Tuning the metamagnetism of an antiferromagnetic metal

We describe a `disordered local moment' (DLM) first-principles electronic structure theory which demonstrates that tricritical metamagnetism can arise in an antiferromagnetic metal due to the dependence of local moment interactions on the magnetisation state. Itinerant electrons can therefore play a defining role in metamagnetism in the absence of large magnetic anisotropy. Our model is used to accurately predict the temperature dependence of the metamagnetic critical fields in CoMnSi-based alloys, explaining the sensitivity of metamagnetism to Mn-Mn separations and compositional variations found previously. We thus provide a finite-temperature framework for modelling and predicting new metamagnets of interest in applications such as magnetic cooling

Trend Analysis of Maximum and Minimum Temperature over Ebonyi State, Nigeria

This study examined the trend in annual maximum and minimum temperature data in Ebonyi State using data collected from the Nigerian Meteorological Agency of Nigeria. The aim is to reveal the spatial and temporal variation in maximum and minimum temperature from 1984-2015. The study made use of statistical tool like linear regression to depict the trend in annual maximum and minimum temperature over time. Results indicate that maximum temperature possesses steepness in slope of 0.0046? and minimum temperature 0.002?. The maximum temperature equation shows a positive time trend which suggests that maximum temperature of Ebonyi State of Nigeria tends to increase over time. Decadal analysis of the climate variable revealed that the 1994-2003 decade has the highest maximum temperature followed by 2004-2015 decade and then 1984-1993. This shows that the 1984-1993 decade was cooler than the later decade which is a pointer to the increasing temperature being experienced in form of global warming with its attendant environmental consequences. The study therefore recommends that proactive measure like making accurate and appropriate weather and climate data available for planning should be encouraged by all to mitigate these consequences especially on population whose livelihood depends on agriculture that is temperature sensitive

Polymers From The Hydrolysis Of Tetraethoxysilane

The hydrolysis of tetraethoxysilane was studied under various solvent and temperature conditions. A method was developed by which reaction rates can be measured even in the presence of alcohols other than ethanol. The structures of the resulting polymers were dependent upon the reaction conditions employed, those formed at high temperature being highly condensed materials. © 1973

The Effect of Electronic Cigarette User Modifications and E-liquid Adulteration on the Particle Size Profile of an Aerosolized Product

Electronic cigarettes (e-cigarettes) are an alternate nicotine delivery system that generate a condensation aerosol to be inhaled by the user. The size of the droplets formed in the aerosol can vary and contributes to drug deposition and ultimate bioavailability in the lung. The growing popularity of e-cigarette products has caused an increase in internet sources promoting the use of drugs other than nicotine (DOTNs) in e-cigarettes. The purpose of this study was to determine the effect of various e-cigarette and e-liquid modifications, such as coil resistance, battery voltage, and glycol and drug formulation, on the aerosol particle size. E-liquids containing 12 mg/mL nicotine prepared in glycol compositions of 100% propylene glycol (PG), 100% vegetable glycerin (VG), or 50:50 PG:VG were aerosolized at three voltages and three coil resistances. Methamphetamine and methadone e-liquids were prepared at 60 mg/mL in 50:50 PG:VG and all e-liquids were aerosolized onto a 10 stage Micro-Orifice Uniform Deposit Impactor. Glycol deposition correlated with drug deposition, and the majority of particles centered between 0.172–0.5 μm in diameter, representing pulmonary deposition. The 100% PG e-liquid produced the largest aerosol particles and the 100% VG and 50:50 PG:VG e-liquids produced ultra-fine particles \u3c0.3 μm. The presence of ultrafine particles indicates that drugs can be aerosolized and reach the pulmonary alveolar regions, highlighting a potential for abuse and risk of overdose with DOTNs aerosolized in an e-cigarette system

A maximum entropy classification scheme for phishing detection using parsimonious features

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM)

Semantics-based clustering approach for similar research area detection

The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of Ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results