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Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance.

To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance.

Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations.

This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed.

The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.

This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.

This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.

In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.

Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

Local governments use taxes on future increases in property values to pay for current economic development through tax incremental financing (TIF). TIF is a powerful tax tool used to spur improvements to a designated area. Proponents of TIF argue that it allows local governments to make investments without affecting previously established government and school district programs. Detractors argue that because the TIF designation denies existing overlapping districts (e.g., schools) the benefits of increases in property values, TIF can have a negative impact on a community. Empirical evidence on the economic and fiscal effects of TIF is mixed. This paper describes the potential costs and benefits associated with the use of TIF and then summarizes prior research on outcomes associated with this widely used property tax program.

This chapter addresses one of the most common and long-standing problems among college students, namely depression, as well as a potential consequence of depression, suicide. A formal definition of depression is presented, and symptoms of depression are discussed. Notably, clinical depression is differentiated from “feeling down” or having “the blues.” Common measures of depression for college students are described, and the current prevalence of depression among college students is explored, along with data pertaining to trends and trajectories. Particular attention is devoted to differences in rates and severity of depression among students of various ethnicities, gender identities, disabilities and sexual orientations. Next, the chapter covers various theories about and studies on the causes and consequences of depression, as well as preventive and remedial efforts that students can engage in to minimize the adverse effects of depression. The chapter concludes with a focus on college student suicide, including its prevalence, predictors of suicidal thoughts and behaviors and prevention and treatment of college student suicide.

Digital transformation in nursing education is crucial for enhancing pedagogical practices and preparing future healthcare professionals for the rapidly evolving healthcare landscape. This chapter explores how the integration of digital technologies in higher education has revolutionising teaching methodologies and offered new opportunities to enhance learning experiences. It identifies gaps in digital learning modalities for undergraduate and postgraduate nursing students and discusses strategies to strengthen online literacy preparation and transition into the healthcare sector's digital transformation landscape and the 4th industrial era economy. The chapter examines best practices and challenges in digital transformation in nursing education such as blended learning environments, simulation and virtual reality, mobile learning applications and gamification strategies. Additionally, it addresses challenges in curriculum development including insufficient technological infrastructure, faculty training and development, assessment strategies and resistance to change among faculty and students. This chapter aims to provide insights and recommendations for educators, curriculum developers and policymakers in implementing successful digital transformation in nursing education.

This chapter discusses the role of school leadership in the face of climate disasters and environmental injustices. These disruptions to schooling are emblematic of an increasing global uncertainty. School leaders play a pivotal role mitigating uncertainty following an environmental crisis or disaster through leadership activities that support their communities. However, preparing school leaders for unexpected disruptions to schooling has often been overlooked by preparation programs and professional development. The goal of this chapter is to equip school leaders with an essential understanding of both the influence of environmental injustice on schools and the tools to respond effectively to these events. First, the chapter contextualizes environmental injustice and inequality as a factor that influences school and student performance, especially for students living below the poverty line and students of color. Next, it synthesizes how school leaders have responded to prior instances of climate disasters and environmental injustices. Finally, it presents key considerations for school leaders confronting future occurrences.

This chapter is about primary and secondary school teachers of history in England, and how they negotiate policy in order to teach sensitive and controversial issues which feature as part of the history curriculum. We present research conducted in two phases that used a bounded case study (Stake, 1995) as a methodological approach. In Phase One, two focus group interviews were undertaken; in Phase Two, six unstructured individual interviews were conducted. Participants were teachers of history in England from Key Stage 1–5 (children aged 4–18 years).

Thematic analysis was used to analyse the data which were informed by reflections on positionality and being a socially conscious researcher (Pillow, 2010). Three key policies were explored as part of this research: the National Curriculum (DfE, 2013), the Teachers' Standards (DfE, 2012) and the Prevent Duty (DfE, 2015). Research findings demonstrate how the context of the school is fundamental in how teachers enact policy in relation to their practice, particularly in light of political changes in society. Self-surveillance was identified as a key strategy, adopted in the teaching of sensitive and controversial issues. We frame this context around Kitson and McCully's (2005) theoretical continuum which indicates that there is a reluctance by some teachers to engage with the teaching of sensitive and controversial issues due to concerns with policy enactment.

The findings of this research illustrate that policy impacts on teachers in numerous ways. Policy was demonstrated to be ambiguous for teachers, and recommendations are made relating to policy and the need for clearer guidance for teachers to support them with their practice.

The lesson study aims to examine college English teachers' growth in technological pedagogical content knowledge (TPACK) and the improvement of students' learning outcomes in the context of MOOC-based and AI-powered flipped teaching and assessment of EFL writing (MAFTA).

Three college EFL teachers and their students (66 in total) participated in three cycles of MAFTA instruction. Triangulated analysis was conducted by considering all relevant data sources, including the teachers' discussions and reflections, the argumentative essays produced by the students before and after the MAFTA instruction, as well as the data gathered through questionnaires and interviews.

The three teachers demonstrated varying degrees of growth in TPACK, as evidenced by their increased knowledge of the technology tools and skills in utilizing the tools to realize their pedagogical beliefs on teaching EFL writing. Substantial improvements were detected in students' essays. The students generally have affirmative perceptions on the MAFTA model and the questionnaire and interviews specified the benefits they gained from each stage of the model.

Firstly, the lesson study is grounded in an innovative approach to teaching EFL writing that incorporates multiple technological affordances. Secondly, it closely scrutinizes the dynamics of both teachers' and students' growths during the innovative practice. The findings could offer insights into teachers' TPACK development and effective integration of technological advancements in EFL education at the tertiary level.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.