Meeting the nutrition challenge of stage 3 renal failure : considerations for nursing practice

Self-care management is needed for effective management of chronic kidney disease. The main aim for treatment or management of chronic kidney disease is to delay the worsening of kidney function, and to prevent or to manage the co-morbidities. Selfcare management is not easy, and patients will face many challenges, especially when they cannot get use to the new treatment plan. One of the challenges they face is dietary restriction, which is a very important aspect in any self-care management programme. Chronic kidney disease patients require a low-protein, low-sodium, low-potassium, and low-phosphorus diet. There are several strategies patients can undertake to ensure adherence, such as self-monitoring their dietary habits and type of food consumed using a food diary; involving social support, such as family members and spouse to help them to adhere to their diet restrictions; setting goals and providing positive reinforcement when they achieved the targeted goals; joining self-management programmes to equip themselves with the necessary skills so that they can better adhere to the treatment regimes, including diet restriction; and lastly, having the knowledge about their regime, and using this knowledge to help them understand and improve their adherence.

Understanding welding practices on shipyards : an ethnographic study for designing an interactive robot welder

The aim of this ethnographic study was to understand welding practices in shipyard environments with the purpose of designing an interactive welding robot that can help workers with their daily job. The robot is meant to be deployed for automatic welding on jack-up rig structures. The design of the robot turns out to be a challenging task due to several problematic working conditions on the shipyard, such as dust, irregular floor, high temperature, wind variations, elevated working platforms, narrow spaces, and circular welding paths requiring a robotic arm with more than 6 degrees of freedom. Additionally, the environment is very noisy and the workers – mostly foreigners – have a very basic level of English. These two issues need to be taken into account when designing the interactive user interface for the robot. Ideally, the communication flow between the two parties involved should be as frictionless as possible. The paper presents the results of our field observations and welders’ interviews, as well as our robot design recommendation for the next project stage.

Development and application of Palygorskite porous ceramsite in a biological aerated filter (BAF)

Novel filter Palygorskite porous ceramsite (PC) was prepared using Palygorskite clay, poreforming material sawdust, and sodium silicate with a mass ratio of 10:2:1 after sintering at 700°C for 180 min. PC was characterized with X-ray diffraction, X-ray fluorescence, scanning electron microscopy, elemental, and porosimetry. PC had a total porosity of 67% and specific surface area of 61 m2/g. In order to assess the usefulness of PC as a medium for biological aerated filters (BAF), PC and (commercially available ceramsite) CAC were used to treat wastewater city in two laboratory-scale upflow BAFs. The results showed that the reactor containing PC was more efficient than the reactor containing CAC in terms of total organic carbon (TOC), ammonia nitrogen (NH3-N), and the removal of total nitrogen (TN) and phosphorus (P). This system was found to be more efficient at water temperatures ranging from 20 to 26°C, an air–water (A/W) ratio of 3:1, dissolved oxygen concentration >4.00 mg/L, and hydraulic retention time (HRT) ranging from 0.5 to 7 h. The interconnected porous structure produced for PC was suitable for microbial growth, and primarily protozoan and metazoan organisms were found in the biofilm. Microorganism growth also showed that, under the same submerged culture conditions, the biological mass in PC was significantly higher than in CAC (34.1 and 2.2 mg TN/g, respectively). In this way, PC media can be considered suitable for the use as a medium in novel biological aerated filters for the simultaneous removal of nitrogen and phosphorus.

Dual-functional gum arabic binder for silicon anodes in lithium ion batteries

Si has attracted enormous research and manufacturing attention as an anode material for lithium ion batteries (LIBs) because of its high specific capacity. The lack of a low cost and effective mechanism to prevent the pulverization of Si electrodes during the lithiation/ delithiation process has been a major barrier in the mass production of Si anodes. Naturally abundant gum arabic (GA), composed of polysaccharides and glycoproteins, is applied as a dualfunction binder to address this dilemma. Firstly, the hydroxyl groups of the polysaccharide in GA are crucial in ensuring strong binding to Si. Secondly, similar to the function of fiber in fiberreinforced concrete (FRC), the long chain glycoproteins provide further mechanical tolerance to dramatic volume expansion by Si nanoparticles. The resultant Si anodes present an outstanding capacity of ca. 2000 mAh/g at a 1 C rate and 1000 mAh/g at 2 C rate, respectively, throughout 500 cycles. Excellent long-term stability is demonstrated by the maintenance of 1000 mAh/g specific capacity at 1 C rate for over 1000 cycles. This low cost, naturally abundant and environmentally benign polymer is a promising binder for LIBs in the future.

Deep geothermal: The ‘Moon Landing’ mission in the unconventional energy and minerals space

Deep geothermal from the hot crystalline basement has remained an unsolved frontier for the geothermal industry for the past 30 years. This poses the challenge for developing a new unconventional geomechanics approach to stimulate such reservoirs. While a number of new unconventional brittle techniques are still available to improve stimulation on short time scales, the astonishing richness of failure modes of longer time scales in hot rocks has so far been overlooked. These failure modes represent a series of microscopic processes: brittle microfracturing prevails at low temperatures and fairly high deviatoric stresses, while upon increasing temperature and decreasing applied stress or longer time scales, the failure modes switch to transgranular and intergranular creep fractures. Accordingly, fluids play an active role and create their own pathways through facilitating shear localization by a process of time-dependent dissolution and precipitation creep, rather than being a passive constituent by simply following brittle fractures that are generated inside a shear zone caused by other localization mechanisms. We lay out a new theoretical approach for the design of new strategies to utilize, enhance and maintain the natural permeability in the deeper and hotter domain of geothermal reservoirs. The advantage of the approach is that, rather than engineering an entirely new EGS reservoir, we acknowledge a suite of creep-assisted geological processes that are driven by the current tectonic stress field. Such processes are particularly supported by higher temperatures potentially allowing in the future to target commercially viable combinations of temperatures and flow rates.

Nutritional risk of cancer patients receiving chemotherapy in the ambulatory care setting : a prospective study

Background Cancer itself can alter the metabolic and physiologic of the body's nutritional needs. As a result, some patients experience some degree of weight loss before the start of the treatment. Aim The aim of the study was to determine at which chemotherapy treatment cycle patients with cancer begin to exhibit signs and symptoms of malnutrition. Methods A prospective descriptive correlational design was used to assess the nutritional risk of 111 patients with cancer receiving chemotherapy in the ambulatory setting. The data were collected by using a nutritional screening tool. Findings The prevalence of malnutrition risk was 45% in patients undergoing the first cycle of chemotherapy. Patients who received the first three cycles of chemotherapy were 2.62 times more likely to develop malnutrition than those who received seven or more cycles. The risk of developing malnutrition varies depending on the type of cancer, the types of chemotherapy regime, the number of chemotherapy cycles, body mass index and the stage of cancer. Conclusion The study found about half of the patients had developed signs and symptoms of nutritional risk at cycle one. Hence, nutritional support may be required even before the start of chemotherapy.

Using viewer’s facial expression and heart rate for sports video highlights detection

Viewer interests, evoked by video content, can potentially identify the highlights of the video. This paper explores the use of facial expressions (FE) and heart rate (HR) of viewers captured using camera and non-strapped sensor for identifying interesting video segments. The data from ten subjects with three videos showed that these signals are viewer dependent and not synchronized with the video contents. To address this issue, new algorithms are proposed to effectively combine FE and HR signals for identifying the time when viewer interest is potentially high. The results show that, compared with subjective annotation and match report highlights, ‘non-neutral’ FE and ‘relatively higher and faster’ HR is able to capture 60%-80% of goal, foul, and shot-on-goal soccer video events. FE is found to be more indicative than HR of viewer’s interests, but the fusion of these two modalities outperforms each of them.

Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: A feasibility study

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

7-point Subjective Global Assessment is more time sensitive than conventional Subjective Global Assessment in detecting nutrition changes

Background: It is important for nutrition intervention in malnourished patients to be guided by accurate evaluation and detection of small changes in the patient’s nutrition status over time. However, the current Subjective Global Assessment (SGA) is not able to detect changes in a short period of time. The aim of the study was to determine whether 7-point SGA is more time sensitive to nutrition changes than the conventional SGA. Methods: In this prospective study, 67 adult inpatients assessed as malnourished using both the 7-point SGA and conventional SGA were recruited. Each patient received nutrition intervention and was followed up post-discharge. Patients were reassessed using both tools at 1, 3 and 5 months from baseline assessment. Results: It took significantly shorter time to see a one-point change using 7-point SGA compared to conventional SGA (median: 1 month vs. 3 months, p = 0.002). The likelihood of at least a one-point change is 6.74 times greater in 7-point SGA compared to conventional SGA after controlling for age, gender and medical specialties (odds ratio = 6.74, 95% CI 2.88-15.80, p<0.001). Fifty-six percent of patients who had no change in SGA score had changes detected using 7-point SGA. The level of agreement was 100% (k = 1, p < 0.001) between 7-point SGA and 3-point SGA and 83% (k=0.726, p<0.001) between two blinded assessors for 7-point SGA. Conclusion: The 7-point SGA is more time sensitive in its response to nutrition changes than conventional SGA. It can be used to guide nutrition intervention for patients.

Exploration of diarrhoea seasonality and its drivers in China

This study investigated the diarrhoea seasonality and its potential drivers as well as potential opportunities for future diarrhoea control and prevention in China. Data on weekly infectious diarrhoea cases in 31 provinces of China from 2005 to 2012, and data on demographic and geographic characteristics, as well as climatic factors, were complied. A cosinor function combined with a Poisson regression was used to calculate the three seasonal parameters of diarrhoea in different provinces. Regression tree analysis was used to identify the predictors of diarrhoea seasonality. Diarrhoea cases in China showed a bimodal distribution. Diarrhoea in children <5 years was more likely to peak in fall-winter seasons, while diarrhoea in persons > = 5 years peaked in summer. Latitude was significantly associated with spatial pattern of diarrhoea seasonality, with peak and trough times occurring earlier at high latitudes (northern areas), and later at low latitudes (southern areas). The annual amplitudes of diarrhoea in persons > = 5 years increased with latitude (r = 0.62, P<0.001). Latitude 27.8° N and 38.65° N were the latitudinal thresholds for diarrhoea seasonality in China. Regional-specific diarrhoea control and prevention strategies may be optimal for China. More attention should be paid to diarrhoea in children <5 years during fall-winter seasons.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.