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Structural Equation Modelling (SEM) has been widely applied in information systems, psychology, marketing, management, and other social science disciplines, providing a powerful framework for analysing relationships among latent variables. However, traditional SEM methods rely on assumptions of linearity and normality, which may limit their ability to represent complex or nonlinear data patterns. Recent advances in computational modelling have introduced Deep SEM (or Neural SEM), an approach that integrates deep learning components within SEM. This hybrid framework combines SEM’s theoretical and explanatory strengths with the representational flexibility of neural networks. In this paper, we provide an overview of Deep SEM, demonstrate its implementation in R using the SEMdeep package, and compare its explanatory and predictive behaviour with that of a traditional covariance-based SEM under identical data conditions. Using an illustrative and parsimonious neural architecture, the results show that Deep SEM yields higher in-sample explained variance across endogenous constructs while preserving the dominant theoretical pathways identified by SEM. These findings suggest that Deep SEM offers a complementary extension to conventional SEM, enabling researchers to explore potential nonlinearities while maintaining interpretability and theoretical coherence.

Wireless Sensor Network (WSN) is composed of numerous tiny smart sensors nodes integrated with Internet of Things (IoT) play a crucial role in many applications. The IoT connects physical devices to form a network which consist of software, sensor for exchange of information. Clustering is most common technique for efficient energy utilization in WNS. Sensor nodes when they have data, forwards it to Cluster Head (CH) and CH transfers the received data from the sensor nodes to the sink. When the sink nodes are far away from CH, long-haul transmission consumes higher power. In this paper we propose efficient data transfer mechanism for clustered IoT network through the cooperation of member nodes. First, we use greedy algorithm to select cooperative sensor nodes to act as relay for long distance transmission. Then, to encourage sensor nodes in data forwarding, cluster head uses priority buffers to prioritize assisting sensor nodes data. Simulation results show that, the proposed approach conserves energy and increases the life-time of clustered IoT network.

Road-safety data in Vehicular Ad Hoc Networks (VANETs) is becoming increasingly complex and diverse which leads to major challenges such as security flaws, ineffective data transmission, and risk of single-point failures. To address these issues, the Information-centric Vehicular Cloud (IVC) network has been adopted to secure data transactions. However, existing IV-based approaches still suffer from high latency and low Packet Delivery Ratio (PDR), which negatively impact the system performance. To overcome these limitations, the research proposed an efficient and secure data validation technique for VANETs. Vehicles generate various types of road safety data, which are transmitted to the destination via unicast forwarding. Caching vehicles are used to store frequently requested data to reduce latency and improve access speed. Also, a probabilistic data verification strategy is implemented, where vehicles verify transient data packets with a certain probability and exchange verification output to enhance accuracy. Each content provider signs the data and attaches verification metadata before transmission, which enables intermediate nodes to validate the content in transit. At the network edge, a Randomized Independent Verification Protocol (RIVP) with a Bloom filter is proposed to rapidly verify content authenticity, even when the complete verification information is embedded in the data itself. Experimental output illustrates that the proposed technique achieves a verification accuracy of 98.95% and a low verification overhead of 1.629%, which outperforms the existing method in terms of both security and efficiency.

This paper compares two popular R packages for structural equation modelling (SEM) – lavaan and seminr – to help researchers understand not only how they work, but also when each is most appropriate. Although both tools allow users to estimate the same structural models, they are grounded in different methodological traditions and are designed to support different research goals. Using an identical model, we estimated results with both covariance-based SEM (lavaan) and variance-based SEM (seminr) and compared their outputs, including model specification syntax, evaluation criteria, and reporting conventions. The results show that both approaches lead to substantively similar conclusions regarding the relationships between constructs, while differing in emphasis: lavaan provides richer global model-fit diagnostics, whereas seminr places greater emphasis on prediction-oriented assessment and convenient access to latent variable scores. The contribution of this study lies in its practical, hands-on demonstration rather than in a theoretical or simulation-based comparison. The findings reinforce that there is no universally “better” SEM approach; instead, methodological choice should be guided by the research objective. Researchers focussed on theory testing may benefit more from lavaan, while those prioritising prediction or exploratory analysis may find seminr more suitable. Ultimately, considering both perspectives can support more transparent, robust, and methodologically appropriate SEM applications.

The violation of trust as a result of interactions that do not proceed as expected gives rise to the question as to whether broken trust can possibly be recovered. Clearly, trust recovery is more complex than trust initialization and maintenance. Trust recovery requires a more complex mechanism to explore different factors that cause the decline of trust and identify the affected individuals of trust violation both directly and indirectly. In this study, an extended framework for recovering trust is presented. Aside from evaluating whether there is potential for recovery based on the outcome of a forgiveness mechanism after a trust violation, encouraging cooperation between interacting parties after a trust violation through incentive mechanisms is also important. Furthermore, a number of experiments are conducted to validate the applicability of the framework and the findings show that the e-marketplace incorporating our proposed framework results in improved efficiency of trading, especially in long-term interactions.

The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)-assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour.

Rainfall variability in tropical climates is influenced by interacting thermal processes, yet maximum (TMAX) and minimum (TMIN) temperatures are often highly collinear, complicating the estimation of their distinct relationships with precipitation. This study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to long-term station-based climate data from Ghana (1981-2020; N = 252 station × calendar month observations) to examine structural associations among TMAX, TMIN, and rainfall (RAIN), including differences between coastal and inland regimes. The model represents a simplified component of the broader hydroclimatic system. The results indicate a statistically significant negative association between TMAX and rainfall (β = -0.454, p < 0.001) and a positive association between TMIN and rainfall (β = 0.166, p < 0.001). The relationship between TMAX and TMIN is positive but not statistically significant (β = 0.152, p = 0.053). Mediation analysis does not support a significant indirect pathway from TMAX to rainfall via TMIN (β = 0.025, p = 0.076). The model explains 21.1% of rainfall variance. Multi-group analysis reveals spatial heterogeneity, with a stronger negative TMAX-rainfall association inland (β = -0.721) than along the coast (β = -0.427), and a stronger TMAX-TMIN association in coastal regions (β = 0.753 vs. 0.337). The findings suggest that TMAX and TMIN exhibit distinct statistical relationships with rainfall that vary across climatic regimes. However, given the limited variables and observational design, the results should be interpreted as structural associations rather than definitive physical mechanisms. The study demonstrates the utility of PLS-SEM for handling multicollinearity and interdependent relationships in climate data while highlighting the need to incorporate additional atmospheric variables to improve explanatory depth.

The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)-assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour.

This paper compares two popular R packages for structural equation modelling (SEM) – lavaan and seminr – to help researchers understand not only how they work, but also when each is most appropriate. Although both tools allow users to estimate the same structural models, they are grounded in different methodological traditions and are designed to support different research goals. Using an identical model, we estimated results with both covariance-based SEM ( lavaan ) and variance-based SEM ( seminr ) and compared their outputs, including model specification syntax, evaluation criteria, and reporting conventions. The results show that both approaches lead to substantively similar conclusions regarding the relationships between constructs, while differing in emphasis: lavaan provides richer global model-fit diagnostics, whereas seminr places greater emphasis on prediction-oriented assessment and convenient access to latent variable scores. The contribution of this study lies in its practical, hands-on demonstration rather than in a theoretical or simulation-based comparison. The findings reinforce that there is no universally “better” SEM approach; instead, methodological choice should be guided by the research objective. Researchers focussed on theory testing may benefit more from lavaan , while those prioritising prediction or exploratory analysis may find seminr more suitable. Ultimately, considering both perspectives can support more transparent, robust, and methodologically appropriate SEM applications.

Software configurable radio with dynamic spectrum support is the inherent property of Cognitive radio. Interoperability of Cognitive radio with wireless sensor network would enable the sensor nodes to access and transmit the application data in licensed PU free channels. Since wireless sensor nodes operate in heavily crowded ISM bands (902 MHz/2.4 GHz), there will be severe performance degradation during channel saturation and increased collision rate. With opportunistic spectrum access, enhanced performance can be achieved by minimizing the channel access collisions and control message overhead delays. To achieve, this paper proposes a novel approach to integrate the sensor nodes with cognitive radio (CR) nodes to route sensor data to sink using licensed channels opportunistically. We further extend our novel approach to cluster sensor node and CR nodes to achieve energy efficiency. Simulation results show that the proposed solution with dynamic spectrum access enhances the throughput, minimizes energy consumption and reduces the delay compared with existing solutions.