Intensive Benchmarking of D2D communication over 5G cellular networks: prototype,integrated features,challenges, and main applications

Wireless Networks - The evolving Fifth generation (5G) cellular wireless networks are envisioned to provide higher data rates, lower end-to-end latency, and lower energy consumption for devices. In...     

Effects of THC on driving performance, physiological state and subjective feelings relative to alcohol

BACKGROUND: The effects of marijuana or THC on driving has been tested in several studies, but usually not in conjunction with physiological and subjective responses and not in comparison to alcohol effects on all three types of measures. OBJECTIVE: To assess the effects of two dosages of THC relative to alcohol on driving performance, physiological strain, and subjective feelings. METHOD: We tested the subjective feelings and driving abilities after placebo, smoking two dosages of THC (13 mg and 17 mg), drinking (0.05% BAC) and 24 h after smoking the high dose THC cigarette, while monitoring physiological activity of the drugs by heart rate. Fourteen healthy students, all recreational marijuana users, participated in the study. RESULTS: Both levels of THC cigarettes significantly affected the subjects in a dose-dependent manner. The moderate dose of alcohol and the low THC dose were equally detrimental to some of the driving abilities, with some differences between the two drugs. THC primarily caused elevation in physical effort and physical discomfort during the drive while alcohol tended to affect sleepiness level. After THC administration, subjects drove significantly slower than in the control condition, while after alcohol ingestion, subjects drove significantly faster than in the control condition. No THC effects were observed after 24 h on any of the measures.     

Elemental composition of Nigerian coals

Three analytical techniques (ASTM, FNAA and INAA) have been used to determine the major elemental composition, the percentage concentrations of Si, Fe, Al, as well as the concentrations of the trace elements Ca, K, Cl, Mg, Na and Ti of coals from seven major deposits in Nigeria. The results show that Nigerian coals are of weak or noncoking grades with low sulphur (0.52-2.04%) and high hydrogen (3.7-6.60%) contents. The coals possess properties which make them suitable feedstocks in gasification processes. They may also be used as blends with coking coals for sintering. Their potentials in the production of form coke in metallurgical and domestic applications, in addition to their suitability as thermal coals for electricity generation and steam raising in industries, provide incentives for their utilization.     

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each feature set. At layer 2 of the proposed model, the Optimized Genetic Algorithm (GA) is used to select one feature set based on the priority value. Modifications are done on standard GA to perform optimization and to fit the proposed model. The Optimized GA is used in the training phase to assign a priority value for each feature set. Also, the priority values are categorized into three categories: high, medium, and low. Besides, the Optimized GA is used in the testing phase to select a feature set based on its priority. The feature set with a high priority will be given a high priority to be selected. At the end of phase 2, an update for feature set priority may occur based on the selected features priority and the calculated F-Measures. The proposed model can learn and modify feature sets priority, which will be reflected in selecting features. For evaluation purposes, two well-known datasets are used in these experiments. The first dataset is UNSW-NB15, the other dataset is the NSL-KDD. Several evaluation criteria are used, such as precision, recall, and F-Measure. The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.     

Preemptive online scheduling with rejection of unit jobs on two uniformly related machines

We consider preemptive online and semi-online scheduling of unit jobs on two uniformly related machines. Jobs are presented one by one to an algorithm, and each job has a rejection penalty associated with it. A new job can either be rejected, in which case the algorithm pays its rejection penalty, or it can be scheduled preemptively on the machines, in which case it may increase the maximum completion time of any machine in the schedule, also known as the makespan of the constructed schedule. The objective is to minimize the sum of the makespan of the schedule of all accepted jobs and the total penalty of all rejected jobs. We study two versions of the problem. The first one is the online problem where the jobs arrive unsorted, and the second variant is the semi-online case, where the jobs arrive sorted by a non-increasing order of penalties. We also show that the variant where the jobs arrive sorted by a non-decreasing order of penalties is equivalent to the unsorted one. We design optimal online algorithms for both cases. These algorithms have smaller competitive ratios than the optimal competitive ratio for the more general problem with arbitrary processing times (except for the case of identical machines), but larger competitive ratios than the optimal competitive ratio for preemptive scheduling of unit jobs without rejection.     

Chitosan graft copolymers‐HA/DBM biocomposites: Preparation,characterization, and in vitro evaluation

The combination of biopolymer with a bioactive component takes advantage of the osteoconductivity and osteoinductivity properties. The studies on composites containing hydroxyapatite (HA), demineralized bone matrix (DBM) fillers and chitosan biopolymer are still conducted. In the present study, the bioactive fillers were loaded onto p(HEMA‐MMA) grafted chitosan copolymer to produce a novel biocomposites having osteoinductive and osteoconductive properties. The produced composites were assessed by TGA, XRD, FTIR, and SEM techniques to prove the interaction between both matrices. In vitro behavior of these composites was performed in SBF to verify the formation of apatite layer onto their surfaces and its enhancement. The results confirmed the formation of thick apatite layer containing carbonate ions onto the surface of biocomposites especially these containing HA‐DBM mixture and pMMA having bone cement formation in their structure. These a novel biocomposites have unique bioactivity properties can be applied in bone implants and tissue engineering applications as scaffolds in future. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

V<Subscript>2</Subscript><Emphasis Type="Italic">O</Emphasis><Subscript>5</Subscript>/SiO<Subscript>2</Subscript> as a Heterogeneous Catalyst in the Synthesis of bis(indolyl)methanes Under Solvent Free Condition

The heterogeneous catalyst of V₂ O ₅/SiO₂ was prepared and characterized with Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy. XRD of the silicon dioxide used reveals the amorphous nature while the spectrum of the prepared catalyst shows sharp intense peaks at about (20.2, 26.1, 31.0 and 47.3^°) and less intense sharp peaks at about (51.1, 55.2, 57.1 and 60.4^°) indicating formation of a crystalline phase with orthorhombic geometry. The FTIR spectra of the catalyst showed characteristic vibration stretching bands of V ?O at their specified position. An efficient and facile approach for the synthesis of bis(indolyl)methanes through a catalytic one pot reaction. Indole and aromatic aldehydes were stirred in the presence of a catalytic amount of the prepared and characterized heterogeneous catalyst V₂ O ₅/SiO₂ at 50^°C under solvent free condition. This procedure has advantages in competition with the previously reported methods, in terms of high yield, green catalyst, mild reaction condition, simple procedure, lack of toxicity, low cost, and simplicity of workup.     

Welfare through regulatory means: eviction and repossession policies in Singapore*

The study and provision of welfare have long been synonymous with direct social spending. The provision of welfare through regulatory means poses a complementary perspective to the study of social policy. In this context, this paper focuses on policies aimed at preventing mortgage borrowers’ eviction and repossession in Singapore, a world leader in state-led owner occupancy but a welfare laggard in terms of social spending. The findings show a disparity between a high rate of arrears on housing credit, and a low level of eviction and repossession. We test several explanations for this disparity, and argue that it is the result of policy aiming to minimize eviction and repossessions. This policy is driven by institutional interdependencies within the state, which have tied citizens’ housing credit to other aspects of their individual welfare savings. The findings shed light on the central role of regulation in welfare.     

On voting-based consensus of cluster ensembles

Voting-based consensus clustering refers to a distinct class of consensus methods in which the cluster label mismatch problem is explicitly addressed. The voting problem is defined as the problem of finding the optimal relabeling of a given partition with respect to a reference partition. It is commonly formulated as a weighted bipartite matching problem. In this paper, we present a more general formulation of the voting problem as a regression problem with multiple-response and multiple-input variables. We show that a recently introduced cumulative voting scheme is a special case corresponding to a linear regression method. We use a randomized ensemble generation technique, where an overproduced number of clusters is randomly selected for each ensemble partition. We apply an information theoretic algorithm for extracting the consensus clustering from the aggregated ensemble representation and for estimating the number of clusters. We apply it in conjunction with bipartite matching and cumulative voting. We present empirical evidence showing substantial improvements in clustering accuracy, stability, and estimation of the true number of clusters based on cumulative voting. The improvements are achieved in comparison to consensus algorithms based on bipartite matching, which perform very poorly with the chosen ensemble generation technique, and also to other recent consensus algorithms.