The Relationship Between the Emotion Management Proficiencies of School Admininstrators and The Motivation Level of The Teachers

The aim of this research is to put forth the relationship between the emotion management proficiencies of the managers and the motivation level of the teachers. In this study, the opinions of school administrators on the emotion management behavior and the motivation level of the teachers have been examined.It was determined whether or not the opinions of the teachers differentiate according to the changes of genre, marital status, school type, the number of the teachers and the working time at the school they work.In this scanning model was used in this study. 374 teachers chosen by random sampling from 650 teachers working in primary, secondary and high schools in the district of Simav in Kütahya created the research phase. Two scales, Mottaz's (1985) “Job Motivation Scale” and “Emotion Management Behaviors of Managers in Terms of Management Process developed by Çoruk and Akçay (2012) were used. According to the findings obtained from the research, the motivation perceptions of the teachers and the proficiency levels of the emotion management of the school principals were found. While the proficiency level of the emotion management of the school administrators differentiates significantly depending on the numbers of the teachers in the whole dimensions of the emotion management, it differentiates depending on the school type in the communication and evaluation dimension. The intrinsic and extrinsic motivation opinions of the teachers differentiate significantly depending on the number of the teachers at school. As a result, it has been reached that there is a positive directional strong relationship between the emotional management and and the teacher motivation. Keywords: Emotion management, motivation, School Administrator

Artifact Concept Pluralism

We have a rough idea of what artifacts are: artifacts are objects made to serve a certain purpose. However, there is no consensus on how to specify this definition. Essentialists argue that objects are grouped into artifact kinds by sharing non-trivial artifact essences, while anti-essentialists argue that there is no such essence to be found. However, the prominent essentialist and anti-essentialist accounts suffer from extensional and definitional problems. I argue that the problems current essentialist and anti-essentialist accounts face mainly stem from the assumption of artifact concept monism. According to artifact concept monism, there is only a single way to group objects into artifact kinds. To remedy the problems that stem from artifact concept monism, this paper offers an alternative framework by drawing parallels from the debates on species concept pluralism and art concept pluralism

An augmented Lagrangian method for image reconstruction with multiple features

We present an Augmented Lagrangian Method (ALM) for solving image reconstruction problems with a cost function consisting of multiple regularization functions with a data fidelity constraint. The presented technique is used to solve inverse problems related to image reconstruction, including compressed sensing formulations. Our contributions include an improvement for reducing the number of computations required by an existing ALM method, an approach for obtaining the proximal mapping associated with p-norm based regularizers, and lastly a particular ALM for the constrained image reconstruction problem with a hybrid cost function including a weighted sum of the p-norm and the total variation of the image. We present examples from Synthetic Aperture Radar imaging and Computed Tomography

Hellenistic and Roman Period Ceramic Finds from the Balatlar Church Excavations in Sinop between 2010-2012

The ancient city of Sinop which was located in Paphlagonia during the Roman and Byzantine periods, is situated in the middle of the Anatolian Black Sea coast. The peninsula sheltering the city is the most northerly point of Anatolia and extends North-eastward (Fig. 1). Thanks to its localization, this area has always been an important port. History of Sinop and its environment goes back to Bronze Age. Ancient sources mention that Sinop was re-founded as a Greek colony of the city of Miletus i..

An augmented Lagrangian method for autofocused compressed SAR imaging

We present an autofocus algorithm for Compressed SAR Imaging. The technique estimates and corrects for 1-D phase errors in the phase history domain, based on prior knowledge that the reflectivity field is sparse, as in the case of strong scatterers against a weakly-scattering background. The algorithm relies on the Sparsity Driven Autofocus (SDA) method and Augmented Lagrangian Methods (ALM), particularly Alternating Directions Method of Multipliers (ADMM). In particular, we propose an ADMM-based algorithm that we call Autofocusing Iteratively Re-Weighted Augmented Lagrangian Method (AIRWALM) to solve a constrained formulation of the sparsity driven autofocus problem with an ℓp-norm, p ≤ 1 cost function. We then compare the performance of the proposed algorithm's performance to Phase Gradient Autofocus (PGA) and SDA [2] in terms of autofocusing capability, phase error correction, and computation time

Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions

Purpose: A time-efficient strategy to acquire high-quality multi-contrast images is to reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause leakage of uncommon features among contrasts, compromising diagnostic utility. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Theory: Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. Methods: The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. Results: The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms. Conclusion: The proposed compressive sensing method performs fast reconstruction of multi-channel multi-contrast MRI data with improved image quality. It offers reliability against feature leakage in joint reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio

Autofocused compressive SAR imaging based on the alternating direction method of multipliers

We present an alternating direction method of multipliers (ADMM) based autofocused Synthetic Aperture Radar (SAR) imaging method in the presence of unknown 1-D phase errors in the phase history domain, with undersampled measurements. We formulate the problem as one of joint image formation and phase error estimation. We assume sparsity of strong scatterers in the image domain, and as such use sparsity priors for reconstruction. The algorithm uses l(p)-norm minimization (p <= 1) [8] with an improvement by integrating the phase error updates within the alternating direction method of multipliers (ADMM) steps to correct the unknown 1-D phase error. We present experimental results comparing our proposed algorithm with a coordinate descent based algorithm in terms of convergence speed and reconstruction quality

Left Atrial Mechanical Functions in Professional Soccer Players: A Pilot Study

Long-term regular exercise is associated with physiologic and morphologic alterations in the heart chambers. The aim of this study to evaluate left atrium (LA) phasic functions in professional football players and compare with control subjects. Left atrial volume was calculated at end-systole (Vmax), end-diastole and pre-atrial contraction by echocardiography in 20 professional male football players (mean age, 20.15+2.11 years) and 20 male control subjects (mean age, 22.3+1.49 years). Echocardiographic assessments were per­formed were performed using the criteria of the American Society of Echocardiography. The following LAVs were measured: maximal volume (Vmax), minimal volume (Vmin) and LAV before atrial contraction (VpreA) at the onset of the P wave of the simultaneously recorded ECG. Left atrial ejection fraction (LAEF), expansion index (LAEI), active emptying volume index (LAAEVI) and fraction(LAAEFr), passive emptying volume index (LAPEVI) and fraction (LAPEFr) were calculated. Baseline characteristics, demographics, two dimensional and tissue Doppler echocardiographic parameters were not statistically significant between the groups (Table 1). Both groups were similar in terms of Vmax index but Vmin index and VpreA index were significantly higher in football players. LAEF, LAEI, LAAEVI and LAAEFr were lesser in football player but they were not statistically significant. Also LAPEVI and LAPEFr were similar in both groups (Table 2). Professional football playing can be associated with morphologic alteration in left atrium mechanical functions. Further prospective, randomized, controlled trials with long term follow-up are necessary to make more robust interpretations of this issue

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging