Global Cement Industry: Competitive and Institutional Dimensions

The cement industry is a capital intensive, energy consuming, and vital industry for sustaining infrastructure of nations. The international cement market –while constituting a small share of world industry output—has been growing at an increasing rate relative to local production in recent years. Attempts to protect the environment in developed countries –especially Europe—have caused cement production plants to shift to countries with less stringent environmental regulations. Along with continually rising real prices, this has created a concerning pattern on economic efficiency and environmental compliance. This paper attempts to critically analyze the forces affecting pricing and production of cement from two perspectives. Porter’s five forces serve as our tool to analyze the competitive forces that move the industry from a market economy standpoint. On the other hand, the institutional economics framework serves to explain how governments and policymakers influence the structure and production distribution in the global market. Our findings suggest that the cement industry does not follow expected patterns of a market economy model. Additionally, it does not fully behave along the institutional economics paradigm. Hence, neither perspective explains the pricing or nature of the market on its own. Combining market forces within an institutional setting provides a more clear understanding of price dynamics and industry performance. We find that local regulation alone is insufficient to ensure market efficiency due to weak institutional governance in developing countries aligned with private business interests of global cement firms. Moreover, the global impact of local environmental non-compliance generates economic spillover effects that cannot be corrected by market forces alone. Due to asymmetries in governance and structure, this paper recommends the establishment of an independent international regulatory body for the cement industry that serves to provide sustainable industry development guidelines within a global context.Keywords: cement – global industry– institutional economics – Porter competition – market niche

D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service

In this paper, we consider the use of UAVs to provide wireless connectivity services, for example after failures of wireless network components or to simply provide additional bandwidth on demand, and introduce the concept of UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework, dubbed D3S, which consists of four phases: demand, decision, deployment, and service. The main objective of this framework is to develop efficient and realistic solutions to implement these four phases. The technical problems include determining the type and number of UAVs to be deployed, and also their final locations (e.g., hovering or on-ground), which is important for serving certain applications. These questions will be part of the decision phase. They also include trajectory planning of UAVs when they have to travel between charging stations and deployment locations and may have to do this several times. These questions will be part of the deployment phase. The service phase includes the implementation of the backbone communication and data routing between UAVs and between UAVs and ground control stations

SURE: A Novel Approach for Self Healing Battery Starved Users using Energy Harvesting

Radio Frequency (RF) energy harvesting holds a promising future for energizing low power mobile devices in next generation wireless networks. Harvesting from a dedicated RF energy source acquires much more energy than simply harvesting from ambient RF sources. In this paper, novel Self-healing of Users equipment by RF Energy transfer scheme is introduced between the network operator and battery starved users to heal and extend their battery life time by sending dedicated energy from different sources in order to be aggregated and harvested by starved users. This approach depends on the concept of Energy as a Service where the network operator delivers energy to battery starved users in the next generation networks. A mixed integer non-linear optimization problem is formulated and solved efficiently using three heuristic algorithms. Simulation results prove that sufficient amounts of energy can be delivered to starved users while minimizing their uplink power requirements and guaranteeing a minimum uplink data rate

Blockage Prediction for Mobile UE in RIS-assisted Wireless Networks: A Deep Learning Approach

Due to significant blockage conditions in wireless networks, transmitted signals may considerably degrade before reaching the receiver. The reliability of the transmitted signals, therefore, may be critically problematic due to blockages between the communicating nodes. Thanks to the ability of Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with different reflection angles, this may counter the blockage effect by optimally reflecting the transmit signals to receiving nodes, hence, improving the wireless network's performance. With this motivation, this paper formulates a RIS-aided wireless communication problem from a base station (BS) to a mobile user equipment (UE). The BS is equipped with an RGB camera. We use the RGB camera at the BS and the RIS panel to improve the system's performance while considering signal propagating through multiple paths and the Doppler spread for the mobile UE. First, the RGB camera is used to detect the presence of the UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is then used to detect if the UE is either "present but blocked" or "absent". The problem is determined as a ternary classification problem with the goal of maximizing the probability of UE communication blockage detection. We find the optimal solution for the probability of predicting the blockage status for a given RGB image and RIS-assisted data rate using a deep neural learning model. We employ the residual network 18-layer neural network model to find this optimal probability of blockage prediction. Extensive simulation results reveal that our proposed RIS panel-assisted model enhances the accuracy of maximization of the blockage prediction probability problem by over 38\% compared to the baseline scheme

Entreprenuerial Intention Among Nigerian University Students

Entreprenuerial intention (EI) is one of the major contributing factors to the formation, growth and development of entrepreneurship. It promotes self reliance and brings about initiatives. Entreprenuership on the other hand, has been considered as an engine of growth for economic growth and development of developed and emerging economies. Acadamic discussions and policy initiatives are increasing nowadays on the vital role of entrepreneurial development in a society. This has been buttressed by the recent loss of jobs due to 2008-2009 financial crisis as well as increasing unemloyment across the globe. In Nigeria, the official rate of unemployment is around 24 percent while 46.5 percent of the youth population are unemployed. The unemployed rate among gradutes has increased from 25.6 percent in 2003 to 42.7 percent in 2011.  This unwelcome development requires a concerted effort at academic and governmental levels.  The objective of this paper is to examine the entreprenuerial intenton among University students in Nigeria.  This will help to identify entreprenuerial intention which determines behaviour as well as the need to gauge entrepreneurial awareness among the respondents.  The paper uses a modified version of Theory of Planned Behaviour (TPB) as the main framework of examining entrepreneurial intention.  A sample size of 205 was drawn from Abubakar Tafawa Balewa Univesity (ATBU).  Data was analysed using structural equation modeling. The findings show that, entrepreneurial attitude, subjective norm and power of behavioural control are all significant predictors of EI. In addition, other indirect relationships were also found to be significant.  Overall the result shows the model fits the the data well