Bahçeşehir University

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

Machine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this study, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved 567 relevant studies from six electronic databases, of which we have selected 50 studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is Artificial Neural Networks in these models. After this observation based on the analysis of machine learning-based 50 papers, we performed an additional search in electronic databases to identify deep learning-based studies, reached 30 deep learning-based papers, and extracted the applied deep learning algorithms. According to this additional analysis, Convolutional Neural Networks (CNN) is the most widely used deep learning algorithm in these studies, and the other widely used deep learning algorithms are Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN).

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In today's competitive industry marketplace, the companies face growing demands to improve process efficiencies, comply with environmental regulations, and meet corporate financial objectives. Given the increasing age of many industrial systems and the dynamic industrial manufacturing market, intelligent and low-cost industrial automation systems are required to improve the productivity and efficiency of such systems. The collaborative nature of industrial wireless sensor networks (IWSNs) brings several advantages over traditional wired industrial monitoring and control systems, including self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability. In this regard, IWSN plays a vital role in creating a highly reliable and self-healing industrial system that rapidly responds to real-time events with appropriate actions. In this paper, first, technical challenges and design principles are introduced in terms of hardware development, system architectures and protocols, and software development. Specifically, radio technologies, energy-harvesting techniques, and cross-layer design for IWSNs have been discussed. In addition, IWSN standards are presented for the system owners, who plan to utilize new IWSN technologies for industrial automation applications. In this paper, our aim is to provide a contemporary look at the current state of the art in IWSNs and discuss the still-open research issues in this field and, hence, to make the decision-making process more effective and direct. </para>

The collaborative and low-cost nature of wireless sensor networks (WSNs) brings significant advantages over traditional communication technologies used in today's electric power systems. Recently, WSNs have been widely recognized as a promising technology that can enhance various aspects of today's electric power systems, including generation, delivery, and utilization, making them a vital component of the next-generation electric power system, the smart grid. However, harsh and complex electric-power-system environments pose great challenges in the reliability of WSN communications in smart-grid applications. This paper starts with an overview of the application of WSNs for electric power systems along with their opportunities and challenges and opens up future work in many unexploited research areas in diverse smart-grid applications. Then, it presents a comprehensive experimental study on the statistical characterization of the wireless channel in different electric-power-system environments, including a 500-kV substation, an industrial power control room, and an underground network transformer vault. Field tests have been performed on IEEE 802.15.4-compliant wireless sensor nodes in real-world power delivery and distribution systems to measure background noise, channel characteristics, and attenuation in the 2.4-GHz frequency band. Overall, the empirical measurements and experimental results provide valuable insights about IEEE 802.15.4-compliant sensor network platforms and guide design decisions and tradeoffs for WSN-based smart-grid applications.

Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.

Combined ATLAS and CMS measurements of the Higgs boson production and decay rates, as well as constraints on its couplings to vector bosons and fermions, are presented. The combination is based on the analysis of five production processes, namely gluon fusion, vector boson fusion, and associated production with a W or a Z boson or a pair of top quarks, and of the six decay modes H → ZZ, W W , γγ, ττ, bb, and μμ. All results are reported assuming a value of 125.09 GeV for the Higgs boson mass, the result of the combined measurement by the ATLAS and CMS experiments. The analysis uses the CERN LHC proton-proton collision data recorded by the ATLAS and CMS experiments in 2011 and 2012, corresponding to integrated luminosities per experiment of approximately 5 fb$^{−1}$ at $\sqrt{s}$=7 TeV and 20 fb−1 at $\sqrt{s}$=8 TeV. The Higgs boson production and decay rates measured by the two experiments are combined within the context of three generic parameterisations: two based on cross sections and branching fractions, and one on ratios of coupling modifiers. Several interpretations of the measurements with more model-dependent parameterisations are also given. The combined signal yield relative to the Standard Model prediction is measured to be 1.09 ± 0.11. The combined measurements lead to observed significances for the vector boson fusion production process and for the H → ττ decay of 5.4 and 5.5 standard deviations, respectively. The data are consistent with the Standard Model predictions for all parameterisations considered.

We report genomic analysis of 300 meningiomas, the most common primary brain tumors, leading to the discovery of mutations in TRAF7, a proapoptotic E3 ubiquitin ligase, in nearly one-fourth of all meningiomas. Mutations in TRAF7 commonly occurred with a recurrent mutation (K409Q) in KLF4, a transcription factor known for its role in inducing pluripotency, or with AKT1(E17K), a mutation known to activate the PI3K pathway. SMO mutations, which activate Hedgehog signaling, were identified in ~5% of non-NF2 mutant meningiomas. These non-NF2 meningiomas were clinically distinctive-nearly always benign, with chromosomal stability, and originating from the medial skull base. In contrast, meningiomas with mutant NF2 and/or chromosome 22 loss were more likely to be atypical, showing genomic instability, and localizing to the cerebral and cerebellar hemispheres. Collectively, these findings identify distinct meningioma subtypes, suggesting avenues for targeted therapeutics.

Artificial intelligence (AI) introduces new tools to the educational environment with the potential to transform conventional teaching and learning processes. This study offers a comprehensive overview of AI technologies, their potential applications in education, and the difficulties involved. Chatbots and related algorithms that can simulate human interactions and generate human-like text based on input from natural language are discussed. In addition to the advantages of cutting-edge chatbots like ChatGPT, their use in education raises important ethical and practical challenges. The authors aim to provide insightful information on how AI may be successfully incorporated into the educational setting to benefit teachers and students, while promoting responsible and ethical use.

During 2015 the ATLAS experiment recorded [Formula: see text] of proton-proton collision data at a centre-of-mass energy of [Formula: see text]. The ATLAS trigger system is a crucial component of the experiment, responsible for selecting events of interest at a recording rate of approximately 1 kHz from up to 40 MHz of collisions. This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton-proton collision data.

There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.

collision data, measurements of the reconstruction efficiency, as well as of the momentum scale and resolution, are presented and compared to Monte Carlo simulations. The reconstruction efficiency is measured to be close to [Formula: see text] over most of the covered phase space ([Formula: see text] and [Formula: see text] GeV). The isolation efficiency varies between 93 and [Formula: see text] depending on the selection applied and on the momentum of the muon. Both efficiencies are well reproduced in simulation. In the central region of the detector, the momentum resolution is measured to be [Formula: see text] ([Formula: see text]) for muons from [Formula: see text] ([Formula: see text]) decays, and the momentum scale is known with an uncertainty of [Formula: see text]. In the region [Formula: see text], the [Formula: see text] resolution for muons from [Formula: see text] decays is [Formula: see text] while the precision of the momentum scale for low-[Formula: see text] muons from [Formula: see text] decays is about [Formula: see text].

Purpose The purpose of this study is to determine the underlying dimensions of supply chain management (SCM) practices and to empirically test a framework identifying the relationships among SCM practices, operational performance and SCM-related organizational performance with special emphasis on small and medium size enterprises (SMEs) in Turkey. Design/methodology/approach Data for the study were collected from a sample of 203 manufacturing SMEs operating in the manufacture of fabricated metal products and general purpose machinery (NACE codes 28 and 29) within the city of Istanbul in Turkey. The research framework was tested using partial least squares method, which is a variance-based structural equation modeling approach. Findings Based on exploratory factor analysis (EFA), SCM practices were grouped in two factors: outsourcing and multi-suppliers (OMS), and strategic collaboration and lean practices (SCLP). The results indicate that both factors of SCLP and OMS have direct positive and significant impact on operational performance. In contrast, both SCLP and OMS do not have a significant and direct impact on SCM-related organizational performance. Also, as the direct relationship between the two performance-constructs was found significant, both factors of SCM practices have an indirect and significant positive effect on ORG through OPER. Research limitations/implications Perhaps, the most serious limitation of this study was its narrow focus on Turkish manufacturing SMEs, thus precluding the generalization of findings to other emerging countries as well as other sectors such as service and government sectors that may benefit from a sound SCM strategy. Practical implications By developing and validating a multi-dimensional construct of SCM practices and by exhibiting its value in improving operational performance of SMEs, it provides SCM managers with useful tool for evaluating the efficiency of their current SCM practices. Second, the analysis of the relationship between SCM practices and operational performance indicates that SCM practices might directly influence operational performance of SMEs. Originality/value This paper adds to the body of knowledge by providing new data and empirical insights into the relationship between SCM practices and performance of SMEs operating in Turkey.

Untethered mobile microrobots have the potential to leverage minimally invasive theranostic functions precisely and efficiently in hard-to-reach, confined, and delicate inner body sites. However, such a complex task requires an integrated design and engineering, where powering, control, environmental sensing, medical functionality, and biodegradability need to be considered altogether. The present study reports a hydrogel-based, magnetically powered and controlled, enzymatically degradable microswimmer, which is responsive to the pathological markers in its microenvironment for theranostic cargo delivery and release tasks. We design a double-helical architecture enabling volumetric cargo loading and swimming capabilities under rotational magnetic fields and a 3D-printed optimized 3D microswimmer (length = 20 μm and diameter = 6 μm) using two-photon polymerization from a magnetic precursor suspension composed from gelatin methacryloyl and biofunctionalized superparamagnetic iron oxide nanoparticles. At normal physiological concentrations, we show that matrix metalloproteinase-2 (MMP-2) enzyme could entirely degrade the microswimmer in 118 h to solubilized nontoxic products. The microswimmer rapidly responds to the pathological concentrations of MMP-2 by swelling and thereby boosting the release of the embedded cargo molecules. In addition to delivery of the drug type of therapeutic cargo molecules completely to the given microenvironment after full degradation, microswimmers can also release other functional cargos. As an example demonstration, anti-ErbB 2 antibody-tagged magnetic nanoparticles are released from the fully degraded microswimmers for targeted labeling of SKBR3 breast cancer cells in vitro toward a potential future scenario of medical imaging of remaining cancer tissue sites after a microswimmer-based therapeutic delivery operation.

Combined measurements of Higgs boson production and decay using up to 80

Unequivocal international guidelines regarding the diagnosis and management of patients with acute appendicitis are lacking. The aim of the consensus meeting 2015 of the EAES was to generate a European guideline based on best available evidence and expert opinions of a panel of EAES members. After a systematic review of the literature by an international group of surgical research fellows, an expert panel with extensive clinical experience in the management of appendicitis discussed statements and recommendations. Statements and recommendations with more than 70 % agreement by the experts were selected for a web survey and the consensus meeting of the EAES in Bucharest in June 2015. EAES members and attendees at the EAES meeting in Bucharest could vote on these statements and recommendations. In the case of more than 70 % agreement, the statement or recommendation was defined as supported by the scientific community. Results from both the web survey and the consensus meeting in Bucharest are presented as percentages. In total, 46 statements and recommendations were selected for the web survey and consensus meeting. More than 232 members and attendees voted on them. In 41 of 46 statements and recommendations, more than 70 % agreement was reached. All 46 statements and recommendations are presented in this paper. They comprise topics regarding the diagnostic work-up, treatment indications, procedural aspects and post-operative care. The consensus meeting produced 46 statements and recommendations on the diagnostic work-up and management of appendicitis. The majority of the EAES members supported these statements. These consensus proceedings provide additional guidance to surgeons and surgical residents providing care to patients with appendicitis.

The ATLAS inner detector comprises three different sub-detectors: the pixel detector, the silicon strip tracker, and the transition-radiation drift-tube tracker. The Insertable $B$-Layer, a new innermost pixel layer, was installed during the shutdown period in 2014, together with modifications to the layout of the cables and support structures of the existing pixel detector. The material in the inner detector is studied with several methods, using a low-luminosity $\sqrt{s}=13$ TeV $pp$ collision sample corresponding to around $2.0\,\mathrm{nb}^{-1}$ collected in 2015 with the ATLAS experiment at the LHC. In this paper, the material within the innermost barrel region is studied using reconstructed hadronic interaction and photon conversion vertices. For the forward rapidity region, the material is probed by a measurement of the efficiency with which single tracks reconstructed from pixel detector hits alone can be extended with hits on the track in the strip layers. The results of these studies have been taken into account in an improved description of the material in the ATLAS inner detector simulation, resulting in a reduction in the uncertainties associated with the charged-particle reconstruction efficiency determined from simulation.

Abstract Purpose – The principal aim of this paper is to determine the critical factors of total quality management (TQM) and to measure their effect on organizational performance of SMEs operating in Turkish textile industry. Design/methodology/approach – Data for this study was collected using a self‐administered questionnaire that was distributed to 500 SMEs in textile industry in the city of Istanbul in Turkey selected randomly from the database of Turkish Small Business Administration (KOSGEB). Of the 500 questionnaires posted, a total of 163 questionnaires were returned. Findings – Using exploratory and confirmatory factor analyses, seven empirically validated dimensions of TQM were identified. The structural equation modelling technique was employed to investigate the relationship between the implementation of TQM practices and organizational performance. Data analysis reveals that there is a strong positive relationship between TQM practices and non‐financial performance of SMEs, while there is only weak influence of TQM practices on financial performance of SMEs. With only a mediating effect of non‐financial performance that the TQM practices has a strong positive impact on financial performance of SMEs. Research limitations/implications – The sample is restricted to only a single region and a single industry, so it would be strongly recommended that data be gathered from various parts of Turkey including both various manufacturing and service industries. As the data in this study were collected from top managers of organizations on the basis of their subjective evaluations, objective performance indicators should also be employed in the analysis. Originality/value – Despite some attempts on the applicability of TQM practices and advanced manufacturing technologies as well as their impact on organizational performance of SMEs, there is a lack of systematic empirical evidence regarding the extent of TQM implementation and its effect on performance of SMEs in emerging market economies. This paper presents new data and empirical insights into the relationship between TQM implementation and organizational performance in SMEs operating in Turkey.

Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L(2) Lebesgue measure of the γ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., H(1) Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.

Mobile microrobots offer great promise for minimally invasive targeted medical theranostic applications at hard-to-access regions inside the human body. The circulatory system represents the ideal route for navigation; however, blood flow impairs propulsion of microrobots especially for the ones with overall sizes less than 10 micrometers. Moreover, cell- and tissue-specific targeting is required for efficient recognition of disease sites and long-term preservation of microrobots under dynamic flow conditions. Here, we report cell-sized multifunctional surface microrollers with ~3.0 and ~7.8-micrometer diameters, inspired by leukocytes in the circulatory system, for targeted drug delivery into specific cells and controlled navigation inside blood flow. The leukocyte-inspired spherical microrollers are composed of magnetically responsive Janus microparticles functionalized with targeting antibodies against cancer cells (anti-HER2) and light-cleavable cancer drug molecules (doxorubicin). Magnetic propulsion and steering of the microrollers resulted in translational motion speeds up to 600 micrometers per second, around 76 body lengths per second. Targeting cancer cells among a heterogeneous cell population was demonstrated by active propulsion and steering of the microrollers over the cell monolayers. The multifunctional microrollers were propelled against physiologically relevant blood flow (up to 2.5 dynes per square centimeter) on planar and endothelialized microchannels. Furthermore, the microrollers generated sufficient upstream propulsion to locomote on inclined three-dimensional surfaces in physiologically relevant blood flow. The multifunctional microroller platform described here presents a bioinspired approach toward in vivo controlled propulsion, navigation, and targeted active cargo delivery in the circulatory system.

Purpose Corporate social responsibility is important and fundamental to the sustainable operations of corporations. Similarly financial performance is undoubtedly fundamental to the continuing operating of any corporation. This paper aims to investigate the relationship between corporate social responsibility and firm financial performance. Design/methodology/approach The main part of this paper is based upon an exploration of the relationship between corporate social responsibility and financial performance in developing countries. The authors do this by investigating the Istanbul Stock Exchange (ISE) 100 index companies and their social responsibility policy and financial indicators. The relationship between CSR and financial performance is empirically examined between 2005 and 2007 with different approaches and measurement methods. The authors show that some causality is related to lagging between periods for financial performance and CSR. Based upon previous empirical studies, this study conducts the analysis based on the assumption that there may be a relationship between firm size, profitability, risk level and CSR. Findings In doing this analysis the authors found a relationship between firm size and corporate social responsibility. However the authors were not able to find any significant relationship between corporate social responsibility and financial performance/profitability. Research limitations/implications The paper has implications in enhancing the understanding of performance management through understanding the relationship between corporate social responsibility and financial performance particularly in a developing country, although it is necessarily limited by the size of the sample. Originality/value This paper increases the understanding of the relationship between corporate social responsibility and financial performance. This research is also the first research that has examined Turkish companies.