Partially pyrolyzed-non-activated olive stones: Characterization and utilization of olive stones partially-pyrolyzed at various temperatures for 2-chlorophenol removal from water

This paper reports the development of the chemical and structural properties of partially pyrolyzed olive stones (OS) (at 250, 400, 500, 600, 700 and 850 °C) intended for use as a less expensive and more environmental-friendly adsorbent within water treatment applications. The following properties were followed: mass loss, surface chemistry (acid/base titrations and IR analysis), crystalline matter and elemental analysis, SEM, BET and TGA analysis. The major mass loss (68%) occurred between 250 and 400 °C. Acidic oxides disappeared after 500 °C, while surface basicity increased with increasing pyrolysis temperature. The partially pyrolyzed-non-activated OS sorbents were used for 2-chlorohenol (2-CP) removal from water, where 2-CP uptake increased with increasing pyrolysis temperature. The maximum adsorption was recorded at pH 7 using the pyrolyzed OS at 850 °C, which was only 13% more than that of OS pyrolyzed at 600 °C (sorbent carb600). So that carb600 (adsorption capacity: 34.1 mg g−1) was recommended as a cost-effective-environmental-friendly adsorbent. The re-usability of carb600 for removing 2-chlorophenol from real water sample was evident, where ∼70% of its adsorption efficiency was reserved even in the presence of competing ions

CONTEST : a Controllable Test Matrix Toolbox for MATLAB

Large, sparse networks that describe complex interactions are a common feature across a number of disciplines, giving rise to many challenging matrix computational tasks. Several random graph models have been proposed that capture key properties of real-life networks. These models provide realistic, parametrized matrices for testing linear system and eigenvalue solvers. CONTEST (CONtrollable TEST matrices) is a random network toolbox for MATLAB that implements nine models. The models produce unweighted directed or undirected graphs; that is, symmetric or unsymmetric matrices with elements equal to zero or one. They have one or more parameters that affect features such as sparsity and characteristic pathlength and all can be of arbitrary dimension. Utility functions are supplied for rewiring, adding extra shortcuts and subsampling in order to create further classes of networks. Other utilities convert the adjacency matrices into real-valued coefficient matrices for naturally arising computational tasks that reduce to sparse linear system and eigenvalue problems

Using periodicity intensity to detect long term behaviour change

This paper introduces a new way to analyse and visualize quantified-self or lifelog data captured from any lifelogging device over an extended period of time. The mechanism works on the raw, unstructured lifelog data by detecting periodicities, those repeating patters that occur within our lifestyles at different frequencies including daily, weekly, seasonal, etc. Focusing on the 24 hour cycle, we calculate the strength of the 24-hour periodicity at 24-hour intervals over an extended period of a lifelog. Changes in this strength of the 24-hour cycle can illustrate changes or shifts in underlying human behavior. We have performed this analysis on several lifelog datasets of durations from several weeks to almost a decade, from recordings of training distances to sleep data. In this paper we use 24 hour accelerometer data to illustrate the technique, showing how changes in human behavior can be identified

Fuchsian analysis of singularities in Gowdy spacetimes beyond analyticity

Fuchsian equations provide a way of constructing large classes of spacetimes whose singularities can be described in detail. In some of the applications of this technique only the analytic case could be handled up to now. This paper develops a method of removing the undesirable hypothesis of analyticity. This is applied to the specific case of the Gowdy spacetimes in order to show that analogues of the results known in the analytic case hold in the smooth case. As far as possible the likely strengths and weaknesses of the method as applied to more general problems are displayed.Comment: 14 page

A meta-analysis of metal biosorption by suspended bacteria from three phyla

Biosorption of heavy metals by bacterial biomass has been the subject of significant research interest in last decades due to its efficiency, relatively low cost and minimal negative effects for the surrounding environment. In this meta-analysis, the biosorption efficiencies of different bacterial strains for Cu(II), Cd(II), Zn(II), Cr(III), Mn(II), Pb(II) and Ni(II) were evaluated. Optimum conditions for the biosorption process such as initial metal concentration, temperature, pH, contact time, metal type, biomass dosage and bacterial phyla, were evaluated for each heavy metal. According to the results, the efficiencies of bacterial biomass for removal of heavy metal were as follows: Cd(II) > Cr(III) > Pb(II) > Zn(II) > Cu(II) > Ni(II) > Mn(II). Firmicute phyla showed the highest overall (living and dead) biosorption efficiency for heavy metals. Living biomass of Proteobacteria had the best biosorption performance. Living bacterial biomass was significantly more efficient in biosorption of Cu(II), Zn(II) and Pb(II) than dead biomass. The maximum biosorption efficiency of bacterial strains for Cd(II), Pb(II) and Zn(II) was achieved at pH values between 6 and 7.5. High temperatures (>35 °C) reduced the removal efficiencies for Cu(II) and Zn(II) and increased the efficiencies for Cd(II) and Cr(III) ions. The maximum biosorption efficiency of non-essential heavy metals occurred with short contact times (24 h). The mean biosorption capacity of bacterial biomass was between 71.26 and 125.88 mg g−1. No publication bias existed according to Egger’s and Begg’s test results

Behavioral periodicity detection from 24h wrist accelerometry and associations with cardiometabolic risk and health-related quality of life

Periodicities (repeating patterns) are observed in many human behaviors. Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health. We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data. GENEActiv accelerometer data were collected from 20 participants (17 men, 3 women, aged 35–65) continuously for (range: 13.9 to 102.0) consecutive days. Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline. Periodograms were constructed to determine patterns emergent from the accelerometer data. Periodicity strength was calculated using circular autocorrelations for time-lagged windows. The most notable periodicity was at 24 h, indicating a circadian rest-activity cycle; however, its strength varied significantly across participants. Periodicity strength was most consistently associated with LDL-cholesterol (’s = 0.40–0.79, ’s < 0.05) and triglycerides (’s = 0.68–0.86, ’s < 0.05) but also associated with hs-CRP and health-related quality of life, even after adjusting for demographics and self-rated physical activity and insomnia symptoms. Our framework demonstrates a new method for characterizing behavior patterns longitudinally which captures relationships between 24 h accelerometry data and health outcomes