuels offer a more reliable means of energy storage that can be converted into usable energy, renewables are more dependent on external factors used for generation. Efficient storage of renewables is more difficult often relying on batteries that have a limited number of charge cycles. A robust and efficient system for forecasting power generation from renewable sources can help alleviate some of the difficulties associated with the transition toward renewable energy. Therefore, this study proposes an attention-based recurrent neural network approach for forecasting power generated from renewable sources. To help networks make more accurate forecasts, decomposition techniques utilized applied the time series, and a modified metaheuristic is introduced to optimized hyperparameter values of the utilized networks. This approach has been tested on two real-world renewable energy datasets covering both solar and wind farms. The models generated by the introduced metaheuristics were compared with those produced by other state-of-the-art optimizers in terms of standard regression metrics and statistical analysis. Finally, the best-performing model was interpreted using SHapley Additive exPlanations