Smart Bandage for Monitoring and Treatment of Chronic Wounds

Chronic wounds are a major health concern and they affect the lives of more than 25 million people in the United States. They are susceptible to infection and are the leading cause of nontraumatic limb amputations worldwide. The wound environment is dynamic, but their healing rate can be enhanced by administration of therapies at the right time. This approach requires real‐time monitoring of the wound environment with on‐demand drug delivery in a closed‐loop manner. In this paper, a smart and automated flexible wound dressing with temperature and pH sensors integrated onto flexible bandages that monitor wound status in real‐time to address this unmet medical need is presented. Moreover, a stimuli‐responsive drug releasing system comprising of a hydrogel loaded with thermo‐responsive drug carriers and an electronically controlled flexible heater is also integrated into the wound dressing to release the drugs on‐demand. The dressing is equipped with a microcontroller to process the data measured by the sensors and to program the drug release protocol for individualized treatment. This flexible smart wound dressing has the potential to significantly impact the treatment of chronic wounds.     

Multi-objective feasibility enhanced particle swarm optimization

This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO, which is based on the particle swarm optimization technique, employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight. Unlike its counterparts, MOFEPSO does not require any feasible solutions in the initialized swarm. Additionally, objective functions are not assessed for infeasible particles. Such particles can only fly along sensitive directions, and particles are not allowed to move to a position where any previously satisfied constraints become violated. These unique features help MOFEPSO gradually increase the overall feasibility of the swarm and to finally attain the optimal solution. In this study, multi-objective versions of a classical gear-train optimization problem are also described. For the given problems, the article comparatively evaluates the performance of MOFEPSO against several popular optimization algorithms found in the literature.     

Microfluidics‐Enabled Multimaterial Maskless Stereolithographic Bioprinting

A stereolithography‐based bioprinting platform for multimaterial fabrication of heterogeneous hydrogel constructs is presented. Dynamic patterning by a digital micromirror device, synchronized by a moving stage and a microfluidic device containing four on/off pneumatic valves, is used to create 3D constructs. The novel microfluidic device is capable of fast switching between different (cell‐loaded) hydrogel bioinks, to achieve layer‐by‐layer multimaterial bioprinting. Compared to conventional stereolithography‐based bioprinters, the system provides the unique advantage of multimaterial fabrication capability at high spatial resolution. To demonstrate the multimaterial capacity of this system, a variety of hydrogel constructs are generated, including those based on poly(ethylene glycol) diacrylate (PEGDA) and gelatin methacryloyl (GelMA). The biocompatibility of this system is validated by introducing cell‐laden GelMA into the microfluidic device and fabricating cellularized constructs. A pattern of a PEGDA frame and three different concentrations of GelMA, loaded with vascular endothelial growth factor, are further assessed for its neovascularization potential in a rat model. The proposed system provides a robust platform for bioprinting of high‐fidelity multimaterial microstructures on demand for applications in tissue engineering, regenerative medicine, and biosensing, which are otherwise not readily achievable at high speed with conventional stereolithographic biofabrication platforms.     

Naturally nonanemic dialysis patients: Who are they?

Introduction Not only anemia, but also erythropoiesis stimulating agent (ESA)s for treating anemia may adversely affect prognosis of chronic hemodialysis patients. Various features of naturally (with no ESA usage) nonanemic patients may be useful for defining several factors in the pathogenesis of anemia. Methods Data, retrieved from the European Clinical Database (EuCliD)‐Turkey on naturally nonanemic prevalent chronic hemodialysis patients (n: 201) were compared with their anemic (those who required ESA treatment) counterparts (n: 3948). Findings Mean hemoglobin values were 13.5 ± 0.8 and 11.5 ± 0.9 g/dL in nonanemic and anemic patients, respectively (P < 0.001). Nonanemia status was associated with younger age, male gender, longer dialysis vintage, nondiabetic status, more frequent hepatitis‐C virus seropositivity and more frequent arteriovenous fistula usage. Serum ferritin and CRP levels and urea reduction ratio were higher in ESA‐requiring patients. One (99%) and two (95.3%) years survival rates of the “naturally nonanemic” patients were superior as compared to anemics (91.0% and 82.6%, respectively), (P < 0.001). Discussion “Naturally nonanemic” status is associated with better survival in prevalent chronic hemodialysis patients; underlying mechanisms in this favorable outcome should be investigated by randomized controlled trials including large number of patients.     

Esophageal perforation during cuffed tunneled catheter introduction: First case in literature

The percutaneous catheterization of central veins is increasingly used in nephrological practice as a temporary or permanent vascular access. Some mechanical complications may occur during insertion of catheter such as misplaced catheter. In this report, we present a case that was misplaced the catheter in esophagus and to analyze the reason of catheter malposition during percutaneous tunneled hemodialysis catheter insertion.     

Offer acceptance prediction of academic placement

The paper examines the validation of prediction models of acceptance of academic placement offers by students in the context of international applications at a large metropolitan Australian University using data mining techniques. Earlier works in enrolment management have examined various classification problems such as inquiry to enrol, persistence and graduation. The data and settings from different institutions are often different, which implies that in order to find out which models and techniques are applicable at a given university, the dataset from that university needs to be used in the validation effort. The whole dataset from the Australian university comprised 24,283 offers made to international applicants from the year 2008 to 2013. Every year around 2000–2500 new international students who accept offers of academic placement commence their studies. The important predictors for the acceptance of offers were as follows: the chosen course and faculty, whether the student was awarded any form of scholarship, and also the visa assessment level of the country by the immigration department. Prediction models were developed using a number of classification methods such as logistic regression, Naïve Bayes, decision trees, support vector machines, random forests, k-nearest neighbour, neural networks and their performances compared. Overall, the neural network prediction model with a single hidden layer produced the best result.     

Efficient tag detection in RFID systems

Recent technological advances have motivated large-scale deployment of RFID systems. However, a number of critical design issues relating to efficient detection of tags remain unresolved. In this paper, we address three important problems associated with tag detection in RFID systems: (i) accurately detecting RFID tags in the presence of reader interference (reader collision avoidance problem); (ii) eliminating redundant tag reports by multiple readers (optimal tag reporting problem); and (iii) minimizing redundant reports from multiple readers by identifying a minimal set of readers that cover all tags present in the system (optimal tag coverage problem). The underlying difficulties associated with these problems arise from the lack of collision detection mechanisms, the potential inability of RFID readers to relay packets generated by other readers, and severe resource constraints on RFID tags. In this paper we present a randomized, distributed and localized Reader Collision Avoidance (RCA) algorithm and provide detailed probabilistic analysis to establish the accuracy and the efficiency of this algorithm. Then, we prove that the optimal tag coverage problem is NP-hard even with global knowledge of reader and tag locations. We develop a distributed and localized Redundant Reader Elimination (RRE) algorithm, that efficiently identifies redundant readers and avoids redundant reporting by multiple readers. In addition to rigorous analysis of performance and accuracy, we provide results from elaborate simulations for a wide range of system parameters, demonstrating the correctness and efficiency of the proposed algorithms under various scenarios.     

An artificial immune system approach to CNC tool path generation

Reduced machining time and increased accuracy for a sculptured surface are both very important when producing complicated parts, so, the step-size and tool-path interval are essential components in high-speed and high-resolution machining. If they are too small, the machining time will increase, whereas if they are too large, rough surfaces will result. In particular, the machining time, which is a key factor in high-speed machining, is affected by the tool-path interval more than the step size. The present paper introduces a ‘system software’ developed to reduce machining time and increased accuracy for a sculptured surface with Non-Uniform Rational B-Spline (NURBS) patches. The system is mainly based on a new and a powerful artificial intelligence (AI) tool, called artificial immune systems (AIS). It is implemented using C programming language on a PC. It can be used as stand alone system or as the integrated module of a CNC machine tool. With the use of AIS, the impact and power of AI techniques have been reflected on the performance of the tool path optimization system. The methodology of the developed tool path optimization system is illustrated with practical examples in this paper.     

Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes

This paper introduces different classification systems based on artificial neural networks for the automatic detection of epileptic spikes in electroencephalogram records. Different multilayer perceptron networks are constructed and trained with different algorithms. The inputs of the networks consist of either raw data or extracted features. To improve the generalization performance of the classifiers, “training with noise” method is used whereby new training data is constructed by adding uncorrelated Gaussian noise to real data. The performances of the constructed classifiers are examined and compared both with each other and with other similar systems found in literature based on sensitivity, specificity and selectivity measures.     

Online mining of fuzzy multidimensional weighted association rules

This paper addresses the integration of fuzziness with On-Line Analytical Processing (OLAP) based association rules mining. It contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube for knowledge discovery. A data cube is mainly constructed to provide users with the flexibility to view data from different perspectives as some dimensions of the cube contain multiple levels of abstraction. The first step of the process described in this paper involves introducing fuzzy data cube as a remedy to the problem of handling quantitative values of dimensional attributes in a cube. This facilitates the online mining of fuzzy association rules at different levels within the constructed fuzzy data cube. Then, we investigate combining the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules from the constructed fuzzy data cube. For this purpose, three different methods are introduced for single dimension, multidimensional and hybrid (integrates the other two methods) fuzzy weighted association rules mining. Each of the three methods utilizes a fuzzy data cube constructed to suite the particular method. To the best of our knowledge, this is the first effort in this direction. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results obtained for each of the three methods on a synthetic dataset and on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach. OLAP is one of the most popular tools for on-line, fast and effective multidimensional data analysis. In the OLAP framework, data is mainly stored in data hypercubes (simply called cubes).