Is tatmini yuksek olan isgorenlerin; is performansi, motivasyonu, isi benimsemesi, orgutsel bagliligi, orgutsel vatandaslik davranisi artarken ise devamsizligi, algilanan stres ve isten ayrilma niyetinin azaldigi bircok arastirmada belirtilmistir. Bu sebeple is tatmininin oncullerinin ve etki derecelerinin belirlenmesi orgutler acisindan onem arz etmektedir. Bu arastirmada, kisi-orgut uyumunun is tatmini uzerindeki etki derecesini ve yas ile is tatmini arasindaki iliskiyi belirlemek amaclanmistir. Arastirmanin orneklemini, Ankara ilinde faaliyet gosteren is makineleri sektorunde calisan toplam 212 kisi olusturmaktadir. Orneklemi olusturan 212 kisiye anket uygulanmis, iclerinden 21 kisi secilmis, is ortaminda davranislari gozlemlenerek performans ciktilari incelenmis ve bu kisilerle mulakat yapilmistir. Elde edilen veriler icerik tahlili yontemi ile tahlil edilerek isgorenlerin olcek maddelerini dogru algilayip algilamadiklari arastirilmistir. Analizler sonucunda, kisi-orgut uyumu ile is tatmini arasinda anlamli ve pozitif bir iliski oldugu, yas arttikca is tatmininin arttigi ve anket maddelerini isgorenlerin dogru algiladiklari tespit edilmistir
Gunumuzde kamusal hizmetlerin boyutu ekonomik ve sosyal gelismeye bagli olarak suratle artmaktadir. Toplumsal hayatin temel unsurlarindan biri olan vergiler modern devletin vazgecilmez gelir kaynaklarindan biri olarak kabul edilmektedir. Ekonomik, mali, sosyal, siyasal ve hukuki boyutlari bulunan verginin, insan unsurunu esas alan bir yaklasimla ele alinmasi, onun sosyo-psikolojik boyutunu ortaya koymaktadir. Kamusal ihtiyaclarin finansmaninin saglanmasi icin vergi yukumlulerinin vergi bilincine sahip olmalari onem arz etmektedir. Modern toplumda vergi bilincinin olusmasi, vergilerin tahakkuk ve tahsilat oranlarini etkileyecegi bilinmektedir. Toplumsal hayatta vergi bilincinin olculmesi, arastirilmasi, olusturulmasi toplumlarin gelistirilmesi acisindan dikkate alinmalidir. Toplumlarin gelecegini olusturacak olan universiteli genclerin konuya bakis acilarinin tespit edilmesi calismanin ozunu olusturmaktadir. Bu amacla Gaziosmanpasa Universitesi Iktisadi ve Idari Bilimler Fakultesi son sinif ogrencilerinin vergi bilinc duzeyleri arastirilmistir. Arastirmanin verileri anket yontemiyle 400 ogrenciden derlenmistir. Calismada Tek-yonlu ANOVA, Ki-kare ve bagimsiz orneklem t testlerinden yararlanilmistir.
Objective Cardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still, they are often overlooked as their detection and correct clinical interpretation require expert skills. In this work, we aim to predict the presence of murmurs and clinical outcomes from multiple PCG recordings employing an explainable multitask model. Approach Our approach consists of a two-stage multitask model. In the first stage, we predict the murmur presence in single PCGs using a multiple instance learning (MIL) framework. MIL also allows us to derive sample-wise classifications (i.e. murmur locations) while only needing one annotation per recording (“weak label”) during training. In the second stage, we fuse explainable hand-crafted features with features from a pooling-based artificial neural network (PANN) derived from the MIL framework. Finally, we predict the presence of murmurs and the clinical outcome for a single patient based on multiple recordings using a simple feed-forward neural network. Main results We show qualitatively and quantitatively that the MIL approach yields useful features and can be used to detect murmurs on multiple time instances and may thus guide a practitioner through PCGs. We analyze the second stage of the model in terms of murmur classification and clinical outcome. We achieved a weighted accuracy of 0.714 and an outcome cost of 13612 when using the PANN model and demographic features on the CirCor dataset (hidden test set of the George B. Moody PhysioNet challenge 2022, team “Heart2Beat”, rank 12 / 40). Significance To the best of our knowledge, we are the first to demonstrate the usefulness of MIL in PCG classification. Also, we showcase how the explainability of the model can be analyzed quantitatively, thus avoiding confirmation bias inherent to many post-hoc methods. Finally, our overall results demonstrate the merit of employing MIL combined with handcrafted features for the generation of explainable features as well as for a competitive classification performance.
Tax compliance has become the main issue for all taxation authorities. Tax sensitivity, tax consciousness and tax awareness plays very important role in increasing tax revenues by increasing the level of tax compliance. Understanding and measuring of those factors is very important to generate more tax revenue and serving more public services. Tax sensitivity and tax consciousness of citizens are not only related to external variables such as tax rate, income and probability of audits and severity of fines, but also related to internal variables, such as citizens' knowledge of tax law, their attitudes towards the government and taxation, personal norms, perceived social norms. This study aimed to understand the perspective of university students for tax sensitivity. For this purpose, tax sensitivity levels of senior students of Gaziosmanpaşa University Faculty of Economics and Administrative Sciences were investigated. A total of 290 students' tax sensitivity level was surveyed using questionnaire survey. Factor analysis, One-way ANOVA and independent sample t tests were used in the study.
Abstract Objective Cardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still they are often overlooked as their detection and correct clinical interpretation requires expert skills. In this work, we aim to predict the presence of murmurs and clinical outcome from multiple PCG recordings employing an explainable multitask model. Approach Our approach consists of a two-stage multitask model. In the first stage, we predict the murmur presence in single PCGs using a multiple instance learning (MIL) framework. MIL also allows us to derive sample-wise classifications (i.e. murmur locations) while only needing one annotation per recording (“weak label”) during training. In the second stage, we fuse explainable hand-crafted features with features from a pooling-based artificial neural network (PANN) derived from the MIL framework. Finally, we predict the presence of murmurs as well as the clinical outcome for a single patient based on multiple recordings using a simple feed-forward neural network. Main results We show qualitatively and quantitatively that the MIL approach yields useful features and can be used to detect murmurs on multiple time instances and may thus guide a practitioner through PCGs. We analyze the second stage of the model in terms of murmur classification and clinical outcome. We achieved a weighted accuracy of 0.714 and an outcome cost of 13612 when using the PANN model and demographic features on the CirCor dataset (hidden testset of the George B. Moody PhysioNet challenge 2022, team “Heart2Beat”, rank 12 / 40). Significance To the best of our knowledge, we are the first to demonstrate the usefulness of MIL in PCG classification. Also, we showcase how the explainability of the model can be analyzed quantitatively, thus avoiding confirmation bias inherent to many post-hoc methods. Finally, our overall results demonstrate the merit of employing MIL combined with handcrafted features for the generation of explainable features as well as for a competitive classification performance.
Abstract The glabellar tapping reflex (GTR) is a sign related to brain conditions and can be analyzed by clinicians for diagnostic purposes. To facilitate the quantitative analysis of this reflex, we developed a video-based tool using the MediaPipe framework. We tested our approach on healthy subjects to assess the effect of age and gender on reaction time and blinking duration. The reaction time results show that the young group has a mean value (±standard deviation) of 0.091 (±0.066) seconds and the old group has 0.085 (±0.052) seconds, while female and male subjects have 0.097 (±0.053) seconds and 0.080 (±0.064) seconds respectively. For blinking duration, males have a mean value of 0.216 (±0.077) seconds and, females have 0.189 (±0.115) seconds, while old and young groups have 0.132 (±0.039) seconds 0.267(±0.084) seconds respectively.
Abstract With the recent increase in interest in machine learning and computer vision, camera-based pose estimation has emerged as a promising new technology. One of the most popular libraries for camera-based pose estimation is MediaPipe Pose due to its computational efficiency, ease of use, and the fact that it is open-source. However, little work has been performed to establish how accurate the library is and whether it is suitable for usage in, for example, physical therapy. This paper aims to provide an initial assessment of this. We find that the pose estimation is highly dependent on the camera’s viewing angle as well as the performed exercise. While high accuracy can be achieved under optimal conditions, the accuracy quickly decreases when the conditions are less favourable.
Abstract We developed a video-based tool to quantitatively assess the Glabellar Tap Reflex (GTR) in patients with idiopathic Parkinson’s disease (iPD) as well as healthy age-matched participants. We also video-graphically assessed the effect of dopaminergic medication on the GTR in iPD patients, as well as the frequency and blinking duration of reflex and non-reflex blinks. The Glabellar Tap Reflex is a clinical sign seen in patients e.g. suffering from iPD. Reliable tools to quantify this sign are lacking. Methods: We recorded the GTR in 11 iPD patients and 12 healthy controls (HC) with a consumer-grade camera at a framerate of at least 180 images/s. In these videos, reflex and non-reflex blinks were analyzed for blink count and blinking duration in an automated fashion. Results: With our setup, the GTR can be extracted from high-framerate cameras using landmarks of the MediaPipe face algorithm. iPD patients did not habituate to the GTR; dopaminergic medication did not alter that response. iPD patients’ non-reflex blinks were higher in frequency and higher in blinking duration (width at half prominence); dopaminergic medication decreased the median frequency (Before medication—HC: p < 0.001, After medication—HC: p = 0.0026) and decreased the median blinking duration (Before medication—HC: p = 0.8594, After medication—HC: p = 0.6943)—both in the direction of HC. Conclusion: We developed a quantitative, video-based tool to assess the GTR and other blinking-specific parameters in HC and iPD patients. Further studies could compare the video data to electromyogram (EMG) data for accuracy and comparability, as well as evaluate the specificity of the GTR in patients with other neurodegenerative disorders, in whom the GTR can also be present. Significance: The video-based detection of the blinking parameters allows for unobtrusive measurement in patients, a safer and more comfortable option.
Photoacoustic microscopy (PAM) is a new and emerging imaging method in last decades, based on photoacoustic effect. PAM, which does not contain ionizing radiation, is used to obtain the structural and functional images of different cancer cells based on the difference of the optical absorption properties. The recorded signal in the PAM is exposed to noise due to system components and environmental sources. In this study, pre-filtering for the reduction of the effect of noise components on the signals obtained by the photoacoustic microscope system and reconstruction of optical contrast images from these signals was studied.
