Deep-Submicron Rectilinear Steiner Tree Problem

Erzin,Adil I.,Kim,Sang Ha,Cho,Jun Dong 대한전자공학회 1997 ICVC : International Conference on VLSI and CAD Vol.5 No.1

This paper presents a fast polynomial approximation algorithm for constructing the Steiner tree of minimum total cost (the cost of the edge (i, j) equal to the rectilinear distance between these nodes) with different requirements on delays along each path to destination. The flexibility of the proposed algorithm permits to drive the number of Steiner nodes in the solution without increasing the tree's cost. Simulation results shows the high efficiency of algorithm. Thus the constructed trees are near the optimal. With the help of node's weights vary, in more than 64% cases, the number of Steiner vertices was reduced without increasing of the total tree's cost. The costs of the best trees (between ones, yielded by our algorithm with different values of the node's weights) were decreased in average on 11.4% and the number of Steiner nodes - on 39.1% with respect to the trees constructed without node's weights.

The use of neural networks for the prediction of swell pressure

Erzin, Yusuf Techno-Press 2009 Geomechanics & engineering Vol.1 No.1

Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural system of human brain. The prediction of swell pressures from easily determined soil properties, namely, initial dry density, initial water content, and plasticity index, have been investigated by using artificial neural networks. The results of the constant volume swell tests in oedometers, performed on statically compacted specimens of Bentonite-Kaolinite clay mixtures with varying soil properties, were trained in an ANNs program and the results were compared with the experimental values. It is observed that the experimental results coincided with ANNs results.

The use of neural networks for the prediction of the settlement of pad footings on cohesionless soils based on standard penetration test

Erzin, Yusuf,Gul, T. Oktay Techno-Press 2013 Geomechanics & engineering Vol.5 No.6

In this study, artificial neural networks (ANNs) were used to predict the settlement of pad footings on cohesionless soils based on standard penetration test. To achieve this, a computer programme was developed to calculate the settlement of pad footings from five traditional methods. The footing geometry (length and width), the footing embedment depth, $D_f$, the bulk unit weight, ${\gamma}$, of the cohesionless soil, the footing applied pressure, Q, and corrected standard penetration test, $N_{cor}$, varied during the settlement analyses and the settlement value of each footing was calculated for each method. Then, an ANN model was developed for each traditional method to predict the settlement by using the results of the analyses. The settlement values predicted from the ANN model were compared with the settlement values calculated from the traditional method for each method. The predicted values were found to be quite close to the calculated values. It has been demonstrated that the ANN models developed can be used as an accurate and quick tool at the preliminary designing stage of pad footings on cohesionless soils without a need to perform any manual work such as using tables or charts. Sensitivity analyses were also performed to examine the relative importance of the factors affecting settlement prediction. According to the analyses, for each traditional method, $N_{cor}$ is found to be the most important parameter while ${\gamma}$ is found to be the least important parameter.

The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions

Erzin, Yusuf,Cetin, T. Techno-Press 2014 Geomechanics & engineering Vol.6 No.1

In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety ($F_s$) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of $F_s$ in 5184 nos. of homogeneous finite slopes having different slope, soil and earthquake parameters were calculated by using the Simplified Bishop method and the minimum (critical) $F_s$ for each of the case was determined and used in the development of the ANN and MR models. The results obtained from both the models were compared with those obtained from the calculations. It is found that the ANN model exhibits more reliable predictions than the MR model. Moreover, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed. Also, the receiver operating curves were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models developed. The performance level attained in the ANN model shows that the ANN model developed can be used for predicting the critical $F_s$ of the homogeneous finite slopes subjected to earthquake forces.

Thiol/Disulfide Homeostasis in Bipolar and Unipolar Depression

Gamze Erzin,Güven Özkaya,Canan Topçuoğlu,Rabia Nazik Yüksel,Özcan Erel,Emine Feyza Yurt,Erol Göka,Sinan Gülöksüz 대한정신약물학회 2020 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.18 No.3

Objective: Bipolar disorder and unipolar depressive disorder are complex phenotypes. There appear to be phenotypical, mechanistic, and therapeutic differences between bipolar depression (BD) and unipolar depression (UD). There is a need for understanding the underlying biological variation between these clinical entities. The role of oxidative processes underlying bipolar disorder and depression has been demonstrated. Thiol-disulfide homeostasis (TDH) is a recent oxidative stress marker. In this study, we aimed to inspect patients with bipolar depression and unipolar depression in terms of thiol-disulfide balance and to compare them with healthy controls. Methods: Patients admitted to the outpatient clinic of Ankara Numune Training and Research Hospital and diagnosed either as a depressive episode with bipolar disorder (n = 37) or unipolar depression (n = 24) according to DSM-5 criteria, along with healthy controls (HC) (n = 50), were included in the study. Native thiol, total thiol, and disulfide levels were compared across the groups. Results: In comparison to HC, both BD and UD groups had higher disulfide levels, disulfide/native thiol ratio, and disulfide/total thiol ratio. No significant differences between BD and UD were detected in terms of disulfide level, disulfide/ native thiol ratio, and disulfide/total thiol ratio. Conclusion: Increased levels of disulfide, native thiol, and disulfide/total thiol ratios compared to healthy controls in both UD and BD groups may be indicative of the presence of oxidative damage in these two clinical conditions. To clarify the role of oxidative stress in the pathophysiology of depressive disorders and investigate TDH, longitudinal studies in patients with medication-free UD and BD are required.

Could Irisin Levels be Affected by Physical Activity in Patients with Schizophrenia?

Gamze Erzin,Olga Güriz,Ali Yalçındağ,Akfer Kahıloğulları,Sibel Örsel 대한정신약물학회 2021 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.19 No.4

Objective: The aim of this study was to explore the effect of physical activity and metabolic parameters on irisin levels in patients with schizophrenia and healthy controls. Methods: Ninety-six patients with schizophrenia and 63 healthy controls comprised the study population. The participants were separated into three groups: inactive, low activity, and sufficiently active according to International Physical Activity Questionnaire short form (IPAQ-SF). We measured irisin levels using Enzyme linked immunosorbent assay. We also calculated exercise levels by using the IPAQ-SF for each individual. The independent samples t test was used in the data analysis to compare irisin levels according to the activity levels of the patients with schizophrenia and controls. Results: The levels of irisin were higher in the healthy controls (p < 0.001) compared to schizophrenia groups. When the activity levels of the schizophrenia and healthy control groups were compared, the irisin levels of the low activity and sufficiently active groups with schizophrenia were found to be lower than those of the low activity and sufficiently active groups in the healthy controls (respectively p = 0.014; p < 0.001). Conclusion: Irisin levels could be affected by physical activity and these results must be supported with new studies.

A provably tight delay-driven concurrently congestion mitigating global routing algorithm

Samanta, R.,Erzin, A.I.,Raha, S.,Shamardin, Y.V.,Takhonov, I.I.,Zalyubovskiy, V.V. Elsevier [etc.] 2015 Applied mathematics and computation Vol.255 No.-

Routing is a very important step in VLSI physical design. A set of nets are routed under delay and resource constraints in multi-net global routing. In this paper a delay-driven congestion-aware global routing algorithm is developed, which is a heuristic based method to solve a multi-objective NP-hard optimization problem. The proposed delay-driven Steiner tree construction method is of O(n<SUP>2</SUP>logn) complexity, where n is the number of terminal points and it provides n-approximation solution of the critical time minimization problem for a certain class of grid graphs. The existing timing-driven method (Hu and Sapatnekar, 2002) has a complexity O(n<SUP>4</SUP>) and is implemented on nets with small number of sinks. Next we propose a FPTAS Gradient algorithm for minimizing the total overflow. This is a concurrent approach considering all the nets simultaneously contrary to the existing approaches of sequential rip-up and reroute. The algorithms are implemented on ISPD98 derived benchmarks and the drastic reduction of overflow is observed.

Evaluation of the Relationships between Irisin Levels and Cognitive Functions in Individuals with Schizophrenia

Hatice Ayça Kaloğlu,Sibel Örsel,Gamze Erzin 대한정신약물학회 2023 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.21 No.4

Objective: Irisin is a myokine that is involved in neurogenesis, neuronal proliferation, and neuronal differentiation. Many research examine the relationship between irisin and schizophrenia. In this study, we aimed to evaluate the relationship between irisin levels and cognitive functions in individuals with schizophrenia. Methods: Ninety-six individuals who were diagnosed with schizophrenia were included. The Brief Psychiatric Rating Scale (BPRS) was used to assess disease severity. To evaluate the cognitive functions of the patients, the trail-making test was evaluated with the A and B forms and the verbal memory processes scale. After a 12-hour night fast, samples of fasting blood were obtained from the participants. Results: There was no significant correlation between irisin, duration of disease, and BPRS total score. In the analysis performed, a positive correlation was found between the plasma irisin level and the error score of the trail-making test form B. Other than that, no correlation was found between irisin level and cognitive performance in schizophrenia patients. In addition, in subgroup analysis between genders, it was determined that the duration of the trail-making test B was longer in female schizophrenia patients. Conclusion: In this study, there was a positive correlation between the trail-making test B-form error scores and the irisin levels. This relationship between impaired executive functions and irisin levels may suggest that the irisin level is increased as compensation for the impairment in executive functions. More research is needed to understand the role of irisin in cognitive impairment and schizophrenia.

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Gokhan Guney,Busra Ozgode Yigin,Necdet Guven,Yasemin Hosgoren Alici,Burcin Colak,Gamze Erzin,Gorkem Saygili 대한정신약물학회 2021 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.19 No.2

Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algo-rithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, esti-mating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuro-psychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.