Modelling, analysis, and acceleration of a printed circuit board fabrication process

Product design and fabrication constitute an important business activity in any manufacturing firm. Designing an optimized product fabrication process is an important problem in itself and is of significant practical and research interest. In this paper, we look into a printed circuit board (PCB) fabrication process and investigate ways in which the fabrication cycle time can be minimized. Single class queueing networks constitute the modelling framework for our study. The model developed in this paper and the analysis experiments carried out are based on extensive data collected on a PCB fabrication company located in Bangalore, India. This is a representative PCB fabrication company involving multiple, concurrent fabrication works with contention for human/technical resources. Our model seeks to capture faithfully the flow of the fabrication process in this company and such other organisations, using queueing networks. Using the model developed, we explore how the cycle times can be reduced using input control, load balancing, and variability reduction. The model presented is sufficiently generic and conceptual; its scope extends beyond that of a PCB fabrication organization

Nonconvex piecewise linear knapsack problems

This paper considers the minimization version of a class of nonconvex knapsack problems with piecewise linear cost structure. The items to be included in the knapsack have a divisible quantity and a cost function. An item can be included partially in the given quantity range and the cost is a nonconvex piecewise linear function of quantity. Given a demand, the optimization problem is to choose an optimal quantity for each item such that the demand is satisfied and the total cost is minimized. This problem and its close variants are encountered in manufacturing planning, supply chain design, volume discount procurement auctions, and many other contemporary applications. Two separate mixed integer linear programming formulations of this problem are proposed and are compared with existing formulations. Motivated by different scenarios in which the problem is useful, the following algorithms are developed: (1) a fast polynomial time, near-optimal heuristic using convex envelopes; (2) exact pseudo-polynomial time dynamic programming algorithms; (3) a 2-approximation algorithm; and (4) a fully polynomial time approximation scheme. A comprehensive test suite is developed to generate representative problem instances with different characteristics. Extensive computational experiments show that the proposed formulations and algorithms are faster than the existing techniques

Water-triggered frontal polymerization

A totally new mode of frontal polymerization (FP) of acrylamide is established which is triggered by the simple addition of a minute, specific volume of water. Experimental conditions under which this mode of polymerization yields linear and water-soluble polyacrylamide were carefully established, paving the way to synthesize commercially pertinent homo- and copolymers. A new redox couple was identified to circumvent the imidization and the ensuing gelation, hitherto associated with FP of acrylamide. Effects of reaction variables such as type and concentration of redox couple and volume of water on measurable parameters of FP such as front velocity, front temperature, shape of front and yield have been studied. Two types of redox couples are reported. Nonplanar frontal regime was observed in few redox couples. We could visually observe helical patterns with naked eyes, while layered patterns were observable under SEM. Additionally, micro-phase separation and heterogeneity in the polymer matrix was observed due to unreacted pockets of monomer which evolve via bulk mode. This nonlinear phenomenon is described

Detecting Diseases in Gastrointestinal Biopsy Images Using CNN

Discovering illnesses in gastrointestinal biopsy photos is a complicated job that must be executed swiftly and with an excellent level of precision. In the interest of boosting the precision and rapidity of illness identification in clinical photographs, models based on deep learning have shown flashes of brilliance. This paper outlines the processes for designing a deep learning model to identify diseases in gastrointestinal biopsy imagery. The procedures involve gathering data, processing, adopting a model, training, assessing and optimizing. It is observed that in order to certify the detection rate, trustworthiness, and safety for clinical evaluation, it is vital that it be developed together with experts in deep learning and medical imaging.&nbsp

The bicyclo[2.1.1]hexan-2-one system: a new probe for the experimental and computational study of electronic effects in π-facial selectivity in nucleophilic additions

The remotely substituted 5-exo-bicyclo[2.1.1]hexan-2-one system is introduced as a new probe to study long range electronic effects on π -face selectivity during hydride reduction and a systematic computational study demonstrates good predictability at the semi-empirical level

Multiattribute electronic procurement using goal programming

One of the key challenges of current day electronic procurement systems is to enable procurement decisions transcend beyond a single attribute such as cost. Consequently, multiattribute procurement have emerged as an important research direction. In this paper, we develop a multiattribute e-procurement system for procuring large volume of a single item. Our system is motivated by an industrial procurement scenario for procuring raw material. The procurement scenario demands multiattribute bids, volume discount cost functions, inclusion of business constraints, and consideration of multiple criteria in bid evaluation. We develop a generic framework for an e-procurement system that meets the above requirements. The bid evaluation problem is formulated as a mixed linear integer multiple criteria optimization problem and goal programming is used as the solution technique. We present a case study for which we illustrate the proposed approach and a heuristic is proposed to handle the computational complexity arising out of the cost functions used in the bids

Prevalence of vitamin D deficiency in chronic obstructive pulmonary disease and it’s correlation with forced expiratory volume in one second: a tertiary care centre study

Background: Vitamin D deficiency is common all over the Indian subcontinent, with a prevalence of 70-100% in the general population. Vitamin D deficiency has a role in several diseases of the respiratory system including chronic obstructive pulmonary disease (COPD). Studies have shown that vitamin D deficient COPD patients have lower lung function measured by FEV1. We conducted a study to see prevalence of Vitamin D deficiency in COPD patients and it’s correlation with Forced Expiratory Volume in 1 second.Methods: A cross sectional observational study was performed in a tertiary care hospital in Hyderabad, Telangana during the period of one and half year. This included 104 COPD cases attending outpatient department of pulmonary medicine. Serum vitamin D levels were measured, and spirometry was done in all patients and data was analyzed accordingly.Results: Among 104 patients, 87 were males and 17 were females. Most of the study population (44.2%) was aged between 60-70 years. Mean BMI was 26.40 (±5.77) kg/m2. Majority of study populations (66.34%) were in GOLD stage 1 and 2. The mean Vitamin D value of the study population was 20.77±11.74ng/ml. Majority of the COPD patients were vitamin D deficient (69.2%). 25.0 % was severely deficient of vitamin D. The mean FEV1 volume (%) was 83.15±11.53, 60.97±17.47, 30.71±7.96 in sufficient, deficient and severe deficient patients.Conclusions: Vitamin D deficiency is common in COPD patients. Serum vitamin D deficiency increases with increased severity of COPD. There is positive correlation between serum vitamin D levels and post bronchodilator FEV1 (%).

Regression rate studies in hypergolic system

Regression rates of a hypergolic combination of fuel and oxidiser have been experimentally measured as a function of chamber pressure, mass flux and the percentage component of the hypergolic compound in natural rubber. The hypergolic compound used is difurfurylidene cyclohexanone (DFCH) which is hypergolic with the oxidiser red fuming nitric acid (RFNA) with ignition delay of 60-70 ms. The data of weight loss versus time is obtained for burn times varying between 5 and 20 seconds. Two methods of correlating the data using mass flux of oxidiser and the total flux of hot gases have shown that index n of the regression law r=aGoxn or r=aGnxa-1 (x the axial distance) is about 0.5 or a little lower and not 0.8 even though the flow through the port is turbulent. It is argued that the reduction of index n is due to heterogeneous reaction between the liquid oxidiser and the hypergolic fuel component on the surface