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This paper describes an in-class activity to introduce random assignment, paired data, and learning effect. The activity requires minimal materials, can be completed in a single class period, and is suitable for those using technology to conduct data exploration but can also be adapted for use in a technology-free classroom. The activity consists of students filling in two lines of circles, one using their dominant and the other using their nondominant hand. The difference in time to fill in the circles is compared. Results typically show a significant difference in time to fill in circles with the nondominant hand taking significantly longer, a significant learning effect, and a difference in time to fill in circles using a pencil compared to pen.

This systematic review presents the first synthesis of the scientific literature on estimating school and teacher/class effects on student academic performance using random-effects (RE) models with three or more levels. The review delves into the theoretical framework underpinning the estimation of educational effects, the associated statistical methods, and the empirical findings from multilevel analyses to date. It includes 94 analyses in 32 papers from diverse geographical regions, focusing on both primary and secondary education levels. The findings reveal a broad spectrum of Variance Partition Coefficients (VPCs) from RE models, highlighting the variability in the impact of school and teacher/class levels on student outcomes. The review emphasizes the necessity for comprehensive and diverse datasets, particularly the inclusion of detailed teacher-level data in African studies and the utilization of robust official educational databases in Chinese research. It also acknowledges the contributions of Latin American countries in advancing multilevel modeling and the holistic approach to educational analysis demonstrated in Cyprus. The conclusions underscore the importance of contextualizing educational policies and practices based on the multifaceted influences on student performance. The review calls for future research to expand data collection efforts, adopt standardized reporting practices, and explore advanced statistical techniques to address methodological challenges and approximate causal relationships more accurately.

Starting with the question of why there is so much religiously motivated resistance against compulsory sex education, this article explores and discusses entanglements of norms about sexuality, gender, and religion in education. Based on predominantly Swedish data, the aim of the paper is to offer perspectives on connections between religiosity and discourses about sexuality. The first part centres around religious resistance against compulsory sex education. The second part draws on phenomenology of religions to explore roots for this religiously motivated resistance. In the third part, the perspective is reversed, discussing how and why secular education dismisses religious perspectives and values. The conclusion is that awareness of how arguments are based on differing presumptions about religion offers opportunities for dialogue, and that considering human existence as vulnerable, interdependent, and responsible might be a valuable religious contribution to sex education.

Measurement-based care (MBC) research and practice, including clinical workflows and systems to support MBC, are grounded in adult-serving mental health systems. MBC research evidence is building in child and adolescent services, but MBC practice is inherently more complex due to identified client age, the family system and the need to involve multiple reporters. This paper seeks to address a gap in the literature by providing practical guidance for youth-serving clinicians implementing MBC with children and their families. We focus on MBC as a data-informed, client-centered communication process, and present three key strategies to enhance usual care child and adolescent psychotherapy via developmentally-appropriate MBC. These strategies include (1) go beyond standardized measures; (2) lean into discrepancies; and (3) get curious together. Case-based examples drawn from various child-serving settings illustrate these key strategies of MBC in child psychotherapy.

Capital markets companies are in the midst of a series of challenges that they cannot solve with revenue growth. This has increased the impetus for companies to look inwards and think about efficiencies and cost savings. The operating model that serves capital markets — and the legacy technology that powers it — is struggling to keep up, but change has historically been fraught with risk and the chance of failure looms large. This paper explores the need for a 'next-generation' operating model, the transformation pitfalls companies need to avoid when delivering one, the technology that enables it, and why people and data must be at the heart of any change. [ABSTRACT FROM AUTHOR]

From July 2019 to January 2021, six models of PM2.5 air sensors were operated at seven air quality monitoring sites across the U.S. in Arizona, Colorado, Delaware, Georgia, North Carolina, Oklahoma, and Wisconsin. Common PM sensor data issues were identified, including repeat zero measurements, false high outliers, baseline shift, varied relationships between the sensor and monitor, and relative humidity (RH) influences. While these issues are often easy to identify during colocation, they are more challenging to identify or correct during deployment since it is hard to differentiate between real pollution events and sensor malfunctions. Air sensors may exhibit wildly different performances even if they have the same or similar internal components. Commonly used RH corrections may still have variable bias by hour of the day and seasonally. Most sensors show promise in achieving the U.S. Environmental Protection Agency (EPA) performance targets, and the findings here can be used to improve their performance and reliability further. This evaluation generated a robust dataset of colocated air sensor and monitor data, and by making it publicly available along with the results presented in this paper, we hope the dataset will be an asset to the air sensor community in understanding sensor performance and validating new methods. [ABSTRACT FROM AUTHOR]

Flipped learning reverses traditional direct instruction by having students complete homework before in-person classes. When supported by technology and data, flipped learning becomes a variant of blended learning, where blended learning refers to the integration of online and in-person learning experiences. This paper highlights the sector-wide shift to flipped learning in Singapore's Polytechnics. The intent of the shift is to provide opportunities to improve self-directed learning, a critical skill set for the workforce. The authors consider seven presentations of practitioners' early studies of flipped learning in action, all of which were delivered at the e-Learning Forum Asia 2023 conference. Key themes and takeaways from the seven presentations are considered, including the use of data and learning analytics to improve tailored support for self-directed learning, the value of learning-design models, examples of designing for social interaction during online asynchronous learning, to improve students' confidence in learning and operational challenges such as the need for more time to implement quality flipped learning. [ABSTRACT FROM AUTHOR]

This paper introduces new, interdisciplinary perspectives for understanding and analysing what diversity data is and works to do. Combining critical data studies with research on diversity and inclusion in the UK television industry, we explore the case of sexual orientation data. We discuss the ambiguous relationship between diversity data and its use to remedy structural inequity and exclusion. We show that diversity data brings into view a narrow selection of queer workers’ lives and experiences while further marginalising others, and that it is generative and productive rather than merely representative of the world around us. In so doing we provide novel and transferable insights for scholars and practitioners to improve both our understanding of diversity data and its practical use in workplace interventions. [ABSTRACT FROM AUTHOR]

Objective: This study qualitatively examined road traffic crash (RTC) data collection and management in southwest Nigeria, with the goal of addressing the tenets of the UN Decade of Action Plan on Road Safety's call for an effective data system. Methods: Data on RTCs data collection in the study area was obtained through key informant interviews with five hospital accident emergency unit directors, three Federal Road Safety Commission (FRSC) Sector commanders, and three Nigeria Police Force (NPF) commissioners. Datasets were transcribed, categorized, and interpreted using content analysis, descriptive statistics, and WHO recommended minimal crash data element requirements. Results: The study found disparate systems, a lack of synergy, and discrepancy in the various data sources in the country when compared with WHO recommendations on road traffic data collection systems, with 55% of agencies using non-standardized paper forms. The study also reveals that the minimum crash data elements recommended by WHO are not consistently captured in the NPF, FRSC, and hospital traffic crash data sources. Conclusions: The study suggested the use of an upgraded National Road Traffic Crash Data Management System (NRTCDMS) Data Template to unify data gathering and linkage issues, but recommends a digital version of the template. [ABSTRACT FROM AUTHOR]

Digital immortality, the idea of endless life and ultimate happiness in a virtual afterlife, has become a subject of human fascination. This article reports the results of a comprehensive research project focused on identifying the challenges and potential options related to digital immortality. Analyzing 39 relevant studies, our research concentrates on two main themes: the barriers to achieve the digital immortality and the tools created to preserve digital memories. Our findings reveal that the challenges associated with digital immortality are deeply rooted in legal, ethical, and social issues. Importantly, our focus is the challenges related to digital content left by the deceased, its collection method, and integrity in digital immortality research, as content forms the basis for achieving this objective. Furthermore, the research highlights the need for more advanced technology, as the number of studies is limited and current progress is primarily future-oriented. However, our analysis demonstrates that the digital content left by the deceased is paramount, as it constitutes the raw material for achieving digital immortality. [ABSTRACT FROM AUTHOR]

Data security has become paramount, especially with the exponential growth of data, the rise of cyber threats, and the increasing prevalence of remote work. Data confidentiality, integrity, and availability are crucial, particularly in sensitive domains like healthcare and cloud computing. While robust access control (AC) is essential, it must be balanced with maintaining operational efficiency. In access control models, there is a general lack of a comprehensive cross-domain overview, insufficient focus on evolving access control requirements, and inadequate analysis of challenges and issues. This paper comprehensively analyzes access control models (ACMs) through a two-pronged approach. First, we conduct a Narrative Literature Review(NLR) to classify traditional ACMs, evaluating their security strengths and weaknesses and compatible security protocols. Second, we perform a Systematic Literature Review (SLR) of emerging ACMs developed over the past decade across various domains. This systematic review has three primary objectives: (1) to introduce and analyze these emerging ACMs, (2) to present their current technological status, and (3) to identify key challenges and promising research directions. By combining these approaches, this paper offers a comprehensive overviewofACMapplications and techniques, identifies existing challenges, and explores future research directions to guide advancements in access control.

Despite its growing success, open banking (OB) struggles to present a coherent identity. Indeed, despite its widespread adoption around the world, various models can be identified based on rationales, the nature of data-sharing obligations, and the standardisation process. Against this background, the paper aims to evaluate the consistency of OB policies. To this end, our analysis adopts a novel approach by examining the primary rationales behind OB regulatory initiatives in some major countries (i.e., the EU, UK, Australia, the US, India, and Singapore). Identifying these rationales is crucial for assessing whether the specific features of OB solutions implemented in each country are aligned with the intended policy goals. Therefore, the paper first identifies the primary rationales supporting OB initiatives in these countries and then examines their data-sharing and standardisation approaches. By mapping the primary rationales and models in terms of data-sharing obligations and standardisation solutions, the comparative analysis shows that variations in models and approaches among the examined jurisdictions do not necessarily reflect differences in the policy goals pursued through the OB regime. As a result, by recommending regulatory and technical solutions that better align with the intended policy goals of an OB regime, such a comparative analysis can assist policymakers in countries considering the introduction of open banking to design a model that best suits their needs.

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have been advanced. Implementation of these frameworks require multi-modal data and a shift towards open science and principles such as requiring data to be FAIR (Findable, Accessible, Interoperable and Reusable). Advanced analytics such as artificial intelligence require data to be interoperable so data can be exchanged among different technology systems and software applications. The TBI field has multiple ongoing initiatives to promote sharing and data reuse for both pre-clinical and clinical studies, which is an opportunity for the SCI field given these injuries can often occur concomitantly. The adoption of interoperable standards, data sharing, open science, and the use of advanced analytics in SCI is needed to facilitate translation in research and care. It is critical that people with lived experience are engaged to ensure data are relevant and enhances quality of life. • Data elements and standards are available for spinal cord injury (SCI). • Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have been proposed. • Opportunities exist to learn from the traumatic brain injury field which has experience with pre-clinical and clinical data standards, and data sharing. • Engagement of people living with SCI is critical to ensure data collected is meaningful and improves quality of life. • The adoption of interoperable standards, data sharing, open science and the use of advanced analytics will facilitate translation in SCI research and care. [ABSTRACT FROM AUTHOR]

• A method is presented to assess certainty in fault data and interpretations. • It is based on the matrix commonly used in risk assessment. • The method justifiable interpretations and decisions about faults to be made. This paper introduces an approach for expressing certainty in the analysis and interpretation of faults, using a modification of the risk matrix commonly used in risk assessment. The certainty matrix uses qualitative or semi-quantitative analyses of both the data used and an interpreted characteristic of individual faults or fault systems. These characteristics may include the existence of faults, the certainty of trace lengths, the age of faults, or the influence of faults on sub-surface fluid flow. This approach improves the ability to make justifiable interpretations and decisions about faults and fault-affected areas, including about issues relevant to geothermal energy exploration or the underground storage of radioactive waste. The use of this approach is illustrated using three faults from Somerset, UK. [ABSTRACT FROM AUTHOR]

In the arena of renewable energy integration for electric vehicle (EV) infrastructure, it is a problem to efficiently use solar power with EV charging, considering grid constraints and user preferences. The current study proposes a new smart charging algorithm that utilizes IoT data for the dynamic optimization of EV charging patterns. A novel aspect of the study was a deep learning model, "EVGridNet", that reliably predicts solar energy output and grid prices. EVGridNet uses deep learning approaches to process accumulated data via IoT devices, facilitating fine-tuned adjustments to charging patterns through predictive analytics. This algorithm receives actual data on the generation of solar energy, the price of grid electricity, and other characteristics set by the user to optimize the usage of solar energy, limit the usage of grid electricity during the peak hours and meet all the needs of the user. The optimization process of the algorithm strategically manages energy sources, uses battery storage systems to exploit solar power effectively and uses grid electricity during low-cost periods, all within user preference parameters. The proposed system has the potential of reducing grid electric use by up to 25% for EV charging, and increasing the renewable energy electricity in EV charging to 40% as per simulation results. This will enable quick EV infrastructure scalability, low carbon emission, and energy independence. EVGridNet is an outstanding innovation in smart charging technology, which is a cost-effective and scalable solution to the renewable energy sector's primary challenge. One of the key aspects of the optimization process is the control of energy sources where battery storage systems allow for flexibility in using solar energy and grid electricity within certain pre-set thresholds. [ABSTRACT FROM AUTHOR]

Parliamentary Monitoring Organisations [PMOs] seek to make legislators more accountable and reduce the potential moral hazards of delegation. This study of the UK finds that PMOs do reduce such hazards and make for greater 'informatory' accountability from MPs and Peers. The primary effect is on the House of Commons, and on individual MPs, where information seeking is most frequently local. The exact impact is variable, and depends on whether a Member is in government or opposition, how long they have served and, more subtly, whether they are male or female. The impact of PMOs is uneven, and can provoke resistance and gaming or fuel political conflict. [ABSTRACT FROM AUTHOR]

Background: The study aimed to understand the acceptability, satisfaction, uptake, utility and feasibility of a quality improvement (QI) intervention to improve care for coronary heart disease (CHD) patients in Australian primary care practices and identify barriers and enablers, including the impact of COVID-19. Methods: Within the QUality improvement for Effectiveness of care for people Living with heart disease (QUEL) study, 26 Australian primary care practices, supported by five Primary Health Networks (PHN) participated in a 1-year QI intervention (November 2019 – November 2020). Data were collected from practices and PHNs staff via surveys and semi-structured interviews. Quantitative and qualitative data were analysed with descriptive statistics and thematic analysis, respectively. Results: Feedback was received from 64 participants, including practice team members and PHN staff. Surveys were completed after each of six workshops and at the end of the study. Interviews were conducted with a subgroup of participants (n = 9). Participants reported positive satisfaction with individual QI features such as learning workshops and monthly feedback reports. Overall, the intervention was well-received, with most participants expressing interest in participating in similar programs in the future. COVID-19 and lack of time were identified as common barriers, whereas team collaboration and effective leadership enabled practices' participation in the QI program. Additionally, 90% of the practices reported COVID-19 effected their participation due to vaccination rollout, telehealth set-up, and continuous operational review shifting their focus from QI. Conclusion: Data-driven QI programs in primary care can boost practice staff confidence and foster increased implementation. Barriers and enablers identified can also support other practices in prioritising effective strategies for future implementation. The use of data-driven quality improvement (QI) activities is increasingly being used in healthcare globally. This study delves into the implementation and impact of a year-long QI intervention, shedding light on barriers and enablers in improving care for coronary heart disease patients. These findings emphasise the need for collaborative approaches and highlight the potential of technology-driven solutions in enhancing patient outcomes and shaping the future of healthcare improvement initiatives. [ABSTRACT FROM AUTHOR]

As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like "system" and "energy" are among the most frequent, signaling that Topic 1 revolves around data systems and IoT technologies, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the application of technologies, as indicated by terms such as "technologies", "industry" and "research". It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on machine learning and IoT applications. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on smart technologies and systems. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on digital transformation. Topic 6 focuses on technical aspects such as modeling, system performance and prediction algorithms. It delves into the efficiency of IoT networks with terms like "accuracy", "power" and "performance" standing out. [ABSTRACT FROM AUTHOR]

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. [ABSTRACT FROM AUTHOR]

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Incorporating findings from 74 meticulously selected studies, this analysis delves into the complex challenge of measuring food waste (FW) in the food service industry (FSI). The objectives are to aggregate findings on FW quantities, scrutinize quantification methods, identify research gaps, and outline key areas for future exploration. The analysis uncovers an average FW of 167.2 g (min 4.7 g, max 1100 g, standard deviation 202) per meal (26.1%), with studies focusing on plate waste indicating an average of 125.0 g (min 4.7 g, max 292 g, standard deviation 97.3) per meal (29.0%). Variability stems from FW quantification from diverse terminology, inconsistent methodologies, and contextual factors, revealing a lack of standardized concepts. Geographic and sub-sector differences further contribute to the variation in reported FW quantities, affecting the generalizability and comparability of quantities across different FSI establishments and studies. Notably, the majority of the articles offer case-specific insights, limiting their relevance to broader FW generation estimates at the local, national, or sectoral level. Beyond the need for precise quantification, the study acknowledges that the primary challenge lies in addressing wasteful behavioral drivers and actions. Within this perspective, this study underscores the influential role of sociocultural factors and behavioral tendencies and advocates for the need for a deeper understanding of FW generation to formulate practical, sustainable FW prevention strategies. This study provides essential groundwork for harmonizing quantification techniques and guides policymakers and industry stakeholders for the development of practical policies and targeted measures and interventions to effectively reduce FW in the FSI. [ABSTRACT FROM AUTHOR]

Can Punnett square simulations help demonstrate how probability and chance affect the outcome of a genetic cross? Genetic crosses come in a variety of different styles. One such cross that involves two parents (P1's) being homozygous or heterozygous for a single trait is called a monohybrid cross. The offspring (F1's) or zygotes of such a cross could also exhibit various genotypes and phenotypes depending on what the parents contribute. The monohybrid Punnett square is a graphical means to provide a visual understanding of all the possible outcomes between two parent contributions of a single trait (i.e. P1's = Aa x AA). Nature does not always follow a strict pattern of probability due to environmental changes or genetic imperfections. So, gathering a significantly large amount of data while avoiding anything that may influence that data is important in research. [ABSTRACT FROM AUTHOR]