Post-disaster 4G/5G Network Rehabilitation using Drones: Solving Battery and Backhaul Issues

Drone-based communications is a novel and attractive area of research in cellular networks. It provides several degrees of freedom in time (available on demand), space (mobile) and it can be used for multiple purposes (self-healing, offloading, coverage extension or disaster recovery). This is why the wide deployment of drone-based communications has the potential to be integrated in the 5G standard. In this paper, we utilize a grid of drones to provide cellular coverage to disaster-struck regions where the terrestrial infrastructure is totally damaged due to earthquake, flood, etc. We propose solutions for the most challenging issues facing drone networks which are limited battery energy and limited backhauling. Our proposed solution based mainly on using three types of drones; tethered backhaul drone (provides high capacity backhauling), untethered powering drone (provides on the fly battery charging) and untethered communication drone (provides cellular connectivity). Hence, an optimization problem is formulated to minimize the energy consumption of drones in addition to determining the placement of these drones and guaranteeing a minimum rate for the users. The simulation results show that we can provide unlimited cellular service to the disaster-affected region under certain conditions with a guaranteed minimum rate for each user.Comment: 2018 IEEE Global Communications Conference: Workshops: 9th International Workshop on Wireless Networking and Control for Unmanned Autonomous Vehicle

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

Report of the Librarian of the State Library, for the Year Ending September 30, 1875

The use of Unmanned Aerial Vehicles (UAVs) has gained interest in wireless networks for its many uses and advantages such as rapid deployment and multi-purpose functionality. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. They can be used as flying base-stations, which can be deployed in case of ground Base-Stations (GBSs) failures. Such failures can be short-term or longterm. Based on the type and duration of the failure, we propose a framework that uses drones or helikites to mitigate GBS failures. Our proposed short-term and long-term cell outage compensation framework aims to mitigate the effect of the failure of any GBS in 5G networks. Within our framework, outage compensation is done with the assistance of sky BSs (UAVs), An optimization problem is formulated to jointly minimize communication power of the UAVs and maximize the minimum rates of the Users\u27 Equipment (UEs) affected by the failure. Also, the optimal placement of the UAVs is determined. Simulation results show that the proposed framework guarantees the minimum quality of service for each UE in addition to minimizina the UAVs\u27 consumed energy

LONG-TERM CARDIOMETABOLIC EFFECTS OF PRECONCEPTIONAL AND PRENATAL OPIOID EXPOSURE IN THE ADULT OFFSPRING

Opioid use disorder (OUD), one of the greatest public health threats in the US, requires urgent attention. In 2022, the CDC reported 107,941 overdose deaths, over 70% attributed to synthetic opioids such as fentanyl (FEN). An alarming increase in opioid use disorder (OUD) diagnoses in pregnant people resulted in a 5-fold rise in cases of neonatal opioid withdrawal syndrome (NOWS). This condition elicits sympathetic overactivity, neural hyperexcitability, and a stress reaction characterized by tachycardia and hypertension. Whether prenatal exposure to fentanyl induces cardiovascular dysfunction in the offspring remains undetermined, impeding the timely development of strategies to mitigate OUD-related health risks in this vulnerable population. In the general population, OUD doubles the risk of developing cardiovascular disease, metabolic-like syndrome, dyslipidemia, and atherogenesis while increasing the risk of coronary artery disease by 16%. Preclinical reports have shown that opiates and the endogenous opioid system (EOS), which comprises opioid receptors (mu, delta, Kappa) and endogenous opioid peptides (enkephalins, dynorphins, endorphins), are critical modulators of cardiovascular and metabolic function in both normotensive and hypertensive states. In the fetus, natural and synthetic opioids can cross the placenta and blood-brain barrier, exposing infants to short and long-term health risks, including premature or still-births, NOWS, and altered neurodevelopment. However, the transplacental effects of opioids on the offspring\u27s cardiometabolic outcomes are poorly understood in the clinical and non-clinical literature. Therefore, this body of work aimed to develop preclinical OUD paradigms to investigate the effects of prenatal opioid exposure on the cardiometabolic outcomes of the offspring, identify potential underlying mechanisms, and test therapeutic approaches. First, we developed an animal model exposing Sprague Dawley dams with ramping doses of morphine (MOR), a typical mu-opioid receptor agonist, using 5-20mg/kg/day. A single MOR injection (5mg/kg/day, s.c.) from gestational day 1 (GD1-3) and increasing 5 mg/kg/day every 5 days from GD4 to 19. Prenatally, MOR-exposed adult female and male rats showed increased conscious mean arterial pressure (MAP) and sympathetic index, which were measured using arterial catheters, impaired vascular function, insulin resistance, glucose intolerance, increased low-density lipoprotein, and reduced renal function. Second, we developed a FEN self-administration (SA-FEN) paradigm to enhance further the translatability of the prenatal OUD model, where dams develop opioid dependence prior to conception and FEN-SA is continued until GD 14. Pups were video-recorded from postnatal days (PND) 1, 3, and 5. Somatic withdrawal signs were quantified to determine the global withdrawal score (GWS). The panel included body curls and stretching, foreleg, hindleg, and head movements, locomotion, spasms, and quietness. GWS was increased in FEN-exposed offspring up to postnatal day 5. Treatment with buprenorphine (1-0.2 mg/kg, s.c., 0.2 decrement/day, PND 1-5) trend to reduce GWS in FEN-exposed offspring. The cephalization index, a marker for brain development and is linked to an increased risk of neurodevelopmental disorders, was significantly higher in FEN-exposed offspring compared to VEH-exposed. FEN-exposed offspring displayed increased MAP and sympathetic activity. Additionally, Proenkephalin (PENK), a neurotransmitter that shows inhibitory effects on neuronal activation, protein levels were decreased in brain areas involved in blood pressure regulation. Thus, long-term PENK reduction could potentially contribute to impairing the mechanisms controlling pressor responses. Third, we tested the effect of prenatal opioid exposure in well-characterized opioid-mediated responses in the adult offspring. Overall, we found that prenatal opioid exposure reduced tolerance development to the hypoactive effect of morphine on locomotion, increased latency of thermal nociception, and increased FEN plasma levels in response to an acute FEN dose, without changes in FEN clearance. Altogether, our data suggest that prenatal opioid exposure permanently alters the response to opioids and pain threshold. Using these models, we determined that prenatal exposure to opioids increases signs of withdrawal, blood pressure and sympathetic tone, metabolic dysfunction, renal damage, differences in their response to analgesics, molecular changes in cardiovascular regulation, and dysregulation of PENK. Our study identifies the dysregulation of EOS components as a potential link between prenatal opioid exposure and cardiovascular and metabolic dysfunction. Further, we showed that the model developed can serve as a unique tool to test traditional medications used for OUD, and novel non-opioid therapeutic alternatives to improve pregnancy outcomes and offspring health

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