Performance of reactivated carbon nanotubes in adsorbing cadmium from aqueous solution

cadmium loaded carbon nanotubes (CNTs) for re-adsorption of the same metal cadmium (Cd). The desorption of cadmium ions (Cd2+) by batch mode laboratory experiments was investigated for hydrochloric and nitric acids, where the former gave better desorption compared to the other acid. Initial experiments revealed that hydrochloric acid (HCl) was better than nitric acid (HNO3)for the desorption of Cd2+ from the CNTs. Optimization study was conducted by design expert software using various molarities of HCl, which resulted in pH of 1.39 (0.1 M), pH 2.43 (0.01 M) and pH 3.53 (0.005 M). Contact time of 20, 50, 80, 110 and 140 minutes were used with fixed agitation of 200 rpm to study the effect of time on the desorption process. Statistical model was developed for the optimum desorption process, which provided a regression model with R2 value of 0.987. The desorbed CNTs were washed with deionized water to remove residual acids and then dried for re-adsorption process. The re-adsorption capacity of cadmium was also determined by batch mode experiments. This study revealed that pH and contact time influenced the desorption and re-adsorption capacity of the CNTs. The optimum condition for desorption was pH 1.39 (0.1 M) hydrochloric acid for 50 minutes agitation. This study also revealed that with this optimum condition, three (3) cycles of desorption process was necessary to remove all cadmium ion from the used CNTs. Re-adsorption capacity of the CNTs, after 3 cycles, was reduced from 8.28 mg/g to 4.23 mg/g. This indicated that about 50% of the adsorption capacity of the CNTs were destroyed or reduced due to the desorption process. Such reduction can also be linked to the destruction of the active sites of the CNT adsorbents

Autonomic arousal in a foreign language in the context of decision making

We test emotional distancing in a second language (L2) by replicating an experiment by Keysar, Hayakawa, and An (2012) on making decisions under the framing effect (Kahneman and Tversky, 1979). With their participants' average Age of Acquisition (AoA) being around and beyond puberty, autonomic arousal was evident in native language (L1) but absent in L2. Our study showed no difference between L1 and L2 when AoA was around 4. However, when average AoA was around 7.7, autonomic arousal was evident in L1 but absent in L2, predicting an AoA threshold affecting L2 affective processing significantly earlier than puberty

Influence of Storage Temperature and Duration of Tomato Leaf Samples on Proline Content

In arid and semi-arid countries such as Jordan, shortage in water sources might affect agricultural development and reduces the effectiveness of economic benefits of most crops planted in such areas. Tomato is an important agricultural crop and faces severe drought stress due to climate changes, therefore, measurement of proline accumulation in plant tissues is used as an indicator for drought stress tolerance. This research was conducted at Jarash University Campus in northern Jordan. A field experiment was carried out to investigate the impact of different storage temperature (+4ºC, - 20ºC and -80ºC) and different storage durations (0, 3, 6 and 11 weeks) on proline content in five different Jordanian tomato landraces. Results indicated that the average free proline content for samples tested directly after leaves collection was 7.1 µmol/g. Proline content in leaves stored at +4 ºC for 3, 6, and 11 weeks was 4.8, 1.8, and 1.1µmol/g, respectively, while for -20ºC was 11.8, 7.9, and 9.5 µmol/g for samples stored for 3, 6, 11 weeks respectively. In contrast the highest values for these parameters were obtained from samples stored at -80ºC, the average measured values of free proline content were 9.5, 7.8, and 12.9 µmol/g at 3, 6, and 11 weeks of storage, respectively. Based on the results obtained by this research, it is recommended to measure proline content directly after leaves collection. However, for large number of samples, keeping the samples at -20ºC not longer than six weeks could be a solution. Finally, we highly recommend the development of in-field method for measurement of free proline content

Optimisation of arsenic adsorption from water by carbon nanofibres grown on powdered activated carbon impregnated with nickel

Contamination of water due to arsenic (As) is increasing in many parts of the world. The removal of As from aqueous solution by using impregnated carbon nanofibres (CNFs) as the adsorbent is reported in this paper. The effects of pH, CNFs dosage, contact time and initial concentration of arsenic were studied at room temperature (±25°C). The interactions among the parameters were also investigated. The data obtained from the adsorption experiment were analysed using statistical software in order to develop a regression equation to represent the optimum operating conditions. The interactions of each parameters were considered during this analysis and the result indicated that the highest removal (97.25%) of As can be attained at pH 6, initial concentration of arsenic of 0.08 mg L-1, contact time of 60 min and CNF dosage of 200 mg L-1. Comparison between impregnated CNF and Powdered Activated Carbon (PAC) were also done and it is determined that impregnated CNF has better removal compared to PAC alone. The final concentration of As after the treatment using CNFs was about 8 ~ 10 times less than that of using PAC. Therefore, it can be concluded that CNFs are highly potential for the adsorption of As from water

Sri Lankan tsunami refugees: a cross sectional study of the relationships between housing conditions and self-reported health

BACKGROUND: On the 26th December 2004 the Asian tsunami devastated the Sri Lankan coastline. More than two years later, over 14,500 families were still living in transitional shelters. This study compares the health of the internally displaced people (IDP), living in transitional camps with those in permanent housing projects provided by government and non-government organisations in Sri Lanka. METHODS: This study was conducted in seven transitional camps and five permanent housing projects in the south west of Sri Lanka. Using an interviewer-led questionnaire, data on the IDPs' self-reported health and housing conditions were collected from 154 participants from transitional camps and 147 participants from permanent housing projects. Simple tabulation with non-parametric tests and logistic regression were used to identify and analyse relationships between housing conditions and the reported prevalence of specific symptoms. RESULTS: Analysis showed that living conditions were significantly worse in transitional camps than in permanent housing projects for all factors investigated, except 'having a leaking roof'. Transitional camp participants scored significantly lower on self-perceived overall health scores than those living in housing projects. After controlling for gender, age and marital status, living in a transitional camp compared to a housing project was found to be a significant risk factor for the following symptoms; coughs OR: 3.53 (CI: 2.11-5.89), stomach ache 4.82 (2.19-10.82), headache 5.20 (3.09-8.76), general aches and pains 6.44 (3.67-11.33) and feeling generally unwell 2.28 (2.51-7.29). Within transitional camp data, the only condition shown to be a significant risk factor for any symptom was household population density, which increased the risk of stomach aches 1.40 (1.09-1.79) and headaches 1.33 (1.01-1.77). CONCLUSION: Internally displaced people living in transitional camps are a vulnerable population and specific interventions need to be targeted at this population to address the health inequalities that they report to be experiencing. Further studies need to be conducted to establish which aspects of their housing environment predispose them to poorer health

Is Adherence to Imatinib Mesylate Treatment Among Patients with Chronic Myeloid Leukemia Associated with Better Clinical Outcomes in Qatar?

Background: Despite the revolutionary success of introducing tyrosine kinase inhibitors (TKIs), such as imatinib mesylate (IM), for treating chronic myeloid leukemia (CML), a substantial proportion of patients' treatments fail. Aim: This study investigates the correlation between patient adherence and failure of TKIs' treatment in a follow-up study. Methods: This is a follow-up study of a new cohort of CML patients. Adherence to IM is assessed using the Medication Event Monitoring System (MEMS 6 TrackCap, AARDEX Ltd). The 9-item Morisky Medication Adherence Scale, medication possession ratio (MPR) calculation, and the electronic medical records are used for identifying potential factors that influence adherence. Clinical outcomes are assessed according to the European LeukemiaNet 2013 guidelines via reverse transcriptase quantitative polymerase chain reaction measurement of the level of BCR-ABL1 transcripts in peripheral blood. Response is classified at the hematological, cytogenetic, and molecular levels into optimal, suboptimal, or failure. Results: A total of 36 CML patients (5 citizens and 31 noncitizen residents) consented to participate in the study. The overall mean MEMS score was 89. Of the 36 patients, 22 (61%) were classified as adherent (mean: 95) and 14 (39%) were classified as nonadherent (mean: 80.2). Adherent patients were significantly more likely to obtain optimal response (95%) compared to the nonadherent group (14.3%; P < 0.0001). The rate of poor adherence was as high as 39% using MEMS, which correlates with 37% treatment failure rate. The survey results show that 97% of patients increased the IM dose by themselves when they felt unwell and 31% of them took the missing IM dose when they remembered. Other factors known to influence adherence show that half of patients developed one or more side effects, 65% of patients experienced lack of funds, 13% of patients declared unavailability of the drug in the NCCCR pharmacy, and 72% of patients believed that IM would cure the disease. The MPR results reveal that 16% of patients had poor access to treatment through the hospital pharmacy. Discussion and conclusion: This is the first prospective study to evaluate CML patients' adherence and response to IM in Qatar. The high rate of treatment failure observed in Qatar is explained by poor adherence. An economic factor (unaffordable drug prices) is one of the main causes of nonadherence and efforts should be made locally to improve access to medication for cancer diseases. Other risk factors associated with poor adherence could be improved by close monitoring and dose adjustment. Monitoring risk factors for poor adherence and patient education that include direct communication between the health-care teams, doctors, nurses, pharmacists, and patients are essential components for maximizing the benefits of TKI therapy and could rectify this problem. The preliminary results show that patients' response to treatment may be directly linked to patients' adherence to treatment. However, further in-depth and specific analysis may be necessary in a larger cohort

Short-term abstinence from alcohol and changes in cardiovascular risk factors, liver function tests and cancer-related growth factors: a prospective observational study

OBJECTIVE: To assess changes in metabolic risk factors and cancer-related growth factors associated with short-term abstinence from alcohol. DESIGN: Prospective, observational study. SETTING: Single tertiary centre. PARTICIPANTS: Healthy subjects were recruited based on intention to: (1) abstain from alcohol for 1 month (abstinence group), or (2) continue to drink alcohol (control group). Inclusion criteria were baseline alcohol consumption >64 g/week (men) or >48 g/week (women). Exclusion criteria were known liver disease or alcohol dependence. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was change in insulin resistance (homeostatic model assessment (HOMA) score). Secondary outcomes were changes in weight, blood pressure (BP), vascular endothelial growth factor (VEGF), epidermal growth factor (EGF) and liver function tests. Primary and secondary outcomes were adjusted for changes in diet, exercise and cigarette smoking. RESULTS: The abstinence group comprised 94 participants (mean age 45.5 years, SD ±1.2) and the control group 47 participants (mean age 48.7 years, SD ±1.8). Baseline alcohol consumption in the abstinence group was 258.2 g/week, SD ±9.4, and in the control group 233.8 g, SD ±19.0. Significant reductions from baseline in the abstinence group (all p<0.001) were found in: HOMA score (-25.9%, IQR -48.6% to +0.3%), systolic BP (-6.6%, IQR -11.8% to 0.0%), diastolic BP (-6.3%, IQR -14.1% to +1.3%), weight (-1.5%, IQR -2.9% to -0.4%), VEGF (-41.8%, IQR -64.9% to -17.9%) and EGF (-73.9%, IQR -86.1% to -36.4%). None of these changes were associated with changes in diet, exercise or cigarette smoking. No significant changes from baseline in primary or secondary outcomes were noted in the control group. CONCLUSION: These findings demonstrate that abstinence from alcohol in moderate-heavy drinkers improves insulin resistance, weight, BP and cancer-related growth factors. These data support an independent association of alcohol consumption with cancer risk, and suggest an increased risk of metabolic diseases such as type 2 diabetes and fatty liver disease

A cost effectiveness analysis of salt reduction policies to reduce coronary heart disease in four Eastern Mediterranean countries.

BACKGROUND: Coronary Heart Disease (CHD) is rising in middle income countries. Population based strategies to reduce specific CHD risk factors have an important role to play in reducing overall CHD mortality. Reducing dietary salt consumption is a potentially cost-effective way to reduce CHD events. This paper presents an economic evaluation of population based salt reduction policies in Tunisia, Syria, Palestine and Turkey. METHODS AND FINDINGS: Three policies to reduce dietary salt intake were evaluated: a health promotion campaign, labelling of food packaging and mandatory reformulation of salt content in processed food. These were evaluated separately and in combination. Estimates of the effectiveness of salt reduction on blood pressure were based on a literature review. The reduction in mortality was estimated using the IMPACT CHD model specific to that country. Cumulative population health effects were quantified as life years gained (LYG) over a 10 year time frame. The costs of each policy were estimated using evidence from comparable policies and expert opinion including public sector costs and costs to the food industry. Health care costs associated with CHDs were estimated using standardized unit costs. The total cost of implementing each policy was compared against the current baseline (no policy). All costs were calculated using 2010 PPP exchange rates. In all four countries most policies were cost saving compared with the baseline. The combination of all three policies (reducing salt consumption by 30%) resulted in estimated cost savings of $235,000,000 and 6455 LYG in Tunisia;$39,000,000 and 31674 LYG in Syria; $6,000,000 and 2682 LYG in Palestine and$1,3000,000,000 and 378439 LYG in Turkey. CONCLUSION: Decreasing dietary salt intake will reduce coronary heart disease deaths in the four countries. A comprehensive strategy of health education and food industry actions to label and reduce salt content would save both money and lives

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods