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Galaxy clusters provide an excellent probe in various research fields in astrophysics and cosmology. However, the number of galaxy clusters detected so far in the $AKARI$ North Ecliptic Pole (NEP) field is limited. In this work, we provide galaxy cluster candidates in the $AKARI$ NEP field with the minimum requisites based only on coordinates and photometric redshift (photo-$z$) of galaxies. We used galaxies detected in 5 optical bands ($g$, $r$, $i$, $z$, and $Y$) by the Subaru Hyper Suprime-Cam (HSC), assisted with $u$-band from Canada-France-Hawaii Telescope (CFHT) MegaPrime/MegaCam, and IRAC1 and IRAC2 bands from the $Spitzer$ space telescope for photo-$z$ estimation. We calculated the local density around every galaxy using the 10$^{th}$-nearest neighbourhood. Cluster candidates were determined by applying the friends-of-friends algorithm to over-densities. 88 cluster candidates containing 4390 member galaxies below redshift 1.1 in 5.4 deg$^2$ have been detected. The reliability of our method was examined through false detection tests, redshift uncertainty tests, and applications on the COSMOS data, giving false detection rates of 0.01 to 0.05 and recovery rate of 0.9 at high richness. 3 X-ray clusters previously observed by $ROSAT$ and $Chandra$ were recovered. The cluster galaxies show higher stellar mass and lower star formation rate (SFR) compared to the field galaxies in two-sample Z-tests. These cluster candidates are useful for environmental studies of galaxy evolution and future astronomical surveys in the NEP, where $AKARI$ has performed unique 9-band mid-infrared photometry for tens of thousands of galaxies., Comment: 19 pages, 16 figures, has been accepted for publication in MNRAS

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

Multimodal steganography consists of concealing a signal into another one of a different medium, such that the latter is only very slightly distorted and the hidden information can be later recovered. A previous work employed deep learning techniques to this end by hiding an image inside an audio signal's spectrogram in a way that the encoding of one is independent of the other. In this work we explore the way in which images were being encoded previously and present a collection of improvements that produce a significant increase in the quality of the system. These mainly consist in encoding the image in a smarter way such that more information is able to be transmitted in a container of the same size. We also explore the possibility of using the short-time Fourier transform phase as an alternative to the magnitude and to randomly permute the signal to break the structure of the noise. Finally, we report results when using a larger container signal and outline possible directions for future work.

The aim of this paper is to review NATO’s Defence Education Enhancement Programme (DEEP) and to bring to the fore the measures of effectiveness regarding its implementation and to determine its shortcomings in the South Caucasus region. At the same time, the paper highlights the efforts of the DEEP teams to influence the partner educators through dialogue and encouragement. The authors refer to the best examples considering the measures of effectiveness. Considering the outcomes achieved by the partner nations, they endeavoured to apply Kirkpatrick model for measuring DEEP’s effectiveness in the South Caucasus countries. The paper discusses how they can more effectively address the challenges in their professional military education (PME) through their tailored DEEPs. The paper analyzes the processes that led by the South Caucasus nations, their national interests and what steps still need to be taken to realize their ambitions to be intellectually interoperable with NATO forces.

Focusing light through a multimode fibre (MMF) has attracted significant research interest, mainly driven by the need for miniature endoscopes in biomedicine. In recent years, digital micromirror devices (DMD) have become increasingly popular as a high-speed alternative to liquid-crystal spatial light modulators for light focusing via wavefront shaping based on binary amplitude modulations. To exploit the potentials and limitations of the state-of-the-art DMD-based wavefront shaping methods, in this study, for the first time, we compared four representative, non-holographic and DMD-based methods that are reported so far in literature with the same experimental and simulation conditions, including a real-valued intensity transmission matrix (RVITM)-based algorithm, a complex-valued transmission matrix (TM)-based algorithm, a conditional probability algorithm and a genetic algorithm. We investigated the maximum achievable peak-to-background ratio (PBR) in comparison to theoretical expectations, and further improved the performance of the RVITM-based method. With both numerical simulations and experiments, we found that the genetic algorithm offered the highest PBR but suffered from the lowest focusing speed, while the RVITM-based algorithm provided a comparable PBR to that of the genetic algorithm, and the highest focusing speed. ispartof: OPTICS EXPRESS vol:29 issue:10 pages:14269-14281 ispartof: location:United States status: published

A soundscape is the recording of all sounds present in an area, creating a holistic view of the acoustic profile in an ecosystem. Studying acoustic parameters of marine soundscapes as a whole has been shown to give an indication of the health status of the location, as well as correlate to which species may be present and using the area. With the rapid innovation of technology, especially data storage and declining cost of equipment, marine soundscape research is fast increasing, and these previous limitations have been switched for computing capacity for data analysis. Here, we perform a systematic assessment of literature of marine soundscape studies, from 1978, when the first soundscape study was reported, until 2021. We identified 200 primary research studies that recorded soundscapes and captured their geographical location, depth, habitat, duration of the study, and number of sites in each study. Using this data, we summarize the state of play in marine soundscapes studies, and identify knowledge gaps in the spatial coverage, depth profiles, habitat representation and study duration. Spatially, studies are biased towards the northern hemisphere. They are also more prevalent in more easily accessible ecosystems, in order from most to least studied, in coastal (38%), pelagic (20%), tropical coral reef (17%), rocky reef (7%), polar (5.5%), seagrass meadows, oyster reef and kelp/algal forest (4000 m. With anthropogenic noise and other pollution sources increasing globally, these gaps in research should be further addressed, especially as they pertain to vulnerable ecosystems, many of which are affected by global climate change and anthropogenic influences. It is crucial that marine soundscape studies continue to be developed and pursued, to establish baselines for healthy ecosystems and/or document recovery following management actions. info:eu-repo/semantics/publishedVersion

Semi-automatic and fully automatic contouring tools have emerged as an alternative to fully manual segmentation to reduce time spent contouring and to increase contour quality and consistency. Particularly, fully automatic segmentation has seen exceptional improvements through the use of deep learning in recent years. These fully automatic methods may not require user interactions, but the resulting contours are often not suitable to be used in clinical practice without a review by the clinician. Furthermore, they need large amounts of labeled data to be available for training. This review presents alternatives to manual or fully automatic segmentation methods along the spectrum of variable user interactivity and data availability. The challenge lies to determine how much user interaction is necessary and how this user interaction can be used most effectively. While deep learning is already widely used for fully automatic tools, interactive methods are just at the starting point to be transformed by it. Interaction between clinician and machine, via artificial intelligence, can go both ways and this review will present the avenues that are being pursued to improve medical image segmentation.

In stroke imaging, CT angiography (CTA) is used for detecting arterial occlusions. These images could also provide information on the extent of ischemia. The study aim was to develop and evaluate a convolutional neural network (CNN)–based algorithm for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke. These results were compared to volumes reported by widely used CT perfusion–based RAPID software (IschemaView). A 42-layer-deep CNN was trained on 50 CTA volumes with manually delineated targets. The lower bound for predicted lesion size to reliably discern stroke from false positives was estimated. The severity of false positives and false negatives was reviewed visually to assess the clinical applicability and to further guide the method development. The CNN model corresponded to the manual segmentations with voxel-wise sensitivity 0.54 (95% confidence interval: 0.44–0.63), precision 0.69 (0.60–0.76), and Sørensen–Dice coefficient 0.61 (0.52–0.67). Stroke/nonstroke differentiation accuracy 0.88 (0.81–0.94) was achieved when only considering the predicted lesion size (i.e., regardless of location). By visual estimation, 46% of cases showed some false findings, such as CNN highlighting chronic periventricular white matter changes or beam hardening artifacts, but only in 9% the errors were severe, translating to 0.91 accuracy. The CNN model had a moderately strong correlation to RAPID-reported Tmax > 10 s volumes (Pearson’s r = 0.76 (0.58–0.86)). The results suggest that detecting anterior circulation ischemic strokes from CTA using a CNN-based algorithm can be feasible when accompanied with physiological knowledge to rule out false positives.

This study focused on the possibility of implementing a vision-based architecture to monitor and detect the presence of trash or valuables in shared cars. The system was introduced to take pictures of the rear seating area of a four-door passenger car. Image capture was performed with a stationary wide-angled camera unit, and image classification was conducted with a prediction model in a remote server. For classification, a convolutional neural network (CNN) in the form of a fine-tuned VGG16 model was developed. The CNN yielded an accuracy of 91.43% on a batch of 140 test images. To determine the correlation among the predictions, a confusion matrix was used, and in addition, for each predicted image, the certainty of the distinct output classes was examined. The execution time of the system, from capturing an image to displaying the results, ranged from 5.7 to 17.2 s. Misclassifications from the prediction model were observed in the results primarily due to the variation in ambient light levels and shadows within the images, which resulted in the target items lacking contrast with their neighbouring background. Developments pertaining to the modularity of the camera unit and expanding the dataset of training images are suggested for potential future research.

Chronic limb-threatening ischemia (CLTI) is a significant facet of atherosclerotic disease that has major medical and functional consequences. With the number of diabetic patients on the rise and improved life expectancy, has also come a greater awareness and wider adoption of endovascular techniques; thus, amputation rates continue to fall.1-3 We expect that patients will be older, have more advanced comorbidities, and will have had more prior interventions. This may translate to a greater proportion of them presenting with no-option CLTI. Venous arterialization may be a viable alternative to preserving these limbs. This thesis starts with a general introduction that provides an update on the history and development of the venous arterialization. After this the thesis consists of three parts: Part 1 is entitled; the open venous arterialization Part 2 is entitled; percutaneous deep venous arterialization (pDVA, LimFlow procedure) Part 3 is entitled; postoperative care and follow-up of the venous arterialization The first part provides information about the open superficial venous arterialization. In Chapter 2, a superficial venous arterialization cohort and pedal bypass cohort of two Dutch hospitals is analysed, with promising results in a group of patients for whom no other reconstructive treatment options were available. Although the pedal bypass is still the gold standard (if a target artery is present), distal venous arterialization is a good alternative due to comparable results and its simplicity. The current evidence was analysed in a systematic review and meta-analysis in Chapter 3 and suggests that venous arterialization is a valuable treatment option in selected patients with no-option CLTI. These otherwise unsalvageable legs can be treated with acceptable morbidity and mortality. However, optimization and standardization of techniques are needed. The venous arterialization should always be considered in patients without distal arterial outflow vessels. The second part is about the percutaneous deep venous arterialization (pDVA). In Chapter 4, the first experience with the pDVA in humans is published, applied to a cohort of patients with no-option CLTI. The dual catheters, guided by ultrasound imaging, provide a reliable way to percutaneously create the AVF between a tibial artery and a deep tibial vein. Assisted by a percutaneously introduced valvulotome, arterial blood can now be directed to the veins of the foot. In this small cohort of patients, pDVA appears to be a safe and feasible procedure that effectively improves limb oxygenation, encourages wound healing, and potentially, avoids major amputation. The experience, after this first publication, from four centers between 2014 and 2018, is analysed in Chapter 5. This study presents midterm results from the largest population of patients with no-option CLTI treated with pDVA using the LimFlow device. In this complex group of patients, the LimFlow device demonstrated high technical success and amputation-free survival rates of 67% coupled with good wound healing at up to 24 months. In selected patients with no-option CLTI, pDVA is a safe and effective treatment to prevent amputation and heal wounds. A new international multicenter prospective study, PROMISE International, has been started to validate these outcomes in a larger cohort, and the protocol is described in Chapter 6. The last part of this thesis is about the postinterventional care of the venous arterialization. Chapter 7 is the first analysis of duplex ultrasound (DUS) measurements in post-pDVA patients. The venous arterialization needs time to mature before becoming effective. Stenoses or occlusions can frequently occur during this period, and therefore, there is a high need for more insight in DUS interpretation to detect failure of the arteriovenous circuit and preserve the limb. This study showed that surveillance of the arteriovenous circuit can be performed by DUS and we determined cut-off values to define the presence of stenosis or occlusions, but the small sample size of the study does not allow firm conclusions to be drawn. A guideline for post-pDVA care is given in Chapter 8. Based on literature and clinical experience of 24 patients from Alkmaar and Singapore, we summarize the different techniques proposed and provide an algorithm for the challenges that interventionalists can face in the follow-up period after a pDVA. The most important, is the acknowledgement that the pDVA needs 6 weeks to develop before becoming effective. Other key elements are edema treatment, pain control, wound care and a staged amputation strategy. The postprocedural management can be as challenging as the procedure itself but has not been a priority for further investigation.

Chronic limb-threatening ischemia (CLTI) is a significant facet of atherosclerotic disease that has major medical and functional consequences. With the number of diabetic patients on the rise and improved life expectancy, has also come a greater awareness and wider adoption of endovascular techniques; thus, amputation rates continue to fall.1-3 We expect that patients will be older, have more advanced comorbidities, and will have had more prior interventions. This may translate to a greater proportion of them presenting with no-option CLTI. Venous arterialization may be a viable alternative to preserving these limbs. This thesis starts with a general introduction that provides an update on the history and development of the venous arterialization. After this the thesis consists of three parts: Part 1 is entitled; the open venous arterialization Part 2 is entitled; percutaneous deep venous arterialization (pDVA, LimFlow procedure) Part 3 is entitled; postoperative care and follow-up of the venous arterialization The first part provides information about the open superficial venous arterialization. In Chapter 2, a superficial venous arterialization cohort and pedal bypass cohort of two Dutch hospitals is analysed, with promising results in a group of patients for whom no other reconstructive treatment options were available. Although the pedal bypass is still the gold standard (if a target artery is present), distal venous arterialization is a good alternative due to comparable results and its simplicity. The current evidence was analysed in a systematic review and meta-analysis in Chapter 3 and suggests that venous arterialization is a valuable treatment option in selected patients with no-option CLTI. These otherwise unsalvageable legs can be treated with acceptable morbidity and mortality. However, optimization and standardization of techniques are needed. The venous arterialization should always be considered in patients without distal arterial outflow vessels. The second part is about the percutaneous deep venous arterialization (pDVA). In Chapter 4, the first experience with the pDVA in humans is published, applied to a cohort of patients with no-option CLTI. The dual catheters, guided by ultrasound imaging, provide a reliable way to percutaneously create the AVF between a tibial artery and a deep tibial vein. Assisted by a percutaneously introduced valvulotome, arterial blood can now be directed to the veins of the foot. In this small cohort of patients, pDVA appears to be a safe and feasible procedure that effectively improves limb oxygenation, encourages wound healing, and potentially, avoids major amputation. The experience, after this first publication, from four centers between 2014 and 2018, is analysed in Chapter 5. This study presents midterm results from the largest population of patients with no-option CLTI treated with pDVA using the LimFlow device. In this complex group of patients, the LimFlow device demonstrated high technical success and amputation-free survival rates of 67% coupled with good wound healing at up to 24 months. In selected patients with no-option CLTI, pDVA is a safe and effective treatment to prevent amputation and heal wounds. A new international multicenter prospective study, PROMISE International, has been started to validate these outcomes in a larger cohort, and the protocol is described in Chapter 6. The last part of this thesis is about the postinterventional care of the venous arterialization. Chapter 7 is the first analysis of duplex ultrasound (DUS) measurements in post-pDVA patients. The venous arterialization needs time to mature before becoming effective. Stenoses or occlusions can frequently occur during this period, and therefore, there is a high need for more insight in DUS interpretation to detect failure of the arteriovenous circuit and preserve the limb. This study showed that surveillance of the arteriovenous circuit can be performed by DUS and we determined cut-off values to define the presence of stenosis or occlusions, but the small sample size of the study does not allow firm conclusions to be drawn. A guideline for post-pDVA care is given in Chapter 8. Based on literature and clinical experience of 24 patients from Alkmaar and Singapore, we summarize the different techniques proposed and provide an algorithm for the challenges that interventionalists can face in the follow-up period after a pDVA. The most important, is the acknowledgement that the pDVA needs 6 weeks to develop before becoming effective. Other key elements are edema treatment, pain control, wound care and a staged amputation strategy. The postprocedural management can be as challenging as the procedure itself but has not been a priority for further investigation.

Today, with the evolution of social behavior models, European regulations on emissions due to climate change and new transport alternatives available on the market, there has been a massive increase in the so-called personal mobility vehicles (PMV) in large cities. The fact that this increase has been so fast has meant that in many cases these vehicles do not have their own lanes or detailed regulations. This means that the public highway is often a shared space between pedestrians and PMV, in the case of sidewalks. Many of these PMVs have in their design a traffic mode that limits their speed to circulate on roads shared with pedestrians, but the reality is that in most cases the PMVs users circulate at a higher speed, putting the rest of the users at risk. This thesis aims to take a first step towards being able to create, in the future, PMVs capable of adapting their speed autonomously depending on the road they travel on.

This report aims to summarize the fundamental concepts of Artificial Intelligence (AI), and to provide a non-exhaustive overview of AI applications in dental imaging, comprising diagnostics, forensics, image processing and image reconstruction. AI has arguably become the hottest topic in radiology in recent years owing to the increased computational power available to researchers, the continuing collection of digital data, as well as the development of highly efficient algorithms for machine learning and deep learning. It is now feasible to develop highly robust AI applications that make use of the vast amount of data available to us, and that keep learning and improving over time.

Methane is produced microbially in vast quantities in sediments throughout the world’s oceans. However, anaerobic oxidation of methane (AOM) provides a near-quantitative sink for the produced methane and is primarily responsible for preventing methane emissions from the oceans to the atmosphere. AOM is a complex microbial process that involves several different microbial groups and metabolic pathways. The role of different electron acceptors in AOM has been studied for decades, yet large uncertainties remain, especially in terms of understanding the processes in natural settings. This study reports whole-core incubation methane oxidation rates along an estuarine gradient ranging from near fresh water to brackish conditions, and investigates the potential role of different electron acceptors in AOM. Microbial community structure involved in different methane processes is also studied in the same estuarine system using high throughput sequencing tools. Methane oxidation in the sediments was active in three distinct depth layers throughout the studied transect, with total oxidation rates increasing seawards. We find extensive evidence of non-sulphate AOM throughout the transect. The highest absolute AOM rates were observed below the sulphate-methane transition zone (SMTZ), strongly implicating the role of alternative electron acceptors (most likely iron and manganese oxides). However, oxidation rates were ultimately limited by methane availability. ANME-2a/b were the most abundant microbial phyla associated with AOM throughout the study sites, followed by ANME-2d in much lower abundances. Similarly to oxidation rates, highest abundances of microbial groups commonly associated with AOM were found well below the SMTZ, further reinforcing the importance of non-sulphate AOM in this system.

3D Concrete Printing (3DCP) has been gaining popularity in the past few years. Due to the nature of line-by-line printing and the slump of the material deposition in each extruded line, 3D printed structures exhibit obvious lines or marks at the layer interface, which affects surface finish quality and potentially affect bonding strength between layers. This makes it necessary to control the extrudate formation in 3DCP. However, it is difficult to directly analyse the extrudate formation process because the extrudate shape depends on many parameters. In this paper, a machine learning technique is applied to correlate the formation of the extrudate to the printing parameters using an Artificial Neural Network model. The training data for the model development was obtained from extrudates printed in 3DCP experiments. The performance of the trained model was experimentally validated and the predicted extrudate geometry resulting from the developed model showed good agreement to the actual extrudate geometry. Subsequently, the developed model was used to find proper nozzle shapes to produce designated extrudate geometries. Significant improvement on the printing quality was demonstrated using nozzle shapes generated from the model on 3D printed objects consisting a vertical wall, an inclined wall and a curved part. National Research Foundation (NRF) Accepted version

Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance). To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN). AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features. The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context. Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map. We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively. AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis.

Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.

Precise prescription of medication dosing is crucial to patients, especially among Intensive Care Unit (ICU) patients. However, improper administration of some sensitive therapeutic medications (e.g., heparin) might place patients at unneeded risk, even life-threatening. Numerous factors such as a patient's clinical phenotype, genotype, and environmental factors will affect the heparin dose response. As a result, it is challenging to prescribe the optimal initial dose of heparin. In this paper, an individualized dosing policy is proposed to determine the optimal initial dose and minimize the risk of mis-dosing, as well as preventing the patients from late complications associated with medications dosing. A latent batch-constrained deep reinforcement learning (RL) algorithm is proposed to guarantee the safety of the medication recommendation system. The agent can observe a latent representation of patents and generate medication dosing solutions in successive and limited action spaces. The individualized dosing policy aims to reduce the extrapolation errors in the off-policy algorithms, by evaluating over-dosing and under-dosing of heparin in patients. Our results evaluated on Medical Information Mart for Intensive Care III (MIMIC-III) database demonstrate that the latent batch-constrained RL algorithm can work effectively from the retrospective data, showing promise to be used in future medication dosing policies.(C)& nbsp;2021 Elsevier B.V. All rights reserved.

Тема выпускной квалификационной работы: Модификация правила мутации в методе разностной эволюции с использованием Ð°Ñ€Ñ Ð¸Ð²Ð° разностей. Данная работа посвящена модификации правила мутации в методе разностной эволюции, реализованном в программном пакете DEEP, путём внедрения Ð°Ñ€Ñ Ð¸Ð²Ð° Ñ€Ð°Ð·Ð½Ð¾ÑÑ‚Ð½Ñ‹Ñ Ð²ÐµÐºÑ‚Ð¾Ñ€Ð¾Ð². В Ñ Ð¾Ð´Ðµ работы был разработан программный код на языке C, реализующий данную модификацию. Для проверки влияния данной модификации на производительность алгоритма были проведены численные эксперименты по оптимизации параметров тестовой функции Растригина и имитационной модели роста, развития и урожайности нута, разработанной Афширом Солтани и проанализированы полученные результаты. Показано, что модификация правила мутации позволяет в среднем снизить число вычислений целевой функции, что позволяет судить о перспективности алгоритма для оптимизации Ð¼Ð°Ñ‚ÐµÐ¼Ð°Ñ‚Ð¸Ñ‡ÐµÑÐºÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ в целом и Ð¸Ð¼Ð¸Ñ‚Ð°Ñ†Ð¸Ð¾Ð½Ð½Ñ‹Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð±Ð¸Ð¾Ð»Ð¾Ð³Ð¸Ñ‡ÐµÑÐºÐ¸Ñ Ð¿Ñ€Ð¾Ñ†ÐµÑÑÐ¾Ð² в частности., The subject of the graduate of qualification work is «Modification of mutation rule in Differential Evolution method using archive of difference vectors». This work is devoted to the modification of the mutation rule in the difference evolution method implemented in the DEEP software package by introducing an archive of difference vectors. In the course of the work a program code in the C language was developed that implements this modification. To test the effect of this modification on the performance of the algorithm, numerical experiments were carried out to optimize the parameters of the Rastrigin test function and the chickpea growth, development and yield simulation model developed by Afshir Soltani and analyzed the results. It is shown that the modification of the mutation rule allows, on average, to reduce the number of objective function calculations, which makes it possible to judge the prospects of the algorithm for optimizing mathematical models in general as well as simulation models of biological processes.

This study aims at timing the eclipses of the binary star TU UMi. The times of minima are taken from the literature, from our observations in April 2004 and from TESS observations between 2019 and 2022. The orbital period analysis of the system indicates that there is a cyclic oscillation with an amplitude of 0.0081d and a period of 9.03 yr, accompanied by a continuous decrease at a rate of dP/dt = ???1.12 ?? 10???7 d yr???1. We study the secular evolution of the orbital period of the system and the possibility of the existence of a third companion or the magnetic activity cycle of the primary component in the system.

Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource- and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner, and support online adaptation space reduction only for specific goals. To tackle these limitations, we present 'Deep Learning for Adaptation Space Reduction Plus' -- DLASeR+ in short. DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two state-of-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach, and supports three common types of adaptation goals beyond the state-of-the-art approaches., Comment: 41 pages

Full list of authors: Borlaff, A. S.; Gómez-Alvarez, P.; Altieri, B.; Marcum, P. M.; Vavrek, R.; Laureijs, R.; Kohley, R.; Buitrago, F.; Cuillandre, J. -C.; Duc, P. -A.; Gaspar Venancio, L. M.; Amara, A.; Andreon, S.; Auricchio, N.; Azzollini, R.; Baccigalupi, C.; Balaguera-Antolínez, A.; Baldi, M.; Bardelli, S.; Bender, R.; Biviano, A.; Bodendorf, C.; Bonino, D.; Bozzo, E.; Branchini, E.; Brescia, M.; Brinchmann, J.; Burigana, C.; Cabanac, R.; Camera, S.; Candini, G. P.; Capobianco, V.; Cappi, A.; Carbone, C.; Carretero, J.; Carvalho, C. S.; Casas, S.; Castander, F. J.; Castellano, M.; Castignani, G.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Colodro-Conde, C.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Coupon, J.; Courtois, H. M.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Di Ferdinando, D.; Douspis, M.; Dubath, F.; Duncan, C. A. J.; Dupac, X.; Dusini, S.; Ealet, A.; Fabricius, M.; Farina, M. ; Farrens, S.; Ferreira, P. G.; Ferriol, S.; Finelli, F.; Flose-Reimberg, P.; Fosalba, P.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; Ganga, K.; Garilli, B.; Gillis, B.; Giocoli, C.; Gozaliasl, G.; Graciá-Carpio, J.; Grazian, A.; Grupp, F.; Haugan, S. V. H.; Holmes, W.; Hormuth, F.; Jahnke, K.; Keihanen, E.; Kermiche, S.; Kiessling, A.; Kilbinger, M.; Kirkpatrick, C. C.; Kitching, T.; Knapen, J. H.; Kubik, B.; Kümmel, M.; Kunz, M.; Kurki-Suonio, H.; Liebing, P.; Ligori, S.; Lilje, P. B.; Lindholm, V.; Lloro, I.; Mainetti, G.; Maino, D.; Mansutti, O.; Marggraf, O.; Markovic, K.; Martinelli, M.; Martinet, N.; Martínez-Delgado, D.; Marulli, F.; Massey, R.; Maturi, M.; Maurogordato, S.; Medinaceli, E.; Mei, S.; Meneghetti, M.; Merlin, E.; Metcalf, R. B.; Meylan, G.; Moresco, M.; Morgante, G.; Moscardini, L.; Munari, E.; Nakajima, R.; Neissner, C.; Niemi, S. M.; Nightingale, J. W.; Nucita, A.; Padilla, C.; Paltani, S.; Pasian, F.; Patrizii, L.; Pedersen, K.; Percival, W. J.; Pettorino, V.; Pires, S.; Poncet, M.; Popa, L.; Potter, D.; Pozzetti, L.; Raison, F.; Rebolo, R.; Renzi, A.; Rhodes, J.; Riccio, G.; Romelli, E.; Roncarelli, M.; Rosset, C.; Rossetti, E.; Saglia, R.; Sánchez, A. G.; Sapone, D.; Sauvage, M.; Schneider, P.; Scottez, V.; Secroun, A.; Seidel, G.; Serrano, S.; Sirignano, C.; Sirri, G.; Skottfelt, J.; Stanco, L.; Starck, J. L.; Sureau, F.; Tallada-Crespí, P.; Taylor, A. N.; Tenti, M.; Tereno, I.; Teyssier, R.; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I.; Valentijn, E. A.; Valenziano, L.; Valiviita, J.; Vassallo, T.; Viel, M.; Wang, Y.; Weller, J.; Whittaker, L.; Zacchei, A.; Zamorani, G.; Zucca, E.; Euclid Collaboration., Context. While Euclid is an ESA mission specifically designed to investigate the nature of dark energy and dark matter, the planned unprecedented combination of survey area (∼15 000 deg2), spatial resolution, low sky-background, and depth also make Euclid an excellent space observatory for the study of the low surface brightness Universe. Scientific exploitation of the extended low surface brightness structures requires dedicated calibration procedures that are yet to be tested. Aims. We investigate the capabilities of Euclid to detect extended low surface brightness structure by identifying and quantifying sky-background sources and stray-light contamination. We test the feasibility of generating sky flat-fields to reduce large-scale residual gradients in order to reveal the extended emission of galaxies observed in the Euclid survey. Methods. We simulated a realistic set of Euclid/VIS observations, taking into account both instrumental and astronomical sources of contamination, including cosmic rays, stray-light, zodiacal light, interstellar medium, and the cosmic infrared background, while simulating the effects of background sources in the field of view. Results. We demonstrate that a combination of calibration lamps, sky flats, and self-calibration would enable recovery of emission at a limiting surface brightness magnitude of μlim = 29.5−0.27+0.08 mag arcsec−2 (3σ, 10 × 10 arcsec2) in the Wide Survey, and it would reach regions deeper by 2 mag in the Deep Surveys. Conclusions.Euclid/VIS has the potential to be an excellent low surface brightness observatory. Covering the gap between pixel-to-pixel calibration lamp flats and self-calibration observations for large scales, the application of sky flat-fielding will enhance the sensitivity of the VIS detector at scales larger than 1″, up to the size of the field of view, enabling Euclid to detect extended surface brightness structures below μlim = 31 mag arcsec−2 and beyond. © ESO 2022., A. B. was supported by an appointment to the NASA Postdoctoral Program at the NASA Ames Research Center, administered by Universities Space Research Association under contract with NASA, and the European Space Agency (ESA), through the European Space Astronomy Center Faculty. We acknowledge a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid website (http://www.euclid-ec.org). This work has made use of data from the ESA mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research made use of NumPy (Van Der Walt et al. 2011), Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2013). All of the figures on this publication were generated using Matplotlib (Hunter 2007). This work was partly done using GNU Astronomy Utilities (Gnuastro, ascl.net/1801.009) version 0.11.22-dc86., With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709.

In this paper, a new method has been proposed to optimize the hyper parameters in Deep neural network (DNN). For this purpose, a new version of Grey Wolf Optimizer named New Balance Grey Wolf Optimizer (NB-GWO) is successfully developed for the first time. NB-GWO introduces an equation to control the movement strategies at each iteration. Based on this equation, the exploitation ability will be prioritized during the first few iterations to improve the convergence rate and gain quick access to potential search spaces. Meanwhile, during the last few iterations, the exploration ability will be biased toward exploitation to increase the opportunities of escaping the local optima's problem or exploring new spaces with the hope of finding better solutions. Because of the diversity of movement strategies, the NB-GWO has established a better balance between the ability of exploitation and exploration than that of in the original GWO. To demonstrate the effectiveness of NB-GWO, 23 classical benchmark functions combined with eight well-known optimization algorithms are used to evaluate the performance of NB-GWO as the first example. Then, NB-GWO will be used to optimize the hyper parameters in deep neural networks (DNN) for damage detection in 2D concrete frame. The results show that NB-GWO is a grown-up version of GWO, the results obtained in this work have proved the effectiveness and reliability of NB-GWO in solving optimization problems, especially, for optimizing the hyper parameters in DNN.