Showing papers by "Istanbul Technical University published in 2019"

Twenty-three unsolved problems in hydrology (UPH)–a community perspective

Abstract: This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

Combined measurements of Higgs boson couplings in proton–proton collisions at √s=13Te

Abstract: Combined measurements of the production and decay rates of the Higgs boson, as well as its couplings to vector bosons and fermions, are presented. The analysis uses the LHC proton–proton collision data set recorded with the CMS detector in 2016 at $\sqrt{s}=13\,\text {Te}\text {V} $ , corresponding to an integrated luminosity of 35.9 ${\,\text {fb}^{-1}} $ . The combination is based on analyses targeting the five main Higgs boson production mechanisms (gluon fusion, vector boson fusion, and associated production with a $\mathrm {W}$ or $\mathrm {Z}$ boson, or a top quark-antiquark pair) and the following decay modes: $\mathrm {H} \rightarrow \gamma \gamma $ , $\mathrm {Z}\mathrm {Z}$ , $\mathrm {W}\mathrm {W}$ , $\mathrm {\tau }\mathrm {\tau }$ , $\mathrm {b} \mathrm {b} $ , and $\mathrm {\mu }\mathrm {\mu }$ . Searches for invisible Higgs boson decays are also considered. The best-fit ratio of the signal yield to the standard model expectation is measured to be $\mu =1.17\pm 0.10$ , assuming a Higgs boson mass of $125.09\,\text {Ge}\text {V} $ . Additional results are given for various assumptions on the scaling behavior of the production and decay modes, including generic parametrizations based on ratios of cross sections and branching fractions or couplings. The results are compatible with the standard model predictions in all parametrizations considered. In addition, constraints are placed on various two Higgs doublet models.

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

Abstract: We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. To the best of our knowledge, this is the first publicly available dataset with comprehensive annotations to address FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset, which can be downloaded at https://guillaumejaume.github.io/FUNSD.

Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s} =$ 13 TeV using the CMS detector

Abstract: The performance of missing transverse momentum (Tmiss) reconstruction algorithms for the CMS experiment is presented, using proton-proton collisions at a center-of-mass energy of 13 TeV, collected at the CERN LHC in 2016. The data sample corresponds to an integrated luminosity of 35.9 fb-1. The results include measurements of the scale and resolution of Tmiss, and detailed studies of events identified with anomalous Tmiss. The performance is presented of a Tmiss reconstruction algorithm that mitigates the effects of multiple proton-proton interactions, using the "pileup per particle identification" method. The performance is shown of an algorithm used to estimate the compatibility of the reconstructed Tmiss with the hypothesis that it originates from resolution effects.

Combination of Searches for Higgs Boson Pair Production in Proton-Proton Collisions at √s = 13 TeV

Abstract: This Letter describes a search for Higgs boson pair production using the combined results from four final states: bbγγ, bbττ, bbbb, and bbVV, where V represents a W or Z boson. The search is performed using data collected in 2016 by the CMS experiment from LHC proton-proton collisions at s=13 TeV, corresponding to an integrated luminosity of 35.9 fb-1. Limits are set on the Higgs boson pair production cross section. A 95% confidence level observed (expected) upper limit on the nonresonant production cross section is set at 22.2 (12.8) times the standard model value. A search for narrow resonances decaying to Higgs boson pairs is also performed in the mass range 250–3000 GeV. No evidence for a signal is observed, and upper limits are set on the resonance production cross section.

Insights on the Use of α-Lipoic Acid for Therapeutic Purposes

Abstract: α-lipoic acid (ALA, thioctic acid) is an organosulfur component produced from plants, animals, and humans. It has various properties, among them great antioxidant potential and is widely used as a racemic drug for diabetic polyneuropathy-associated pain and paresthesia. Naturally, ALA is located in mitochondria, where it is used as a cofactor for pyruvate dehydrogenase (PDH) and α-ketoglutarate dehydrogenase complexes. Despite its various potentials, ALA therapeutic efficacy is relatively low due to its pharmacokinetic profile. Data suggests that ALA has a short half-life and bioavailability (about 30%) triggered by its hepatic degradation, reduced solubility as well as instability in the stomach. However, the use of various innovative formulations has greatly improved ALA bioavailability. The R enantiomer of ALA shows better pharmacokinetic parameters, including increased bioavailability as compared to its S enantiomer. Indeed, the use of amphiphilic matrices has capability to improve ALA bioavailability and intestinal absorption. Also, ALA’s liquid formulations are associated with greater plasma concentration and bioavailability as compared to its solidified dosage form. Thus, improved formulations can increase both ALA absorption and bioavailability, leading to a raise in therapeutic efficacy. Interestingly, ALA bioavailability will be dependent on age, while no difference has been found for gender. The present review aims to provide an updated on studies from preclinical to clinical trials assessing ALA’s usages in diabetic patients with neuropathy, obesity, central nervous system-related diseases and abnormalities in pregnancy.

A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond

Abstract: Today's wireless networks allocate radio resources to users based on the orthogonal multiple access (OMA) principle. However, as the number of users increases, OMA based approaches may not meet the stringent emerging requirements including very high spectral efficiency, very low latency, and massive device connectivity. Nonorthogonal multiple access (NOMA) principle emerges as a solution to improve the spectral efficiency while allowing some degree of multiple access interference at receivers. In this tutorial style paper, we target providing a unified model for NOMA, including uplink and downlink transmissions, along with the extensions tomultiple inputmultiple output and cooperative communication scenarios. Through numerical examples, we compare the performances of OMA and NOMA networks. Implementation aspects and open issues are also detailed.

Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum

Abstract: Results are reported from a search for supersymmetric particles in the final state with multiple jets and large missing transverse momentum. The search uses a sample of proton-proton collisions at $ \sqrt{s} $ = 13 TeV collected with the CMS detector in 2016–2018, corresponding to an integrated luminosity of 137 fb$^{−1}$, representing essentially the full LHC Run 2 data sample. The analysis is performed in a four-dimensional search region defined in terms of the number of jets, the number of tagged bottom quark jets, the scalar sum of jet transverse momenta, and the magnitude of the vector sum of jet transverse momenta. No significant excess in the event yield is observed relative to the expected background contributions from standard model processes. Limits on the pair production of gluinos and squarks are obtained in the framework of simplified models for supersymmetric particle production and decay processes. Assuming the lightest supersymmetric particle to be a neutralino, lower limits on the gluino mass as large as 2000 to 2310 GeV are obtained at 95% confidence level, while lower limits on the squark mass as large as 1190 to 1630 GeV are obtained, depending on the production scenario.

Production of epoxy composites reinforced by different natural fibers and their mechanical properties

Abstract: The aim of this research is the production of epoxy resin composites reinforced by birch, palm, and eucalyptus fibers with resin transfer molding technique and molded fiber production technique combination. The tensile stress of birch, palm, and eucalyptus reinforced epoxy composites were determined as 29.53, 42.24, and 45.28 MPa, respectively. Bending stress of birch, palm and eucalyptus reinforced epoxy composites were found as 58.83, 68.58, and 79.92 MPa, respectively. The birch epoxy composite had 0.105 J impact energy while palm and eucalyptus epoxy composites were determined as 0.130 and 0.124 J, respectively. It is clearly observed that fiber type was very effective on the mechanical properties of composites. The results of studies showed that molded fiber production method had a very promising future for the development of natural fiber reinforced composites.

Recent developments on thermal municipal sludge pretreatment technologies for enhanced anaerobic digestion

Abstract: Sludge management is still one of the most challenging issues in wastewater treatment plants due to a dramatic increase in sludge production, high sludge disposal costs, legal constraints as well as social and environmental concerns. There is a great effort to develop more environmentally friendly and economical technologies for minimization of excess sludge production and converting wastewater treatment sludge from waste into a renewable resource for bioenergy recovery. Recently, among these technologies, pretreatment processes applied before anaerobic sludge digestion have received a growing attention with several advantages over conventional digestion process. The main goal of the present paper is to present a state-of-the-art review of recent developments on advanced anaerobic digestion employed in municipal wastewater treatment plants. Thermal pretreatment technologies documented in the literature are presented extensively. The effectiveness of thermal pretreatment methods, namely conventional, microwave and radio frequency heatings, are discussed and compared in terms of heating principles, sludge disintegration, digester performance, and sludge rheology. The effectiveness and practicality of the aforementioned methods at industrial-scale and some challenges associated with the implementation at full-scale are also reviewed. Particular attention is paid to integration of combined heat and power systems with thermal hydrolysis for achieving energy self-sufficiency in full-scale plants. Furthermore, the municipal sludge production around the world as well as current sludge disposal and reuse options are addressed.

Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods

Abstract: Interval type-2 fuzzy neural networks ( IT2FNNs ) can be seen as the hybridization of interval type-2 fuzzy systems ( IT2FSs ) and neural networks ( NNs ). Thus, they naturally inherit the merits of both IT2FSs and NNs. Although IT2FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2FNNs, which increases the difficulties of their design. In this paper, big bang-big crunch ( BBBC ) optimization and particle swarm optimization ( PSO ) are applied in the parameter optimization for Takagi-Sugeno-Kang ( TSK ) type IT2FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions ( IT2FMFs ) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2FNNs.

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

Abstract: . The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated the performance of 36 Coupled Model Intercomparison Project 5 (CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation and maximum and minimum temperature. The approach proposed in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.

A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis

Abstract: Objective : This paper proposes a multi attribute decision support model in a supply chain in order to solve complex decision problems. The paper provides a platform to ease decision process through the integration of quality function deployment (QFD) and grey relational analysis (GRA) in demonstrating main supply chain drivers under fuzzy environment. Methodology : The proposed method is important because of several points: First of all, in a supply chain system, evaluation factors are not really independent and must be addressed in relation to the external factors such as customer requirements. Hence, we have applied QFD tool. Second, due to the constant uncertainty in the supply chain environment, fuzziness among the factors has to be considered. So, an interval valued fuzzy model was implemented. Third, to examine the proposed decision system in reality, it was applied in Risk and Uncertain Conditions for Agriculture Production Systems (RUC-APS) project. Contribution : An integrated version of QFD and GRA is presented. It is assumed that QFD can act to measure optimal solutions based on the distance to ideal solutions. In an interval-valued fuzzy environment the enormous volume of computation by Euclidean distance doesn't allow decision makers to obtain the results easily. This drawback is addressed using gray relational analysis. The gray relational coefficient is integrated to the fuzzy QFD to measure the distance of potential solutions from ideal solutions. This integration facilitates decision making process in further problems once big data are available. Results : To obtain the importance degrees of logistic indicators in the supply chain, expert team considered the environmental, social & cultural, and economic factors as external dimension of the QFD. The other dimension of QFD includes supply chain drivers such as quality, environmental management system, supply chain flexibility, corporate social responsibility, transportation service condition, and financial stability. The decision model is solved and the ranking of indicators is achieved. A sensitivity analysis helps to test and check the performance of the decision model.

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

Abstract: Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator, and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path, and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed.

Flexible polyaniline-carbon nanofiber supercapacitor electrodes

Abstract: Flexible polyaniline-carbon nanofiber (PANI-CNF) composites were fabricated and evaluated for use as supercapacitor electrodes. Sol-gel and electrospinning techniques were employed to produce flexible carbon nanofibers and polyaniline coating was applied via in-situ chemical polymerization to further improve the electrochemical properties of the electrodes. The performance of flexible PANI-CNF electrodes was investigated in symmetric supercapacitor cells. Results showed that binder-free flexible PANI-CNF electrodes had high capacitance of 234 F/g and excellent cycling stability with capacitance retention of about 90% after 1000 cycles. Ragone plots were also presented and a high energy density of 32 Wh/kg at the power density of 500 W/kg was achieved for the flexible PANI-CNF electrode prepared with 12 h polymerization. In addition, mechanical tests demonstrated that free-standing PANI-CNF electrodes were durable and highly flexible. Therefore, combining sol-gel and electrospinning techniques is a facile and effective way to achieve flexible carbon nanofiber electrodes and this work provides a new approach for designing flexible electrodes with exceptional electrochemical performance, which is very promising for practical application in the energy storage field.

Thermal Properties of Woven Kenaf/Carbon Fibre-Reinforced Epoxy Hybrid Composite Panels

Abstract: The effects of carbon fibre hybridisation on the thermal properties of woven kenaf-reinforced epoxy composites were studied. Woven kenaf hybrid composites of different weave designs of plain and satin and fabric counts of and were manually prepared by a vacuum infusion technique. A composite made from 100% carbon fibre was served for a comparison purpose. Thermal properties of pure carbon fibre and hybrid composites were determined by using a thermogravimetric analyser (TGA) and differential scanning calorimeter (DSC). It was found that a hybrid composite with higher kenaf fibre content (fabric count ) showed better thermal stability while the highest thermal stability was found in the pure carbon fibre composite. The TG and DTG results showed that the amount of residue decreased in the plain-designed hybrid composite compared to the satin-designed hybrid composite. The DSC data revealed that the presence of woven kenaf increased the decomposition temperature.

Cucurbits Plants: A Key Emphasis to Its Pharmacological Potential.

Abstract: Cucurbita genus has received a renowned interest in the last years. This plant species, native to the Americas, has served worldwide folk medicine for treating gastrointestinal diseases and intestinal parasites, among other clinical conditions. These pharmacological effects have been increasingly correlated with their nutritional and phytochemical composition. Among those chemical constituents, carotenoids, tocopherols, phenols, terpenoids, saponins, sterols, fatty acids, and functional carbohydrates and polysaccharides are those occurring in higher abundance. However, more recently, a huge interest in a class of triterpenoids, cucurbitacins, has been stated, given its renowned biological attributes. In this sense, the present review aims to provide a detailed overview to the folk medicinal uses of Cucurbita plants, and even an in-depth insight on the latest advances with regards to its antimicrobial, antioxidant and anticancer effects. A special emphasis was also given to its clinical effectiveness in humans, specifically in blood glucose levels control in diabetic patients and pharmacotherapeutic effects in low urinary tract diseases.