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PurposeThe study seeks to investigate the factors that influence MOOC usage by students in tertiary institutes in Ghana.Design/methodology/approachAs this study sought both to test existing UTAUT variables and potentially identify additional variables impacting MOOC usage, a mixed method approach was used. The quantitative study was used to test the significance of UTAUT variables on MOOC usage while the qualitative study was conducted to validate the quantitative results and potentially determine additional factors impacting MOOC usage.FindingsThe results of the quantitative data analysis showed that computer self-efficacy, performance expectancy and system quality had a significant influence on MOOC usage intention. Facilitating conditions, instructional quality and MOOC usage intention were found to have a significant influence on actual MOOC usage. The results of the qualitative data analysis showed that information-seeking behaviour and functional Internet access were dominant non-UTAUT factors that influence actual MOOC usage, while teacher motivation was a dominant non-UTAUT factor that influenced MOOC usage intention.Research limitations/implicationsThe study employed a non-probability sampling technique which imposes limitations on the generalizability of the findings. Additionally, the study was conducted in two out of the ten geographical and administration regions of Ghana; this also imposes limitations on the generalizability of the findings.Practical implicationsIt is important that lecturers and university management find ways of motivating students to participate in MOOCs. Lecturers can influence students to use MOOCs if they regularly and persistently spur the students on to use MOOCs. Lecturers can also adopt other innovative strategies such as posting MOOC information on student noticeboards, the formation of MOOC clubs and the commissioning of MOOC champions on campuses.Social implicationsThe significance of functional Internet access in MOOC usage implies that good Internet connectivity is critical for online learning in developing countries. Regulators of Internet service providers must enforce strict adherence to quality of service standards regarding the provision of Internet service. The Internet service pricing regime must favour the use of the Internet for learning purposes.Originality/valueThe study adopted a mixed method approach to explore MOOC usage in a West African university context. The non-significance of two key UTAUT variables (effort expectancy and social influence) points to a key difference between the application of adoption and usage models to information systems compared to e-learning systems. Additionally, three other variables, namely information-seeking behaviour, functional Internet access and teacher motivation, were found to impact MOOC usage. The study presents a model of MOOC usage (MMU).

China’s special access policies for unapproved drugs and medical devices represent a key institutional innovation in its healthcare regulatory system. In recent years, tailored versions of this policy have been implemented in Hainan Lecheng, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), and Beijing Tianzhu, each exhibiting differentiated trajectories in institutional design and management mechanisms. This study identifies a pattern of policy evolution termed Strategy-Embedded Diffusion, a model wherein the central government does not mandate a uniform policy model, but strategically deploys this tool to different strategic regions in alignment with national objectives. To analyze this phenomenon, this study develops a three-tiered interactive framework encompassing central delegation, local innovation, and social feedback. A comparative case analysis reveals that: the central government delineates distinct scopes of authorization based on each region’s strategic positioning and risk tolerances; local governments engage in selective learning and institutional reproduction tailored to local conditions; and key societal stakeholders actively shape policy refinement through continuous feedback.

Deep neural networks have demonstrated impressive performance across various fields such as computer vision, natural language processing, and autonomous systems. However, their static architectural configurations often result in computational inefficiencies and lack of flexibility, which limit their applicability in intelligent scheduling tasks involving complex structural spaces. To address these challenges, we propose a novel optimization framework for deep learning architectures that enables dynamic and knowledge-driven adaptation, specifically tailored for intelligent scheduling in structural environments. The proposed framework integrates a Dynamic Compositional Architecture (DCA) and a Knowledge-Embedded Adaptive Strategy (KEAS). DCA models the network as a directed acyclic graph composed of modular units, where each module is conditionally activated based on input semantics and spatial scheduling requirements. This enables real-time adjustment of computation depth, width, and routing pathways, allowing for efficient processing across varying structural configurations. KEAS enhances the model’s adaptability by embedding symbolic domain knowledge and semantic constraints into the learning process, guiding the architecture to align with scheduling objectives and structural semantics. Experimental results on multiple benchmark datasets demonstrate that our approach not only achieves state-of-the-art prediction accuracy, but also significantly improves scheduling efficiency, reduces inference latency, and minimizes resource usage. This research offers a scalable and interpretable deep learning paradigm for intelligent scheduling of structural spaces in dynamic, resource-constrained environments.

A theoretical framework based on information and embeddedness is constructed to analyze the micro structure of the impact of heterogeneous social capital on corporate performance. It is empirically tested based on data collected from a sample of 155 Chinese firms. Results indicate that hierarchical social capital has a positive association with corporate market power but little impact on corporate operational efficiency. Furthermore, social capital can promote operational efficiency but contribute little to corporate market power. There is a complementary structure between the two types of heterogeneous social capital. The embedded inertia of social capital into institutional environment is negatively related with corporate performance. From the perspective of economic transition, firms in developed regions rely far more on hierarchical social capital to acquire market power than firms in developing areas.

This chapter contains sections titled: Abstract Introduction Economics of Lithic Raw Material Acquisition and Transport Three Hypotheses and their Empirical Consequences Methods Results Discussion and Conclusions Acknowledgments References

Long-range wireless connectivity technologies for sensors and actuators open the door for a variety of new Internet of Things (IoT) applications. These technologies can be deployed to establish new monitoring capabilities and enhance efficiency of services in a rich diversity of domains. Low energy consumption is essential to enable battery-powered IoT nodes with a long autonomy. This paper explains the challenges posed by combining low-power and long-range connectivity. An energy breakdown demonstrates the dominance of transmit and sleep energy. The principles for achieving both low-power and wide-area are outlined, and the landscape of available networking technologies that are suited to connect remote IoT nodes is sketched. The typical anatomy of such a node is presented, and the subsystems are zoomed into. The art of designing remote IoT devices requires an application-oriented approach, where a meticulous design and smart operation are essential to grant a long battery life. In particular we demonstrate the importance of strategies such as “think before you talk” and “race to sleep”. As maintenance of IoT nodes is often cumbersome due to being deployed at hard to reach places, extending the battery life of these devices is critical. Moreover, the environmental impact of batteries further demonstrates the need for a longer battery life in order to reduce the number of batteries used.

Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra’s algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies.

With the profound convergence and advancement of the Internet of Things, big data analytics, and artificial intelligence technologies, edge computing—a novel computing paradigm—has garnered significant attention. While edge computing simulation platforms offer convenience for simulations and tests, the disparity between them and real-world environments remains a notable concern. These platforms often struggle to precisely mimic the interactive behaviors and physical attributes of actual devices. Moreover, they face constraints in real-time responsiveness and scalability, thus limiting their ability to truly reflect practical application scenarios. To address these obstacles, our study introduces an innovative physical verification platform for edge computing, grounded in embedded devices. This platform seamlessly integrates KubeEdge and Serverless technological frameworks, facilitating dynamic resource allocation and efficient utilization. Additionally, by leveraging the robust infrastructure and cloud services provided by Alibaba Cloud, we have significantly bolstered the system’s stability and scalability. To ensure a comprehensive assessment of our architecture’s performance, we have established a realistic edge computing testing environment, utilizing embedded devices like Raspberry Pi. Through rigorous experimental validations involving offloading strategies, we have observed impressive outcomes. The refined offloading approach exhibits outstanding results in critical metrics, including latency, energy consumption, and load balancing. This not only underscores the soundness and reliability of our platform design but also illustrates its versatility for deployment in a broad spectrum of application contexts.

Hardware/software partitioning is an essential step in hardware/software co-design. For large size problems, it is difficult to consider both solution quality and time. This paper presents an efficient GPU-based parallel tabu search algorithm (GPTS) for HW/SW partitioning. A single GPU kernel of compacting neighborhood is proposed to reduce the amount of GPU global memory accesses theoretically. A kernel fusion strategy is further proposed to reduce the amount of GPU global memory accesses of GPTS. To further minimize the transfer overhead of GPTS between CPU and GPU, an optimized transfer strategy for GPU-based tabu evaluation is proposed, which considers that all the candidates do not satisfy the given constraint. Experiments show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW partitioning. The proposed parallelization is significant when considering the ordinary GPU platform.

With the increasing application of computer vision technology in autonomous driving, robot, and other mobile devices, more and more attention has been paid to the implementation of target detection and tracking algorithms on embedded platforms. The real-time performance and robustness of algorithms are two hot research topics and challenges in this field. In order to solve the problems of poor real-time tracking performance of embedded systems using convolutional neural networks and low robustness of tracking algorithms for complex scenes, this paper proposes a fast and accurate real-time video detection and tracking algorithm suitable for embedded systems. The algorithm combines the object detection model of single-shot multibox detection in deep convolution networks and the kernel correlation filters tracking algorithm, what is more, it accelerates the single-shot multibox detection model using field-programmable gate arrays, which satisfies the real-time performance of the algorithm on the embedded platform. To solve the problem of model contamination after the kernel correlation filters algorithm fails to track in complex scenes, an improvement in the validity detection mechanism of tracking results is proposed that solves the problem of the traditional kernel correlation filters algorithm not being able to robustly track for a long time. In order to solve the problem that the missed rate of the single-shot multibox detection model is high under the conditions of motion blur or illumination variation, a strategy to reduce missed rate is proposed that effectively reduces the missed detection. The experimental results on the embedded platform show that the algorithm can achieve real-time tracking of the object in the video and can automatically reposition the object to continue tracking after the object tracking fails.