A queuing approach for inventory planning with batch ordering in multi-echelon supply chains

This paper presents stylized models for conducting performance analysis of the manufacturing supply chain network (SCN) in a stochastic setting for batch ordering. We use queueing models to capture the behavior of SCN. The analysis is clubbed with an inventory optimization model, which can be used for designing inventory policies . In the first case, we model one manufacturer with one warehouse, which supplies to various retailers. We determine the optimal inventory level at the warehouse that minimizes total expected cost of carrying inventory, back order cost associated with serving orders in the backlog queue, and ordering cost. In the second model we impose service level constraint in terms of fill rate (probability an order is filled from stock at warehouse), assuming that customers do not balk from the system. We present several numerical examples to illustrate the model and to illustrate its various features. In the third case, we extend the model to a three-echelon inventory model which explicitly considers the logistics process.

A Dual Seven-Level Inverter Supply for an Open-End Winding Induction Motor Drive

This paper develops a seven-level inverter structure for open-end winding induction motor drives. The inverter supply is realized by cascading four two-level and two three-level neutral-point-clamped inverters. The inverter control is designed in such a way that the common-mode voltage (CMV) is eliminated. DC-link capacitor voltage balancing is also achieved by using only the switching-state redundancies. The proposed power circuit structure is modular and therefore suitable for fault-tolerant applications. By appropriately isolating some of the inverters, the drive can be operated during fault conditions in a five-level or a three-level inverter mode, with preserved CMV elimination and DC-link capacitor voltage balancing, within a reduced modulation range.

Structural Basis of Drug Resistance by Genetic Variants of HIV Type 1 Clade C Protease from India

Using computer modeling of three-dimensional structures and structural information available on the crystal structures of HIV-1 protease, we investigated the structural effects of mutations, in treatment-naive and treatment-exposed individuals from India and postulated mechanisms of resistance in clade C variants. A large number of models (14) have been generated by computational mutation of the available crystal structures of drug bound proteases. Localized energy minimization was carried out in and around the sites of mutation in order to optimize the geometry of interactions present. Most of the mutations result in structural differences at the flap that favors the semiopen state of the enzyme. Some of the mutations were also found to confer resistance by affecting the geometry of the active site. The E35D mutation affects the flap structure in clade B strains and E35N and E35K mutation, seen in our modeled strains, have a more profound effect. Common polymorphisms at positions 36 and 63 in clade C also affected flap structure. Apart from a few other residues Gln-58, Asn-83, Asn-88, and Gln-92 and their interactions are important for the transition from the closed to the open state. Development of protease inhibitors by structure-based design requires investigation of mechanisms operative for clade C to improve the efficacy of therapy.

Structure and Conformation of an Antidepressant Drug, Nitroxazepine Hydrochloride Monohydrate

CIsH20N3Oa+.C1-.H2 O, M r = 395, orthorhombic, Pn21a, a = 7.710 (4), b = 11.455 (3), c -- 21.199 (3)/k, Z = 4, V = 1872.4/k 3, D m = 1.38, D C = 1.403 g cm -3, F(000) = 832, g(Cu Kct) = 20.94 cm -l. Intensities for 1641 reflections were measured on a Nonius CAD-4 diffractometer; of these, 1470 were significant. The structure was solved by direct methods and refined to an R index of 0.045 using a blockdiagonal least-squares procedure. The angle between the least-squares planes through the benzene rings is 125.0 (5) ° and the side chain is folded similarly to one of the independent molecules of imipramine hydrochloride.

Structure and conformation of an anti-depressant drug, nitroxazepine hydrochloride monohydrate

CIsH20N3Oa+.C1-.H2 O, M r = 395, orthorhombic, Pn21a, a = 7.710 (4), b = 11.455 (3), c -- 21.199 (3)/k, Z = 4, V = 1872.4/k 3, D m = 1.38, D C = 1.403 g cm -3, F(000) = 832, g(Cu Kct) = 20.94 cm -l. Intensities for 1641 reflections were measured on a Nonius CAD-4 diffractometer; of these, 1470 were significant. The structure was solved by direct methods and refined to an R index of 0.045 using a blockdiagonal least-squares procedure. The angle between the least-squares planes through the benzene rings is 125.0 (5) ° and the side chain is folded similarly to one of the independent molecules of imipramine hydrochloride.

Biochemical effects of the porphyrinogenic drug allylisopropylacetamide. A comparative study with phenobarbital

Successive administrations of allylisopropylacetamide, a potent porphyrinogenic drug, increase liver weight, microsomal protein and phospholipid contents. There is an increase in the rate of microsomal protein synthesis in vivo and in vitro. The drug decreases microsomal ribonuclease activity and increases NADPH–cytochrome c reductase activity. Phenobarbital, which has been reported to exhibit all these changes mentioned, is a weaker inducer of δ-aminolaevulinate synthetase and increases the rate of haem synthesis only after a considerable time-lag in fed female rats, when compared with the effects observed with allylisopropylacetamide. Again, phenobarbital does not share the property of allylisopropylacetamide in causing an initial decrease in cytochrome P-450 content. Haematin does not counteract most of the biochemical effects caused by allylisopropylacetamide, although it is quite effective in the case of phenobarbital. Haematin does not inhibit the uptake of [2-14C]allylisopropylacetamide by any of the liver subcellular fractions.

Dual Drug Delivery Microcapsules via Layer-by-Layer Self-Assembly

The integration of hydrophobic and hydrophilic drugs in the polymer microcapsule offers the possibility of developing a new drug delivery system that combines the best features of these two distinct classes of material. Recently, we have reported the encapsulation of an uncharged water-insoluble drug in the polymer membrane. The hydrophobic drug is deposited using a layer-by-layer (LbL) technique, which is based on the sequential adsorption of oppositely charged polyelectrolytes onto a charged substrate. In this paper, we report the encapsulation of two different drugs, which are invariably different in structure and in their solubility in water. We have characterized these dual drug vehicular capsules by confocal laser scanning microscopy, atomic force microscopy, visible microscopy, and transmission electron microscopy. The growth of a thin film on a flat substrate by LbL was monitored by UV−vis spectra. The desorption kinetics of two drugs from the thin film was modeled by a second-order rate model.

Computer-Aided Drug Design of Pyranopyrazoles and Related Compounds for Checkpoint Kinase-1

Checkpoint-1 kinase plays an important role in the G(2)M cell cycle control, therefore its inhibition by small molecules is of great therapeutic interest in oncology. In this paper, we have reported the virtual screening of an in-house library of 2499 pyranopyrazole derivatives against the ATP-binding site of Chk1 kinase using Glide 5.0 program, which resulted in six hits. All these ligands were docked into the site forming most crucial interactions with Cys87, Glu91 and Leu15 residues. From the observed results these ligands are suggested to be potent inhibitors of Chk1 kinase with sufficient scope for further elaboration.

Influence of surface chemistry of mesoporous alumina with wide pore distribution on controlled drug release

The crucial role of the drug carrier surface chemical moeities on the uptake and in vitro release of drug is discussed here in a systematic manner. Mesoporous alumina with a wide pore size distribution (2-7 nm) functionalized with various hydrophilic and hydrophobic surface chemical groups was employed as the carrier for delivery of the model drug ibuprofen. Surface functionalization with hydrophobic groups resulted in low degree of drug loading (approximately 20%) and fast rate of release (85% over a period of 5 h) whereas hydrophilic groups resulted in a significantly higher drug payloads (21%-45%) and slower rate of release (12%-40% over a period of 5 h). Depending on the chemical moiety, the diffusion controlled (proportional to time(-0.5)) drug release was additionally observed to be dependent on the mode of arrangement of the functional groups on the alumina surface as well as on the pore characteristics of the matrix. For all mesoporous alumina systems the drug dosages were far lower than the maximum recommended therapeutic dosages (MRTD) for oral delivery. We envisage that the present study would aid in the design of delivery systems capable of sustained release of multiple drugs.

Computational systems approach for drug target discovery

Importance of the field: The shift in focus from ligand based design approaches to target based discovery over the last two to three decades has been a major milestone in drug discovery research. Currently, it is witnessing another major paradigm shift by leaning towards the holistic systems based approaches rather the reductionist single molecule based methods. The effect of this new trend is likely to be felt strongly in terms of new strategies for therapeutic intervention, new targets individually and in combinations, and design of specific and safer drugs. Computational modeling and simulation form important constituents of new-age biology because they are essential to comprehend the large-scale data generated by high-throughput experiments and to generate hypotheses, which are typically iterated with experimental validation. Areas covered in this review: This review focuses on the repertoire of systems-level computational approaches currently available for target identification. The review starts with a discussion on levels of abstraction of biological systems and describes different modeling methodologies that are available for this purpose. The review then focuses on how such modeling and simulations can be applied for drug target discovery. Finally, it discusses methods for studying other important issues such as understanding targetability, identifying target combinations and predicting drug resistance, and considering them during the target identification stage itself. What the reader will gain: The reader will get an account of the various approaches for target discovery and the need for systems approaches, followed by an overview of the different modeling and simulation approaches that have been developed. An idea of the promise and limitations of the various approaches and perspectives for future development will also be obtained. Take home message: Systems thinking has now come of age enabling a `bird's eye view' of the biological systems under study, at the same time allowing us to `zoom in', where necessary, for a detailed description of individual components. A number of different methods available for computational modeling and simulation of biological systems can be used effectively for drug target discovery.

A 100MHz to 1GHz, 0.35V to 1.5V supply 256 x 64 SRAM mock using symmetrized 9T SRAM cell with controlled read

In this paper, we present Dynamic Voltage and Frequency Managed 256 x 64 SRAM block in 65nm technology, for frequency ranging from 100MHz to 1GHz. The total energy is minimized for any operating frequency in the above range and leakage energy is minimized during standby mode. Since noise margin of SRAM cell deteriorates at low voltages, we propose Static Noise Margin improvement circuitry, which symmetrizes the SRAM cell by controlling the body bias of pull down NMOS transistor. We used a 9T SRAM cell that isolates Read and Hold Noise Margin and has less leakage. We have implemented an efficient technique of pushing address decoder into zigzag-super-cut-off in stand-by mode without affecting its performance in active mode of operation. The Read Bit Line (RBL) voltage drop is controlled and pre-charge of bit lines is done only when needed for reducing power wastage.

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 Bayesian incentive compatible mechanism for decentralized supply chain formation

In this paper we consider a decentralized supply chain formation problem for linear multi-echelon supply chains when the managers of the individual echelons are autonomous, rational, and intelligent. At each echelon, there is a choice of service providers and the specific problem we solve is that of determining a cost-optimal mix of service providers so as to achieve a desired level of end-to-end delivery performance. The problem can be broken up into two sub-problems following a mechanism design approach: (1) Design of an incentive compatible mechanism to elicit the true cost functions from the echelon managers; (2) Formulation and solution of an appropriate optimization problem using the true cost information. In this paper we propose a novel Bayesian incentive compatible mechanism for eliciting the true cost functions. This improves upon existing solutions in the literature which are all based on the classical Vickrey-Clarke-Groves mechanisms, requiring significant incentives to be paid to the echelon managers for achieving dominant strategy incentive compatibility. The proposed solution, which we call SCF-BIC (Supply Chain Formation with Bayesian Incentive Compatibility), significantly reduces the cost of supply chain formation. We illustrate the efficacy of the proposed methodology using the example of a three echelon manufacturing supply chain.

Prospecting for alternate sources of shikimic acid, a precursor of Tamiflu, a bird-flu drug

Shikimic acid, more commonly known by its anionic form, shikimate, is an important intermediate compound of the ‘shikimate pathway’ in plants and microorganisms1. It is the principal precursor for the synthesis of aromatic amino acids, phenylalanine, tryptophan and tyrosine and other compounds such as alkaloids, phenolics and phenyl propanoids2. It is used extensively as a chiral building block for the synthesis of a number of compounds in both pharmaceutical and cosmetic industries3. In the recent past, the focus on shikimic acid has increased since it is the key precursor for the synthesis of Tamiflu, the only drug against avian flu caused by the H5N1 virus4,5. Shikimic acid is converted to a diethyl ketal intermediate, which is then reduced in two steps to an epoxide that is finally transformed to Tamiflu6.

Ultrasound-Triggered Controlled Drug Delivery and Biosensing Using Silica Nanotubes

Silica nanotubes (SNTs) have been demonstrated here as a versatile host for controlled drug delivery and biosensing. The sol-gel template synthesized SNTs have a slow rate of drug release. Application of an external stimulus in the form of ultrasound to or chemical functionalization of synthesized SNT results in higher yield of drug release as well as yield of drug release varying linearly with time. In case of controlled drug delivery triggered by ultrasound, drug yield as function of time is found to be heavily dependent on the ultrasound impulse protocol. Impulses of shorter duration (similar to 0.5 min) and shorter time intervals between successive impulses resulted in higher drug yields. Confinement of hemoglobin (Hb) inside nanometer sized channels of SNT does not have any detrimental effect on the native protein structure and function. Observance of significant enhancement in direct electron transfer of Hb makes the SNTs also promising for application in biosensors.