Relevant Research Questions Research Articles

Why Can’t My Child Play Too? Current Challenges of Public Playgrounds for Children with Disabilities.

Current Australian legislation promotes playground inclusion for all children, and playgrounds serve as local, safe, and vital spaces for children of all ages to play. The World Health Organisation International Classification of Functioning, Disability and Health, Children and Youth Version states that play is a key activity to engage children with disability in various areas of their lives, and there is no doubt that playgrounds can promote community and social engagement for all children. Consequently, accessible playgrounds are important because they can offer motor, psychological, and social skill development in a fun-filled environment. Nonetheless, some children encounter challenges in playground settings. Surprisingly, very little research has been conducted in Australia on the experiences of children with disabilities in public playgrounds. According to the National Autism Strategy announced by the Australian Government in 2022, there is a growing number of Australians on the autism spectrum. For many individuals with an autistic spectrum disorder (ASD), life outcomes in education, health, and family functioning are worse than they should be. Consequently, while this paper addresses a general perspective of inclusive playground experience for children with disabilities, there is also a focus on the experiences of children with ASD in playgrounds. As a result, this literature review begins with an introduction to the prevalence of autistic spectrum disorder and its associated characteristics. It presents existing research on play, examining various playground factors that impact the experiences of children with disabilities, including ASD. In addition, the role of parents/carers in influencing the outdoor experiences of children with disability is also explored. In conclusion, this paper summarises key findings and proposes relevant research questions to address these gaps.

Exploring retinal degenerative diseases through CRISPR-based screening.

The CRISPR (Clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein9) system has emerged as a powerful genetic tool, gaining global recognition as a versatile and efficient gene-editing technique. Its transformation into a high-throughput research platform, CRISPR Screening, has demonstrated wide applicability across various fields such as cancer biology, virology, and drug target discovery, resulting in significant advances. However, its potential in studying retinal degenerative diseases remains largely unexplored, despite the urgent need for effective treatments arising from an incomplete understanding of disease mechanisms. This review aims to present a comprehensive overview of the evolution and current state of CRISPR tools and CRISPR screening methodologies. Noteworthy pioneering studies utilizing these technologies are discussed, alongside experimental design guidelines, including positive and negative selection strategies and delivery methods for sgRNAs (single guide RNAs) and Cas proteins. Furthermore, we explore existing in vitro models appropriate for CRISPR screening in retinal research and identify relevant research questions that could be addressed through this approach. It is anticipated that this review will stimulate innovation in retinal research, facilitating a deeper comprehension of retinal pathophysiology and paving the way for groundbreaking therapeutic interventions and enhanced patient outcomes in the management of retinal degenerative disorders.

Research Advancement in Forest Property Rights: A Thematic Review over Half a Decade Using Natural Language Processing

This paper proposes a thematic literature review of advances in the literature on forest property rights over the first half of this decade. From a methodological point of view, we exploited a corpus of scientific articles published between 2019 and 2023, extracted from the Scopus and Web of Science databases. We then performed a co-word analysis using the Louvain algorithm to reveal thematic clusters. This approach enabled us to identify seven distinct trends over the past half-decade, each highlighting specific aspects and geographical diversity of forest property rights, which were then evaluated using a thematic map. The main findings revealed that (policies, livelihoods and agroforestry) and (institutions, sustainability and non-timber forest products) stand out as central and influential themes, which have a significant impact and present notable advances that have shaped recent discussions and practices. Furthermore, themes (forest policy and resource management) and (conservation and the rights of indigenous communities) are well developed, which have a solid research base, offering an in-depth understanding of the relevant research questions. Conversely, themes (institutional change and forest conservation) and (deforestation, indigenous rights and community management) represent emerging themes, which are receiving increasing attention and are likely to become major of focus. In contrast, (forest governance, community management and land conflict resolution) which indicates a waning influence in terms of miscellaneous implications. The findings of this thematic literature review will help guide policymakers, researchers, and practitioners based on recent developments in international forest property rights, during a half-decade marked by a break with previous trends, leading to legislative changes and strategic adaptations essential for the sustainable management of forest resources, bolstered by technological advancements and environmental efforts.

Abstract C011: The Critical Role of Developmental Evaluation in Supporting and Enhancing an Equitable Transdisciplinary Approach to Address Cancer Disparities

Abstract Background: Complex problems such as cancer disparities require deeper integration of social and scientific problem-solving and a broader systems approach. Given the nature of disparities, it also demands that we not just seek equitable outcomes but collaborate in ways that reflect equity in the process. To address breast and lung cancer disparities, we developed a unique transdisciplinary approach that creates teams inclusive of community, clinicians, and researchers and builds capacity to understand at a deep level the factors that underpin disparities and design and implement newer sustainable solutions. Methods: This study focuses on a phase I planning period for a transdisciplinary process with five teams locally embedded where lung and breast cancer disparities are significant. A leadership team that included primary investigators from academic medicine and public health supported community-based facilitators and evaluators responsible for guiding local teams. Deep equity, considerations on the biology to policy spectrum, and systems thinking are grounding principles in our approach. At the end of phase I, an evaluator facilitated After-Action Reviews (AAR) to assess effectiveness in maintaining fidelity to the framework and to identify successful practices and areas for improvement. These data informed changes to our approach to implementation to further root our commitment to equity during implementation. Seven AAR sessions were conducted: individual teams members, facilitators and evaluators, and the leaderships team (n-=50). The evaluator triangulated the data from the different AARs and facilitated a discussion of the integrated findings to the expanded leadership team. Results: The AAR highlighted three primary themes for critical self-reflection including (1) strengthening the understanding and use of our approach, (2) reducing ambiguity while preserving slack (e.g. flexibility with resources) and creativity, and (3) integrating project’s partners intentionally into our wider transdisciplinary network. In examining these themes, we identified several strategies for improving our transdisciplinary approach during implementation and for wider replication of our framework with future integrated science-social problem solving. These areas included improved systems for 360-degree communication, clear expectations of roles, and additional support for transdisciplinary collaboration and application of grounded principles to better self-organize a transdisciplinary system within a wider less resilient system like academic medicine. Discussion: To successfully innovate socially and scientifically within complexities of cancer disparities, we need to push beyond conventional models of research and program intervention. This means incorporating methods across fields and sectors including evaluation. An After-Action Review is an effective evaluation tool for assessing a complex process with numerous contributors to ensure a stronger outcome including more refined and relevant research questions and sustainability program, policy and practice. Citation Format: Tobi Cawthra, Laura Pinsoneault, Jerica Broeckling, Kristen Gardner-Volle, Tim Meister, Kailey Taebel, Jada Proctor, Felicia Fairfield, Kelly Hackett, David Frazer, Staci Young, Melinda Stolley. The Critical Role of Developmental Evaluation in Supporting and Enhancing an Equitable Transdisciplinary Approach to Address Cancer Disparities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C011.

Abstract B163: Researcher, Clinician, and Community Focus Groups to Inform an Understanding of Cancer Disparities in the Transgender and Gender Diverse

Abstract Background: A growing number of Americans identify as transgender or gender diverse (TGD). This population experiences barriers and challenges across to socio-ecological spectrum including healthcare. Simultaneously, there is limited data on cancer in TGD persons. While advances in data-capture provide promising opportunity for future research, a comprehensive understanding of cancer disparities for the TGD community is needed to inform clinically and socially relevant research questions – particularly on cancer prevention, risk, screening, and treatment. Our study aimed to develop a transdisciplinary (TDS) academic-community team to create a shared understanding of factors associated with cancer disparities in the TGD population with aid from discussion sessions and interviews with researchers, clinicians, and community members. Methods: The TDS team engaged in literature reviews and discussion to ground their collective understanding. The team also conducted focus groups and interviews with researchers, clinicians, and community members (N=55) to enrich the team’s understanding of factors associated with cancer disparities. Data were summarized and thematized and groups were compared. Results: Community organizations/members noted systemic issues such as insufficient training of healthcare providers and staff, individual mistrust, and not believing they would live long enough to worry about cancer development. Providers identified personal uncertainty working with TGD patients, knowledge of appropriate recommendations for cancer screening, and individual risk behaviors (i.e. tobacco and alcohol use) as potential causes for cancer disparities. Researchers indicated they had not considered including TGD individuals as a specific subject population, were unsure how to study given lack of mouse models for early-stage research and acknowledged the opportunity for future study inclusion. Further, community members discussed the persistent stress of existing as a TGD person during a time of increasingly aggressive anti-TNB legislation and social attitudes. Healthcare providers and researchers reflected on the behavioral and physiological impact of stress on an individual’s health and cancer risk. Conclusion: The findings from our focus groups elucidated areas of shared understanding between the three different audiences; however, they also highlighted concepts that were not shared which offer opportunities for further collaboration and learning by key stakeholders. Through our TDS and collaborative approach, we will continue deep learning between team members to identify specific research questions to address the root causes of TGD cancer disparities through TDS collaboration and innovative research. Citation Format: Tobi Cawthra, Chandler Cortina, laura Pinsoneault, Michael Munson, Andrew Petroll, Melinda Stolley. Researcher, Clinician, and Community Focus Groups to Inform an Understanding of Cancer Disparities in the Transgender and Gender Diverse [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B163.

Edge Computing in Healthcare: Innovations, Opportunities, and Challenges

Edge computing promising a vision of processing data close to its generation point, reducing latency and bandwidth usage compared with traditional cloud computing architectures, has attracted significant attention lately. The integration of edge computing in modern systems takes advantage of Internet of Things (IoT) devices and can potentially improve the systems’ performance, scalability, privacy, and security with applications in different domains. In the healthcare domain, modern IoT devices can nowadays be used to gather vital parameters and information that can be fed to edge Artificial Intelligence (AI) techniques able to offer precious insights and support to healthcare professionals. However, issues regarding data privacy and security, AI optimization, and computational offloading at the edge pose challenges to the adoption of edge AI. This paper aims to explore the current state of the art of edge AI in healthcare by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and analyzing more than 70 Web of Science articles. We have defined the relevant research questions, clear inclusion and exclusion criteria, and classified the research works in three main directions: privacy and security, AI-based optimization methods, and edge offloading techniques. The findings highlight the many advantages of integrating edge computing in a wide range of healthcare use cases requiring data privacy and security, near real-time decision-making, and efficient communication links, with the potential to transform future healthcare services and eHealth applications. However, further research is needed to enforce new security-preserving methods and for better orchestrating and coordinating the load in distributed and decentralized scenarios.

Promoting Collaborative Scholarship During the COVID-19 Pandemic Through an Innovative COVID-19 Data Explorer and Repository at Yale School of Medicine: Development and Usability Study.

The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

IDcare – a longitudinal register study of pre-pandemic and pandemic health care utilization and diagnostic profiles among people with intellectual disabilities in southern Sweden

The aim of the creation of this cohort was to investigate patterns of health and health care utilisation before and during the COVID-19 pandemic, overall and in relation to specific diagnoses, among people with intellectual disabilities (ID) compared to the general population. People living in Skåne, the southernmost region of Sweden, on 1st of January 2014 with at least one diagnosis of ID (ICD-10 codes F70-F79) or Down syndrome (DS; Q90), or support and/or services according to the LSS act comprised the ID cohort (n = 14 716). People living in the same family and/or household as a person in the ID cohort constituted the ID family cohort (n = 31 688), and those remaining comprised the general population cohort (gPop; n = 1 226 955). Data has been collected for all three cohorts from several national and regional registers. These include registers for health care (2014–2021), deaths (2014–2021), COVID-19-related health care (vaccinations, intensive care, palliative care, 2020–2021). The prevalence of ID was 1.2%. In the ID cohort, 77.9% had at least one measure of support, 5.8% at least one Q90-diagnosis and 63.8% had at least one F7-diagnosis (26.9% mild (F70), 7.4% moderate (F71), 2.8% severe (F72), 1.4% profound (F73), and 25.4% other/unknown (F78/F79)). Compared to the gPop there were more people in the younger age groups in the ID cohort. At this point, no additional collection of data will be carried out. However, there is a possibility to add data from the registers to include years after 2021 or from additional registers. Future publications will explore relevant research questions and report key findings in relation to health among people with ID. Future results will be used to inform policy and practice on people with ID.

Introducing the brief reverse correlation: An improved tool to assess visual representations

AbstractThe reverse correlation is an innovative method to capture visual representations (i.e., classification images, CIs) of social targets. However, this method necessitates many trials to compute high‐quality CIs, which poses important practical and economic challenges. We introduce a new version of the reverse correlation method, namely, the Brief‐RC. By increasing the number of stimuli (i.e., noisy faces) presented at each trial, the Brief‐RC improves the quality of individual (and average) CIs and lowers the overall task length. In two experiments, assessments by external judges confirmed that the new method delivers equally good (Experiment 1) or higher‐quality (Experiment 2) outcomes than the traditional method for the same number of trials, time length and number of stimuli. The informational values of CIs were also compared using a more objective metric (infoVal). Because the Brief‐RC facilitates the production of higher‐quality individual CIs, social psychology researchers may more easily address a series of relevant research questions.

Quantile regression in the field of liver transplantation: A case study-based tutorial.

We present a tutorial on quantile regression, an underutilized yet valuable class of multivariable linear regression models that allow researchers to understand more fully the conditional distribution of response as compared to models based on conditional means. Quantile regression models are flexible, have attractive interpretations, and are implemented in most statistical software packages. Our focus is on intuitive understanding of quantile regression models, particularly as compared with more familiar regression methods such as conditional mean models as estimated using ordinary least squares (OLS). We frame our tutorial through two clinical case studies in the field of liver transplantation: one in the context of estimating recipient financial burden after transplantation and another in estimating intraoperative blood transfusion needs. Our real-world cases demonstrate how quantile regression models give researchers a richer understanding of relationships in the data and provide more nuanced clinical understanding compared to more commonly used linear regression models. We encourage researchers to explore quantile regression as a tool to answer relevant clinical research questions and support their more widespread adoption.

Behavioural differences and similarities between dog breeds: proposing an ecologically valid approach for canine behavioural research.

The behaviour of dogs holds great relevance for not only scientists from fundamental and applied research areas, but also due to the widespread roles of dogs in our societies as companions and working animals; their behaviour is also an important factor in animal and human welfare. A large proportion of dogs currently under human supervision belong to one of roughly 400 recognised breeds. Dog breeds can be characterised by distinctive, predictable and reproducible features, including some of their behavioural traits. To the scientist, the comparative analysis of the behaviour of dog breeds provides an opportunity for investigating an array of intriguing phenomena within an easily accessible model organism created from natural and human-driven evolutionary processes. There are many ways to design and conduct breed-related behavioural investigations, but such endeavours should always be based around biologically relevant research questions and lead to ecologically valid conclusions. In this review, we surveyed recent research efforts that included dog behaviour-related comparisons and applied a critical evaluation according to their methods of breed choice and the subsequent research design. Our aim was to assess whether these two fundamentally important components of experimental design provide a solid basis to reach valid conclusions. Based on 97 publications that fulfilled our selection criteria, we identified three primary methods used by researchers to select breeds for their investigations: (i) convenience sampling; (ii) hypothesis-driven, ancestry-based sampling; and (iii) hypothesis-driven, functional sampling. By using the SWOT (Strengths, Weaknesses, Opportunities, Threats) evaluation system, we highlight each of these techniques' merits and shortcomings. We identify when particular methods may be inherently unable to produce biologically meaningful results due to a mismatch between breed choice and the initial research goals. We hope that our evaluation will help researchers adopt best practices in experimental design regarding future dog breed comparisons.

Edulyze: Learning Analytics for Real-World Classrooms at Scale

Classroom sensing systems can capture data on teacher-student behaviours and interactions at a scale far greater than human observers can. These data, translated to multi-modal analytics, can provide meaningful insights to educational stakeholders. However, complex data can be difficult to make sense of. In addition, analyses done on these data are often limited by the organization of the underlying sensing system, and translating sensing data into meaningful insights often requires custom analyses across different modalities. We present Edulyze, an analytics engine that processes complex, multi-modal sensing data and translates them into a unified schema that is agnostic to the underlying sensing system or classroom configuration. We evaluate Edulyze’s performance by integrating three sensing systems (Edusense, ClassGaze, and Moodoo) and then present data analyses of five case studies of relevant pedagogical research questions across these sensing systems. We demonstrate how Edulyze’s flexibility and customizability allow us to answer a broad range of research questions made possible by Edulyze’s translation of a breadth of raw sensing data from different sensing systems into relevant classroom analytics.

Development of Powtoon-Based Learning Videos to Improve Learning Outcomes in Siroh Subjects at Markazur Quraan Baitun Najaah

Powtoon-based learning media has become a popular choice in education to improve learning effectiveness. This scientific article was prepared using the Systematic Literature Review (SLR) method. The SLR method is used to identify, review, evaluate and interpret all available research with the topic area of the phenomenon of interest, with specific relevant research questions. The researcher used 10 articles as references in this study. Results Previous studies have shown that the use of Powtoon in learning maths, science and history has yielded positive results. It not only improves comprehension of the material, but also enhances students' visual appeal, creativity, innovation, learning experience and learning motivation. With attractive animation features, Powtoon creates a dynamic, interactive, and fun learning environment for students. Therefore, the use of Powtoon media can be an effective strategy in improving the quality of learning and student learning outcomes in various subjects.

A Disruptive Research Playbook for Studying Disruptive Innovations

As researchers today, we are witnessing a fundamental change in our technologically-enabled world due to the advent and diffusion of highly disruptive technologies such as generative Artificial Intelligence (AI), Augmented Reality (AR) and Virtual Reality (VR). In particular, software engineering has been profoundly affected by the transformative power of disruptive innovations for decades, with a significant impact of technical advancements on social dynamics due to its socio-technical nature. In this paper, we reflect on the importance of formulating and addressing research problems in software engineering through a socio-technical lens, thus ensuring a holistic understanding of the complex phenomena in this field. We propose a research playbook with the aim of providing a guide to formulate compelling and socially relevant research questions and to identify the appropriate research strategies for empirical investigations, with an eye on the long-term implications of technologies or their use. We showcase how to apply the research playbook. Firstly, we show how it can be used retrospectively to reflect on a prior disruptive technology, Stack Overflow, and its impact on software development. Secondly, we show how it can be used to question the impact of two current disruptive technologies: AI and AR/VR. Finally, we introduce a specialized GPT model to support the researcher in framing future investigations. We conclude by discussing the broader implications of adopting the playbook for both researchers and practitioners in software engineering and beyond.

WallStreetBets: Assessing the Collective Intelligence of Reddit for Investment Advice

The WallStreetBets (WSB) community on Reddit gained prominence for its role in the GameStop saga and the resulting meme stock phenomenon. Concurrently, this has boosted the popularity of finance-related communities on Reddit, with the top five totalling more than 25 million subscribers at the time of writing. However, little is known about the reliability of the advice disseminated in these communities, which is a relevant research question within the field of social computing. In this study, we examine the collective intelligence of WSB, the largest and most active subreddit focused on the stock market, and assess its potential as a democratizing force in enabling access to financial knowledge. First, we establish that WSB meets several criteria to be considered a collectively intelligent crowd. Then, we test our hypothesis quantitatively by analyzing Reddit posts and financial data from a 28-month period to evaluate how successful an investor relying on WSB recommendations could have been. We define a portfolio of WSB’s most discussed stocks which shows significant growth, outperforming the S&P 500 index over the reviewed time frame. We further find that following buy signals at the time they are posted on WSB leads to positive outcomes over the long run, and that the GameStop hype merely amplified previously existing characteristics. The WSB portfolio underperforms the broader market during downturns, but recovers more quickly and achieves higher profits afterwards. The results of our work can be generalized to comparable finance-related communities, indicating that their original purpose of leisurely entertainment has already been extended towards tangible real-world value.

Risk factors for PPCs in laparoscopic non–robotic vs. laparoscopic robotic abdominal surgery (LapRas): rationale and protocol for a patient–level analysis of LAS VEGAS and AVATaR

IntroductionPostoperative pulmonary complications (PPCs) vary amongst different surgical techniques. We aim to compare the incidence of PPCs after laparoscopic non–robotic versus laparoscopic robotic abdominal surgery. Methods and analysisLapRas (Risk Factors for PPCs in Laparoscopic Non–robotic vs Laparoscopic robotic abdominal surgery) incorporates harmonized data from 2 observational studies on abdominal surgery patients and PPCs: ‘Local ASsessment of VEntilatory management during General Anaesthesia for Surgery’ (LAS VEGAS), and ‘Assessment of Ventilation during general AnesThesia for Robotic surgery' (AVATaR). The primary endpoint is the occurrence of one or more PPCs in the first five postoperative days. Secondary endpoints include the occurrence of each individual PPC, hospital length of stay and in–hospital mortality. Logistic regression models will be used to identify risk factors for PPCs in laparoscopic non–robotic versus laparoscopic robotic abdominal surgery. We will investigate whether differences in the occurrence of PPCs between the two groups are driven by differences in duration of anesthesia and/or the intensity of mechanical ventilation. Ethics and disseminationThis analysis will address a clinically relevant research question comparing laparoscopic and robotic assisted surgery. No additional ethical committee approval is required for this metanalysis. Data will be shared with the scientific community by abstracts and original articles submitted to peer-reviewed journals. RegistrationThe registration of this post-hoc analysis is pending; individual studies that were merged into the used database were registered at clinicaltrials.gov: LAS VEGAS with identifier NCT01601223, AVATaR with identifier NCT02989415.

Deep Neural Networks and Tabular Data: A Survey.

Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous datasets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted. However, their adaptation to tabular data for inference or data generation tasks remains highly challenging. To facilitate further progress in the field, this work provides an overview of state-of-the-art deep learning methods for tabular data. We categorize these methods into three groups: data transformations, specialized architectures, and regularization models. For each of these groups, our work offers a comprehensive overview of the main approaches. Moreover, we discuss deep learning approaches for generating tabular data and also provide an overview over strategies for explaining deep models on tabular data. Thus, our first contribution is to address the main research streams and existing methodologies in the mentioned areas while highlighting relevant challenges and open research questions. Our second contribution is to provide an empirical comparison of traditional machine learning methods with 11 deep learning approaches across five popular real-world tabular datasets of different sizes and with different learning objectives. Our results, which we have made publicly available as competitive benchmarks, indicate that algorithms based on gradient-boosted tree ensembles still mostly outperform deep learning models on supervised learning tasks, suggesting that the research progress on competitive deep learning models for tabular data is stagnating. To the best of our knowledge, this is the first in-depth overview of deep learning approaches for tabular data; as such, this work can serve as a valuable starting point to guide researchers and practitioners interested in deep learning with tabular data.

Systematic Literature Review Method for Identifying Efficiency and Photovoltaic Technology as a Renewable Energy Innovation

Photovoltaics or solar cells are a device that can convert/convert solar energy into electrical energy. The use of photovoltaics as a renewable energy innovation is very necessary in developing photovoltaic technology and increasing the efficiency of the energy produced. The main electricity supply is PLN, which is not always continuous in its distribution due to the limited availability of fossil energy. This can result in disruption of human activities and productivity. The technology developed is organic and inorganic photovoltaic technology. Photovoltaic efficiency refers to the ability of solar panels to convert sunlight energy into electrical energy. Currently it is not known how much technology has been developed by photovoltaics and the efficiency that photovoltaics have produced. This research aims to identify developments in photovoltaic efficiency research and technology used in the world whose data was obtained from related journals in 2013-2022. There are 1,503 studies related to photovotaic efficiency. The method used in this research is the Systematic Literature Review (SLR) Method. The SLR method is used to identify, review, evaluate, and interpret all available research on a topic area of ​​interest, with specific relevant research questions. By using the SLR method, a systematic review and identification of journals can be carried out, with each process following predetermined steps or protocols. The research results show that the technology that is widely used in developing photovoltaic efficiency is organic photovoltaic technology and the highest efficiency that has been obtained is 38.9%.

The welfare of ill and injured feedlot cattle: a review of the literature and implications for managing feedlot hospital and chronic pens.

By definition, ill and injured animals are on the negative valence of animal welfare. For beef cattle kept in feedlot settings, advances in cattle health management have resulted in a greater understanding and prevention of illness and injury. However, the management of cattle once they become ill and injured is an understudied area, and there are gaps in knowledge that could inform evidence-based decision-making and strengthen welfare for this population. The aim of this review is to provide a comprehensive overview of the acquired knowledge regarding ill and injured feedlot cattle welfare, focusing on existing knowledge gaps and implications for hospital and chronic pen management and welfare assurance. Ill and injured feedlot cattle consist of acutely impaired animals with short-term health conditions that resolve with treatment and chronically impaired animals with long-term health conditions that may be difficult to treat. A literature search identified 110 articles that mentioned welfare and ill and injured feedlot cattle, but the population of interest in most of these articles was healthy cattle, not ill and injured cattle. Articles about managing ill and injured cattle in specialized hospital (n = 12) or chronic (n = 2) pens were even more sparse. Results from this literature search will be used to outline the understanding of acutely and chronically ill and injured feedlot cattle, including common dispositions and welfare considerations, behavior during convalescence, and strategies for identifying and managing ill and injured cattle. Finally, by working through specific ailments common in commercial feedlot environments, we illustrate how the Five Domains Model can be used to explore feelings and experiences and subsequent welfare state of individual ill or injured feedlot cattle. Using this approach and our knowledge of current industry practices, we identify relevant animal-based outcomes and critical research questions to strengthen knowledge in this area. A better understanding of this overlooked topic will inform future research and the development of evidence-based guidelines to help producers care for this vulnerable population.

Strategi Menumbuhkan Kreativitas dan Keterampilan Kewirausahaan Anak melalui Kegiatan Bermain di Sekolah Dasar

Creativity and entrepreneurial skills are important aspects in developing individuals who are able to face future challenges resiliently. This research aims to identify and analyze effective strategies in fostering children's creativity and entrepreneurial skills through play activities in elementary schools. This study uses a qualitative method, where the approach is through a systematic literature review (SLR) to explore the best way to understand the essence of entrepreneurship education for prospective teachers and observe the implementation of this strategy in several elementary schools. The SLR method is used to identify, review, evaluate, and interpret all available research on a topic area of interest, with specific relevant research questions. The findings show that a structured and directed play approach can be an effective means of developing children's creativity and entrepreneurial skills. The implications of this research provide a better understanding of the importance of integrating play activities in the elementary school curriculum to strengthen aspects of creativity and entrepreneurship in children.