An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

A Low-Complexity Near-ML Performance Achieving Algorithm for Large MIMO Detection

In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 x 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10(-3) at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q(root SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNt) per symbol, where N-t and N-tau denote the number of transmit and receive antennas.

An efficient algorithm for scheduling in bluetooth piconets and scatternets

Bluetooth is a short-range radio technology operating in the unlicensed industrial-scientific-medical (ISM) band at 2.45 GHz. A piconet is basically a collection of slaves controlled by a master. A scatternet, on the other hand, is established by linking several piconets together in an ad hoc fashion to yield a global wireless ad hoc network. This paper proposes a scheduling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in bluetooth piconets and scatternets. We propose a novel algorithm for scheduling slots to slaves for both piconets and scatternets using multi-layered parameterized policies. Our scheduling scheme works with real data and obtains an optimal feedback policy within prescribed parameterized classes of these by using an efficient two-timescale simultaneous perturbation stochastic approximation (SPSA) algorithm. We show the convergence of our algorithm to an optimal multi-layered policy. We also propose novel polling schemes for intra- and inter-piconet scheduling that are seen to perform well. We present an extensive set of simulation results and performance comparisons with existing scheduling algorithms. Our results indicate that our proposed scheduling algorithm performs better overall on a wide range of experiments over the existing algorithms for both piconets (Das et al. in INFOCOM, pp. 591–600, 2001; Lapeyrie and Turletti in INFOCOM conference proceedings, San Francisco, US, 2003; Shreedhar and Varghese in SIGCOMM, pp. 231–242, 1995) and scatternets (Har-Shai et al. in OPNETWORK, 2002; Saha and Matsumot in AICT/ICIW, 2006; Tan and Guttag in The 27th annual IEEE conference on local computer networks(LCN). Tampa, 2002). Our studies also confirm that our proposed scheme achieves a high throughput and low packet delays with reasonable fairness among all the connections.

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

A feedback-based algorithm forMotion analysis with application to object tracking

We present a motion detection algorithm which detects direction of motion at sufficient number of points and thus segregates the edge image into clusters of coherently moving points. Unlike most algorithms for motion analysis, we do not estimate magnitude of velocity vectors or obtain dense motion maps. The motivation is that motion direction information at a number of points seems to be sufficient to evoke perception of motion and hence should be useful in many image processing tasks requiring motion analysis. The algorithm essentially updates the motion at previous time using the current image frame as input in a dynamic fashion. One of the novel features of the algorithm is the use of some feedback mechanism for evidence segregation. This kind of motion analysis can identify regions in the image that are moving together coherently, and such information could be sufficient for many applications that utilize motion such as segmentation, compression, and tracking. We present an algorithm for tracking objects using our motion information to demonstrate the potential of this motion detection algorithm.

An efficient and optimized bluetooth scheduling algorithm for piconets

Bluetooth is an emerging standard in short range, low cost and low power wireless networks. MAC is a generic polling based protocol, where a central Bluetooth unit (master) determines channel access to all other nodes (slaves) in the network (piconet). An important problem in Bluetooth is the design of efficient scheduling protocols. This paper proposes a polling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in a Bluetooth Piconet. We present an extensive set of simulation results and performance comparisons with two important existing algorithms. Our results indicate that our proposed scheduling algorithm outperforms the Round Robin scheduling algorithm by more than 40% in all cases tried. Our study also confirms that our proposed policy achieves higher throughput and lower packet delays with reasonable fairness among all the connections.

Dynamic simulation of activated sludge based wastewater treatment processes: Case studies with Titagarh Sewage Treatment Plant, India

STOAT has been extensively used for the dynamic simulation of an activated sludge based wastewater treatment plant in the Titagarh Sewage Treatment Plant, near Kolkata, India. Some alternative schemes were suggested. Different schemes were compared for the removal of Total Suspended Solids (TSS), b-COD, ammonia, nitrates etc. A combination of IAWQ#1 module with the Takacs module gave best results for the existing scenarios of the Titagarh Sewage Treatment Plant. The modified Bardenpho process was found most effective for reducing the mean b-COD level to as low as 31.4 mg/l, while the mean TSS level was as high as 100.98 mg/l as compared to the mean levels of TSS (92 62 mg/l) and b-COD (92.0 mg/l) in the existing plant. Scheme 2 gave a better scenario for the mean TSS level bringing it down to a mean value of 0.4 mg/l, but a higher mean value for the b-COD level at 54.89 mg/l. The Scheme Final could reduce the mean TSS level to 2.9 mg/l and the mean b-COD level to as low as 38.8 mg/l. The Final Scheme looks to be a technically viable scheme with respect to the overall effluent quality for the plant. (C) 2009 Elsevier B.V. All rights reserved.

Effect of laser surface treatment on microstructure and properties of MRI 230D Mg alloy

A creep resistant Mg alloy MRI 230D was subjected to laser surface treatment using Nd:YAG laser equipped with a fiber optics beam delivery system in argon atmosphere. The laser surface treatment produced a fine dendritic microstructure and this treatment was beneficial for the corrosion and wear resistance of the alloy. Long-term linear polarisation resistance and Electrochemical Impedance Spectroscopy measurements confirmed that the polarisation resistance values of laser treated material were twice as high as that for the untreated material. This improved behaviour was due to the finer and more homogenous microstructure of the laser treated surface. The laser treatment also increased surface hardness two times and reduced the wear rate by 25% due to grain refinement and solid solution strengthening.

Pharmacological inhibition of gut-derived serotonin synthesis is a potential bone anabolic treatment for osteoporosis

Osteoporosis is a disease of low bone mass most often caused by an increase in bone resorption that is not sufficiently compensated for by a corresponding increase in bone formation(1). As gut-derived serotonin (GDS) inhibits bone formation(2), we asked whether hampering its biosynthesis could treat osteoporosis through an anabolic mechanism (that is, by increasing bone formation). We synthesized and used LP533401, a small molecule inhibitor of tryptophan hydroxylase-1 (Tph-1), the initial enzyme in GDS biosynthesis. Oral administration of this small molecule once daily for up to six weeks acts prophylactically or therapeutically, in a dose-dependent manner, to treat osteoporosis in ovariectomized rodents because of an isolated increase in bone formation. These results provide a proof of principle that inhibiting GDS biosynthesis could become a new anabolic treatment for osteoporosis.

Novel melody line identification algorithm for polyphonic MIDI music

The problem of automatic melody line identification in a MIDI file plays an important role towards taking QBH systems to the next level. We present here, a novel algorithm to identify the melody line in a polyphonic MIDI file. A note pruning and track/channel ranking method is used to identify the melody line. We use results from musicology to derive certain simple heuristics for the note pruning stage. This helps in the robustness of the algorithm, by way of discarding "spurious" notes. A ranking based on the melodic information in each track/channel enables us to choose the melody line accurately. Our algorithm makes no assumption about MIDI performer specific parameters, is simple and achieves an accuracy of 97% in identifying the melody line correctly. This algorithm is currently being used by us in a QBH system built in our lab.

Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

The use of circular surface aerators in wastewater treatment tanks

Aeration experiments were conducted in different sized baffled and unbaffled circular surface aeration tanks to study their relative performance on oxygen transfer process while aerating the same volume of water. Experiments were carried out with the objective of ascertaining the effect of baffle on oxygen transfer coefficient k. Simulation equations govern the oxygen transfer coefficient with the theoretical power per unit volume, X and actual power per unit volume, P-V. It has been found that, for any given X, circular tanks with baffle produce higher values of k than unbaffled circular tanks, but in terms of actual power consumption unbaffled tanks consume less power when compared to baffled circular tanks to achieve the same value of k. It has been found that in terms of energy consumption, epsilon, baffled tanks consume more energy than unbaffled tanks at any value of X. This suggests that the unbaffled circular tank gives a better performance as far as energy consumption is concerned and hence better economy. An example illustrating the energy conservation to aerate the same volume of water in both types of aerators is given. (c) 2007 Society of Chemical Industry.

Profiling k-Iteration Paths : A Generalization of the Ball-Larus Profiling Algorithm

The Ball-Larus path-profiling algorithm is an efficient technique to collect acyclic path frequencies of a program. However, longer paths -those extending across loop iterations - describe the runtime behaviour of programs better. We generalize the Ball-Larus profiling algorithm for profiling k-iteration paths - paths that can span up to to k iterations of a loop. We show that it is possible to number suchk-iteration paths perfectly, thus allowing for an efficient profiling algorithm for such longer paths. We also describe a scheme for mixed-mode profiling: profiling different parts of a procedure with different path lengths. Experimental results show that k-iteration profiling is realistic.

Micro-treatment options for components of organic fraction of MSW in residential areas

There is a growing interest in management of MSW through micro-treatment of organic fraction of municipal solid wastes (OFMSW) in many cities of India. The OFMSW fraction is high (> 80%) in many pockets within South Indian cities like Bangalore, Chikkamagalur, etc. and is largely represented by vegetable, fruit, packing and garden wastes. Among these, the last three have shown problems for easy decomposition. Fruit wastes are characterized by a large pectin supported fraction that decomposes quickly to organic acids (becomes pulpy) that eventually slow down anaerobic and aerobic decomposition processes. Paper fraction (newsprint and photocopying paper) as well as paddy straw (packing), bagasse (from cane juice stalls) and tree leaf litter (typical garden waste and street sweepings) are found in reasonably large proportions in MSW. These decompose slowly due to poor nutrients or physical state. We have examined the suitability of these substrates for micro-composting in plastic bins by tracking decomposition pattern and physical changes. It was found that fruit wastes decompose rapidly to produce organic acids and large leachate fraction such that it may need to be mixed with leachate absorbing materials (dry wastes) for good composting. Leaf litter, paddy straw and bagasse decompose to the tune of 90, 68 and 60% VS and are suitable for composting micro-treatment. Paper fractions even when augmented with 10% leaf compost failed to show appreciable decomposition in 50 days. All these feedstocks were found to have good biological methane potential (BMP) and showed promise for conversion to biogas under a mixed feed operation. Suitability of this approach was verified by operating a plug-flow type anaerobic digester where only leaf litter gathered nearby (as street sweepings) was used as feedstock. Here only a third of the BMP was realized at this scale (0.18 m(3) biogas/kg VS 0.55 m(3)/kg in BMP). We conclude that anaerobic digestion in plug-flow like digesters appear a more suitable micro-treatment option (2-10 kg VS/day) because in addition to compost it also produces biogas for domestic use nearby.