Volumetric analysis of MR images for glioma classification and their effect on brain tissues

This work aims to identify non-invasive quantitative parameters from three-dimensional brain magnetic resonance images in order: (1) to classify brain tumor (glioma) as low grade (LG) or high grade (HG) and (2) to analyze effect of tumor on brain gray matter (GM) and white matter (WM). In proposed model features were extracted from segmented tumor region based on its volume and shape for distinguishing the tumor grade. Statistical analysis revealed good correlation between segmented tumor volume and tumor grade. Various morphological parameters extracted from segmented tumor region were also significantly different for LG and HG cases (\(p<0.05\)). Also, for brain tumor patients a considerable variation in normalized GM (%GM) volume was obtained compared to normalized WM (%WM) volume for LG and HG cases. We also found that, relative to controls, there was higher effect on %GM and %WM volumes for HG glioma patients as compared to LG glioma patients. The experimental results show that proposed feature set achieves LG/HG classification with high accuracy using support vector machines classifier.     

Physicochemical Analysis of Tetrakis (Thiourea) Palladium Chloride: A Prospective Non Linear Optical Crystal

The solubility test and nucleation kinetics studies of Tetrakis (thiourea) palladium chloride (TTPC) have been carried out and optical quality crystals of TTPC have been harvested by means of slow evaporation method. The single crystal X-ray diffraction studies revealed the orthorhombic structure and Pna2₁ space group of the crystals. To ascertain the stoichiometry and the purity of the crystals, elemental analysis has been performed. The etching studies of the crystal suggested the dimensional nucleation mechanism. Fourier Transform Infrared spectral studies have been employed in order to establish the metal—sulphur coordination prevailing in the crystal. The Ultra Violet transmittance study has been conducted to calculate the transmittance, band gap energy, Urbach’s energy, nature of electronic transitions, reflectance, refractive index, optical and electrical conductivities, extinction coefficient and electrical susceptibility. The mechanical stability of the TTPC crystals has been examined by Vicker’s hardness test in terms of hardness number, elastic stiffness constant, Meyer’s Index, minimum level of indentation load, load dependent constant, brittleness index and corrected hardness. Kurtz method has been adopted which showed the phase matching nature of TTPC and concluded that TTPC showed 1.15 times second harmonic generation efficiency as that of KDP. By using the dielectric and ac conductivity study, the activation energy values of the electrical process have been measured. Using theoretical approach, important solid state parameters such as valence electron plasma energy, Penn gap, Fermi energy and polarisability have been derived. The photoconductivity studies revealed the negative conductivity of the title crystal.     

Sustainability trends in the process industries: A text mining-based analysis

Sustainability is widely recognized as one of the most important challenges facing the world today. Companies publish sustainability reports that present their efforts and achievements in meeting sustainability goals and targets. In this paper, text mining is used to identify sustainability trends and practices in the process industries. Four main sectors of the industry are studied: oil/petrochemicals, bulk/specialty chemicals, pharmaceuticals, and consumer products. Our study reveals that the top sustainability focuses of the four sectors are very similar: health and safety, human rights, reducing GHG, conserving energy/energy efficiency, and community investment. Sector-specific sustainability issues have also been identified, for example oil spill prevention in the oil/petrochemicals sector and access to medicine in the pharmaceuticals sector. Environment is identified to be the predominant sustainability aspect in the process industries. The text mining methodology, results, and findings are detailed in the paper.     

An ultrasonic collision detection system

Robots in manufacturing are increasingly being called on to do complex tasks that require intelligence beyond merely following a preprogrammed path. In robotic assembling of mechanisms, welding, machine tending and other tasks, sensing enables robots to adapt to their environments. In this research, an ultrasonic collision detection system for an industrial robot was designed, constructed and tested. Two ultrasonic transducers and ranging modules were mounted on the robot wrist to detect and prevent collisions with objects placed in the end effector's path. Experiments were conducted to determine objectto-robot distance as a function of robot speed after the robot had stopped. Two robot motions and two loads were studied. Statistical methods of stopping distance vs. robot speed are presented and will be useful in planning robot tool paths. This ultrasonic collision detection system can be used on stationary and mobile robots, automatic-guided vehicles, and other manufacturing applications.     

Mercury concentrations in water from an unconfined aquifer system, New Jersey coastal plain

Concentrations of total mercury (Hg) from 2 microg/L (the USEPA maximum contaminant level) to 72 microg/L in water from about 600 domestic wells in residential parts of eight counties in southern New Jersey have been reported by State and county agencies. The wells draw water from the areally extensive (7770 km(2)) unconfined Kirkwood-Cohansey aquifer system, in which background concentrations of Hg are about 0.01 microg/L or less. Hg is present in most aquifer materials at concentrations <50 microg/kg, but is at 100--150 microg/kg in undisturbed surficial soils. No point sources of contamination to the affected areas have been conclusively identified. To determine whether high levels of Hg in ground water are related to a particular land use and (or) water chemistry, water samples from 105 wells that tap the aquifer system were collected by the United States Geological Survey. These included randomly selected domestic wells, domestic and observation wells in selected land uses, and sets of clustered observation wells--including two sets that are downgradient from residential areas with Hg-contaminated ground water. Hg concentrations in filtered samples (Hg(f)) were at or near background levels in water from most wells, but ranged from 0.1 to 3.8 microg/L in water from nearly 20% of wells. Hg(f) concentrations from 0.0001 to 0.1 microg/L correlated significantly and positively with concentrations of other constituents associated with anthropogenic inputs (Ca, Cl, Na, and NO(3)) and with dissolved organic carbon. Hg(f) concentrations >0.1 microg/L did not correlate significantly with concentrations of the inorganic constituents. Hg(f) concentrations near or exceeding 2 microg/L were found only in water from wells in areas with residential land use, but concentrations were at background levels in most water samples from undeveloped land. The spatial distribution of Hg-contaminated ground water appears to be locally and regionally heterogeneous; no extensive plumes of Hg contamination have yet been identified.     

Multi-model based process condition monitoring of offshore oil and gas production process

Offshore oil and gas production platforms are uniquely hazardous in which the operating personnel have to work in a perilous environment surrounded by extremely flammable hydrocarbons. A failure in an equipment could quickly propagate to others resulting in leaks, fires and explosions, causing loss of life, capital invested and production downtime. A method for preventing such accidents is to deploy intelligent monitoring tools which continuously supervise the process and the health of equipments to provide context-specific decision support to operators during safety-critical situations. Such an intelligent system, which is condition driven is developed and described in this paper. Since relevant process data is unavailable in the literature, a dynamic model of an offshore oil and gas production platform was developed using gPROMS and data to reflect operating conditions under normal, fault conditions and maintenance activities were simulated. The different maintenance activities and normal conditions are explicitly considered as separate states of the process. The simulated data are then used to train principal component analysis monitoring models for each of these states. Online fault detection and identification are performed by identifying the operating state in real-time and triggering the respective model. In this paper, the dynamic model and the condition monitoring system are described.     

Development and application of a multi-scalar,participant-driven water poverty index in post-tsunami India

This article presents a modified water poverty index that captures several waterscape attributes to better understand complex issues surrounding water. Household surveys (n = 300), water quality tests (n = 375) and qualitative methods were deployed to examine 14 post-tsunami settlements in Nagapattinam and Karaikal Districts (India) through the lens of water. Data were used to develop a contextualized, participant-driven water poverty index to measure water poverty at several scales. Statistical tests revealed significant differences between the two districts (p ≤ .0001) and between rural and urban areas within each district (p ≤ .0001). Three weight schemes (one dictated entirely by research participants) produced analogous outcomes though predicated on different indicator arrangements.     

Synthesis of cenosphere supported heterogeneous catalyst and its performance in esterification reaction

A heterogeneous catalyst has been developed from cenosphere, a byproduct generated in thermal power plant. The performance of catalyst was investigated in the esterification of propionic acid and ethanol. The catalyst was characterized by FTIR, X-ray diffraction, field emission scanning electron microscope, and Brunauer–Emmett–Teller surface area and surface acidity analysis. The response surface methodology (RSM) with the Box–Behnken design was employed to design the experiments as well as to optimize the various process parameters such as catalyst loading, alcohol/acid molar ratio, and reaction temperature. The characterization revealed that the cenosphere supported catalyst possessed increased amounts of silica content, surface hydroxyl groups, surface area as well as surface acidity as compared with that of pristine cenosphere. The catalyst showed an excellent activity in the esterification reaction with a maximum conversion up to 91%. The RSM model well fitted the experimental data and predicted optimal conditions which were validated experimentally with a good agreement. The recyclability study showed a significant catalytic stability up to three reaction cycles. This study reveals that the cenosphere supported catalyst can be used as a potential catalyst in the esterification reaction may replace the conventional homogeneous acid catalyst.     

Model-predictive control of mixed-mode buildings with rule extraction

A series of model-predictive control (MPC) techniques have been explored for optimizing control sequences for window operation in mixed-mode (MM) buildings using EnergyPlus, and results for a simplified MM office building have been presented. Initial results for a small office in Boulder, Colorado show the ability to save upwards of 40% of cooling energy through near-optimal night cooling strategies, even in existing facilities. Strategies can be tuned to avoid overcooling the space by introducing heating energy into the objective function used in the MPC process. A complementary statistical technique has been introduced that allows for the “extraction” of logistic decision models from the optimal control results. The process works best when some time-lagged information is present as a predictor variable to ensure that some process memory is preserved. A generalized linear model (GLM) in the form of a multi-logistic regression was able to mimic the general characteristics of the optimizer results, achieving 70–90% of optimizer energy savings, but at a small fraction of the computational expense. Given the simple mathematical formulation of the logistic regression, it would be possible to implement this sort of decision model into modern direct digital control systems to control MM buildings in a near-optimal manner in real time.