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International audience; Biogas production is rising in the context of fossil fuel decline and the future circular economy, yet raw biogas requires purification steps before use. Here, we review biogas upgrading using physical, chemical and biological methods such as water scrubbing, physical absorption, pressure swing adsorption, cryogenic separation, membrane separation, chemical scrubbing, chemoautotrophic methods, photosynthetic upgrading and desorption. We also discuss their techno-economic feasibility. We found that physical and chemical upgrading technologies are near-optimal, but still require high energy and resources. Biological methods are less explored despite their promising potential. High-pressure water scrubbing is more economic for small-sized plants, whereas potassium carbonate scrubbing provides the maximum net value for largesized plants.

Part 1: Computational Intelligence for Text Analysis; International audience; Twitter is the most popular social platform where common people reflect their personal, political and business views that obliquely build an active online repository. The data presented by users on social networking sites are usually composed of sensitive or private data that is highly potential for cyber threats. The most frequently presented sensitive private data is analyzed by collecting real-time tweets based on benchmarked cyber-keywords under personal, professional and health categories. This research work aims to generate a Topic Keyword Extractor by adapting the Automatic Acronym - Abbreviation Replacer which is specially developed for social media short texts. The feature space is modeled using the Latent Dirichlet Allocation technique to discover topics for each cyber-keyword. The user’s context and intentions are preserved by replacing the internet jargon and abbreviations. The originality of this research work lies in identifying sensitive keywords that reveal Tweeter’s Personally Identifiable Information through the novel Topic Keyword Extractor. The potential sensitive topics in which the social media users frequently exhibit personal information and unintended information disclosures are discovered for the benchmarked cyber-keywords by adapting the proposed qualitative topic-wise keyword distribution approach. This experiment analyzed cyber-keywords and the identified sensitive topic keywords as bi-grams to predict the most common sensitive information leaks happening in Twitter. The results showed that the most frequently discussed sensitive topic was ‘weight loss’ with the cyber-keyword ‘weight’ of the health tweet category.

Part 3: Data Science; International audience; Existing practices of crop irrigation is manual and based on generic traditional recommendations. Crops when provided lesser water, shows reduced growth and reduced uptake of calcium. Excessive irrigation leads to root death and water wastage. Hence, irrigating crops with precise water becomes an important problem. Towards this objective, an IoT based crop field monitoring and precise irrigation system is proposed that monitors crop-field and computes precise crop water requirement based on its life cycle and climatic conditions. Using this computed crop water requirement, a pump motor is operated automatically whenever soil moisture decreases below permanent welting point. The motor is shut down once the required water is pumped out to crops. The proposed system is installed in a crop-field of brindle plant and the crop is irrigated for 6 months. It is observed that 53% of water has been saved from wastage.

Part 1: Computational Intelligence for Text Analysis; International audience; Detecting paraphrases in Indian languages require critical analysis on the lexical, syntactic and semantic features. Since the structure of Indian languages differ from the other languages like English, the usage of lexico-syntactic features vary between the Indian languages and plays a critical role in determining the performance of the system. Instead of using various lexico-syntactic similarity features, we aim to apply a complete end-to-end system using deep learning networks with no lexico-syntactic features. In this paper we exploited the encoder-decoder model of deep neural network to analyze the paraphrase sentences in Tamil language and to classify. In this encoder-decoder model, LSTM, GRU units and gNMT are used as layers along with attention mechanism. Using this end-to-end model, there is an increase in f1-measure by 0.5% for the subtask-1 when compared to the state-of-the-art systems. The system was trained and evaluated on DPIL@FIRE2016 Shared Task dataset. To our knowledge, ours is the first deep learning model which validates the training instances of both the subtask-1 and subtask-2 dataset of DPIL shared task.

Data centers are constructed with a huge number of network devices to support the expanding cloud based services. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. Consequently, many devices are set into idle state and cause a huge energy waste leading to a non-proportionality between the network load and the energy consumed. In this paper, we propose a new approach to improve the efficiency of data centers in terms of energy consumption. Our approach exploits the correlation in time of the inter-node communication traffic and some topological features to maximize energy saving with only a minor increase in the average path length. Our approach dynamically controls the number of active communication links by turning off and on ports in the network (switches ports and nodes ports). Simulations results confirmed the energy saving gain procured by the proposed approach with a low impact on the average path length. This publication was made possible by NPRP grant 6-718-2-298 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

International audience; Sustainability benchmarking is gaining importance in industry. Despite its increasing popularity, the existing research utilizing theory to explain the organizations intention to shape performance measurement systems (PMS) for sustainability benchmarking is limited. Drawing upon institutional theory and organizational culture, this study investigates how institutional pressures motivate organizations to shape PMS for sustainability benchmarking and how such effects are moderated by organizational culture. The results of a survey of 277 respondents, gathered from Indian manufacturing firms, suggest that two of the dimensions of the institutional pressures (i.e. coercive pressures and normative pressures) are positively related to the PMS whereas the third dimension (i.e. mimetic pressures) does not affect PMS. Furthermore, organizational culture (i.e. flexible orientation and control orientation) plays a different role on the differential effect of coercive pressures, normative pressures and mimetic pressures on shaping PMS for sustainability benchmarking. The current manuscript offers an interesting contribution to the sustainability benchmarking literature: we integrate the perspectives of ‘external pressures’ and ‘organizational culture’ –as neither perspective, can on, its own can shape the PMS for sustainability benchmarking–, and ‘organizational structure’ under which the external pressures are most effective. From a practitioners' perspective, our study provides theory-driven and empirically-proven guidance for managers to understand the effect of external pressures and the role of organizational structure on PMS for sustainability benchmarking.

International audience; In recent years, data centers have witnessed an exponential growth for hosting hundreds of thousands of servers as well as to accommodating a very large demand for resources. To fulfill the required level of demand, some approaches tackled network aspects so to host a huge number of servers while others focused on delivering rapid services to the clients by minimizing the path length between any two servers. In general, network devices are often designed to achieve 1:1 oversubscription. Alternatively, in a realistic data center environment, the average utilization of a network could vary between 5% and 25%, and thus the energy consumed by idle devices is wasted. This paper proposes a new parameterizable data center topology, called PTNet. PTNet offers a gradual scalability that interconnects small to large networks covering different ranges of sizes. This new interconnection network provides also a small path length between any two servers even in large sized data centers. PTNet does not only reduce path length and latency, it also uses a power-aware routing algorithm which saves up to 40% of energy with an acceptable computation time. In comparison to existing solutions (e.g. Flatnet, BCube, DCell and Fat-tree), PTNet shows substantial improvements in terms of capacity, robustness, cost-effectiveness and power efficiency: this improvement reaches up to 50% in some cases. (C) 2017 Elsevier Inc. All rights reserved.

Nowadays, data centers are designed to offer the highest performance in case of high traffic load and peak utilisation of the network. However, in a realistic data center environment, the peak capacity of the network is rarely reached and the average utilisation of devices varies between 5% and 25% which results into a huge loss of energy since most of the time links and servers are idle or under-utilized. The high impact of this wasted power on environmental effects, energy needs and electricity costs raised the concerns to seek for an efficient solution to make data centers more power effective while keeping the desired quality of service. In this paper, we propose a power-aware routing algorithm that saves a considerable amount of energy with a negligible trade-off on the performance of the network and a guaranteed reliability of the system. The key idea is to keep active only the vital and critical nodes participating in the communication traffic and ensuring the reliability while the unneeded devices are turned-off. Vital nodes between clusters (parts of the network) are calculated only once during the initialization of the system and consequently used with a constant time complexity. Besides its short computation time, our routing algorithm guarantees over 50% of energy saving by maintaining the minimum number of needed devices and over 20% when adding backup routes. This power efficiency is accompanied by a guaranteed performance and reliability against failures. 1 2017 Elsevier B.V. Scopus

One of the fundamental challenges of existing data centers is to design a network that interconnects massive number of servers, and therefore providing an efficient and fault-tolerant routing service to upper-layer applications. Several solutions have been proposed (e.g. FatTree, DCell and BCube), however they either scale too fast (i.e., double exponentially) or too slow. This paper proposes a new data center topology, called LaCoDa, that combines the advantages of previous topologies while avoiding their limitations. LaCoDa uses a small node degree that matches physical restriction for servers, and it also interconnects a large number of servers while reducing the wiring complexity and without affecting the existing servers. LaCoDa can interconnect up to millions of servers with a small number of port switches by increasing the number of directly connected clusters per layer, and therefore avoiding the cluster connections repetitions. The theoretical and experimental results show that the average path length (APL) of a 1000-node LaCoDa is smaller than the APL of a 23-node FlatTree and 11-node BCube and DCell. Moreover, with 6-port switches and a node degree of 4, the bisection bandwidth of 4.19 Million-node LaCoDa is 7% and 14% bigger than the bisection bandwidth of 310-node DCell and BCube, respectively. Consequently, LaCoDa provides better performance than DCel, BCube, FatTree in terms of average path length, throughput, latency and bisection bandwidth. This publication was made possible by the NPRP award [NPRP 6-718-2-298] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the author[s]. Scopus

Part 3: Analytics for Smart Governance; International audience; In this work, an efficient vehicle tracking with intelligent prediction system is designed and implemented for tracking the movement of any vehicle from any location. The proposed system uses an inexpensive technology that combines a smartphone application with a microcontroller. The users will be able to continuously monitor a moving vehicle on demand using the smartphone application and determine the estimated distance and time for the vehicle to arrive at a chosen destination. Apart from this, the system is designed further to account for the case of failing to catch one’s vehicle by dynamically suggesting a new vehicle within one’s reach with an estimated time and a route. The data collected using the tracking system is effectively used to predict the estimated arrival time of the vehicles using sophisticated machine learning technique Artificial Neural Networks (ANN). While there are other systems on vehicle tracking, we present a vehicle tracking, dynamic route suggestion on failing to catch one’s vehicle and intelligent arrival time prediction system together making it a comprehensive system for users. The feasibility and effectiveness of the system are presented through experimental results of the vehicle tracking system with the advantages and challenges.

International audience; In order to satisfy the need for the critical computing resources, many data center architectures proposed to house a huge number of network devices. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. Consequently, many devices are set into idle state and cause a huge energy waste leading to a non-proportionality between the network load and the energy consumed. In this paper, we propose a power-aware routing algorithm that saves energy consumption with a negligible trade-off on the performance of the network. The idea is to keep active only the source and destination devices and the vital nodes participating in the communication. Vital nodes in the network are calculated only once and can be used with a constant time complexity. Besides its short computation time, our routing algorithm guarantees also a high performance and shows over 50% of energy saving.

Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment., 12 pages, 8 figures

Wireless powered communication networks (WPCNs) will act as a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. The MTC networks will be deployed by using low-power transceivers with finite discrete configurations. This paper considers minimum length scheduling problem for full-duplex WPCNs, where users transmit information to a hybrid access point at a rate chosen from a finite set of discrete-rate levels. The optimization problem considers energy causality, data and maximum transmit power constraints, and is proven to be NP-hard. As a solution strategy, we define the minimum length scheduling (MLS) slot, which is slot of minimum transmission completion time while starting transmission at anytime after the decision time. We solve the problem optimally for a given transmission order based on the optimality analysis of MLS slot. For the general problem, we categorize the problem based on whether the MLS slots of users overlap over time. We propose optimal algorithm for non-overlapping scenario by allocating the MLS slots, and a polynomial-time heuristic algorithm for overlapping scenario by allocating the transmission slot to the user with earliest MLS slot. Through simulations, we demonstrate significant gains of scheduling and discrete rate allocation., Scientific and Technological Research Council of Turkey (TÜBİTAK)

We propose a novel hub location model that jointly eliminates some of the traditional assumptions on the structure of the network and on the discount as a result of economies of scale in an effort to better reflect real-world logistics and transportation systems. Our model extends the hub literature in various facets: instead of connecting nonhub nodes directly to hub nodes, we consider routes with stopovers; instead of connecting pairs of hubs directly, we design routes that can visit several hub nodes; rather than dimensioning pairwise connections, we dimension routes of vehicles; and rather than working with a homogeneous fleet, we use intermodal transportation. Decisions pertinent to strategic and tactical hub location and transportation network design are concurrently made through the proposed optimization scheme. An effective branch-and-cut algorithm is developed to solve realistically sized problem instances and to provide managerial insights., Science Academy of Turkey (TÜBA) BAGEP Program

Purpose: this study was planned to assess the application of three-dimensional (3D) cardiac modeling in preoperative evaluation for complex congenital heart surgeries. Methods: from July 2015 to September 2019, 18 children diagnosed with complex congenital heart diseases (CHDs) were enrolled in this study (double outlet right ventricle in nine patients, complex types of transposition of the great arteries in six patients, congenitally corrected transposition of the great arteries in two patients, and univentricular heart in one patient). The patients' age ranged from 7 months to 19 years (median age, 14 months). Before the operation, 3D patient-specific cardiac models were created based on computed tomography (CT) data. Using each patient's data, a virtual computer model (3D mesh) and stereolithographic (SLA) file that would be printed as a 3D model were generated. These 3D cardiac models were used to gather additional data about cardiac anatomy for presurgical decision-making. Results: all 18 patients successfully underwent surgeries, and there were no mortalities. The 3D patient-specific cardiac models led to a change from the initial surgical plans in 6 of 18 cases (33%), and biventricular repair was considered feasible. Moreover, the models helped to modify the planned biventricular repair in five cases, for left ventricular outflow tract obstruction removal and ventricular septal defect enlargement. 3D cardiac models enable pediatric cardiologists to better understand the spatial relationships between the ventricular septal defect and great vessels, and they help surgeons identify risk structures more clearly for detailed planning of surgery. There was a strong correlation between the models of the patients and the anatomy encountered during the operation. Conclusion: 3D cardiac models accurately reveal the patient's anatomy in detail and are therefore beneficial for planning surgery in patients with complex intracardiac anatomy., NA

Automatic generation of video descriptions in natural language, also called video captioning, aims to understand the visual content of the video and produce a natural language sentence depicting the objects and actions in the scene. This challenging integrated vision and language problem, however, has been predominantly addressed for English. The lack of data and the linguistic properties of other languages limit the success of existing approaches for such languages. In this paper we target Turkish, a morphologically rich and agglutinative language that has very different properties compared to English. To do so, we create the first large-scale video captioning dataset for this language by carefully translating the English descriptions of the videos in the MSVD (Microsoft Research Video Description Corpus) dataset into Turkish. In addition to enabling research in video captioning in Turkish, the parallel English-Turkish descriptions also enable the study of the role of video context in (multimodal) machine translation. In our experiments, we build models for both video captioning and multimodal machine translation and investigate the effect of different word segmentation approaches and different neural architectures to better address the properties of Turkish. We hope that the MSVD-Turkish dataset and the results reported in this work will lead to better video captioning and multimodal machine translation models for Turkish and other morphology rich and agglutinative languages., Scientific and Technological Research Council of Turkey (TÜBİTAK); British Council; Newton Fund Institutional Links Grant Programme; MMVC Project; European Union (EU); Horizon 2020; ERC Starting Grant; MultiMT; Turkish Academy of Sciences GEBIP 2018 Award; Science Academy BAGEP 2021 Award

In services where teams come together for short collaborations, managers are often advised to strive for high team familiarity so as to improve coordination and consequently, performance. However, inducing high team familiarity by keeping team membership intact can limit workers’ opportunities to acquire useful knowledge and alternative practices from exposure to a broader set of partners. We introduce an empirical measure for prior partner exposure and estimate its impact (along with that of team familiarity) on operational performance using data from the London Ambulance Service. Our analysis focuses on ambulance transports involving new paramedic recruits, where exogenous changes in team membership enable identification of the performance effect. Specifically, we investigate the impact of prior partner exposure on time spent during patient pickup at the scene and patient handover at the hospital. We find that the effect varies with the process characteristics. For the patient pickup process, which is less standardized, greater partner exposure directly improves performance. For the more standardized patient handover process, this beneficial effect is triggered beyond a threshold of sufficient individual experience. In addition, we find some evidence that this beneficial performance impact of prior partner exposure is amplified during periods of high workload, particularly for the patient handover process. Finally, a counterfactual analysis based on our estimates shows that a team formation strategy emphasizing partner exposure outperforms one that emphasizes team familiarity by about 9.2% in our empirical context., Deloitte Institute of Innovation and Entrepreneurship

Background: There is limited evidence on the modification or stopping of antiretroviral therapy (ART) regimens, including novel antiretroviral drugs. The aim of this study was to evaluate the discontinuation of first ART before and after the availability of better tolerated and less complex regimens by comparing the frequency, reasons and associations with patient characteristics. Methods: A total of 3019 ART-naive patients registered in the HIV-TR cohort who started ART between Jan 2011 and Feb 2017 were studied. Only the first modification within the first year of treatment for each patient was included in the analyses. Reasons were classified as listed in the coded form in the web-based database. Cumulative incidences were analysed using competing risk function and factors associated with discontinuation of the ART regimen were examined using Cox proportional hazards models and Fine-Gray competing risk regression models. Results: The initial ART regimen was discontinued in 351 out of 3019 eligible patients (11.6%) within the first year. The main reason for discontinuation was intolerance/toxicity (45.0%), followed by treatment simplification (9.7%), patient willingness (7.4%), poor compliance (7.1%), prevention of future toxicities (6.0%), virologic failure (5.4%), and provider preference (5.4%). Non-nucleoside reverse transcriptase inhibitor (NNRTI)-based (aHR = 4.4, [95% CI 3.0-6.4]; p < 0.0001) or protease inhibitor (PI)-based regimens (aHR = 4.3, [95% CI 3.1-6.0]; p < 0.0001) relative to integrase strand transfer inhibitor (InSTI)-based regimens were significantly associated with ART discontinuation. ART initiated at a later period (2015-Feb 2017) (aHR = 0.6, [95% CI 0.4-0.9]; p < 0.0001) was less likely to be discontinued. A lower rate of treatment discontinuation for intolerance/toxicity was observed with InSTI-based regimens (2.0%) than with NNRTI- (6.6%) and PI-based regimens (7.5%) (p < 0.001). The percentage of patients who achieved HIV RNA < 200 copies/mL within 12 months of ART initiation was 91% in the ART discontinued group vs. 94% in the continued group (p > 0.05). Conclusion: ART discontinuation due to intolerance/toxicity and virologic failure decreased over time. InSTI-based regimens were less likely to be discontinued than PI- and NNRTI-based ART., Gilead SciencesGilead Sciences, This study was funded by Gilead Sciences.

Urine analysis via a toilet-based device can enable continuous health monitoring, a transformation away from hospital-based care towards more proactive medicine. To enable reliable sample collection for a toilet-attached analyzer, here a novel sample collector is proposed. The applicability of the proposed sample collector is validated for long-term use. Geometric parameters of the 3D-printed sample collector are optimized. The collected and leftover volumes are quantified for a range of urination speeds and design parameters. For long-term cyclic use, the protein concentrations of samples are quantified and the effectiveness of washing the sample collector is assessed., NA

We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries. The corpus contains document-, sentence-, and token-level annotations. This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information, constructing knowledge bases that enable comparative social and political science studies. For each news source, the annotation starts with random samples of news articles and continues with samples drawn using active learning. Each batch of samples is annotated by two social and political scientists, adjudicated by an annotation supervisor, and improved by identifying annotation errors semi-automatically. We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting, contributing to the generalizability and robustness of automated text processing systems. This corpus and the reported results will establish a common foundation in automated protest event collection studies, which is currently lacking in the literature., European Union (EU); Horizon 2020; European Research Council (ERC); Starting Grant; Emerging Welfare

Relay nodes are used to improve the throughput, delay and reliability performance of energy harvesting networks by assisting both energy and information transfer between sources and access point. Previous studies on radio frequency energy harvesting networks are limited to single-source-single/multiple-relay networks. In this paper, a novel joint relay selection, scheduling and power control problem for multiple-source-multiple-relay network is formulated with the objective of minimizing the total duration of wireless power and information transfer. The formulated problem is non-convex mixed-integer non-linear programming problem, and proven to be NP-hard. We first formulate a sub-problem on scheduling and power control for a given relay selection. We propose an efficient optimal algorithm based on a bi-level optimization over power transfer time allocation. Then, for optimal relay selection, we present optimal exponential-time Branch-and-Bound (BB) based algorithm where the nodes are pruned with problem specific lower and upper bounds. We also provide two BB-based heuristic approaches limiting the number of branches generated from a BB-node, and a relay criterion based lower complexity heuristic algorithm. The proposed algorithms are demonstrated to outperform conventional harvest-then-cooperate approaches with up to 87% lower schedule length for various network settings with at least 7.88 times higher algorithm runtime., Scientific and Technological Research Council of Turkey (TÜBİTAK)

Magnetooptical Faraday effect enables ultrafast photonic devices based on nonreciprocal polarization rotation; however, the intrinsic weakness of Faraday effect prevents miniaturization and practical applications of nonreciprocal photonic devices. Magnetoplasmonics offers new mechanisms for enhancing magnetooptical effects using surface plasmon resonances, which generally have narrow bandwidths. Using finite-difference time-domain modeling, we demonstrate a magnetoplasmonic metasurface, which remarkably enhances the Faraday effect in a wide spectral range. While Faraday rotation in a bare bismuth-substituted yttrium iron garnet film is below 0.02 degrees in the studied range of 600-1600 nm, the proposed metasurface yields few degrees of rotation in a broad band with a maximum exceeding 6.5 degrees, which indicates about three orders of magnitude enhancement. We also show that by optimizing the configuration of the system including the geometry and excitation parameters, the metasurface response and operation band can be tuned further, and rotation values higher than 20 degrees can be achieved. Finally, we present guidelines for designing magnetoplasmonic metasurfaces., European Union (European Union); European Research Council (ERC), Advanced Grant (ERC-AdG) Wear3D Project; Scientific and Technological Research Council of Turkey (TÜBİTAK); Turkish Academy of Sciences (TÜBA)-GEBIP Award by the Turkish Academy of Sciences

Purpose: this study aims to quantify the patient-specific hemodynamics of complex conduit routing configurations of coronary artery bypass grafting (CABG) operation which are specifically suitable for off-pump surgeries. Coronary perfusion efficacy and local hemodynamics of multiple left internal mammary artery (LIMA) with sequential and end-to-side anastomosis are investigated. Using a full anatomical model comprised of aortic arch and coronary artery branches the optimum perfusion configuration in multi-vessel coronary artery stenosis is desired. Methodology: two clinically relevant CABG configurations are created using a virtual surgical planning tool where for each configuration set, the stenosis level, anastomosis distance and angle were varied. A non-Newtonian computational fluid dynamics solver in OpenFOAM incorporated with resistance boundary conditions representing the coronary perfusion physiology was developed. The numerical accuracy is verified and results agreed well with a validated commercial cardiovascular flow solver and experiments. For segmental performance analysis, new coronary perfusion indices to quantify deviation from the healthy scenario were introduced. Results: the first simulation configuration set;-a CABG targeting two stenos sites on the left anterior descending artery (LAD), the LIMA graft was capable of 31 mL/min blood supply for all the parametric cases and uphold the healthy LAD perfusion in agreement with the clinical experience. In the second end-to-side anastomosed graft configuration set;-the radial artery graft anastomosed to LIMA, a maximum of 64 mL/min flow rate in LIMA was observed. However, except LAD, the obtuse marginal (OM) and second marginal artery (m2) suffered poor perfusion. In the first set, average wall shear stress (WSS) were in the range of 4 to 35 dyns/cm(2)for in LAD. Nevertheless, for second configuration sets the WSS values were higher as the LIMA could not supply enough blood to OM and m2. Conclusion: the virtual surgical configurations have the potential to improve the quality of operation by providing quantitative surgical insight. The degree of stenosis is a critical factor in terms of coronary perfusion and WSS. The sequential anastomosis can be done safely if the anastomosis angle is less than 90 degrees regardless of degree of stenosis. The smaller proposed perfusion index value,O(0.04 - 0) x 10(2), enable us to quantify the post-op hemodynamic performance by comparing with the ideal healthy physiological flow., Scientific and Technological Research Council of Turkey (TÜBİTAK); 1003 Priority-Research Program Grant; European Union (EU); Horizon 2020; European Research Council (ERC) Proof of Concept Grant; KidsSurgicalPlan

International audience; Aims: Over the last 30 years, many medicine development programmes in acute and chronic heart failure (HF) with preserved ejection fraction (HFpEF) have failed, in contrast to those in HF with reduced ejection fraction (HFrEF). We explore how the neutral results in larger HF trials may be attributable to chance and/or the dilution of statistical power.Methods and results: Using simulations, we examined the probability that a positive finding in a Phase 2 trial would result in the study of a truly effective medicine in a Phase 3 trial. We assessed the similarity of clinical trial and registry patient populations. We conducted a meta-analysis of paired Phase 2 and 3 trials in HFrEF and acute HF examining the associations of trial phase and size with placebo event rates and treatment effects for HF events and death. We estimated loss in trial power attributable to dilution with increasing trial size. Appropriately powered Phase 3 trials should have yielded ∼35% positive results. Patient populations in Phase 3 trials are similar to those in Phase 2 trials but both differ substantially from the populations of 'real-life' registries. We observed decreasing placebo event rates and smaller treatment effects with increasing trial size, especially for HF events (and less so for mortality). This was more pronounced in trials in acute HF patients.Conclusions: The selection of more positive Phase 2 trials for further development does not explain the failure of HFpEF and acute HF medicine development. Increasing sample size may lead to reduced event rates and smaller treatment effects, resulting in a high rate of neutral Phase 3 trials.

Attention deficit/hyperactivity disorder (ADHD) is a common and heritable phenotype frequently accompanied by insomnia, anxiety, and depression. Here, using a reverse phenotyping approach, we report heterozygous coding variations in the core circadian clock gene cryptochrome 1 in 15 unrelated multigenerational families with combined ADHD and insomnia. The variants led to functional alterations in the circadian molecular rhythms, providing a mechanistic link to the behavioral symptoms. One variant, CRY1Δ11 c.1657+3A>C, is present in approximately 1% of Europeans, therefore standing out as a diagnostic and therapeutic marker. We showed by exome sequencing in an independent cohort of patients with combined ADHD and insomnia that 8 of 62 patients and 0 of 369 controls carried CRY1Δ11. Also, we identified a variant, CRY1Δ6 c.825+1G>A, that shows reduced affinity for BMAL1/CLOCK and causes an arrhythmic phenotype. Genotype-phenotype correlation analysis revealed that this variant segregated with ADHD and delayed sleep phase disorder (DSPD) in the affected family. Finally, we found in a phenome-wide association study involving 9438 unrelated adult Europeans that CRY1Δ11 was associated with major depressive disorder, insomnia, and anxiety. These results defined a distinctive group of circadian psychiatric phenotypes that we propose to designate as "circiatric" disorders., NIH Clinical and Translational Science Award (CTSA) Program; NIH; French National Research Agency (ANR) under the “Investments for the future” Program; Integrative Biology of Emerging Infectious Diseases Laboratoire d’Excellence; IEIHSEER Grant; SEAe-Host Factors Grant; PNEUMOID Project Grant; INCA/Cancéropole Ile-de-France; Turkish Academy of Sciences (TÜBA); National Center for Advancing Translational Sciences (NCAST); Rockefeller University, INSERM; HHMI, University of Paris; St. Giles Foundation; Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai

Numerous cultural assets host a great historical and moral value, but due to their degradation, this value is heavily affected as their attractiveness is lost. One of the solutions that most heritage organizations and museums currently choose is to leverage the knowledge of art and history experts in addition to curators to recover and restore the damaged assets. This process is labor-intensive, expensive and more often results in just an assumption over the damaged or missing region. In this work, we tackle the issue of completing missing regions in artwork through advanced deep learning and image reconstruction (inpainting) techniques. Following our analysis of different image completion and reconstruction approaches, we noticed that these methods suffer from various limitations such as lengthy processing times and hard generalization when trained with multiple visual contexts. Most of the existing learning-based image completion and reconstruction techniques are trained on large datasets with the objective of retrieving the original data distribution of the training samples. However, this distribution becomes more complex when the training data is diverse making the training process difficult and the reconstruction inefficient. Through this paper, we present a clustering-based low-delay image completion and reconstruction approach which combines supervised and unsupervised learning to address the highlighted issues. We compare our technique to the current state of the art using a real-world dataset of artwork collected from various cultural institutions. Our approach is evaluated using statistical methods and a surveyed audience to better interpret our results objectively and subjectively. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors (www.ceproqha.qa ). The authors would also like to thank the Museum of Islamic Art (MIA), the MIA Multimedia team, Mr. Marc Pelletreau, the Art Curators and the management staff of the Museum of Islamic Art, Doha Qatar for their help and contribution in the data acquisition. Scopus

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International audience; Polarity-controlled growth of ZnO by chemical bath deposition provides a method for controlling the crystal orientation of vertical nanorod arrays. The ability to define the morphology and structure of the nanorods is essential to maximizing the performance of optical and electrical devices such as piezoelectric nanogenerators; however, well-defined Schottky contacts to the polar facets of the structures have yet to be explored. In this work, we demonstrate a process to fabricate metal−semiconductor−metal device structures from vertical arrays with Au contacts on the uppermost polar facets of the nanorods and show that the Opolar nanorods (∼0.44 eV) have a greater effective barrier height than the Znpolar nanorods (∼0.37 eV). Oxygen plasma treatment is shown by cathodoluminescence spectroscopy to affect midgap defects associated with radiative emissions, which improves the Schottky contacts from weakly rectifying to strongly rectifying. Interestingly, the plasma treatment is shown to have a much greater effect in reducing the number of carriers in O-polar nanorods through quenching of the donor-type substitutional hydrogen on oxygen sites (HO) when compared to the zinc-vacancy-related hydrogen defect complexes (VZn −nH) in Zn-polar nanorods that evolve to lowercoordinated complexes. The effect on HO in the O-polar nanorods coincides with a large reduction in the visible-range defects, producing a lower conductivity and creating the larger effective barrier heights. This combination can allow radiative losses and charge leakage to be controlled, enhancing devices such as dynamic photodetectors, strain sensors, and light-emitting diodes while showing that the O-polar nanorods can outperform Zn-polar nanorods in such applications.

Recent advances in real-time Magnetic Resonance Imaging (rtMRI) provide an invaluable tool to study speech articulation. In this paper, we present an effective deep learning approach for supervised detection and tracking of vocal tract contours in a sequence of rtMRI frames. We train a single input multiple output deep temporal regression network (DTRN) to detect the vocal tract (VT) contour and the separation boundary between different articulators. The DTRN learns the non-linear mapping from an overlapping fixed-length sequence of rtMRI frames to the corresponding articulatory movements, where a blend of the overlapping contour estimates defines the detected VT contour. The detected contour is refined at a post-processing stage using an appearance model to further improve the accuracy of VT contour detection. The proposed VT contour tracking model is trained and evaluated over the USC-TIMIT dataset. Performance evaluation is carried out using three objective assessment metrics for the separating landmark detection, contour tracking and temporal stability of the contour landmarks in comparison with three baseline approaches from the recent literature. Results indicate significant improvements with the proposed method over the state-of-the-art baselines., NA

In this paper, a novel opportunistic spectrum sharing scheme, based on orthogonal frequency division multiplexing with index modulation (OFDM-IM), is proposed for cognitive radio (CR) networks. In the considered OFDM-IM based CR (OFDM-IM-CR) model, the primary transmitter (PT) communicates with the primary receiver with the aid of an amplify-and-forward (AF) relay by transmitting OFDM-IM signals. Meanwhile, the secondary transmitter (ST) passively senses the spectrum and transmits its own information over those inactive subcarriers of the primary network to the secondary receiver if the signal-to-noise ratio of the PT -> ST link is above a predefined threshold; otherwise, the ST stays in silent mode. Two different types of maximum-likelihood (ML) detectors are designed for the primary network, based on the knowledge of either the estimated channel state information or the statistical channel information of the secondary network. A complexity-reducing method, which is applicable to both types and achieves near optimal performance, is further proposed. To evaluate the performance, a tight upper bound on the bit error rate (BER) is derived, assuming the first type of ML detection. Simulation results corroborate the analysis and show that OFDM-IM-CR has the potential of outperforming OFDM-CR and OFDM-IM-AF in terms of BER with higher spectral efficiency., National Natural Science Foundation of China; Natural Science Foundation of Guangdong; State Key Laboratory of Integrated Service Networks of Xidian University; Pearl River Nova Program of Guangzhou; U.S. National Science Foundation; China Scholarship Council; Turkish Academy of Sciences

In recent years, both the scientific community and the industry have focused on moving computational resources with remote data centres from the centralized cloud to decentralised computing, making them closer to the source or the so called "edge" of the network. This is due to the fact that the cloud system alone cannot sufficiently support the huge demands of future networks with the massive growth of new, time-critical applications such as self-driving vehicles, Augmented Reality/Virtual Reality techniques, advanced robotics and critical remote control of smart Internet-of-Things applications. While decentralised edge computing will form the backbone of future heterogeneous networks, it still remains at its infancy stage. Currently, there is no comprehensive platform. In this article, we propose a novel decentralised edge architecture, a solution called OMNIBUS, which enables a continuous distribution of computational capacity for end-devices in different localities by exploiting moving vehicles as storage and computation resources. Scalability and adaptability are the main features that differentiate the proposed solution from existing edge computing models. The proposed solution has the potential to scale infinitely, which will lead to a significant increase in network speed. The OMNIBUS solution rests on developing two predictive models: (i) to learn timing and direction of vehicular movements to ascertain computational capacity for a given locale, and (ii) to introduce a theoretical framework for sequential to parallel conversion in learning, optimisation and caching under contingent circumstances due to vehicles in motion., Universiti Teknologi Malaysia; Research University Grant Scheme Tier 2; Ambeent Inc.

The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication., Turkish Directorate of Strategy and Budget, TAM Project

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy constrained relay-based systems. Most of the existing literature on relay-based SWIPT systems incorporate linear energy harvesting (EH) model. This article incorporates a non-linear EH model into the full-duplex (FD) amplify-and-forward (AF) relay-based system for the first time in the literature. We consider a practical non-linear energy harvester model namely constant-linear-constant (CLC) EH model, which takes into account the sensitivity and saturation characteristics of the EH circuit. First, the end-to-end outage probability of the system is derived for the time-switching (TS) based relay protocol. To prevent the outage performance degradation, the outage throughput and energy efficiency (EE) of the system is maximized by optimizing the TS parameter. Since the formulated problems are convex in nature, the golden-section method is used to find the optimal TS solution. Numerical results reveal the significance of employing a non-linear EH model by demonstrating the difference of the proposed model from the traditional linear EH model system and importance of using full-duplex relay by showing large performance gain over half-duplex relay-based (HDR) system, in terms of outage probability, throughput, and EE., Scientific and Technological Research Council of Turkey (TÜBİTAK)

Visual discomfort and fatigue due to accommodation-convergence (AC) conflict in stereoscopic displays has been widely reported, but little is known about its impact on visual acuity, particularly when automotive three-dimensional (3D) head-up displays (HUDs) are involved. This paper presents a study on the visual acuity response when an indigenously developed 75% transparent retroreflective screen is used as a windshield 3D HUD. The simulated optical collimation technique was used to provide the virtual content at a farther depth (i.e. on the road while driving). Two user test experiments were performed. The first test was performed under the see-through condition, where the real scene (i.e. roadside view) was perceived through the 3D HUD, while the second test was performed under the simulated collimation condition, where a stereo-collimated virtual content was projected on the HUD at a farther depth. The results showed a slightly declining trend (from 20/20 to 20/25) in visual acuity response when the HUD screen was placed between the viewer and the scene. An inverse relation between the amount of AC conflict and visual acuity was observed under the simulated collimation condition. The >100 cm user-to-screen distance was found to be comfortable, providing the highest acuity response., European Union (European Union); Horizon 2020; European Research Council (ERC); European Union's Seventh Framework Program (FP7/2007-2013)

Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) ( NRF-2019R1A2C1010557 ). The authors would also like to thank the University of Malaya for supporting Ph.D. student through research grant GPF046A-2018 . Publisher Copyright: © 2020 The Author(s) The demand for renewable energy sources is gradually escalating due to the spontaneously growing population and global economic development. The access to fossil fuels is gradually declining due to the limited available reserves. Hence, renewable energy resources, technology choice, and energy policy are always being revised due to the modernization of society. Meanwhile, the liquid energy sources such as methyl ester from locally produced vegetable oils are readily accepted by many countries globally, although it is currently being blended (up to 20%) with diesel. Oxides of nitrogen are the most substantial emissions from diesel engines produced due to high combustion temperature. The addition of alcohol in the fuel reduces the NOx formation since alcohols have high latent heat of evaporation. The present study's primary purpose is to investigate the effect of different alcohol types on engine performance and emission characteristics. For this purpose, seven test fuels and neat diesel were used. The test fuels P20 (20% palm biodiesel with 70% neat diesel and 10% alcohol on a volume basis), D70P20E10, D70P20Pr10, D70P20B10, D70P20Pe10, D70P20H10 were prepared and tested on a single-cylinder, 4-stroke, DI-diesel engine at different speeds at 100 % load. The P20E10 ternary fuel blend illustrated the most practical combination of all the bioethanol-based blends, which considerably improves the BTE, BSFC and reduces NOxformation at high speed compared to other types of alcoholic fuel blends. Also, the P20E10 fuel blend improved the cloud point of neat diesel.

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive and molecule-specific detection technique that uses surface plasmon resonances to enhance Raman scattering from analytes. In SERS system design, the substrates must have minimal or no background at the incident laser wavelength and large Raman signal enhancement via plasmonic confinement and grating modes over large areas (i.e., squared millimeters). These requirements impose many competing design constraints that make exhaustive parametric computational optimization of SERS substrates pro-hibitively time consuming. Here, we demonstrate a genetic-algorithm (GA)-based optimization method for SERS substrates to achieve strong electric field localization over wide areas for recon-figurable and programmable photonic SERS sensors. We analyzed the GA parameters and tuned them for SERS substrate optimization in detail. We experimentally validated the model results by fabricating the predicted nanostructures using electron beam lithography. The experimental Raman spectrum signal enhancements of the optimized SERS substrates validated the model predictions and enabled the generation of a detailed Raman profile of methylene blue fluorescence dye. The GA and its optimization shown here could pave the way for photonic chips and components with arbitrary design constraints, wavelength bands, and performance targets., Scientific and Technological Research Council of Turkey (TÜBİTAK)

Description, A wireless magnetic millirobot navigates inside an ex vivo porcine brain propelled by a permanent magnetic array–based force trap., Creating wireless milliscale robots that navigate inside soft tissues of the human body for medical applications has been a challenge because of the limited onboard propulsion and powering capacity at small scale. Here, we propose around 100 permanent magnet array–based remotely propelled millirobot system that enables a cylindrical magnetic millirobot to navigate in soft tissues via continuous penetration. By creating a strong magnetic force trap with magnetic gradients on the order of 7 T/m inside a soft tissue, the robot is attracted to the center of the array even without active control. By combining the array with a motion stage and a fluoroscopic x-ray imaging system, the magnetic robot followed complex paths in an ex vivo porcine brain with extreme curvatures in sub-millimeter precision. This system enables future wireless medical millirobots that can deliver drugs; perform biopsy, hyperthermia, and cauterization; and stimulate neurons with small incisions in body tissues.

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks., Scientific and Technological Research Council of Turkey (TÜBİTAK); Career Development Program; TÜBİTAK-2211 Fellowship

In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their “self-organized” variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation. In this paper, we propose to replace the convolutional and GDN layers in the variational autoencoder with self-organized operational layers, and propose a novel self-organized variational autoencoder (Self-VAE) architecture that benefits from stronger non-linearity. The experimental results demonstrate that the proposed Self-VAE yields improvements in both rate-distortion performance and perceptual image quality., Scientific and Technological Research Council of Turkey (TÜBİTAK); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Turkish Academy of Sciences (TÜBA)

Small-scale wireless magnetic robots and devices offer an effective solution to operations in hard-to-reach and high-risk enclosed places, such as inside the human body, nuclear plants, and vehicle infrastructure. In order to obtain functionalities beyond the capability of magnetic forces and torques exerted on magnetic materials used in these robotic devices, electronics need to be also integrated into them. However, their capabilities and power sources are still very limited compared to their larger-scale counterparts due to their much smaller sizes. Here, groups of milli/centimeter-scale wireless magnetic modules are shown to enable on-site electronic circuit construction and operation of highly demanding wireless electrical devices with no batteries, that is, with wireless power. Moreover, the mobility of the modular components brings remote modification and reconfiguration capabilities. When these small-scale robotic modules are remotely assembled into specific geometries, they can achieve, if not impossible, challenging electrical tasks for individual modules. Using such a method, several wireless and battery-free robotic devices are demonstrated using milli/centimeter-scale robotic modules, such as a wireless circuit to power light-emitting diodes with lower external fields, a device to actuate relatively high force-output shape memory alloy actuators, and a wireless force sensor, all of which can be modified on-site., Advanced Science, 8 (17), ISSN:2198-3844

This paper describes the elimination of healthcare-associated Acinetobacter baumannii infections in a highly endemic region. A prospective, observational study was performed between October 2012 and October 2017. Acinetobacter baumannii were isolated from 59 patients, and >95% similarity was demonstrated among isolates of seven patients (DiversiLab™, BioMérieux). Carbapenemase activity was detected in 15 of 17 (88%) isolates, and all were OXA-23 type. The control of Acinetobacter baumannii outbreaks can be achieved by close follow-up supported by molecular techniques, strict application of infection control measures, and isolation of transferred patients., NA

Virtual and augmented reality (VR/AR) systems dramatically gained in popularity with various application areas such as gaming, social media, and communication. It is therefore a crucial task to have the knowhow to efficiently utilize, store or deliver 360° videos for end-users. Towards this aim, researchers have been developing deep neural network models for 360° multimedia processing and computer vision fields. In this line of work, an important research direction is to build models that can learn and predict the observers' attention on 360° videos to obtain so-called saliency maps computationally. Although there are a few saliency models proposed for this purpose, these models generally consider only visual cues in video frames by neglecting audio cues from sound sources. In this study, an unsupervised frequency-based saliency model is presented for predicting the strength and location of saliency in spatial audio. The prediction of salient audio cues is then used as audio bias on the video saliency predictions of state-of-the-art models. Our experiments yield promising results and show that integrating the proposed spatial audio bias into the existing video saliency models consistently improves their performance., Scientific and Technological Research Council of Turkey (TÜBİTAK) 1001 Program Award; Turkish Academy of Sciences GEBIP 2018 Award; Science Academy BAGEP 2021 Award

International audience; In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonicinvestigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracyclose to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.

We described the significance of systematic monitoring nationwide antimicrobial stewardship programs (ASPs) in primary care. All the prescriptions given by family physicians were recorded in Prescription Information System established by the Turkish Medicines and Medical Devices Agency of Ministry of Health. We calculated, for each prescription, ""antibiotics amount"" as number of boxes times number of items per box for medicines that belong to antiinfectives for systemic use (i.e., J01 block in the Anatomical Therapeutic Chemical Classification System). We compared the antibiotics amount before (2015) and after (2016) the extensive training programs for the family physicians. We included 266,389,209 prescriptions from state-operated family healthcare units (FHUs) between January 1, 2015 and December 31, 2016. These prescriptions were given by 26,313 individual family physicians in 22,518 FHUs for 50,713,181 individual patients. At least one antimicrobial was given in 37,024,232 (28.31%) prescriptions in 2015 and 36,154,684 (26.66%) prescriptions in 2016. The most common diagnosis was ""acute upper respiratory infections (AURI)"" (i.e., J00-J06 block in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems) with 28.05%. The average antibiotics amount over prescriptions with AURI decreased in 79 out of 81 provinces, and overall rate of decrease in average antibiotics amount was 8.33%, where 28 and 53 provinces experienced decreases (range is between 28.63% and-3.05%) above and below this value, respectively. In the most successful province, the highest decrease in average amount of ""other beta-lactam antibacterials"" per prescription for AURI was 49.63% in January. Computational analyses on a big data set collected from a nationwide healthcare system brought a significant contribution in improving ASPs., Turkish Academy of Sciences (TUBA-GEBIP); The Young Scientist Award Program; Science Academy of Turkey (BAGEP); The Young Scientist Award Program

It is a generally accepted perspective that type-II nanocrystal quantum dots (QDs) have low quantum yield due to the separation of the electron and hole wavefunctions. Recently, high quantum yield levels were reported for cadmium-based typeII QDs. Hence, the quest for finding non-toxic and efficient type-II QDs is continuing. Herein, we demonstrate environmentally benign type-II InP/ZnO/ZnS core/shell/shell QDs that reach a high quantum yield of similar to 91%. For this, ZnO layer was grown on core InP QDs by thermal decomposition, which was followed by a ZnS layer via successive ionic layer adsorption. The small-angle Xray scattering shows that spherical InP core and InP/ZnO core/ shell QDs turn into elliptical particles with the growth of the ZnS shell. To conserve the quantum efficiency of QDs in device architectures, InP/ZnO/ZnS QDs were integrated in the liquid state on blue light-emitting diodes (LEDs) as down-converters that led to an external quantum efficiency of 9.4% and a power conversion efficiency of 6.8%, respectively, which is the most efficient QD-LED using type-II QDs. This study pointed out that cadmium-free type-II QDs can reach high efficiency levels, which can stimulate novel forms of devices and nanomaterials for bioimaging, display, and lighting., European Union;Horizon 2020; European Research Council (ERC); Research and Innovation Programme; Turkish Academy of Sciences (TUBA-GEBIP), Young Scientist Award Program; Science Academy of Turkey (BAGEP), The Young Scientist Award Program; Bilim Kahramanlari Dernegi, Young Scientist Award Program

Setae, fibrils located on a gecko's feet, have been an inspiration of synthetic dry microfibrillar adhesives in the last two decades for a wide range of applications due to unique properties: residue-free, repeatable, tunable, controllable and silent adhesion; self-cleaning; and breathability. However, designing dry fibrillar adhesives is limited by a template-based-design-approach using a pre-determined bioinspired T- or wedge-shaped mushroom tip. Here, a machine learning-based computational approach to optimize designs of adhesive fibrils is shown, exploring a much broader design space. A combination of Bayesian optimization and finite element methods creates novel optimal designs of adhesive fibrils, which are fabricated by two-photon-polymerization-based 3D microprinting and double-molding-based replication out of polydimethylsiloxane. Such optimal elastomeric fibril designs outperform previously proposed designs by maximum 77% in the experiments of dry adhesion performance on smooth surfaces. Furthermore, finite-element-analyses reveal that the adhesion of the fibrils is sensitive to the 3D fibril stem shape, tensile deformation, and fibril microfabrication limits, which contrast with the previous assumptions that mostly neglect the deformation of the fibril tip and stem, and focus only on the fibril tip geometry. The proposed computational fibril design could help design future optimal fibrils with less help from human intuition., Max Planck Society; Projekt DEAL

ARINC 664, which is a heavily used protocol for modern avionics networks, is preferred due to its simplicity although its mixed-criticality support is limited. Time Triggered Ethernet (TTEthernet), and IEEE Time Sensitive Networking (TSN), which utilize time synchronized schedule, are more suitable for supporting mixed-criticality applications; however, both require a fault tolerant time synchronization that makes the certification process more challenging. In this paper, we propose a novel dynamic priority assignment (DPA) concept together with the burst limiting shaper (BLS) from the IEEE TSN standard to enhance the schedulability and the mixed-criticality support of ARINC 664. The decision of flow re-assignment to a new priority class is done by calculating the high priority (HP) and low priority (LP) class worst-case delays using the network calculus framework. The numerical results show that the class utilization rates can be significantly increased by using the DPA concept with and without the BLS while the deadline constraints for all classes are satisfied. Thus, the DPA can improve the schedulability and mixed-criticality of ARINC 664 without using any time synchronization mechanism., NA

Sketch recognition algorithms are engineered and evaluated using publicly available datasets contributed by the sketch recognition community over the years. While existing datasets contain sketches of a limited set of generic objects, each new domain inevitably requires collecting new data for training domain specific recognizers. This gives rise to two fundamental concerns: First, will the data collection protocol yield ecologically valid data? Second, will the amount of collected data suffice to train sufficiently accurate classifiers? In this paper, we draw attention to these two concerns. We show that the ecological validity of the data collection protocol and the ability to accommodate small datasets are significant factors impacting recognizer accuracy in realistic scenarios. More specifically, using sketch-based gaming as a use case, we show that deep learning methods, as well as more traditional methods, suffer significantly from dataset shift. Furthermore, we demonstrate that in realistic scenarios where data is scarce and expensive, standard measures taken for adapting deep learners to small datasets fall short of comparing favorably with alternatives. Although transfer learning, and extensive data augmentation help deep learners, they still perform significantly worse compared to standard setups (e.g., SVMs and GBMs with standard feature representations). We pose learning from small datasets as a key problem for the deep sketch recognition field, one which has been ignored in the bulk of the existing literature., Turkish Academy of Sciences (TÜBA)

Rate-distortion optimization (RDO) of codecs, where distortion is quantified by the mean-square error, has been a standard practice in image/video compression over the years. RDO serves well for optimization of codec performance for evaluation of the results in terms of PSNR. However, it is well known that the PSNR does not correlate well with perceptual evaluation of images; hence, RDO is not well suited for perceptual optimization of codecs. Recently, rate-distortion-perception trade-off has been formalized by taking the Kullback-Leibler (KL) divergence between the distributions of the original and reconstructed images as a perception measure. Learned image compression methods that simultaneously optimize rate, mean-square loss, VGG loss, and an adversarial loss were proposed. Yet, there exists no easy approach to fix the rate, distortion or perception at a desired level in a practical learned image compression solution to perform an analysis of the trade-off between rate, distortion and perception measures. In this paper, we propose a practical approach to fix the rate to carry out perception-distortion analysis at a fixed rate in order to perform perceptual evaluation of image compression results in a principled manner. Experimental results provide several insights for practical rate-distortion-perception analysis in learned image compression., Scientific and Technological Research Council of Turkey (TÜBİTAK) 1001 Project; Scientific and Technological Research Council of Turkey (TÜBİTAK) 2247-A National Leader Researchers Award; Turkish Academy of Sciences (TUBA)

Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help to quantify and to constrain the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).