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ÖZ Amaç: Yumuşak astar maddeleri, esneklik özellikleri ve yumuşak yapılarından dolayı tam ve bölümlü protezlerde sert protez kaide maddelerinin destek dokular üzerindeki olumsuz etkilerinin önlenmesi veya azaltılması amacıyla kullanılırlar. Zamanla sertleşerek esnekliklerini yitirme, bakteri ve mantar kolonizasyonuna neden olma, porözite gösterme, su absorbe etme, renk değiştirme, protez kaidesinden ayrılma ve protezlerin kırılmaya karşı dayanıklılıklarını azaltma gibi klinik kullanım sürelerini sınırlayan önemli dezavantajlara sahiptirler. Bu nedenle çalışma, polimetil metakrilat (PMMA) esaslı protez kaide maddesi ile yumuşak astar materyali arasındaki bağlanma dayanımına yüzey işlemlerinin etkisinin incelenmesi amacıyla yapıldı. Gereç ve Yöntemler: Çalışmada ASTM D 638-08 standardından faydalanılarak 60 mm uzunluğunda, kalın olan bölümü 15 mm, ince olan bölümü ise 2,5 mm çapında olacak şekilde konvansiyonel sıcak akrilik rezinden 32 örnek hazırlandı. Örnekler rastgele numaralandırılarak dört gruba ayrıldı. Ortadan ikiye ayrılan örneklere farklı yüzey işlemleri (kontrol, kumlama, Er: Yag lazer ve monomer uygulama) uygulandıktan sonra silikon esaslı yumuşak astar maddesi üreticinin önerdiği şekilde uygulandı. Hazırlanan örnekler 37°C distile suda 24 saat bekletildikten sonra bağlanma dayanımları Universal test cihazında değerlendirildi. Verilerin istatistiksel olarak değerlendirilmesi tek yönlü varyans analizi ve çoklu karşılaştırma (Tukey) testi ile yapıldı. Bulgular: Yapılan varyans analizi sonucunda; bağlanma dayanımı üzerinde uygulanan yüzey işlemlerinin çok anlamlı (p

The Ball State University Center for Peace and Conflict Studies in the United States has compiled altruistic stories from different countries such as India, Australia, the United States, and England since the onset of the COVID‐19 pandemic. We examined those stories to understand (1) who receives the most support, (2) what type of support is provided, (3) who those supporters are, and (4) why people support each other during the pandemic. Conducting a qualitative content analysis of 104 altruistic stories, we first identified that (a) older individuals , (b) people with sensitive health conditions, and disabilities, (c) frontline workers, and (d) working class and marginalized communities received the primary support. Second, we identified three types of support: (a) material, (b) social/emotional, and (c) psychological. Third, we found that support was coming from (a) different organizations, (b) advantaged groups, and (c) volunteers. Last, (a) sharing a community/humanity identity, (b) allyship, and (c) showing gratitude were the reasons behind altruistic and prosocial behavior. Our findings contribute to the literature by providing some possible reasons and ways that allies have supported disadvantaged communities via sharing the available resources and how showing gratitude can be a reason for altruism and prosocial behavior in a pandemic.

This paper uses ethnography and interviews with 15 participants who were actively involved in the campaign following the 2017 Grenfell Tower fire to address the question of how campaigners use street mobilization to seek justice in the aftermath of a disaster. Analysis of observations and themes suggests that campaigners used street mobilization to do two things. First, they used it to build solidarity with supporters through accessibility, the use of shared social spaces and collectively showing respect for losses. Second, they used it to protest in a way that challenged negative stereotypes and projected their power to the authorities, who could not ignore such a large and dignified mobilization. Whereas previous social psychology research on disaster communities focuses on the determinants of the immediate responses of people who experienced a disaster, we show the strategic aspects of such disaster communities. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

Previous research has shown that disasters often involve a sense of injustice among affected communities. But the process through which ‘disaster communities’ organise to confront such injustices have not been investigated by social psychology. This study addresses this gap by examining how community members impacted by the Grenfell Tower fire self-organized to demand justice in response to government neglect. Thematic analysis of interviews with fifteen campaigners helped us to understand the shared responses of those involved in support campaigns following the fire. Campaigners aimed to: overcome injustice against the government inactions in the aftermath of the fire; empower their community against government neglect; create a sense of community for people who experienced injustice. Community members created a petition calling on the government to build trust in the public inquiry; they achieved their goals with the participation of people from wider communities. We found that reaching out to allies and building shared social identity among supporters were two main ways to achieve campaign goals. The study suggests ways that ‘disaster communities’ can be sustained after the disaster to form the basis of effective collective action. Specifically, shared social identity is a key mechanism for communities who are affected by a disaster, enabling them to act as one when they seek justice as well as overcome the effects of disaster.

Background In the absence of a vaccine, behaviour by the public is key to the response to the COVID-19 pandemic. Yet, as with other types of crises and emergencies, there have been doubts about the extent to which the public are able to engage effectively with the required behaviour. These doubts are based on outdated models of group psychology. Aims and argument We analyse the role of group processes in the COVID-19 pandemic in three domains: recognition of threat, adherence by the public to the required public health behaviours (and the factors that increase such adherence) and actions of the many community mutual aid groups that arose during lockdown. In each case, we draw upon the accumulated research on behaviour in emergencies and disasters, as well as the latest findings in relation to the COVID-19 pandemic, to show that explanations in terms of social identity processes make better sense of the patterns of evidence than alternative explanations. Conclusions If behaviour in the pandemic is a function of mutable group processes rather than fixed tendencies, then behavioural change is possible. There was evidence of significant change in behaviour from the public, particularly in the early days of the pandemic. Understanding the role of group processes means we can help design more effective interventions to support collective resilience in the public in the face of the pandemic and other threats. We draw out from the evidence a set of recommendations on facilitating the public response to COVID-19 by harnessing group processes.

Accumulated evidence demonstrates the centrality of social psychology to the behavior of members of the public as immediate responders in emergencies. Such public behavior is a function of social psychological processes—in particular identities and norms. In addition, what the authorities and relevant professional groups assume about the social psychology of people in emergencies shapes policy and practice in preparedness, response, and recovery. These assumptions therefore have consequences for the public's ability to act as immediate responders. In this Policy and Practice Review, we will do three things. First, we will overview research on the behavior of survivors of emergencies and disasters, drawing out key factors known to explain the extent to which survivors cooperate in these events and contribute to safe collective outcomes. We will demonstrate the utility of the social identity approach as an overarching framework for explaining the major mechanisms of collective supportive behavior among survivors in emergencies. Second, we will critically review recent and current UK government agency guidance on emergency response, focusing particularly on what is stated about the role of survivors in emergencies and disasters. This review will suggest that the “community resilience” agenda has only been partly realized in practice, but that the social identity approach is progressing this. Third, we will derive from the research literature and from dialogue with groups involved in emergencies a set of 12 recommendations for both emergency managers and members of the public affected by emergencies and disasters. These focus on the crucial need to build shared identity and to communicate, and the connection between these two aims. Including our recommendations within emergency guidance and training will facilitate collective psychosocial resilience, which refers to the way a shared identity allows groups of survivors to express and expect solidarity and cohesion, and thereby to coordinate and draw upon collective sources of support. In sum, this evidence-base and the recommendations we derive from it will help professionals involved in emergency management to support public resilient behaviors and will help the public to develop and maintain their own capacity for such resilience.

This study aims to forecast the movement direction of Istanbul Stock Exchange National 100 Index (ISE-100) using Support Vector Machines (SVM). SVMs' classification performance was compared with Logistic Regression (LR), the other method used in this study, in order to forecast the movement direction of ISE-100 Index. Technical indicators that are among the devices useful for technical analysis in stock prediction were used. These indicators included in models were analysed with LR analysis and then, significant ones were used as independent variables. The analysis includes the data from 03.04.1995 to 19.03.2012. 4226 data were established as daily, weekly and monthly data sets. 4 models were built for each dataset and index movement direction forecasting performance of these methods was evaluated by applying different criteria for each model. The results of this study show that SVMs estimate the movement of ISE-100 Index best with weekly Model 1 (70.0%) among 12 models. Additionally, it is observed that this model has a high level of estimation and the closest increase (82.89%) and decrease (54.68%) direction of ISE 100 Index.