3D Transition Matrix Solution for a Path Dependency Problem of Markov Chains-Based Prediction in Cellular Networks

Handover (HO) management is one of the critical challenges in current and future mobile communication systems due to new technologies being deployed at a network level, such as small and femtocells. Because of the smaller sizes of cells, users are expected to perform more frequent HOs, which can increase signaling costs and also decrease user's performance, if a HO is performed poorly. In order to address this issue, predictive HO techniques, such as Markov chains (MC), have been introduced in the literature due to their simplicity and generality. This technique, however, experiences a path dependency problem, specially when a user performs a HO to the same cell, also known as a re-visit. In this paper, the path dependency problem of this kind of predictors is tackled by introducing a new 3D transition matrix, which has an additional dimension representing the orders of HOs, instead of a conventional 2D one. Results show that the proposed algorithm outperforms the classical MC based predictors both in terms of accuracy and HO cost when re-visits are considered

Introducing a Novel Minimum Accuracy Concept for Predictive Mobility Management Schemes

In this paper, an analytical model for the minimum required accuracy for predictive methods is derived in terms of both handover (HO) delay and HO signaling cost. After that, the total HO delay and signaling costs are derived for the worst-case scenario (when the predictive process has the same performance as the conventional one), and simulations are conducted using a cellular environment to reveal the importance of the proposed minimum accuracy framework. In addition to this, three different predictors; Markov Chains, Artificial Neural Network (ANN) and an Improved ANN (IANN) are implemented and compared. The results indicate that under certain circumstances, the predictors can occasionally fall below the applicable level. Therefore, the proposed concept of minimum accuracy plays a vital role in determining this corresponding threshold

Visible Light Generation and Mechanistic Investigation of High-Valent Metal-Oxo Species Supported by Different Ligands

Numerous transition metal catalysts have been designed as biomimetic model compounds for the active site of metalloenzymes found throughout Nature, most notably cytochrome P450 monooxygenases that carry out the oxidative transformations of organic substrates with near-perfect chemo-, regio-, and stereo-selectivity. The primary active oxidants in catalytic and enzymatic cycles are fleeting high-valent metal-oxo intermediates where the oxo ligand can transfer to an organic substrate in a process known as oxygen atom transfer (OAT). In the present work, porphyrin-manganese(III), salen-chromium(III), and salenmanganese( III) derivatives were successfully synthesized and spectroscopically characterized using 1H NMR and UV-Vis spectroscopies. A facile photochemical approach was applied for the successful production of porphyrin-manganese(IV)-oxo, salenchromium( V)-oxo, and salen-manganese(V)-oxo intermediates. The photochemistry in all circumstances was rationalized through the cleavage of the oxygen-halogen bond in bromate and chlorate photo-liable precursors under visible light irradiation. The results serve as a ‘proof-of-concept’ that photolysis reactions are not exclusive to porphyrin or corrole systems. Meanwhile, conventional chemical oxidation methods were applied to the generation of identical high-valent metal-oxo species using terminal oxidants, such as iodobenzene diacetate and m-chloroperoxybenzoic acid. The kinetics of OAT reactions of these generated metal-oxo intermediates with various organic substrates were studied, providing a direct comparison for their reactivities. In addition, the present catalytic studies demonstrated that porphyrinmanganese( III), and salen-chromium(III), and salen-manganese(III) complexes showed excellent activity and selectivity for the oxidation of sulfides, alkenes, and activated alkanes. Furthermore, kinetic and competition studies along with Hammett analyses were conducted on the chemically- and photo-generated metal-oxo species, providing detailed mechanistic insights into the potential reaction pathways and active intermediates. With porphyrin-manganese complexes, a direct oxygen atom transfer event occurs in the presence of reactive nucleophiles such as sulfides and porphyrin-manganese(IV)-oxo species; while a disproportionation mechanism in the case of weak nucleophiles such as hydrocarbons where the premier oxidant is manganese(V)-oxo species, although the manganese(V)-oxo species was spectroscopically detectable. In the instance of salenderivatives, there was no observed rate acceleration effect towards sulfides in the presence of electrophilic chromium(V)-oxo and manganese(V)-oxo cationic species. Presumably, the electrophilicity of these metal-oxo species was hampered due to the strong outer-sphere coordination of sulfoxide to the metal ion. The mechanistic studies imply that the observed chromium(V)-oxo and manganese(V)-oxo species are unlikely to serve as the primary oxidants under catalytic turnover conditions

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

The Effects of Triclosan on the Dvelopment of Rana Palustris

2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

Response of Daphnia Magna to Episodic Exposures of Several Types of Suspended Clay

2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

The Effects of Duration and Concentration of Episodic Zinc Exposure to the Fathead Minnow, P. promelas

2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

Effects of Episodic Copper Exposures on Population Fitness

2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

The Effect of Microplastic Fibers on the Freshwater Amphipod, Hyalella Azteca

Microplastics are a growing and persistent contaminant in aquatic ecosystems. There is a wide variety of shapes that MPs can take, with fibers being the most prominently found in marine systems. Few studies have investigated the toxicological implications of MP exposure to freshwater organisms, and none so far has quantified the effect that fibers, as compared to spherical particles, may have on aquatic organisms. A 42-day chronic exposure to polypropylene MP fibers (0 – 22.5 MPs/mL) was conducted in order to investigate potential effects on mortality, growth, reproduction, and egestion times. Significant mortality was only observed at the highest concentration (22.5 MPs/mL). Growth and reproduction is also significantly less than the control at all exposures to MP fibers, with no mating pairs forming at all in concentrations greater than 5.63 MPs/mL. Interestingly, gut clearance times after exposure to MP fibers is also greater at concentrations greater than 5.63 MPs/mL. Delays in reproduction and growth may result from deficiencies in nutrient uptake. This study provides further insight on how the shape of MPs may hold significant implications on their toxicity to aquatic organisms