Self-construals and happiness: an experimental priming study

Bireysel farklılıkları açıklamada kullanılan kavramlardan benlik kurguları durumsal olarak uyandırılabilmekte ve belirli bir benlik kurgusu baskın hale getirilebilmektedir. Bu çalışma da bağımsız benlik kurgusu ve karşılıklı-bağımlı benlik kurgusunun Türkiye’den seçilen bir örneklemde farklı kültürlerde başarıyla uygulanan deneysel hazırlama (priming) yöntemleri ile belirgin hale getirilip getirilemeyeceği ve belirgin hale getirilen bu benlik kurgularının öznel iyi oluş olarak kavramsallaştırılan mutlulukla ilişkisi incelemiştir. Araştırma grubunu iki üniversiteden seçilen lisans öğrencileri oluşturmuş; bu öğrenciler bağımsız benlik, karşılıklıbağımlı benlik ve kontrol grubu olmak üzere üç farklı gruba ayrılmışlardır. Katılımcıların benlik kurgularını uyandırmak amacıyla daha önceki çalışmalarda uygulanan hazırlama yöntemleri arasında en sıklıkla kullanılan iki görev kullanılmıştır. Katılımcılar bu görevleri tamamladıktan hemen sonra bir benlik tanımlama testine ve yaşam doyumu, pozitif duygu durumu ve negatif duygu durumu ölçeklerine yanıt vermiştir. Benlik tanımlama testinden elde edilen bulgular, benlik kurgusu hazırlama görevlerinin başarılı olduğunu göstermiştir. Ayrıca gruplar arasında öznel iyi oluş bakımından farklılıklar ortaya konulmuştur. Bağımsız benlik kurgusu hazırlanan katılımcılar karşılıklı-bağımlı benlik kurgusu hazırlanan katılımcılara oranla yaşam doyumu ve pozitif duygu durumu ölçeklerinden daha yüksek, negatif duygu durumu ölçeğinden ise daha düşük puan almıştır. Öznel iyi oluş bakımından cinsiyet farkı bulunmamış; kadınların karşılıklı-bağımlı benlik kurgusu erkeklerden daha yüksek, bağımsız benlik kurgusu ise daha düşük olarak bulunmuştur.Self-construals are used in explaining individual differences in the extent to which people define themselves in relation to cultural context. However, the way people consture themselves may also be primed and become salient. Using a sample selected from Turkey, this study tested whether independent and interdependent self-construals can be successfully primed with an experimental manipulation and examined the relationship between primed selfconstruals and happiness. The participants were selected from two universities and were randomly assigned to independent self-construal, interdependent self-construal, and control groups. In order to prime self-construals two tasks which were commonly employed in priming studies in other cultures were used. After completing the priming tasks, participants were requested to fill out questionnaires to examine the way they define themselves, positive affectivity, negative affectivity, and life satisfaction. The findings demonstrated that priming tasks were successful in evoking self-construals. Compared to participants whose interdependent self-construals were primed, those whose independent self-construals were primed scored higher on positive affect and life satisfaction, and lower on negative affect. Finally, while no gender differences were noted for subjective well-being, gender differences were found for self-construals in that females scored higher on interdependent self-construal while males scored higher on independent self-construal

Mutluluk 2.0: iyi yaşama dair bilmediklerimiz

Mutluluk 2.0, mutluluğa dair fikirlerinizi tepe taklak edecek. Bildiklerinizi, öğretilenleri, mutlu olmak için yapmanız gerekenleri yeniden düzenlemeye, düşünmeye hazırlıklı olun. Önemli olan sahip olmadığın bir mutluluğun peşinde koşmak mı, yoksa yanındaki mutluluğu fark edebilmek mi? Mutlu olmak için önce kendisiyle barışık olması gerekmiyor mu kişinin? Peki ya hayatın bize verdikleriyle yetinebiliyor muyuz? Tüm bu sorulara ve daha birçok soruya cevap veriyor yazarlar bu kitapta; “Mutluluk 2.0 ile mutluluğa dair var olan bilgi kirliliğini temizleyerek güncel araştırmalarca destekli, uygulanabilir bilgileri sizlere yalın bir dille sunmak istiyoruz. Genel olarak vermek istediğimiz mesajsa çok açık: Neredeyse hepimizin, eğer istiyorsa, daha sağlıklı bir duygusal yaşam geliştirebilmesi, daha kaliteli bir yaşam sürebilmesi ve potansiyelini kullanarak hedeflerine ulaşabilmesi mümkündür.” Elma yayınevi mutluluk kavramının yeni modeliyle karşınızda. Örneklerle, önerilerle ve uzman görüşlerle Mutluluk 2.0 sizler için

DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images

In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis. Current methods still rely on visual inspection by experts. Vision-based automatic B-line detection methods have been developed, but their performance has yet to improve in terms of both accuracy and computational speed. This paper presents a novel approach to posing B-line detection as an inverse problem via deep unfolding of the Alternating Direction Method of Multipliers (ADMM). It tackles the challenges of data labelling and model training in lung ultrasound image analysis by harnessing the capabilities of deep neural networks and model-based methods. Our objective is to substantially enhance diagnostic accuracy while ensuring efficient real-time capabilities. The results show that the proposed method runs more than 90 times faster than the traditional model-based method and achieves an F1 score that is 10.6% higher.Comment: 4 pages, 3 figures, conferenc

A multilevel analysis of the relationship between leaders’ experiential avoidance and followers’ well-being

Experiential avoidance is defined as a process involving excessive negative evaluations of difficult or unwanted feelings, thoughts, and sensations, an unwillingness to remain in contact with and express these experiences, and habitual attempts to avoid or control them. Experiential avoidance is closely associated with maladaptive functioning. Although the ability to connect with internal experiences has been considered an important element of effective leadership, this assumption has not yet been empirically tested. On the basis of the Acceptance and Commitment Therapy model of experiential avoidance and the propositions of leadership models (e.g., transformational and authentic leadership) that characterize leadership as an emotion-related process, we examined the relationship between leaders’ experiential avoidance and their followers’ well-being in a sample of leader-follower triads. Well-being outcomes were subjective happiness, purpose in life, and job satisfaction. We also tested the mediating roles of followers’ basic psychological need satisfaction and need frustration in this relationship. Multilevel mediation model analyses suggested that followers’ psychological need frustration but not need satisfaction mediated the relationship between leaders’ experiential avoidance and followers’ well-being outcomes. Thus, a rigid attitude toward one’s internal experiences as a leader is a risk factor for followers’ well-being because leaders with such attitudes may pay little attention to their followers and give rise to need frustration in their followers. Organizational efforts to increase leaders’ flexibility in dealing with negative experiences can help foster well-being among both leaders and their followers

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Dubline: a deep unfolding network for B-line detection in lung ultrasound images

In the context of lung ultrasound, the identification of B-lines, which serve as indicators of interstitial lung disease and pulmonary edema, holds immense significance in clinical diagnosis. Presently, although vision-based automatic B-line detection techniques have emerged, their performance remains suboptimal. This paper introduces a novel approach, framing B-line detection as an inverse problem through the deep unfolding of the Alternating Direction Method of Multipliers. By leveraging the capabilities of deep neural networks and model-based methods, this methodology addresses the challenges associated with data labeling and model training in lung ultrasound image analysis. Our primary aim is to significantly augment diagnostic precision while maintaining efficient real-time capabilities. The experiment on 34 patients demonstrates that the proposed method outperforms traditional model-based approaches, achieving a 10.6% higher F 1 score and running over 90 times faster, underscoring its potential for real-time clinical utility

Deep learning enhanced mobile-phone microscopy

Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. After training a convolutional neural network, we successfully imaged various samples, including blood smears, histopathology tissue sections, and parasites, where the recorded images were highly compressed to ease storage and transmission for telemedicine applications. This method is applicable to other low-cost, aberrated imaging systems, and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications