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Background/Objective. The teaching and learning of geology in Morocco's secondary education system seems to face various challenges. This research aims to recognize and analyze these challenges so as to develop a deeper understanding of the problems faced in middle and high school classrooms. Materials/methods. To this end, a survey was conducted among Life and Earth Sciences teachers working in secondary education in Morocco using an online questionnaire (via Google Forms). Participants were selected using a non-probability sampling method based on self-selection; only teachers who voluntarily agreed to respond to the questionnaire were included in the sample. The sample comprised 142 Life and Earth Sciences teachers from public secondary schools under various provincial education directorates across Morocco. The questionnaire was divided into two parts: the first part, comprising four items, gathers general information about the respondents; the second part, composed of 17 items, explores the specific difficulties encountered in the teaching and learning of geology. Data analysis was carried out using Excel for descriptive statistical processing and SPSS software to apply the Chi-square test to examine relationships between certain variables. Results. The data analysis reveals numerous challenges from both teachers' and students' perspectives. These challenges are mainly related to the characteristics of the subject, particularly its use of abstract concepts and the intricate ideas of geological time and spatial relationships. Conclusion. The study found that there are also obstacles related to teaching methods, pedagogy, administration, and individual circumstances. To address these setbacks, the teachers surveyed suggested several solutions to improve the teaching of geology and facilitate student learning.

As a management information systems (MIS) project manager and an action researcher, the author examined, over time, the influence on information system development (ISD) of the informal sociopolitical organizational actions. The paper reports two cases of action research findings concerning the effect on ISD project implementation processes of power-based arbitrary decisional actions in bureaucratic environments. The research focused on the relationship of such unilateral actions to the interconnected subprocesses of deliberate and conscious attempts by the ISD project members to define and resolve system implementation issues. A suggested conceptual framework for the issue-resolution processes (IRP's) and obstacle-coping processes (OCP's) is based on the author's IRP/OCP-related behavioral constructs and theoretical models dealing with soft-systems issues, especially appreciative system theory and soft-systems methodology, and innovative flexible, \"complementarist\" (Sinn, 1998) or pluralist problem-solving approaches. [PUBLICATION ABSTRACT]

Traditionally, autonomous cars treat human-driven vehicles like moving obstacles. They predict their future trajectories and plan to stay out of their way. While physically safe, this results in defensive and opaque behaviors. In reality, an autonomous car’s actions will actually affect what other cars will do in response, creating an opportunity for coordination. Our thesis is that we can leverage these responses to plan more efficient and communicative behaviors. We introduce a formulation of interaction with human-driven vehicles as an underactuated dynamical system, in which the robot’s actions have consequences on the state of the autonomous car, but also on the human actions and thus the state of the human-driven car. We model these consequences by approximating the human’s actions as (noisily) optimal with respect to some utility function. The robot uses the human actions as observations of her underlying utility function parameters. We first explore learning these parameters offline, and show that a robot planning in the resulting underactuated system is more efficient than when treating the person as a moving obstacle. We also show that the robot can target specific desired effects, like getting the person to switch lanes or to proceed first through an intersection. We then explore estimating these parameters online, and enable the robot to perform active information gathering: generating actions that purposefully probe the human in order to clarify their underlying utility parameters, like driving style or attention level. We show that this significantly outperforms passive estimation and improves efficiency. Planning in our model results in coordination behaviors: the robot inches forward at an intersection to see if can go through, or it reverses to make the other car proceed first. These behaviors result from the optimization, without relying on hand-coded signaling strategies. Our user studies support the utility of our model when interacting with real users.

When there are dynamic obstacles in the environment, it is difficult for traditional path-generation algorithms to achieve desired obstacle-avoidance results. To solve this problem, we propose a robot navigation control method based on SAC (Soft Actor-Critic) Deep Reinforcement Learning. Firstly, we use a fast path-generation algorithm to control the robot to generate expert trajectories when the robot encounters danger as well as when it approaches a target, and we combine SAC reinforcement learning with imitation learning based on expert trajectories to improve the safety of training. Then, for the hybrid data consisting of agent data and expert data, we use an improved prioritized experience replay method to improve the learning efficiency of the policies. Finally, we introduce RNN (Recurrent Neural Network) units into the network structure of the SAC Deep Reinforcement-Learning navigation policy to improve the agent’s transfer inference ability in a new environment and obstacle-avoidance ability in dynamic environments. Through simulation and practical experiments, it is fully verified that our method has a higher training efficiency and navigation success rate compared to state-of-the-art reinforcement-learning algorithms, which further enhances the obstacle-avoidance capability of the robot system.

This article aims to look at pedagogical issues and social issues arising in connection with the use of technology in teaching and learning today. Suggested learning environments that include learning strategies, learning theories, learning activities, learning designs and delivery platforms have been proposed by researchers to meet the 21st century learning that emphasizes active involvement of students, active learning, fun learning and collaborating with peers, sharing information with other partners, creative thinking and critical thinking as well as self-directed learning among students are also discussed. Implementation of ICT in the teaching and learning process of pupils will not only provide students with the stimulus to learning, but will also increase their academic interest and achievement.

PurposeSocially responsible investment (SRI) is a niche and upcoming investment strategy in India. Very few researches have been conducted on SRI in the Indian context. This study identifies the SRI awareness level, attitude towards the importance of environmental, social, and governance (ESG) issues, willingness to invest in SRI avenues and obstacles in SRI investment decision-making by Indian retail investors. The second objective was among the awareness, attitude, willingness, obstacle, and demographic constructs to identify the most significant variables that impact an individual investor's SRI decision in India. .Design/methodology/approachData for the study have been collected through a self-structured questionnaire. Descriptive statistics are used to identify the importance of variables for individual investors. This paper used the theory of planned behavior (TPB) to understand the factors impacting individual investors' SRI behavior. Binary logistics regression analysis is used to recognize the variables that affect an individual investor's SRI decision.FindingsThe descriptive statistics indicate a low level of SRI awareness; the majority of the investors agreed that ESG issues are significant in investing and showed a willingness to invest in SRI avenues. However, the investors were not willing to accept lower returns from SRI. The majority of investors found, lower returns on SRIs, no tax benefit, lack of information about SRIs, and low liquidity as important obstacles in SRI investing. Binary logistics regression results indicated that awareness about SR/ESG indices, awareness about SR/ESG funds, and willingness to invest in SRI avenues significantly impact investors' SRI decisions but demographic variables have no significant impact on SRI decision-making.Practical implicationsThis study has implications for the ethical/SR mutual funds managers, policymakers, government, and international asset management companies. The study finds an urgent need for increasing awareness about SRI among individual investors in India. The study suggests that the issuers must provide adequate information about SRI avenues and probable risk and returns involved in these, while the regulators must make efforts to educate investors in India.Originality/valueThe context of the present study is original because hardly any of the earlier studies conducted in India have tried to find out the individual investors' SRI awareness level, investors' willingness towards SRI, investors' attitude towards ESG issues, and obstacles faced by investors in socially responsible investing.

In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in linear temporal logic, such as patrolling a set of rooms, we synthesize an automaton from which the robots can extract valid strategies. This centralized automaton is executed by the robots in the team at runtime, and in conjunction with a distributed motion planner that guarantees avoidance of moving obstacles. Our contribution is a correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles, guarantees satisfaction of the mission specification and resolves encountered deadlocks, where a moving obstacle blocks the robot temporally. Our method provides conditions under which deadlock will be avoided by identifying environment behaviors that, when encountered at runtime, may prevent the robot team from achieving its goals. In particular, (1) it identifies deadlock conditions; (2) it is able to check whether they can be resolved; and (3) the robots implement the deadlock resolution policy locally in a distributed manner. The approach is capable of synthesizing and executing plans even with a high density of dynamic obstacles. In contrast to many existing approaches to mission and motion planning, it is scalable with the number of moving obstacles. We demonstrate the approach in physical experiments with walking humanoids moving in 2D environments and in simulation with aerial vehicles (quadrotors) navigating in 2D and 3D environments.

The interrelationship between regional water, energy, food, and land systems is extremely complex. Hence, accurately assessing the coupling coordination relationship and identifying the influential factors of the water-energy-food-land nexus (WEFL nexus) are of utmost importance. This study proposes a novel analytical framework and evaluation index system for exploring interactions across the WEFL nexus. The comprehensive benefit evaluation index (CBEI), coupling coordination degree (CCD) model, and obstacle factor diagnosis model are integrated to assess and analyze the coupling coordination relationship and spatiotemporal dynamic evolution of the WEFL nexus in the Yangtze River Economic Belt (YREB) from 2006 to 2020. The results indicated that (1) the CBEI and CCD generally increased from 0.23 to 0.79 and 0.45 to 0.88, respectively, revealing the upward trend of the coordination development levels of the WEFL nexus in the YREB. (2) The lower reaches achieved a relatively higher coordination development degree than the upper and middle reaches of the YREB. (3) The findings of obstacle factors reveal that agricultural non-point source pollution control, waterlogging disaster prevention, industrial solid waste efficient treatment, and urban water-saving are the essential fields that need to be improved in YREB’s future development. This study helps to understand the complex interrelation of the WEFL nexus at different spatial–temporal scales and provides a novel framework that can be used as an evaluation system and policy insights for a region’s integrated resources, environmental management, and green sustainable development.

Obstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.

Farm animal welfare in livestock production is a topical and important issue attracting growing interest of policy makers, consumers, stakeholders in the supply chain and others. While there is much public interest in the issue this is not reflected in the supply and market shares of animal food products that are produced under welfare standards that exceed legislative requirements. Given the obstacles to devising stricter legislative standards, higher welfare animal food products are mostly made available through market-based approaches. This paper discusses different challenges and opportunities for a range of public and consumer policies and makes recommendations on how these might be strengthened. The paper does not report primary empirical findings but assembles available knowledge on citizen and consumer attitudes and perceptions towards animal welfare from various research disciplines. We argue that in order for public and consumer policies to be (more) efficient and effective, it is important to develop a segmented and targeted strategy. This paper will thus elaborate on what information could and should be provided to whom. This implies the need for a good understanding of how people conceptualize farm animal welfare. Further, information provisioning should address the needs and expectations of those specific consumer segments most likely to be motivated to purchase higher welfare products. Based on the assembled information, opportunities and challenges for information provisioning and communication to the public and consumers are identified. The merits and limitations of different forms of information provisioning and animal welfare labelling are discussed and recommendations are set forth for future research.