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Online learning is enforced by all higher education in Indonesia due to the COVID-19 pandemic. There is an inability of students to manage time and often delay the work given by the lecturer. This study aimed to reveal the degree of relationship between student time management and academic procrastination in higher education. This research was conducted at the Psychology Faculty of the University Puta Indonesia YPTK Padang, Indonesia. The sample of this research was psychology students as many as 202 respondents. The research instrument was in the form of a questionnaire distributed via Google Form. The results showed that the level of correlation between time management and academic procrastination of psychology students was 0.242 with data processing using SPSS 26. Which means that there is a relationship between students' ability to manage time and delay in completing assignments by students of the faculty of psychology. The role of lecturers in providing direction and motivation to students is very much needed.

Climate change will put millions more people in Africa at risk of food and nutrition insecurity by 2050. Integrated assessments of food systems tend to be limited by either heavy reliance on models or a lack of information on food and nutrition security. Accordingly, we developed a novel integrated assessment framework that combines models with in-country knowledge and expert academic judgement to explore climate-smart and nutrition-secure food system futures: the integrated Future Estimator for Emissions and Diets (iFEED). Here, we describe iFEED and present its application in Malawi, South Africa, Tanzania and Zambia. The iFEED process begins with a participatory scenario workshop. In-country stakeholders identify two key drivers of food system change, and from these, four possible scenarios are defined. These scenarios provide the underlying narratives of change to the food system. Integrated modeling of climate change, food production and greenhouse gas emissions is then used to explore nutrition security and climate-smart agriculture outcomes for each scenario. Model results are summarized using calibrated statements—quantitative statements of model outcomes and our confidence in them. These include statements about the way in which different trade futures interact with climate change and domestic production in determining nutrition security at the national level. To understand what the model results mean for food systems, the calibrated statements are expanded upon using implication statements. The implications rely on input from a wide range of academic experts—including agro-ecologists and social scientists. A series of workshops are used to incorporate in-country expertise, identifying any gaps in knowledge and summarizing information for country-level recommendations. iFEED stakeholder champions help throughout by providing in-country expertise and disseminating knowledge to policy makers. iFEED has numerous novel aspects that can be used and developed in future work. It provides information to support evidence-based decisions for a climate-smart and nutrition-secure future. In particular, iFEED: (i) employs novel and inclusive reporting of model results and associated in-country food system activities, with comprehensive reporting of uncertainty; (ii) includes climate change mitigation alongside adaptation measures; and (iii) quantifies future population-level nutrition security, as opposed to simply assessing future production and food security implications.

International research and development projects (or grand challenge projects) consist of multicultural, multi-country, multi-sectoral, and multi-stakeholder initiatives aimed at poverty reduction. They are usually conceived as partnerships between actors in the global north–south. The COVID-19 pandemic was a major unexpected disruption to ongoing projects and challenged their already complex management. The aim of this paper is to present evidence on how international development projects were impacted by COVID-19 with a particular focus on the relationship between research institutions in the north and south. We conducted a mixed-methods research study, combining a reflective exercise with the co-author team and a survey with principal investigators, project managers, and capacity development leads drawn from 31 Global Challenges Research Fund (GCRF) projects funded through the UK government’s Official Development Assistance (ODA) and focused on social–ecological system research. The survey contained closed- and open-ended questions in order to (i) demonstrate how those involved in managing projects adapted to risks, including both threats and opportunities, presented by the COVID-19 pandemic, and (ii) consider the implications for tailoring adaptive management approaches in international research projects amidst uncertainties, with a special focus on enhancing equities in global north–south partnerships. The paper offers the following recommendations on designing, planning, and implementing international research and development projects: (i) devolve project management in order to enhance project resilience and improve north–south equities; (ii) allocate dedicated resources to enable equitable north–south research partnerships; (iii) rely more on hybrid and agile approaches for managing a project’s life cycle; and (iv) improve resource flexibility, transparency, and communication through enhanced funder–implementer collaboration.

Verifikasi wajah merupakan salah satu teknologi biometrika yang menjadi perhatian para peneliti. Banyak sekali sistem aplikasi yang berbasis kepada verifikasi wajah misalnya: akses pintu, akses mesin ATM, sistem presensi kehadiran, dll. Pada makalah ini akan dibahas perancangan dan pembuatan sistem verifikasi wajah manusia menggunakan metode ekstraksi menggunakan metode SPCA (Simple Principle Component Analysis) dan teknik klasifikasi jaringan saraf tiruan Learning Vector Quantization. Data citra wajah yang digunakan berasal dari 5 orang yang terdiri masing-masing sebanyak 10 citra wajah untuk proses pelatihan dan juga masing-masing sebanyak 10 wajah untuk proses pengujian. Hasil pengujian unjuk kerja sistem didapat nilai FRR rata-rata 0% dan FAR rata-rata = 1,55%.

Verifikasi wajah merupakan salah satu teknologi biometrika yang menjadi perhatian para peneliti. Banyak sekali sistem aplikasi yang berbasis kepada verifikasi wajah misalnya: akses pintu, akses mesin ATM, sistem presensi kehadiran, dll. Pada makalah ini akan dibahas perancangan dan pembuatan sistem verifikasi wajah manusia menggunakan metode ekstraksi menggunakan metode SPCA (Simple Principle Component Analysis) dan teknik klasifikasi jaringan saraf tiruan Learning Vector Quantization. Data citra wajah yang digunakan berasal dari 5 orang yang terdiri masing-masing sebanyak 10 citra wajah untuk proses pelatihan dan juga masing- masing sebanyak 10 wajah untuk proses pengujian. Hasil pengujian unjuk kerja sistem didapat nilai FRR rata-rata 0% dan FAR rata-rata = 1,55%. Kata kunci : verifikasi wajah, SPCA, learning vector quantization