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Negative Shaping Order K in Set Shaping Theory: A Comprehensive Analysis
No full textThis paper delves into an innovative aspect of the Set Shaping Theory, exploring the use of a negative shaping order K. Traditionally, the theory utilizes a positive K to extend the length of data strings, enhancing their testability and compressibility. We propose a paradigm shift by employing a negative K, which shortens data strings and potentially improves compression efficiency. However, this approach sacrifices the local testability of the data, a cornerstone in traditional Set Shaping Theory. We examine the theoretical implications, practical benefits, and challenges of this new methodology
TIME Learning Brief 4
No full textMore information about the TIME (Together in My Education) home-learning programme and materials can be found in open access at: https://wwhomeliteracy.org.za/time/This is the fourth in a series of learning briefs that explore the implementation of the TIME Home Learning programme and learning trajectories of 5- to 7-year-olds. This brief is based on interviews, home visits and observations made between February 2022 and August 2023 with participating families of children who were in Grade R in 2022 and in Grade 1 in 2023. It focuses on the home circumstances of families and their lived experiences while engaging with the TIME programme. This brief seeks to address the following questions:
• How does the diversity of families and homes challenge our mental representations of “family” and “home”?
• What does it take to embed the practice of TIME in the routine of the home?
• What can we learn from caregivers’ experiences with TIME at home, which could help improve the frequency and the quality of families’ engagement?
Reviewing the stories of a few families under a dynamic lens, the brief discusses how home circumstances such as family configurations, poverty, working hours, multilingualism, influence the levels of caregiver engagement with the TIME programme, and draws a typology of caregiver engagement.DG Murray Trus
The Future of eduroam with a Cross-Border eSIM Solution for Seamless Scholarly Connectivity
No full textThe Role of Climate Change for Transboundary Crop Pest Outbreaks in IGAD Member States – Challenges for Integrated EWS and Governance. A Review
Full text linkNote: This paper is not peer-reviewed and should be regarded as preprint.This paper is based on the review of scientific literature and consultations with member states of the Intergovernmental Authority on Development (IGAD). Climate change is having a profound impact on the IGAD region, with rising temperatures and shifting rainfall patterns driving both extreme weather events and agricultural pest outbreaks. This review addresses the threat posed by five major transboundary crop pests: fall armyworm (Spodoptera frugiperda), African armyworm (Spodoptera exempta), tomato leaf miner (Tuta absoluta), red-billed quelea (Quelea quelea), and desert locust (Schistocerca gregaria). The lifecycle, behaviour, and economic impact of each pest are examined, with a particular focus on the role of climate change in intensifying their proliferation and spread. The paper also assesses current pest management strategies and identifies their shortcomings. It advocates for advancements in Early Warning Systems (EWS), emphasizing the need for integrating advanced technologies to prevent and manage the emergence and spread of transboundary pests. The paper calls for a holistic and integrated approach to pest management, incorporating climate services and fostering community-based interventions. It underscores the need to rethink governance to equip EWS for future challenges and stresses the importance of continuous research and international cooperation to build sustainable and resilient agricultural systems.This research was completed with the financial support of the German Federal Ministry for Economic Cooperation and Development
Exploration of Climate Data and Temperature Forecasting using Machine Learning
No full textIn this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.N/