Towards Greater Effectiveness and Accountability in Impact Investing

Impact investing – investing for social and environmental returns alongside financial returns – is a growing phenomenon in financial markets. However, concerns exist regarding the demand for robust impact evidence and accountability for impact claims when compared to a public sector aid model. Drawing from social network analysis on Twitter, preliminary findings indicate the scope and scale of influential actors within this investing network. They reveal a need for asset owners, fund managers and other intermediaries to foster greater collaboration; facilitate greater thought-leadership on evidence and impact measurement and to address the power asymmetries between investors, investees and the global South

Sub-pixel point detection algorithm for point tracking with low-power wearable camera systems: a simplified linear interpolation

With the continuous developments in vision sensor technology, highly miniaturized low-power and wearable vision sensing is becoming a reality. Several wearable vision applications exist which involve point tracking. The ability to efficiently detect points at a sub-pixel level can be beneficial, as the accuracy of point detection is no longer limited to the resolution of the vision sensor. In this work, we propose a novel Simplified Linear Interpolation (SLI) algorithm that achieves high computational efficiency, which outperforms existing algorithms in terms of the accuracy under certain conditions. We present the principles underlying our algorithm and evaluate it in a series of test scenarios. Its performance is finally compared to similar algorithms currently available in the literature

Learning strategies, study habits and social networking activity of undergraduate medical students

Objectives: To determine learning strategies, study habits, and online social networking use of undergraduates at an Irish medical school, and their relationship with academic performance. Methods: A cross-sectional study was conducted in Year 2 and final year undergraduate-entry and graduate-entry students at an Irish medical school. Data about participants’ demographics and educational background, study habits (including time management), and use of online media was collected using a self-report questionnaire. Participants’ learning strategies were measured using the 18-item Approaches to Learning and Studying Inventory (ALSI). Year score percentage was the measure of academic achievement. The association between demographic/educational factors, learning strategies, study habits, and academic achievement was statistically analysed using regression analysis. Results: Forty-two percent of students were included in this analysis (n=376). A last-minute “cramming” time management study strategy was associated with increased use of online social networks. Learning strategies differed between undergraduate- and graduate-entrants, with the latter less likely to adopt a ‘surface approach’ and more likely adopt a ‘study monitoring’ approach. Year score percentage was positively correlated with the ‘effort management/organised studying’ learning style. Poorer academic performance was associated with a poor time management approach to studying (“cramming”) and increased use of the ‘surface learning’ strategy. Conclusions: Our study demonstrates that effort management and organised studying should be promoted, and surface learning discouraged, as part of any effort to optimise academic performance in medical school. Excessive use of social networking contributes to poor study habits, which are associated with reduced academic achievemen

3D ranging and tracking using lensless smart sensors

Target tracking has a wide range of applications in Internet of Things (IoT), such as smart city sensors, indoor tracking, and gesture recognition. Several studies have been conducted in this area. Most of the published works either use vision sensors or inertial sensors for motion analysis and gesture recognition [1, 2]. Recent works use a combination of depth sensors and inertial sensors for 3D ranging and tracking [3, 4]. This often requires complex hardware and the use of complex embedded algorithms. Stereo cameras or Kinect depth sensors used for high precision ranging are instead expensive and not easy to use. The aim of this work is to track in 3D a hand fitted with a series of precisely positioned IR LEDs using a novel Lensless Smart Sensor (LSS) developed by Rambus, Inc. [5, 6]. In the adopted device, the lens used in conventional cameras is replaced by low-cost ultra-miniaturized diffraction optics attached directly to the image sensor array. The unique diffraction pattern enables more precise position tracking than possible with a lens by capturing more information about the scene

Reference point estimation technique for direct validation of subpixel point detection algorithms for Internet of Things

Subpixel point detection algorithms are important in many application spaces, especially those where limitations of the imaging device's resolution need to be overcome. Such algorithms help decrease the overall requirements of the given system. Many factors, such as power consumption and cost, are critical in the context of the Internet of Things. While these algorithms do offer an improvement in the precision of point detection, it is often difficult to directly determine their precision. The main reason for it is the lack of the point of reference that the outputs of subpixel point detection methods can be compared to. In this work, we present a novel method for finding the point of reference for validating the subpixel point detection algorithms directly. Its operation is demonstrated on an experimentally obtained sample dataset

Multimodal sensor fusion for low-power wearable human motion tracking systems in sports applications

This paper presents a prototype human motion tracking system for wearable sports applications. It can be particularly applicable for tracking human motion during executing certain strength training exercises, such as the barbell squat, where an inappropriate technique could result in an injury. The key novelty of the proposed system is twofold. Firstly, it is an inside-out, multimodal, motion tracker that incorporates two complementary sensor modalities, i.e. a camera and an inertial motion sensor, as well as two externally-mounted points of reference. Secondly, it incorporates a novel multimodal sensor fusion algorithm which uses the complementary nature of vision and inertial sensor modalities to perform a computationally efficient 3-Dimensional (3-D) pose detection of the wearable device. The 3-D pose is determined by fusing information about the two external reference points captured by the camera together with the orientation angles captured by the inertial motion sensor. The accuracy of the prototype was experimentally validated in laboratory conditions. The main findings are as follows. The Root Mean Square Error (RMSE) in 3-D position calculation was 36.7 mm and 13.6 mm in the static and mobile cases, respectively. Whereas the static case was aimed at determining the system’s performance at all 3-D poses within the work envelope, the mobile case was used to determine the error in tracking human motion that is involved in the barbell squat, i.e. a mainly repeated vertical motion pattern

Low cost embedded multimodal opto-inertial human motion tracking system

Human motion tracking systems are widely used in various application spaces, such as motion capture, rehabilitation, or sports. There exists a number of such systems in the State-Of-The-Art (SOA) that vary in price, complexity, accuracy and the target applications. With the continued advances in system integration and miniaturization, wearable motion trackers gain in popularity in the research community. The opto-inertial trackers with multimodal sensor fusion algorithms are some of the common approaches found in SOA. However, these trackers tend to be expensive and have high computational requirements. In this work, we present a prototype version of our opto-inertial, motion tracking system that offers a low-cost alternative. The 3D position and orientation are determined by fusing optical and inertial sensor data together with knowledge about two external reference points using a purpose-designed data fusion algorithm. An experimental validation was carried out on one of the use cases that this system is intended for, i.e. barbell squat in strength training. The results showed that the total RMSE in position and orientation was 32.8 mm and 0.89 degree, respectively. It operated in real-time at 20 frames per second

What aspects of periods are most bothersome for women reporting heavy menstrual bleeding? Community survey and qualitative study

Background: Heavy menstrual bleeding is a common symptom amongst women of reproductive age, yet questions remain about why some women experience this as a problem while others do not. We investigated the concerns of women who reported heavy menstrual bleeding on questionnaire. Methods: A cross-sectional postal survey and qualitative interviews were carried out amongst a community-based sample of women in Lothian, Scotland. 906 women aged 25 to 44 reported heavy or very heavy periods in response to a postal survey of 2833 women registered with 19 general practices. Amongst those who had reported heavy menstrual bleeding, analysis was carried out of responses to the free text questionnaire item, "What bothers you most about your periods?" In addition, 32 of these women participated in qualitative interviews and their accounts were analysed to explore how menstrual symptoms and 'problems' with periods were experienced. Results: Even amongst this subgroup of women, selected on the basis of having reported their periods as heavy in the survey, pain was the aspect of their periods that 'most bothered' them, followed by heaviness, mood changes or tiredness, and irregularity or other issues of timing. Interviewees' accounts similarly suggested that a range of menstrual symptoms were problematic and some women did not disentangle which was worst. Judgements of periods as a problem were based on the impact of menstrual symptoms on daily life and this was contingent on social circumstances such as type of paid work and other responsibilities. Although women spoke readily of whether their periods were a problem, there was less clarity in accounts of whether or not menstrual loss was 'heavy'; women said they made judgements based on what was normal for them, degree of difficulty in containing blood loss and pattern of loss. Conclusion: Women with heavy periods are bothered by a range of menstrual symptoms and their impact on everyday life. Clinical emphasis should be on clarifying the presenting problem and providing help and advice for this, as well as on excluding serious disease. Sometimes simple approaches, such as help with analgesia, may be all that is required