Minnesota Project

Aim: Recent innovations in artificial intelligence (AI) transform the supply chain management system and allow organizations to predict demand, regulate the stock, reduce risk, and optimize the logistics process, becoming more specific and quicker. The purpose of this study is to examine how end-to-end supply chain optimization through artificial intelligence (AI)-based predictive analytics can improve the accuracy of the forecast, inventory management, and logistics effectiveness. Methods: Based on modern-day case evidence and measurements, the analysis will be directed towards quantifiable goals: 10 30% reductions in MAPE; 5-15% reductions in days-in-inventory; 10-20% reductions in logistics cost per order; and 4-8% reduction percentage payback in on-time-in-very-full and ROI of over 20%. The study adopted a comparative analytical design using secondary data from multiple industries to assess the effectiveness of AI-driven predictive models (gradient boosting, random forests, and LSTM) against classical time-series forecasting approaches. Results: The findings show that companies with unified AI platforms, feature stores, MLOps pipelines, and balanced data models are binding significantly more adoption and return compared to the competitors, which use individual instruments, since the mutual data resource, lineage, and governance decelerate friction and aid learning speed. Conclusion: The study concludes that predictive analytics, when integrated into unified AI platforms, enhances supply chain resilience and sustainability by converting real-time data into actionable insights for cost and service optimization. Recommendation: The study proposes that organizations should embrace unified AI structures that have controlled data models to achieve optimal predictive analytics performance and scale across supply chain operations.

In an era of rapid digital disruption, organizations must evolve not only technologically but also strategically. This thesis explores the critical role of leadership in driving successful digital transformation, emphasizing the need for strategic alignment, inclusive practices, and adaptive capabilities. Through a quantitative survey of professionals across industries, the study examines how various leadership styles—transformational, servant, transactional, and inclusive—impact innovation, organizational culture, and change management. It also investigates leadership development, succession planning, and the integration of diversity, equity, and inclusion in digital contexts. The findings underscore that effective digital transformation is not merely a technological shift but a leadership-driven cultural evolution, requiring vision, agility, and emotional intelligence to build future-ready enterprises.

In the current context, the issue of improving the quality of continuous teacher education gains special importance. Mentoring and tutoring support are considered key mechanisms of professional assistance for teachers at the pre-service, initial, and ongoing training stages. The aim of this study is to identify and analyze innovative models of mentoring and tutoring support within the continuous teacher education system, and to determine their effectiveness and implementation prospects.

In the face of rapid digitization and the adoption of artificial intelligence (AI) technologies, the education system is undergoing profound changes, reflected in the functional and methodological roles of educators. Modern pedagogical practices impose new competency requirements on teachers, necessitating a rethinking of their professional identity and status within the educational process. This study employs a comprehensive approach. Collected data were processed using thematic coding and pattern comparison to identify transformations in pedagogical activity.

‘The protection of the individual, and the idea of individual criminal responsibility for the worst crimes, would be part of the new legal order. The sovereignty of the state would no longer provide absolute refuge for crimes on such a scale, in theory at least’. (East West Street) First published in 2017, Philippe Sands’ East West Street is a moving and unforgettable story that interweaves a deeply painful and personal journey into the history of his family living under Nazi rule, with a parallel journey that traces the origins of the legal concepts of ‘genocide’ and ‘crimes against humanity’. These concepts have since come to form the foundations of international human rights law and could not hold more resonance today as we enter a dangerous period marked by the slide towards authoritarianism and the retreat from an international rules-based world order created painstakingly in the aftermath of the holocaust and the second world war.

Since 2014, the French National Research Institute for Sustainable Development (IRD) has hosted three interdisciplinary thematic schools on the vulnerability of the reef heritage (VulPaRe) in the Western Indian Ocean. Taking a sustainability science approach, the schools aimed to build capacity, facilitate knowledge transfer, and foster collaboration among a variety of stakeholders involved in research and the governance of coastal resources. The latest VulPaRe school highlighted 1) the need to strengthen the role of social sciences in interdisciplinary studies, 2) the opportunities and threats of market-based mechanisms integrating conservation and ecological restoration, and 3) concerns of justice on the access to common resources. An improvement of this educational process would be to provide more guidance for dialogues with decision makers in these reef areas.

This paper looks at how practices in managing the program can enable faster adoption of AI nationally. The analysis is applied using the quantitative information gathered on 120 professionals working in the fields of governance, engineering, compliance, and MLOps determining the competency of structured processes in speed, stability, and cost-efficiency. Statistical tests demonstrate that there is a strong positive relationship between program governance and data quality controls and cloud readiness and MLOps maturity and the overall results of AI performance. The best predictors of acceleration of AI obtained as the result of regression are governance maturity, cross-functional coordination, and cloud orchestration. The research finds that disciplined program management is necessary in order to scale AI initiatives reliably, minimise the risks, and enhance operational effectiveness is improved

In order to build a vocational practice program for students majoring in preschool education at the college level according to the connection model between pedagogical schools (colleges of preschool education) and kindergartens (preschools), the authors have the study in 2 years (from 2023 to 2024) based on previous studies by many different authors and the authors ourselves. The surveys in the plan of the study obtained positive results but did not clearly demonstrate the diversity ideas of many subjects nor did it specifically supplement the content, the subjects of the on-going building vocational practice program. Therefore, the study should be conducted one more quantitative survey on students to the original plan in order to expand diversity and express the wishes of learners while carrying out the vocational practice program. The results of this survey on students are also hoped by the authors to add some specific content and subjects in the process of building a vocational practice program.