Heterogeneous error-resilient scheme for spectral analysis in ultra-low power wearable electrocardiogram devices

Wearable devices performing advanced bio-signal analysis algorithms are aimed to foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term monitoring must be ensured while relying on a tiny power source. Operating at a scaled supply voltage, just above the threshold voltage, effectively helps in saving substantial energy, but it makes circuits, and especially memories, more prone to errors, threatening the correct execution of algorithms. The use of error detection and correction codes may help to protect the entire memory content, however it incurs in large area and energy overheads which may not be compatible with the tight energy budgets of wearable systems. To cope with this challenge, in this paper we propose to limit the overhead of traditional schemes by selectively detecting and correcting errors only in data highly impacting the end-to-end quality of service of ultra-low power wearable electrocardiogram (ECG) devices. This partition adopts the protection of either significant words or significant bits of each data element, according to the application characteristics (statistical properties of the data in the application buffers), and its impact in determining the output. The proposed heterogeneous error protection scheme in real ECG signals allows substantial energy savings (11% in wearable devices) compared to state-of-the-art approaches, like ECC, in which the whole memory is protected against errors. At the same time, it also results in negligible output quality degradation in the evaluated power spectrum analysis application of ECG signals.

Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms-a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.

'A load too heavy': Critical reflections on the child labor problem in Africa's small-scale mining sector

This paper provides an extended analysis of the child labor problem in the artisanal and small-scale mining (ASM) sector, focusing specifically on the situation in sub-Saharan Africa. In recent years, the issue of child labor in ASM has garnered significant attention from the International Labor Organization (ILO), which has been particularly active in raising public awareness of the problem; and, has proceeded to implement policies and collaborative project work aimed at Curtailing children's participation in ASM activities in a number of African countries. The analysis concludes with a critical appraisal of an ILO project recently launched in the Talensi-Nabdam District in the Upper East Region of Ghana, which sheds light on how the child labor problem is being tackled in practice in ASM communities in sub-Saharan Africa. (c) 2008 Elsevier Ltd. All rights reserved.

A systematic review of the routine monitoring of growth in children of primary school age to identify growth-related conditions

Objectives: To clarify the role of growth monitoring in primary school children, including obesity, and to examine issues that might impact on the effectiveness and cost-effectiveness of such programmes. Data sources: Electronic databases were searched up to July 2005. Experts in the field were also consulted. Review methods: Data extraction and quality assessment were performed on studies meeting the review's inclusion criteria. The performance of growth monitoring to detect disorders of stature and obesity was evaluated against National Screening Committee (NSC) criteria. Results: In the 31 studies that were included in the review, there were no controlled trials of the impact of growth monitoring and no studies of the diagnostic accuracy of different methods for growth monitoring. Analysis of the studies that presented a 'diagnostic yield' of growth monitoring suggested that one-off screening might identify between 1: 545 and 1: 1793 new cases of potentially treatable conditions. Economic modelling suggested that growth monitoring is associated with health improvements [ incremental cost per quality-adjusted life-year (QALY) of pound 9500] and indicated that monitoring was cost-effective 100% of the time over the given probability distributions for a willingness to pay threshold of pound 30,000 per QALY. Studies of obesity focused on the performance of body mass index against measures of body fat. A number of issues relating to human resources required for growth monitoring were identified, but data on attitudes to growth monitoring were extremely sparse. Preliminary findings from economic modelling suggested that primary prevention may be the most cost-effective approach to obesity management, but the model incorporated a great deal of uncertainty. Conclusions: This review has indicated the potential utility and cost-effectiveness of growth monitoring in terms of increased detection of stature-related disorders. It has also pointed strongly to the need for further research. Growth monitoring does not currently meet all NSC criteria. However, it is questionable whether some of these criteria can be meaningfully applied to growth monitoring given that short stature is not a disease in itself, but is used as a marker for a range of pathologies and as an indicator of general health status. Identification of effective interventions for the treatment of obesity is likely to be considered a prerequisite to any move from monitoring to a screening programme designed to identify individual overweight and obese children. Similarly, further long-term studies of the predictors of obesity-related co-morbidities in adulthood are warranted. A cluster randomised trial comparing growth monitoring strategies with no growth monitoring in the general population would most reliably determine the clinical effectiveness of growth monitoring. Studies of diagnostic accuracy, alongside evidence of effective treatment strategies, could provide an alternative approach. In this context, careful consideration would need to be given to target conditions and intervention thresholds. Diagnostic accuracy studies would require long-term follow-up of both short and normal children to determine sensitivity and specificity of growth monitoring.

Climate change impacts on crop productivity in Africa and South Asia

Climate change is a serious threat to crop productivity in regions that are already food insecure. We assessed the projected impacts of climate change on the yield of eight major crops in Africa and South Asia using a systematic review and meta-analysis of data in 52 original publications from an initial screen of 1144 studies. Here we show that the projected mean change in yield of all crops is − 8% by the 2050s in both regions. Across Africa, mean yield changes of − 17% (wheat), − 5% (maize), − 15% (sorghum) and − 10% (millet) and across South Asia of − 16% (maize) and − 11% (sorghum) were estimated. No mean change in yield was detected for rice. The limited number of studies identified for cassava, sugarcane and yams precluded any opportunity to conduct a meta-analysis for these crops. Variation about the projected mean yield change for all crops was smaller in studies that used an ensemble of > 3 climate (GCM) models. Conversely, complex simulation studies that used biophysical crop models showed the greatest variation in mean yield changes. Evidence of crop yield impact in Africa and South Asia is robust for wheat, maize, sorghum and millet, and either inconclusive, absent or contradictory for rice, cassava and sugarcane.

Can environmental performance rating programmes succeed in Africa?: an evaluation of Ghana’s AKOBEN project

The purpose of the article is to describe and analyse Ghana’s AKOBEN programme which is the first environmental performance rating and public disclosure programme in Africa. Furthermore, by means of a SWOT analysis, the article assesses the suitability of AKOBEN as a veritable tool for promoting good environmental governance in Ghana specifically and Africa in general.

Building climate resilience: lessons of early warning in Africa

Given the high levels of uncertainty and substantial variability in local weather and climate, what constitutes successful adaptation for the 800 million food-insecure people in Africa? In this context there is a need for building climate resilience through effective early warning systems, bringing real-time monitoring and decision-making together with stakeholders. The chapter presents two effective operational early warning systems in Africa: The Radio and Internet (RANET) network and the Rainwatch project. These examples were developed in partnership with local climate scientists and tailored to local development needs, enabled and encouraged with only modest international support. They deliver important lessons about how to prepare for crises using simple real-time monitoring. They also help us identify characteristics of managing for resilience in practice. The chapter concludes that successful adaptation requires adaptive, flexible, linked institutions, together with ground-based collaboration and practical tools. In the context of early warning three features stand out that make these systems successful: effective communication of current weather and climate information, a key individual within a bridging organization with the ability to navigate the governance systems, and sufficient time for innovation development.

Rule Type Identification using TRCM for trend analysis in Twitter

This paper considers the use of Association Rule Mining (ARM) and our proposed Transaction based Rule Change Mining (TRCM) to identify the rule types present in tweet’s hashtags over a specific consecutive period of time and their linkage to real life occurrences. Our novel algorithm was termed TRCM-RTI in reference to Rule Type Identification. We created Time Frame Windows (TFWs) to detect evolvement statuses and calculate the lifespan of hashtags in online tweets. We link RTI to real life events by monitoring and recording rule evolvement patterns in TFWs on the Twitter network.

Regional projections of extreme apparent temperature days in Africa and the related potential risk to human health

Regional climate modelling was used to produce high resolution climate projections for Africa, under a “business as usual scenario”, that were translated into potential health impacts utilizing a heat index that relates apparent temperature to health impacts. The continent is projected to see increases in the number of days when health may be adversely affected by increasing maximum apparent temperatures (AT) due to climate change. Additionally, climate projections indicate that the increases in AT results in a moving of days from the less severe to the more severe Symptom Bands. The analysis of the rate of increasing temperatures assisted in identifying areas, such as the East African highlands, where health may be at increasing risk due to both large increases in the absolute number of hot days, and due to the high rate of increase. The projections described here can be used by health stakeholders in Africa to assist in the development of appropriate public health interventions to mitigate the potential health impacts from climate change.

Field report from sediment coring in peat for landscape analysis in Hjaltadalur, Skagafjörður, north Iceland 2010 : Sediment drilling in Hólar, Vidvik and Kolkuós, Hjaltadalur

This field report presents fieldwork undertaken in Hjaltadalur, Skagafjördur, northern Iceland during summer 2010. The main aim was to initiate coring in selected mires in order to determine the composition of organic material and sediments in the mires, sub-sample for sedimentological and palaeoecological analyses, and initiate advanced landscape analysis of Hjaltadalur. Three mires were selected for sediments coring in Hjaltadalur: Ástunga close to Kolkóus, Hólakot at Viðvik, and Hólar. All three represented a landscape transect in NW to SE direction, from close to the coast to valley interior, i.e. close to the old settlement at Hólar.

MONITORING NITROGEN NUTRITION IN COTTON

Both N excess and deficiency may affect cotton yield and quality. It would therefore be useful to base the N management fertilization on the monitoring of the nutritional status. This study investigated the correlations among the following determination methods of the N nutritional status of cotton (Gossypium hirsutum L., var. Latifolia): chlorophyll readings (SPAD-502 (R), Minolta), specific-ion nitrate meter (Nitrate Meter C-141, Horiba-Cardy (R)), and laboratory analysis (conventional foliar diagnosis). Samples were taken weekly from two weeks before flowering to the fifth week after the first flower. The experiment was conducted on the Fazenda Santa Tereza, Itapeva, State of São Paulo, Brazil. The crop was fertilized with 40 kg ha(-1) N at planting and 0, 30, 60, 90, and 120 kg ha(-1) of side-dressed N. The range of leaf N contents reported as adequate for samples taken 80-90 days after plant emergence (traditional foliar diagnosis) may be used as reference from the beginning of flowering when the plant is not stressed. Specific-ion nitrate meter readings can be used as a nutritional indicator of cotton nutrition from one week after pinhead until the third week of flowering. In this case, plants are well-nourished when readings exceed 8,000 mg L(-1) NO(3)(-). The chlorophyll meter can also be used to estimate the nutritional status of cotton from the third week of flowering. In this case the readings should be above 48 in well-nourished plants.

Supercritical fluid extraction for pesticide multiresidue analysis in honey: determination by gas chromatography with electron-capture and mass spectrometry detection

An analytical procedure using supercritical fluid extraction (SFE) and capillary gas chromatography with electron-capture detection was developed to determine simultaneously residues of different pesticides (organochlorine, organophosphorus, organonitrogen and pyrethroid) in honey samples. Fortification experiments were conducted to test conventional extraction (liquid-liquid) and optimize the extraction procedure in SFE by varying the CO2-modifier, temperature, extraction time and pressure. Best efficiency was achieved at 400 bar using acetonitrile as modifier at 90 degreesC. For the clean-up step, Florisil cartridges were used for both methods LLE and SFE. Recoveries for majority of pesticides from fortified samples of honey at fortification level of 0.01-0.10 mg/kg ranged 75-94% from both methods. Limits of detection found were less than 0.01 mg/kg for ECD and confirmation of pesticide identity was performed by gas chromatography-mass spectrometry in selected-ion monitoring mode. The multiresidue methods in real honey samples were applied and the results of developed methods were compared. (C) 2004 Elsevier B.V. All rights reserved.

Immunoassays for pesticide analysis in environmental and food matrices

Immunochemical methods have increased considerably in the past years, and many examples of small and large scale studies have demonstrated the reliability of the immunotechniques for control and monitoring gf contaminant residues in different kinds of samples. Application of the immunoassay (IA) methods in pesticide residue control is an area with enormous potential for growth. The most extensively studied IA is the enzyme-linked absorbent assay (ELISA), but several other approaches, that include radioimmunoassay and immunoaffinity chromatography, have been also developed recently. In comparison with classical analytical methods, IA methods offer the possibility of highly sensitive, relatively vapid, and cost-effective measurements. This paper introduces the general IAs used until now, focusing on their use in pesticide analysis, and discussing briefly the effects of interferences from solvent residues or matrix components on the IA performance. Numerous immunochemical methods commonly used for pesticide determination in different samples such as food, crop and environmental samples are presented.

Integrated assessment of multilevel biomarker responses and chemical analysis in Mussels from Sao Sebastiao, São Paulo, Brazil

The aim of the present study was to investigate the presence of contaminants in the mussel Perna perna from Sao Sebastiao Channel, São Paulo, Brazil, and to evaluate the effects of these contaminants on these organisms at biochemical (catalase [CAT], glutathione-S-transferase [GST], and cholinesterase [ChE]), cellular (neutral red retention time [NRRT] assay), and physiological (cardiac monitoring) levels. Two sampling surveys were performed (winter of 2001 and summer of 2002) at six stations along the channel: Cigarras, station 1; late Clube de Ilhabela, station 2; Oil Terminal, station 3; Toque Toque, station 4; Ponta da Sela, station 5 (reference station); and Taubate, station 6. Differences in CAT activity were observed between mussels from stations 3 and 5 during the winter, but no differences were detected in the summer. No differences in GST activity were found among stations during the winter, although animals from station 3 showed higher activity during the summer. The ChE activity was significantly higher in the mussels from stations I and 2 during the winter and from stations I and 3 during the summer. Organisms from stations I through 4 showed statistically lower NRRT in both seasons. Similar heart rates were observed in the mussels from all stations. Hydrocarbons were detected in organisms from all the stations in both seasons. During the winter, higher polycyclic aromatic hydrocarbon (PAH) levels were observed in organisms from station 3, whereas during the summer, higher levels of metals were found in organisms from stations 1, 3, and 4. The multivariate analyses showed a strong influence of PAHs on the winter biological results, but metals showed higher influence on these responses in the summer, indicating multiple contaminant sources.

Comparing river basins world-wide and contrasting inland fisheries in Africa and Central Amazonia

An analysis of covariance relating basin area (A, km2) to river length (L, km) and discharge rate (D, m3 s-1) was performed for two continents and showed that the two covariates (L and D) were highly significant and that the strength of the relationship changed between continents. For comparison, D was excluded but the result remained the same. Although geomorphological models are useful for establishing global levels of production, these regressions should be applied with caution. Historically, simple statistical models were developed to predict fish catches in rivers. These, based upon regression of catches on channel length or basin area for Africa and Central Amazonia, are contrasted in this paper because of their generally similar approach.