Case-control study of factors associated with virologic failure in HIV-infected children on HAART in Botswana, a developing African country

According to the United Nations Program on HIV/AIDS (UNAIDS, 2008), in 2007 about 67 per cent of all HIV-infected patients in the world were in Sub-Saharan Africa, with 35% of new infections and 38% of the AIDS deaths occurring in Southern Africa. Globally, the number of children younger than 15 years of age infected with HIV increased from 1.6 million in 2001 to 2.0 million in 2007 and almost 90% of these were in Sub-Saharan Africa. (UNAIDS, 2008).^ Both clinical and laboratory monitoring of children on Highly Active Anti-Retroviral Therapy (HAART) are important and necessary to optimize outcomes. Laboratory monitoring of HIV viral load and genotype resistance testing, which are important in patient follow-up to optimize treatment success, are both generally expensive and beyond the healthcare budgets of most developing countries. This is especially true for the impoverished Sub-Saharan African nations. It is therefore important to identify those factors that are associated with virologic failure in HIV-infected Sub-Saharan African children. This will inform practitioners in these countries so that they can predict which patients are more likely to develop virologic failure and therefore target the limited laboratory monitoring budgets towards these at-risk patients. The objective of this study was to examine those factors that are associated with virologic failure in HIV-infected children taking Highly Active Anti-retroviral Therapy in Botswana, a developing Sub-Saharan African country. We examined these factors in a Case-Control study using medical records of HIV-infected children and adolescents on HAART at the Botswana-Baylor Children's Clinical Center of Excellence (BBCCCOE) in Gaborone, Botswana. Univariate and Multivariate Regression Analyses were performed to identify predictors of virologic failure in these children.^ The study population comprised of 197 cases (those with virologic failure) and 544 controls (those with virologic success) with ages ranging from 3 months to 16 years at baseline. Poor adherence (pill count <95% on at least 3 consecutive occasions) was the strongest independent predictor of virologic failure (adjusted OR = 269.97, 95% CI = 104.13 to 699.92; P < 0.001). Other independent predictors of virologic failure identified were: First Line NNRTI with Nevirapine (OR = 2.99, 95% CI = 1.19 to7.54; P = 0.020), Baseline HIV-1 Viral Load >750,000/ml (OR = 257, 95% CI = 1.47 to 8.63; P = 0.005), Positive History of PMTCT (OR = 11.65, 95% CI = 3.04-44.57; P < 0.001), Multiple Care-givers (>=3) (OR = 2.56, 95% CI = 1.06 to 6.19; P = 0.036) and Residence in a Village (OR = 2.85, 95% CI = 1.36 to 5.97; P = 0.005).^ The results of this study may help to improve virologic outcomes and reduce the costs of caring for HIV-infected children in resource-limited settings. ^ Keywords: Virologic Failure, Highly Active Anti-Retroviral Therapy, Sub-Saharan Africa, Children, Adherence.^

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.

An online failure prediction system for private IaaS platforms

The size and complexity of cloud environments make them prone to failures. The traditional approach to achieve a high dependability for these systems relies on constant monitoring. However, this method is purely reactive. A more proactive approach is provided by online failure prediction (OFP) techniques. In this paper, we describe a OFP system for private IaaS platforms, currently under development, that combines di_erent types of data input, including monitoring information, event logs, and failure data. In addition, this system operates at both the physical and virtual planes of the cloud, taking into account the relationships between nodes and failure propagation mechanisms that are unique to cloud environments.

Health monitoring of web plastifying dampers subjected to cyclic loading through vibration tests

This article investigates experimentally the application of health monitoring techniques to assess the damage on a particular kind of hysteretic (metallic) damper called web plastifying dampers, which are subjected to cyclic loading. In general terms, hysteretic dampers are increasingly used as passive control systems in advanced earthquake-resistant structures. Nonparametric statistical processing of the signals obtained from simple vibration tests of the web plastifying damper is used here to propose an area index damage. This area index damage is compared with an alternative energy-based index of damage proposed in past research that is based on the decomposition of the load?displacement curve experienced by the damper. Index of damage has been proven to accurately predict the level of damage and the proximity to failure of web plastifying damper, but obtaining the load?displacement curve for its direct calculation requires the use of costly instrumentation. For this reason, the aim of this study is to estimate index of damage indirectly from simple vibration tests, calling for much simpler and cheaper instrumentation, through an auxiliary index called area index damage. Web plastifying damper is a particular type of hysteretic damper that uses the out-of-plane plastic deformation of the web of I-section steel segments as a source of energy dissipation. Four I-section steel segments with similar geometry were subjected to the same pattern of cyclic loading, and the damage was evaluated with the index of damage and area index damage indexes at several stages of the loading process. A good correlation was found between area index damage and index of damage. Based on this correlation, simple formulae are proposed to estimate index of damage from the area index damage.

Methodology based on FBG sensors for monitoring of FRP-strengthened beams

Advanced composite materials are increasingly used in the strengthening of reinforced concrete (RC) structures. The use of externally bonded strips made of fibre-reinforced plastics (FRP) as strengthening method has gained widespread acceptance in recent years since it has many advantages over the traditional techniques. However, unfortunately, this strengthening method is often associated with a brittle and sudden failure caused by some form of FRP bond failure, originated at the termination of the FRP material or at intermediate areas in the vicinity of flexural cracks in the RC beam. Up to date, little effort in the early prediction of the debonding in its initial instants even though this effect is not noticeable by simple visual observation. An early detection of this phenomenon might help in taking actions to prevent future catastrophes. Fibre-optic Bragg grating (FBG) sensors are able to measure strains locally with high resolution and accuracy. Furthermore, as their physical size is extremely small compared with other strain measuring components, it enables to be embedded at the concrete-FRP interface for determining the strain distribution without influencing the mechanical properties of the host materials. This paper shows the development of a debonding identification methodology based on strains experimentally measured. For, it a simplified model is implemented to simulate the behaviour of FRP-strengthened reinforced concrete beams. This model is taken as a basis to. develop an model updating procedure able to detect minor debonding at the concrete-FRP interface from experimental strains obtained by using FBG sensors embedded at the interface

Application of PZT and FBG impedance sensors for structural health monitoring of reinforced concrete beams strengthened with FRP composite materials

Esta Tesis tiene como objetivo principal el desarrollo de métodos de identificación del daño que sean robustos y fiables, enfocados a sistemas estructurales experimentales, fundamentalmente a las estructuras de hormigón armado reforzadas externamente con bandas fibras de polímeros reforzados (FRP). El modo de fallo de este tipo de sistema estructural es crítico, pues generalmente es debido a un despegue repentino y frágil de la banda del refuerzo FRP originado en grietas intermedias causadas por la flexión. La detección de este despegue en su fase inicial es fundamental para prevenir fallos futuros, que pueden ser catastróficos. Inicialmente, se lleva a cabo una revisión del método de la Impedancia Electro-Mecánica (EMI), de cara a exponer sus capacidades para la detección de daño. Una vez la tecnología apropiada es seleccionada, lo que incluye un analizador de impedancias así como novedosos sensores PZT para monitorización inteligente, se ha diseñado un procedimiento automático basado en los registros de impedancias de distintas estructuras de laboratorio. Basándonos en el hecho de que las mediciones de impedancias son posibles gracias a una colocación adecuada de una red de sensores PZT, la estimación de la presencia de daño se realiza analizando los resultados de distintos indicadores de daño obtenidos de la literatura. Para que este proceso sea automático y que no sean necesarios conocimientos previos sobre el método EMI para realizar un experimento, se ha diseñado e implementado un Interfaz Gráfico de Usuario, transformando la medición de impedancias en un proceso fácil e intuitivo. Se evalúa entonces el daño a través de los correspondientes índices de daño, intentando estimar no sólo su severidad, sino también su localización aproximada. El desarrollo de estos experimentos en cualquier estructura genera grandes cantidades de datos que han de ser procesados, y algunas veces los índices de daño no son suficientes para una evaluación completa de la integridad de una estructura. En la mayoría de los casos se pueden encontrar patrones de daño en los datos, pero no se tiene información a priori del estado de la estructura. En este punto, se ha hecho una importante investigación en técnicas de reconocimiento de patrones particularmente en aprendizaje no supervisado, encontrando aplicaciones interesantes en el campo de la medicina. De ahí surge una idea creativa e innovadora: detectar y seguir la evolución del daño en distintas estructuras como si se tratase de un cáncer propagándose por el cuerpo humano. En ese sentido, las lecturas de impedancias se emplean como información intrínseca de la salud de la propia estructura, de forma que se pueden aplicar las mismas técnicas que las empleadas en la investigación del cáncer. En este caso, se ha aplicado un algoritmo de clasificación jerárquica dado que ilustra además la clasificación de los datos de forma gráfica, incluyendo información cualitativa y cuantitativa sobre el daño. Se ha investigado la efectividad de este procedimiento a través de tres estructuras de laboratorio, como son una viga de aluminio, una unión atornillada de aluminio y un bloque de hormigón reforzado con FRP. La primera ayuda a mostrar la efectividad del método en sencillos escenarios de daño simple y múltiple, de forma que las conclusiones extraídas se aplican sobre los otros dos, diseñados para simular condiciones de despegue en distintas estructuras. Demostrada la efectividad del método de clasificación jerárquica de lecturas de impedancias, se aplica el procedimiento sobre las estructuras de hormigón armado reforzadas con bandas de FRP objeto de esta tesis, detectando y clasificando cada estado de daño. Finalmente, y como alternativa al anterior procedimiento, se propone un método para la monitorización continua de la interfase FRP-Hormigón, a través de una red de sensores FBG permanentemente instalados en dicha interfase. De esta forma, se obtienen medidas de deformación de la interfase en condiciones de carga continua, para ser implementadas en un modelo de optimización multiobjetivo, cuya solución se haya por medio de una expansión multiobjetivo del método Particle Swarm Optimization (PSO). La fiabilidad de este último método de detección se investiga a través de sendos ejemplos tanto numéricos como experimentales. ABSTRACT This thesis aims to develop robust and reliable damage identification methods focused on experimental structural systems, in particular Reinforced Concrete (RC) structures externally strengthened with Fiber Reinforced Polymers (FRP) strips. The failure mode of this type of structural system is critical, since it is usually due to sudden and brittle debonding of the FRP reinforcement originating from intermediate flexural cracks. Detection of the debonding in its initial stage is essential thus to prevent future failure, which might be catastrophic. Initially, a revision of the Electro-Mechanical Impedance (EMI) method is carried out, in order to expose its capabilities for local damage detection. Once the appropriate technology is selected, which includes impedance analyzer as well as novel PZT sensors for smart monitoring, an automated procedure has been design based on the impedance signatures of several lab-scale structures. On the basis that capturing impedance measurements is possible thanks to an adequately deployed PZT sensor network, the estimation of damage presence is done by analyzing the results of different damage indices obtained from the literature. In order to make this process automatic so that it is not necessary a priori knowledge of the EMI method to carry out an experimental test, a Graphical User Interface has been designed, turning the impedance measurements into an easy and intuitive procedure. Damage is then assessed through the analysis of the corresponding damage indices, trying to estimate not only the damage severity, but also its approximate location. The development of these tests on any kind of structure generates large amounts of data to be processed, and sometimes the information provided by damage indices is not enough to achieve a complete analysis of the structural health condition. In most of the cases, some damage patterns can be found in the data, but none a priori knowledge of the health condition is given for any structure. At this point, an important research on pattern recognition techniques has been carried out, particularly on unsupervised learning techniques, finding interesting applications in the medicine field. From this investigation, a creative and innovative idea arose: to detect and track the evolution of damage in different structures, as if it were a cancer propagating through a human body. In that sense, the impedance signatures are used to give intrinsic information of the health condition of the structure, so that the same clustering algorithms applied in the cancer research can be applied to the problem addressed in this dissertation. Hierarchical clustering is then applied since it also provides a graphical display of the clustered data, including quantitative and qualitative information about damage. The performance of this approach is firstly investigated using three lab-scale structures, such as a simple aluminium beam, a bolt-jointed aluminium beam and an FRP-strengthened concrete specimen. The first one shows the performance of the method on simple single and multiple damage scenarios, so that the first conclusions can be extracted and applied to the other two experimental tests, which are designed to simulate a debonding condition on different structures. Once the performance of the impedance-based hierarchical clustering method is proven to be successful, it is then applied to the structural system studied in this dissertation, the RC structures externally strengthened with FRP strips, where the debonding failure in the interface between the FRP and the concrete is successfully detected and classified, proving thus the feasibility of this method. Finally, as an alternative to the previous approach, a continuous monitoring procedure of the FRP-Concrete interface is proposed, based on an FBGsensors Network permanently deployed within that interface. In this way, strain measurements can be obtained under controlled loading conditions, and then they are used in order to implement a multi-objective model updating method solved by a multi-objective expansion of the Particle Swarm Optimization (PSO) method. The feasibility of this last proposal is investigated and successfully proven on both numerical and experimental RC beams strengthened with FRP.

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

Health monitoring of a new hysteretic damper subjected to earthquakes on a shaking table

This paper presents the experimental results obtained by applying frequency-domain structural health monitoring techniques to assess the damage suffered on a special type of damper called Web Plastifying Damper (WPD). The WPD is a hysteretic type energy dissipator recently developed for the passive control of structures subjected to earthquakes. It consists of several I-section steel segments connected in parallel. The energy is dissipated through plastic deformations of the web of the I-sections, which constitute the dissipative parts of the damper. WPDs were subjected to successive histories of dynamically-imposed cyclic deformations of increasing magnitude with the shaking table of the University of Granada. To assess the damage to the web of the I-section steel segments after each history of loading, a new damage index called Area Index of Damage (AID) was obtained from simple vibration tests. The vibration signals were acquired by means of piezoelectric sensors attached on the I-sections, and non-parametric statistical methods were applied to calculate AID in terms of changes in frequency response functions. The damage index AID was correlated with another energy-based damage index-ID- which past research has proven to accurately characterize the level of mechanical damage. The ID is rooted in the decomposition of the load-displacement curve experienced by the damper into the so-called skeleton and Bauschinger parts. ID predicts the level of damage and the proximity to failure of the damper accurately, but it requires costly instrumentation. The experiments reported in this paper demonstrate a good correlation between AID and ID in a realistic seismic loading scenario consisting of dynamically applied arbitrary cyclic loads. Based on this correlation, it is possible to estimate ID indirectly from the AID, which calls for much simpler and less expensive instrumentation.

The Special Monitoring Mission in Ukraine: a useful but flawed OSCE tool. EPC Policy Brief, 23 April 2015

The OSCE’s Special Monitoring Mission in Ukraine functions as the eyes and ears of the international community in Eastern Ukraine; it monitors, impartially, the ongoing conflict between Ukraine and Russia and observes whether or not both parties comply with the Minsk II Agreement. The success, or failure, of this Mission will not only be of crucial importance for the situation in Eastern Ukraine, but will also determine the OSCE’s entire future and ability to operate in conflict regions. Although the SMM is a flawed tool, Dennis Sammut and Joseph D’Urso argue that its deployment and the subsequent reinvigoration of OSCE is the perfect opportunity for the EU to rethink its relation to and position in the organisation, and to assume a bigger and more decisive role.

Failure of normal glycaemic regulation in a patient with severe hypothermia

This report describes the case of an 88-year-old non-diabetic female who presented to the emergency department following a presumed hypoijtycaemic collapse due to self-neglect. Subsequent rewarming and resuscitation demonstrated a number of the significant consequences of severe hypothermia, including apparent secondary impairment of glycaemic autoregulation. The phenomenon of reversible inhibition of insulin secretion due to severe hypothermia has been documented previously in the field of cardiac surgery. The hyperglycaemia was not treated with any antihyperglycaernic agent, and her recovery was uneventful. Subsequent blood sugar level monitoring was normal. If insulin is administered to the hypothermic patient, intensive monitoring of blood glucose is essential due to the increase in endogenous insulin secretion on rewarming. (c) 2005 Elsevier Ireland Ltd. All rights reserved.

The role of BNP testing in heart failure

Brain natriuretic peptide (BNP) levels are simple and objective measures of cardiac function. These measurements can be used to diagnose heart failure, including diastolic dysfunction, and using them has been shown to save money in the emergency department setting. The high negative predictive value of BNP tests is particularly helpful for ruling out heart failure. Treatment with angiotensin-converting enzyme inhibitors, angiotensin-II receptor blockers, spironolactone, and diuretics reduces BNP levels, suggesting that BNP testing may have a role in monitoring patients with heart failure. However, patients with treated chronic stable heart failure may have levels in the normal range (i.e., BNP less than 100 pg per mL and N-terminal proBNP less than 125 pg per mL in patients younger than 75 years). Increases in BNP levels may be caused by intrinsic cardiac dysfunction or may be secondary to other causes such as pulmonary or renal diseases (e.g., chronic hypoxia). BNP tests are correlated with other measures of cardiac status such as New York Heart Association classification. BNP level is a strong predictor of risk of death and cardiovascular events in patients previously diagnosed with heart failure or cardiac dysfunction.

On sensor network segmentation for urban water distribution monitoring

Identifying water wastage in forms of leaks in a water distribution network of any city becomes essential as droughts are presenting serious threats to few major cities. In this paper, we propose a deployment of sensor network for monitoring water flow in any water distribution network. We cover the issues related with designing such a dedicated sensor network by considering types of sensors required, sensors' functionality, data collection, and providing computation serving as leak detection mechanism. The main focus of this paper is on appropriate network segmentation that provides the base for hierarchical approach to pipes' failure detection. We show a method for sensors allocation to the network in order to facilitate effective pipes monitoring. In general, the identified computational problem belongs to hard problems. The paper shows a heuristic method to build effective hierarchy of the network segmentation.

Monitoring Infants By Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.

Blood glucose self-monitoring in non-insulin-treated type 2 diabetes:a qualitative study of patients' perspectives

Background: Self-monitoring of blood glucose is controversial in the management of type 2 diabetes. Some research suggests that self-monitoring improves glycaemic control, whereas other research is sceptical about its value for people with type 2 diabetes who are not on insulin. Although blood glucose meters are widely available and used by this group, patients' own views are absent from the debate. Aim: To explore the pros and cons of glucose monitoring from the patients' perspectives. Design of study: Qualitative repeat-interview study. Setting: Patients were recruited from 16 general practices and three hospital clinics within four local healthcare cooperatives in Lothian, Scotland. Method: Interview data from 40 patients diagnosed with type 2 diabetes within the previous 6 months were analysed using thematic analysis informed by grounded theory. We report findings from round 1 and round 2 interviews. Results: Glucose monitoring can heighten patients' awareness of the impact of lifestyle; for example, dietary choices, on blood glucose levels. Glucose monitoring amplifies a sense of 'success' or 'failure' about self-management, often resulting in anxiety and self-blame if glucose readings remain consistently high. Moreover, monitoring can negatively effect patients' self-management when readings are counter-intuitive. Conclusion: Our analysis highlights the importance of understanding the meanings that newly diagnosed patients attach to glucose self-monitoring. To maximise the positive effects of self-monitoring, health professionals should ensure that patients understand the purpose of monitoring and should clarify with patients how readings should be interpreted. © British Journal of General Practice.

Deep hole drilling with twist drills: aspects of the CNC process and its real time monitoring and adaptive control

Deep hole drilling is one of the most complicated metal cutting processes and one of the most difficult to perform on CNC machine-tools or machining centres under conditions of limited manpower or unmanned operation. This research work investigates aspects of the deep hole drilling process with small diameter twist drills and presents a prototype system for real time process monitoring and adaptive control; two main research objectives are fulfilled in particular : First objective is the experimental investigation of the mechanics of the deep hole drilling process, using twist drills without internal coolant supply, in the range of diarneters Ø 2.4 to Ø4.5 mm and working length up to 40 diameters. The definition of the problems associated with the low strength of these tools and the study of mechanisms of catastrophic failure which manifest themselves well before and along with the classic mechanism of tool wear. The relationships between drilling thrust and torque with the depth of penetration and the various machining conditions are also investigated and the experimental evidence suggests that the process is inherently unstable at depths beyond a few diameters. Second objective is the design and implementation of a system for intelligent CNC deep hole drilling, the main task of which is to ensure integrity of the process and the safety of the tool and the workpiece. This task is achieved by means of interfacing the CNC system of the machine tool to an external computer which performs the following functions: On-line monitoring of the drilling thrust and torque, adaptive control of feed rate, spindle speed and tool penetration (Z-axis), indirect monitoring of tool wear by pattern recognition of variations of the drilling thrust with cumulative cutting time and drilled depth, operation as a data base for tools and workpieces and finally issuing of alarms and diagnostic messages.