PurposeDrawing from social cognitive theory, the purpose of this study is to examine how personal, environmental and behavioral factors can interplay to influence people's use of YouTube as a learning resource.Design/methodology/approachThis study proposed a conceptual model, which was then tested with data collected from a survey with 150 participants who had the experience of using YouTube for learning. The bootstrap method was employed to test the direct and mediation hypotheses in the model.FindingsThe results revealed that personal factors, i.e. learning outcome expectations and attitude, had direct effects on using YouTube as a learning resource (person → behavior). The environmental factor, i.e. the sociability of YouTube, influenced the attitude (environment → person), while the behavioral factor, i.e. prior experience of learning on YouTube, affected learning outcome expectations (behavior → person). Moreover, the two personal factors fully mediated the influences of sociability and prior experience on YouTube usage for learning.Practical implicationsThe factors and their relationships identified in this study provide important implications for individual learners, platform designers, educators and other stakeholders who encourage the use of YouTube as a learning resource.Originality/valueThis study draws on a comprehensive theoretical perspective (i.e. social cognitive theory) to investigate the interplay of critical components (i.e. individual, environment and behavior) in YouTube's learning ecosystem. Personal factors not only directly influenced the extent to which people use YouTube as a learning resource but also mediated the effects of environmental and behavioral factors on the usage behavior.

Underwater Wireless Sensor Networks (UWSNs) face numerous challenges due to small bandwidth, long propagation delay, limited energy resources and high deployment cost. Development of efficient routing strategies is, therefore, mandatory and has remained the focus of researchers over the past few years. To address these challenges and to further improve the performance of the existing protocols, many routing protocols have been designed. In Weighting Depth and Forwarding Area Division-Depth Based Routing (WDFAD-DBR), the forwarding decision is based on the weighting depth difference, which is not an efficient way for void hole avoidance. In this paper, we propose a depth-based routing mechanism called Energy Balanced Efficient and Reliable Routing (EBER 2) protocol for UWSNs. First, energy balancing among neighbors and reliability are achieved by considering residual energy and the number of Potential Forwarding Nodes (PFNs) of the forwarder node, respectively. Secondly, energy efficiency is enhanced by dividing the transmission range into power levels, and the forwarders are allowed to adaptively adjust their transmission power according to the farthest node in their neighbor list. Thirdly, duplicate packets are reduced by comparing depths, residual energy and PFNs among the neighbors. Moreover, network latency is decreased by deploying two sinks at those areas of the network that have high traffic density. The results of our simulations show that EBER 2 has higher Packet Delivery Ratio (PDR), lower energy tax, and lesser duplicate packets than the WDFAD-DBR routing protocol. INDEX TERMS Underwater wireless sensor networks (UWSNs), potential forwarding nodes (PFNs), packet delivery ratio (PDR), end-to-end delay (E2ED), void hole.

The increased need to gather scientific data and the renewed drive to explore underwater natural resources has led more and more researchers to study the underwater environment. This has resulted in enormous attention being given to Underwater Wireless Sensor Networks (UWSNs) all over the world. However, UWSNs are faced with some major challenges including harsh environment, higher propagation delay, and limited battery power of the sensor nodes. To address these challenges, several routing schemes have been proposed. In this paper, we propose a routing strategy, called Reliable Path Selection and Opportunistic Routing (RPSOR) for UWSNs, which is a significantly improved version of Weighting Depth and Forwarding Area Division Depth Based Routing (WDFAD-DBR). RPSOR is based on three main factors: Advancement factor (ADV f), which depends on the depth of current as well as next hop forwarding node; Reliability index (REL i), which depends on the energy of the current forwarder as well as average energy in the next expected forwarding region; and Shortest Path Index (SP i), which is calculated on the basis of number of hops to the sink and average depth of neighbors in the next expected hop. To deal with the void hole problem and improve the Packet Delivery Ratio (PDR), we follow the more reliable path towards the sink by calculating REL i for a node. At the end, we perform extensive simulations and compare our proposed scheme with WDFAD-DBR, the results of which prove that RPSOR shows better performance in terms of PDR and energy tax in comparison to WDFAD-DBR. However, the proposed work compromises end-to-end delay in sparse networks. INDEX TERMS Underwater wireless sensor networks (UWSNs), reliability, potential forwarding nodes (PFNs), end-to-end delay (E2ED), packet delivery ratio (PDR), priority function (PF), void hole.

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Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.