Search

Abstract Drawing upon seven years of police calls for service data (2014–2020), this study examined the effect of the COVID-19 pandemic on calls involving persons with perceived mental illness (PwPMI) using a Bayesian Structural Time Series. The findings revealed that PwPMI calls did not increase immediately after the beginning of the pandemic in March 2020. Instead, a sustained increase in PwPMI calls was identified in August 2020 that later became statistically significant in October 2020. Ultimately, the analysis revealed a 22% increase in PwPMI calls during the COVID-19 pandemic than would have been expected had the pandemic not taken place. The delayed effect of the pandemic on such calls points to a need for policymakers to prioritize widely accessible mental health care that can be deployed early during public health emergencies thus potentially mitigating or eliminating the need for increased police intervention, as was the case here.

Since the pandemic began, online teaching methods have received increased interest and review. Educators rapidly developed delivery techniquesto meet the needs of students forced to become remote learners. Some of these methods were successful; others did not meet program objectives, especially in educator preparation programs (EPPs). The speed of changing to an online environment limited the ability of educators to research and adapt known evidenced-based practices. This systematic literature review provides research and insight into student-focused outcomes related to effective online teaching practices. Over 600 articles, across multiple disciplines were reviewed based on research and results using different techniques, pedagogy, and design in the online environment. The five main strategies, methods, or philosophies that most affected student outcomes in the online community were engagement, collaboration, beliefs, knowledge, and enactment of knowledge/practice. Each topic is discussed in depth and recommendations are made for effective online teaching and also specific applications to EPPs. [ABSTRACT FROM AUTHOR]

The origins of this report, and of the Mental Health and Policing Working Group, can be traced to the unique situation Canadians have faced as a result of the COVID-19 pandemic. The unique circumstances of this global outbreak, which have for many Canadians resulted in serious illness and death, intensified economic uncertainties, altered family and lifestyle dynamics, and generated or exacerbated feelings of loneliness and social dislocation, rightly led the Royal Society of Canada’s COVID-19 Taskforce to consider the strains and other negative impacts on individual, group, and community mental health. With the central role that police too often play in the lives of individuals in mental and (or) emotional crisis, we were tasked with exploring what can be reasonably said about the state of our current knowledge of police responses to persons with mental illness.

The aim of this article is to analyze the pace of expansion of the network of highways and expressways over the past 15 years of membership in the European Union. Joining the European Union by such post-socialist countries as: Poland, Lithuania, Latvia, Estonia, the Czech Republic, Slovakia, Hungary, Slovenia (2004), Bulgaria, Romania (2007) and Croatia (2013) as well as the funds received for the development of transport infrastructure significantly contributed to the boom in the pace of road investments in these countries. The existing networks of highways and expressways in the 11 analyzed countries are at the stage of creating major national and international connections. The most advanced expansion of the national highway system is taking place in Poland and Hungary. Stable development, with a slight annual growth of the length of new highways, is taking place in Bulgaria, Croatia, the Czech Republic, Romania, Slovakia and Slovenia. Stagnation in the development of roads of this type is occurring in Lithuania, Latvia and Estonia. Toll roads are operated in 8 out of 11 analyzed countries. The vignette system for highways is used in Bulgaria, the Czech Republic, Slovakia, Slovenia, Romania and Hungary. Toll is paid at tollbooths in Croatia and Poland. Most of the highways in Central Europe are concentrated around the capital agglomerations, e.g. Budapest, Prague, Bratislava, Ljubljana, Zagreb, Sofia and Bucharest, with the exception of Warsaw, which is slowly rebuilding its central position in the network of expressways (A-2, S-7, S-8, S-17) in Poland. The first sections of highways in the discussed countries, except for Poland, were located around the capital cities. The layout of the highway network is often determined by the orography of the terrain, especially the course of mountain ranges, great rivers and coasts. Due to the varied topography, costs are rising, and the construction time of engineering structures such as tunnels, overpasses and bridges is longer, with countries such as Croatia, Slovenia, Slovakia, and recently also the Czech Republic, Poland and Romania being forced to build them.

Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.

Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in most real datasets, affecting the performance of classification algorithms. Particularly, high levels of imbalance make serious difficulties, often requiring the use of specially designed methods. In such cases the most important issue is often to properly detect minority examples, but at the same time the performance on the majority class cannot be neglected. In this paper we describe a novel resampling technique focused on proper detection of minority examples in a two-class imbalanced data task. The proposed method combines cleaning the decision border around minority objects with guided synthetic oversampling. Results of the conducted experimental study indicate that the proposed algorithm usually outperforms the conventional oversampling approaches, especially when the detection of minority examples is considered.