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The format and display of the MARC 505 contents note: Some recent practical developments
No full textThis article identifies trends in the use of the MARC 505 field through the analysis of records downloaded from the UK's National Bibliographic Knowledgebase. It finds that the proportion of records where this field is formatted in the established way - with items delimited by a double hyphen - is rapidly decreasing, and alternative formatting, including HTML, is becoming increasingly common. A project at the author's library demonstrates that modern public catalogs are capable of sophisticated display without requiring HTML in the underlying records and that ultimately the consistency of the established formatting is probably better for display than HTML
Advanced machine learning techniques for predictive modeling of property prices
No full textReal estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry
An investigation into the exploratory use of additive manufacturing in stepped spillway model testing in open channel flow
No full textStepped spillways provide an excellent form of energy dissipation and are used in many locations around the world. Effective designs are crucial in improving protection to downstream erosion and providing economic benefits to the stilling basin design. Additive manufacturing (AM) provides a wide range of possibilities of testing complex and varied geometries in fluid flow research at relatively low cost. This research builds upon previous studies in utilising AM in open channel flow applications. This paper presents a comparative analysis between two types of stepped spillway using preceding weir designs in terms of fluid velocity profile, flow rate, water height upstream and downstream; associated fluid flow parameters and experimental observations. For validation and further analysis, computational fluid dynamics (CFD) modelling is conducted to show close comparisons to the experimental results. Conditions are varied to compare the differences between models being sealed and unsealed, allowing for significant side flow. AM technology allows for cost-effective small models to be created, that can significantly improve the design process for larger scale testing and enhances the field of study for experimental fluid flow research
Hands-on assessment in a hands-off world: strategic implementation of inclusive assessment in an online environment
No full textWorkload ratio assessment in football: Evaluating simple and exponential moving averages
No full textIntroduction: To identify the optimal technique for examining time series data related to the Acute Chronic Workload Ratio (ACWR), correlations between the Simple Moving Average (SMA) and the Exponentially Weighted Moving Average (EWMA) were investigated in this study utilising a decay factor(λ) over a period of 7/28 days. Five GPS metrics were included in our analysis: Total Distance, Accelerations, Decelerations, High Metabolic Load Distance, and Distance in Speed Zones 3+4+5 (>19,9km/h). These data points were collected from 22 players across 47 days, excluding the first 28 days, for a total of 596 data points per pair [SMA/EWMA].Methods: Shapiro-Wilk and Kolmogorov-Smirnov normality tests were performed on the SMA and EWMA datasets prior to using the Spearman, Kendall Tau, and Distance Correlation techniques to assess correlations and dependencies between pairings. Using Python and libraries including Pandas, NumPy, Matplotlib, SciPy, Scikit-Learn, Stats models, OpenPyXL, Dcor, and IPython.display, the analysis was carried out in Anaconda's Jupyter Notebook. Results and Discussion: Significant departures from the normal distribution were shown by normality tests (p<0.05 for most of the variables). With p-values of 0.00, Spearman analysis showed significant correlations for every pair of variables, ranging from moderate (0.46) to somewhat weak (0.23). Additionally, Kendall's Tau revealed statistically significant correlations (p=0.00) across strengths, ranging from moderate (0.32) to weak (0.16). With values ranging from 0.25 to 0.44, Distance Correlation showed significant connections(p<0.00), while Energy Distance values displayed a range of discrepancies. Interestingly, EWMA frequently displayed values that were marginally lower than SMA, highlighting a significance level of p=0.00. Conclusion: The results show continuous trends and modest to moderate positive correlations between the variables under study. Both SMA and EWMA can be used with the help of distance correlation. EWMA is typically chosen for responsive trend analysis and offering a realistic representation of current conditions in ACWR monitoring due to its emphasis on recent data. The decision between SMA and EWMA, however, may change depending on the coaching needs; in this study, EWMA approaches produced somewhat lower scores than SMA
A comprehensive review of existing corpora and methods for creating annotated corpora for event extraction tasks
No full textPurposeThe purpose of this study is to serve as a comprehensive review of the existing annotated corpora. This review study aims to provide information on the existing annotated corpora for event extraction, which are limited but essential for training and improving the existing event extraction algorithms. In addition to the primary goal of this study, it provides guidelines for preparing an annotated corpus and suggests suitable tools for the annotation task.Design/methodology/approachThis study employs an analytical approach to examine available corpus that is suitable for event extraction tasks. It offers an in-depth analysis of existing event extraction corpora and provides systematic guidelines for researchers to develop accurate, high-quality corpora. This ensures the reliability of the created corpus and its suitability for training machine learning algorithms.FindingsOur exploration reveals a scarcity of annotated corpora for event extraction tasks. In particular, the English corpora are mainly focused on the biomedical and general domains. Despite the issue of annotated corpora scarcity, there are several high-quality corpora available and widely used as benchmark datasets. However, access to some of these corpora might be limited owing to closed-access policies or discontinued maintenance after being initially released, rendering them inaccessible owing to broken links. Therefore, this study documents the available corpora for event extraction tasks.Research limitationsOur study focuses only on well-known corpora available in English and Chinese. Nevertheless, this study places a strong emphasis on the English corpora due to its status as a global lingua franca, making it widely understood compared to other languages.Practical implicationsWe genuinely believe that this study provides valuable knowledge that can serve as a guiding framework for preparing and accurately annotating events from text corpora. It provides comprehensive guidelines for researchers to improve the quality of corpus annotations, especially for event extraction tasks across various domains.Originality/valueThis study comprehensively compiled information on the existing annotated corpora for event extraction tasks and provided preparation guidelines
The role of collaborative podcasting in developing student engagement and reflective practices
No full textCollaborative and interdisciplinary teaching in sport and exercise: Lessons from the development and delivery of an equity, diversity, and inclusion workshop
No full textTraining in sport and exercise that is collaborative and interdisciplinary allows for the delivery of key knowledge and skills that help shape trainees’ practice. Such training also demonstrates how collaborations can take shape and how individuals can work together in the future. We present an example of collaborative and interdisciplinary training used within an equity, diversity, and inclusion workshop that was provided to trainees enrolled on the Sport and Exercise Psychology Accreditation Route (SEPAR)training program offered through the British Association of Sport and Exercise Sciences. The SEPAR program was designed to allow trainees to gain knowledge, skills, and experience to apply and register as Practitioner Psychologists with the Health and Care Professions Council in the United Kingdom. The workshop was a collaboration between individuals trained in sport and exercise psychology and clinical social work. Overall, the workshop helped trainees gain an understanding of key terms and definitions concerning equity, equality, diversity, inclusion, and social justice as well as legal responsibilities. The workshop also demonstrated a variety of perspectives from sport and exercise psychology and clinical social work as to how inclusive and socially just approaches can be used to create safe environments that can foster strong therapeutic relationships with clients
Young sanctuary-living chimpanzees produce more communicative expressions with artificial objects than with natural objects
No full textIn humans, interactions with objects are often embedded in communicative exchanges. Objects offer unique affordances to explore, carry functions and hold cultural relevance, which can shape children’s interactions and communication. Research indicates that the use of artificial objects, such as certain toys, helps promote pre-linguistic communication, consequently impacting language development. Given that chimpanzees use objects extensively compared to other great apes, and considering the differences between chimpanzees and bonobos in intrinsic motivation for tool use and the extended developmental period during which they learn to use objects, it is reasonable to expect that objects may influence chimpanzees’ communication. Here, we examined interactions of 31 immature sanctuary-living chimpanzees with non-novel artificial and natural objects and tested their vocal and facial expressions, applying methods previously designed for children. Our results showed an increase in these expressions associated with artificial objects. These findings provide the first empirical evidence that chimpanzee communicative expressions may be influenced by inherent properties of objects, potentially promoting varied communication, comparable to the impact distinctive objects have on pre-linguistic children. By exploring this connection between object-centric interactions and communication, this study reveals deep phylogenetic roots where objects may have shaped great ape communication and possibly evolutionary foundations of language
Exploratory data analysis and data visualization on accidental drug related deaths
No full textThe drug overdose epidemic in the United States is rapidly getting worse with substantial associated public health effects. Covering 10,654 cases over a decade (2012–2022), this study analyses an extensive dataset of accidental drug-related deaths in Connecticut. We process and analyze this data using Python and Tableau, we then use LSTM to predict how many people will show up in the designated intervals. The ages of individuals were stated as a mean value 43.52 (SD =12.60) years with a range between 13 and 87 years, bimodally distributed around the mid-30s to mid-50's. Overall, 74.14% were male and 85.48 % white in race/ethnicity. Significant increases were seen in accidental drug-related deaths. The most implicated substances were any opioids, fentanyl (alone or in combination), cocaine alone, heroin and ethanol. Crucially, some 89.05% of the cases had co-abuse with multiple drugs by one person who showed evidence that poly-substance use is commonplace in this community. Most deaths involved fentanyl (309, with a mode at 36 years (229 out of 309 deaths) and r (0.51) associated with ‘any opioid’, the primary cause of death). New Haven, Hartford and Fairfield counties stood out as hotspots for overdoses in geographic analysis. The LSTM model achieved a Root Mean Square Error (RMSE) of 7.25 and a Mean Absolute Error (MAE) of 5.62, predicting a sustained annual increase in deaths over the next three years. This federal and state partnership provides a model for using existing surveillance resources to inform targeted overdose intervention strategies, with an emphasis on the rise of fentanyl positivity among decedents
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