A sustainable approach to the degradation of dyes by fungal species isolated from industrial wastewaters: Performance, parametric optimization, kinetics and degradation mechanism.

Fungal abetted processes are among the finest approaches for the transformation or degradation and decolorization of dyes in effluents. In this piece of research; biodegradation and metabolic pathways of two toxic dyes Congo Red (CR) and Reactive black 5 (RB5) by two strains of Aspergillus sp. fungus in batch experiments has been investigated. Morphological characteristics of the isolates were observed with both light and electron microscopies. Based on molecular characterization the isolates were identified as Aspergillus flavus and Aspergillus niger. The degradation was also optimized via. operational parameters such as pH, temperature, incubation time, inoculums size, dye concentration, carbon sources and nitrogen sources. Degradation measurements revealed that the isolates effectively degraded 90% and 96% of CR and RB5 respectively. Metabolites were identified with Liquid chromatography-mass spectrometry (LCMS) and degradation pathways of the dyes were proposed. Toxicity assay Phaseolus mungo seeds showed that pure CR and RB5 dyes exhibits significant toxicity whereas fungal treated dye solution resulted in an abatement of the toxicity and cell viability was increased. The results stipulated in this article clearly showed the effectiveness of the isolates on detoxification of CR and RB5 dyes.

Small Stochastic Data Compactification Concept Justified in the Entropy Basis.

Measurement is a typical way of gathering information about an investigated object, generalized by a finite set of characteristic parameters. The result of each iteration of the measurement is an instance of the class of the investigated object in the form of a set of values of characteristic parameters. An ordered set of instances forms a collection whose dimensionality for a real object is a factor that cannot be ignored. Managing the dimensionality of data collections, as well as classification, regression, and clustering, are fundamental problems for machine learning. Compactification is the approximation of the original data collection by an equivalent collection (with a reduced dimension of characteristic parameters) with the control of accompanying information capacity losses. Related to compactification is the data completeness verifying procedure, which is characteristic of the data reliability assessment. If there are stochastic parameters among the initial data collection characteristic parameters, the compactification procedure becomes more complicated. To take this into account, this study proposes a model of a structured collection of stochastic data defined in terms of relative entropy. The compactification of such a data model is formalized by an iterative procedure aimed at maximizing the relative entropy of sequential implementation of direct and reverse projections of data collections, taking into account the estimates of the probability distribution densities of their attributes. The procedure for approximating the relative entropy function of compactification to reduce the computational complexity of the latter is proposed. To qualitatively assess compactification this study undertakes a formal analysis that uses data collection information capacity and the absolute and relative share of information losses due to compaction as its metrics. Taking into account the semantic connection of compactification and completeness, the proposed metric is also relevant for the task of assessing data reliability. Testing the proposed compactification procedure proved both its stability and efficiency in comparison with previously used analogues, such as the principal component analysis method and the random projection method.

Parameterization of the Stochastic Model for Evaluating Variable Small Data in the Shannon Entropy Basis.

The article analytically summarizes the idea of applying Shannon's principle of entropy maximization to sets that represent the results of observations of the "input" and "output" entities of the stochastic model for evaluating variable small data. To formalize this idea, a sequential transition from the likelihood function to the likelihood functional and the Shannon entropy functional is analytically described. Shannon's entropy characterizes the uncertainty caused not only by the probabilistic nature of the parameters of the stochastic data evaluation model but also by interferences that distort the results of the measurements of the values of these parameters. Accordingly, based on the Shannon entropy, it is possible to determine the best estimates of the values of these parameters for maximally uncertain (per entropy unit) distortions that cause measurement variability. This postulate is organically transferred to the statement that the estimates of the density of the probability distribution of the parameters of the stochastic model of small data obtained as a result of Shannon entropy maximization will also take into account the fact of the variability of the process of their measurements. In the article, this principle is developed into the information technology of the parametric and non-parametric evaluation on the basis of Shannon entropy of small data measured under the influence of interferences. The article analytically formalizes three key elements: -instances of the class of parameterized stochastic models for evaluating variable small data; -methods of estimating the probability density function of their parameters, represented by normalized or interval probabilities; -approaches to generating an ensemble of random vectors of initial parameters.

Parametric optimization of domestic wastewater treatment in an activated sludge sequencing batch reactor using response surface methodology.

In this work, the parametric optimization of real domestic wastewater treated in an activated sludge sequencing batch reactor (SBR) was performed by means of the response surface methodology (RSM). The influences of influent organic matter concentration as chemical oxygen demand (CODinf), biomass concentration (Xs) and aeration time (t) on the COD, organic matter removal efficiency as COD (η) and sludge volume index (SVI) were determined to evaluate the performance of activated sludge SBR. The results showed that organic matter efficiency and maximum SVI were obtained at a t of 12 h, 300 mg L-1 of CODinf and 2000 mg L-1 of Xs. The SBR-activated sludge exhibited a η of 73% and an SVI of 119 mL g-1. Both values indicated a very good performance. Furthermore, the COD of the effluent under these conditions complied with Mexican regulations for wastewater discharged into water bodies.

Sensitivity of thermophysiological models of cryoablation to the thermal and biophysical properties of tissues.

Advancement in biomedical simulation and imaging modality have catalysed the development of in silico predictive models for cryoablation. However, one of the main challenges in ensuring the accuracy of the model prediction is the use of proper thermal and biophysical properties of the patient. These properties are difficult to measure clinically and thus, represent significant uncertainty that can affect the model prediction. Motivated by this, a sensitivity analysis is carried out to identify the model parameters that have the most significant impact on the lesion size during cryoablation. The study is initially carried out using the Morris method to screen for the most dominant parameters. Once determined, analysis of variance (ANOVA) is performed to quantitatively rank the order of importance of each parameter and their interactions. Results from the sensitivity analysis revealed that blood perfusion, water transport and ice nucleation parameters are critical in predicting the lesion size, suggesting that the acquisition of these parameters should be prioritised to ensure the accuracy of the model prediction.

Metaheuristic aided structural topology optimization method for heat sink design with low electromagnetic interference.

This study investigates the application of the Metaheuristic Aided Structural Topology Optimization (MASTO) method as a novel approach to address the multiphysics design challenge of creating a heat sink with both high heat conductivity and minimal Electromagnetic Interference (EMI). A distinctive 2D layout with elongated fins is examined for electromagnetic traits, highlighting resonance-related EMI concerns. MASTO proves to be a valuable tool for navigating the complex design space, yielding thoughtfully optimized solutions that harmonize efficient heat dissipation with effective EMI control. By merging simulation findings with practical observations, this study underscores the potential of the MASTO method in achieving effective designs for intricate multiphysics optimization problems. Specifically, the method's capacity to address the complex interplay of heat transfer with convection and the suppression of electromagnetic emissions is showcased. Moreover, the study demonstrates the feasibility of translating these solutions into tangible outcomes through manufacturing processes.

Reporting delays: A widely neglected impact factor in COVID-19 forecasts.

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean, and devoid of noise? The complexity and variability inherent in data collection and reporting suggest otherwise. While we cannot evaluate the integrity of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Using our framework, we expose and analyze reporting delays in eight regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data by using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50%. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.

Entropy-metric estimation of the small data models with stochastic parameters.

The formalization of dependencies between datasets, taking into account specific hypotheses about data properties, is a constantly relevant task, which is especially acute when it comes to small data. The aim of the study is to formalize the procedure for calculating optimal estimates of probability density functions of parameters of linear and nonlinear dynamic and static small data models, created taking into account specific hypotheses regarding the properties of the studied object. The research methodology includes probability theory and mathematical statistics, information theory, evaluation theory, and stochastic mathematical programming methods. The mathematical apparatus presented in the article is based on the principle of maximization of information entropy on sets determined as a result of a small number of censored measurements of "input" and "output" entities in the presence of noise. These data structures became the basis for the formalization of linear and nonlinear dynamic and static models of small data with stochastic parameters, which include both controlled and noise-oriented input and output measurement entities. For all variants of the above-mentioned small data models, the tasks of determining the optimal estimates of the probability density functions of the parameters were carried out. Formulated optimization problems are reduced to the forms canonical for the stochastic linear programming problem with probabilistic constraints.

Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters.

Professionals in industries are making progress in creating predictive techniques for evaluating critical characteristics and reactions of engineered materials. The objective of this investigation is to determine the optimal settings for a 3D printer made of acrylonitrile butadiene styrene (ABS) in terms of its conflicting responses (flexural strength (FS), tensile strength (TS), average surface roughness (Ra), print time (T), and energy consumption (E)). Layer thickness (LT), printing speed (PS), and infill density (ID) are all quantifiable characteristics that were chosen. For the experimental methods of the prediction models, twenty samples were created using a full central composite design (CCD). The models were verified by proving that the experimental results were consistent with the predictions using validation trial tests, and the significance of the performance parameters was confirmed using analysis of variance (ANOVA). The most crucial element in obtaining the desired Ra and T was LT, whereas ID was the most crucial in attaining the desired mechanical characteristics. Numerical multi-objective optimization was used to achieve the following parameters: LT = 0.27 mm, ID = 84 percent, and PS = 51.1 mm/s; FS = 58.01 MPa; TS = 35.8 MPa; lowest Ra = 8.01 m; lowest T = 58 min; and E = 0.21 kwh. Manufacturers and practitioners may profit from using the produced numerically optimized model to forecast the necessary surface quality for different aspects before undertaking trials.

A comparative study on the influence of single and combined ultrasounds assisted flake graphite flotation.

Ultrasound has emerged as a promising technique for improving the mineral flotation performance. However, limited research exists regarding the influence of different ultrasound types on the flotation process. Specifically, the impact of combined ultrasound and the comparison of horn- and bath-type ultrasounds on flotation have not been fully investigated. To address this knowledge gap, a comprehensive study to explore the effects of different ultrasonic pretreatments on the flotation of flake graphite was conducted. A Box-Behnken design is employed to analyze the effects of combined ultrasound on graphite flotation. By characterizing the properties of graphite samples before and after the ultrasonic treatment, the aim is to elucidate the mechanism underlying the impact of ultrasound on graphite flotation. The experimental results indicated that the ultrasonic cavitation intensity exerted a significant influence on the graphite flotation recovery. Both horn- and bath- type ultrasounds contributed to flotation, but horn-type ultrasound demonstrated a more pronounced effect, leading to a 7% increase in flotation recovery, whereas bath-type ultrasound resulted in only a 2% increase. Furthermore, the cavitation intensity of combined ultrasound was found to be higher than that of single-frequency ultrasound in the same duration. However, the performance of graphite flotation was better with short duration combined ultrasound pretreatment, while the opposite trend was observed for a long duration ultrasound pretreatment. These findings may inform the development of more efficient and effective ultrasonic pretreatments for flotation separation processes.

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance.

In recent years, there has been a growing interest in the field of 3D printing technology. Among the various technologies available, fused deposition modeling (FDM) has emerged as the most popular and widely used method. However, achieving optimal results with FDM presents a significant challenge due to the selection of appropriate process parameters. Therefore, the objective of this research was to investigate the impact of process parameters on the tribological and frictional behavior of acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) 3D-printed parts. The design of experiments (DOE) technique was used considering the input design parameters (infill percentage and layer thickness) as variables. The friction coefficient values and the wear were determined by experimental testing of the polymers on a universal tribometer employing plane friction coupling. Multi-response optimization methodology and analysis of variance (ANOVA) were used to highlight the dependency between the coefficient of friction, surface roughness parameters, and wear on the process parameters. The optimization analysis revealed that the optimal 3D printing input parameters for achieving the minimum coefficient of friction and linear wear were found to be an infill percentage of 50% and layer thickness of 0.1 mm (for ABS material), and an infill percentage of 50%, layer thickness of 0.15 mm (for PLA material). The suggested optimization methodology (which involves minimizing the coefficient of friction and cumulative linear wear) through the optimized parameter obtained provides the opportunity to select the most favorable design conditions contributing to a more sustainable approach to manufacturing by reducing overall material consumption.

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining.

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.

Electrocatalytic oxidation of aromatic amine (4-aminobiphenyl): Kinetics and transformation products with mechanistic approach.

Electrocatalytic oxidation (EO) of carcinogenic 4-aminobiphenyl (4-ABP) aromatic amine was performed using Ti-RuO2 anodes. Current (I), pH, electrolysis time (t), and 4-ABP initial concentration (Co ) were selected as EO parameters, and their effects on %4-ABP removal (R1 ) and energy consumed (R2 ) were studied. Experimental design, parameters optimization, and their interaction with responses R1 and R2 were performed using response surface methodology. At optimized parameters, %TOC removal and 4-BP mineralization current efficiency (%MCE) were assessed to evaluate the potential of Ti/RuO2 anodes towards 4-ABP mineralization. Simultaneous TOC and 4-ABP degradation kinetics were also studied to evaluate the competition in 4-ABP mineralization and degradation. Further, UPLC-Q-TOF-MS analysis was performed to identify the 4-ABP transformation products during the EO, and a mechanism describing the EO transformation was proposed. At optimum parameters (I = 1.2 A; pH = 4.0; t = 30 min; Co = 30 ppm), responses were found to be R1 = 60.25%; R2 = 2.49 kWh/g of 4-ABP removed. %TOC removal and %MCE were 52.4% and 34.2%, respectively. PRACTITIONER POINTS: 4-Aminobiphenyl electro-oxidation (EO) was explored using Ti/RuO2 anode. Achieved 34.2% mineralization current efficiency, 52.4% TOC and 61.3% TKN removal. Three electro-oxidation transformation products of 4-ABP were detected. 4-Aminobiphenyl was found degrading at ≈1.6 times higher rate than TOC A plausible EO transformation pathway and mechanism was proposed.

Surface Modification Using Assisting Electrodes in Wire Electrical Discharge Machining for Silicon Wafer Preparation.

This paper outlines notable advances in the wire electrical discharge machining of polycrystalline silicon workpieces for wafer preparation. Our use of assisting electrodes permits the transfer of aluminum particles to the machined surface of the polycrystalline silicon workpieces, to enhance conductivity and alter surface topography regardless of the silicon's crystallographic structure and diamond-type lattice. This in-process surface modification technique was shown to promote material removal and simultaneously preserve the integrity of the machined surfaces with preferable surface textures. In the validation experiment, the 25 mm-thick assisting electrodes deposited a notable concentration of aluminium on the machined surface (~3.87 wt %), which greatly accelerated the rate of material removal (~9.42 mg/s) with minimal surface roughness (Sa ~5.49 µm) and moderate skewness (-0.23). The parameter combination used to obtain the optimal surface roughness (Sa 2.54 µm) was as follows: open voltage (80 V), electrical resistance (1.7 Ω), pulse-on time (30 µs), and electrode thickness (15 mm). In multiple objective optimization, the preferred parameter combination (open voltage = 80 V, resistance = 1.4 Ω, pulse-on time = 60 µs, and assisting electrode thickness = 25 mm) achieved the following appreciable results: surface modification of 3.26 ± 0.61 wt %, material removal rate of 7.08 ± 2.2 mg/min, and surface roughness of Sa = 4.3 ± 1.67 µm.

Effects of Energy Parameters on Dimensional Accuracy When Joining Stainless-Steel Powders with Heterogeneous Metal Substrates.

This work presents some breakthroughs for obtaining high dimensional accuracy and reliable geometrical tolerance in the joining of stainless-steel powders with heterogeneous substrates. In the laser melting process, the interfacial energy fractions and forces acting at the solid-liquid surface of the melting powders can effectively vary their geometrical shapes and positions before they turn into the liquid phase. When the interfacial free energy is low, the melting powders are near molten, thus the successive volumetric changes can alter the layered geometry and positions. This assumption was validated by a powder-bedding additive manufacturing process to consolidate stainless-steel 316L powders (SLM 316L) on a thin heterogeneous stainless-steel substrate. Experiments were carried out to reveal the effects of the process parameters, such as laser power (100-200 W), exposure duration (50-100 µs) and point distance (35-70 µm) on the resulting material density and porosity and the corresponding dimensional variations. A fractional factorial design of experiment was proposed and the results of which were analyzed statistically using analysis of variances (ANOVA) to identify the influence of each operating factor. High energy densities are required to achieve materials of high density (7.71 g/cm3) or low porosity (3.15%), whereas low energy densities are preferable when the objective is dimensional accuracy (0.016 mm). Thermally induced deflections (~0.108 mm) in the heterogeneous metal substrate were analyzed using curvature plots. Thermally induced deformations can be attributed to volumetric energy density, scanning strategy, and the lay-up orientation. The parametric optimizations for increasing in dimensional accuracy (Z1: ~0.105 mm), or in material density (~7.71 g/cm3) were proven with high conversion rates of 88.2% and 96.4%, respectively, in validation runs.

Achieving the Minimum Roughness of Laser Milled Micro-Impressions on Ti 6Al 4V, Inconel 718, and Duralumin.

Titanium-aluminium-vanadium (Ti 6Al 4V) alloys, nickel alloys (Inconel 718), and duraluminum alloys (AA 2000 series) are widely used materials in numerous engineering applications wherein machined features are required to having good surface finish. In this research, micro-impressions of 12 µm depth are milled on these materials though laser milling. Response surface methodology based design of experiment is followed resulting in 54 experiments per work material. Five laser parameters are considered naming lamp current intensity (I), pulse frequency (f), scanning speed (V), layer thickness (LT), and track displacement (TD). Process performance is evaluated and compared in terms of surface roughness through several statistical and microscopic analysis. The significance, strength, and direction of each of the five laser parametric effects are deeply investigated for the said alloys. Optimized laser parameters are proposed to achieve minimum surface roughness. For the optimized combination of laser parameters to achieve minimum surface roughness (Ra) in the titanium alloy, the said alloy consists of I = 85%, f = 20 kHz, V = 250 mm/s, TD = 11 µm, and LT = 3 µm. Similarly, optimized parameters for nickel alloy are as follows: I = 85%, f = 20 kHz, V = 256 mm/s, TD = 8 µm, and LT = 1 µm. Minimum roughness (Ra) on the surface of aluminum alloys can be achieved under the following optimized parameters: I = 75%, f = 20 kHz, V = 200 mm/s, TD = 12 µm, and LT = 3 µm. Micro-impressions produced under optimized parameters have surface roughness of 0.56 µm, 2.46 µm, and 0.54 µm on titanium alloy, nickel alloy, and duralumin, respectively. Some engineering applications need to have high surface roughness (e.g., in case of biomedical implants) or some desired level of roughness. Therefore, validated statistical models are presented to estimate the desired level of roughness against any laser parametric settings.

Optimization of Flat Ends in Pressure Vessels.

The application of flat ends in pressure boilers is inevitably associated with the presence of stress concentration, which is observed in the vicinity of the junction of the cylinder and the closing flat plate. The analyzed flat end plates with stress relief grooves fall into the group of solutions recognized by the respective Standards of Calculations of Pressure Vessels. Unfortunately, no clear evidence is given in the Standards on how to choose the best groove parameters. This opens up the problem of the optimal choice of the groove parameters providing a minimum stress level. Even for the optimal values defining the stress relief groove geometry, certain plastic deformations are observed in the groove area for materials which exhibit elastic-plastic properties. Such a situation is completely unacceptable during exploitation, and a suitable reduction of the operating pressure is necessary. This paper discusses the effectiveness of other designs for flat ends used in pressure vessels. The proposed modifications took the form of external ribs applied around the top of the endplate circumference. The dimensions of these ribs were set using parametric optimization. The results of the study encouraged the authors to perform a more general analysis with the use of topology optimization. The results of all performed studies proved that the reduction of stress concentration and the full elimination of plastic deformation are possible. All numerical calculations were made using the finite element code (FEM), Ansys.

Ecosystem restoration in Europe: Can analogies to Traditional Chinese Medicine facilitate the cross-policy harmonization on managing socio-ecological systems?

EU's Biodiversity Strategy to 2020 sets a 15% restoration target. However, the understanding of restoration as a management tool remains ambiguous at EU and Member State levels. As a country with rich biodiversity but low GDP, a well-defined priority setting approach is key for Bulgaria. The "Methodological framework for assessment and mapping of ecosystem condition and ecosystem services in Bulgaria" proposes a transition towards ecosystem management and monitoring of the Socio-Ecological System (SES), to be embedded in the environmental policy framework. We extend the analogy between SES and the human body's system in the Traditional Chinese Medicine (TCM) as a way to inform restoration priority setting and development of restoration and monitoring tools at several levels: We apply the analogy and find that spatially explicit decision making on restoration, streamlined ecosystem monitoring and a number of other issues (green infrastructure, designation of protected areas, defragmentation and connectivity, cumulative impact assessment, etc.), are easier to understand, communicate, account for and manage. Ecosystem restoration is priority for China and the country has accumulated research and practical experience, including study of links between ecosystem management and the historical principles of Chinese philosophy. The Bulgarian and European approach to ecosystem based management can benefit from analogies to TCM. We derive policy recommendations by analogy, and illustrate them on the example of Natural Capital Accounting.

Closed-form expressions for flip angle variation that maximize total signal in T1-weighted rapid gradient echo MRI.

PURPOSE: Magnetization-prepared rapid gradient-echo (MPRAGE) sequences are commonly employed for T1-weighted structural brain imaging. Following a contrast preparation radiofrequency (RF) pulse, the data acquisition proceeds under nonequilibrium conditions of the relaxing longitudinal magnetization. Variation of the flip angle can be used to maximize total available signal. Simulated annealing or greedy algorithms have so far been published to numerically solve this problem, with signal-to-noise ratios optimized for clinical imaging scenarios by adhering to a predefined shape of the signal evolution. We propose an unconstrained optimization of the MPRAGE experiment that employs techniques from resource allocation theory. A new dynamic programming solution is introduced that yields closed-form expressions for optimal flip angle variation. METHODS: Flip angle series are proposed that maximize total transverse magnetization (Mxy) for a range of physiologic T1 values. A 3D MPRAGE sequence is modified to allow for a controlled variation of the excitation angle. Experiments employing a T1 contrast phantom are performed at 3T. 1D acquisitions without phase encoding permit measurement of the temporal development of Mxy. Image mean signal and standard deviation for reference flip angle trains are compared in 2D measurements. Signal profiles at sharp phantom edges are acquired to access image blurring related to nonuniform Mxy development. RESULTS: A novel closed-form expression for flip angle variation is found that constitutes the optimal policy to reach maximum total signal. It numerically equals previously published results of other authors when evaluated under their simplifying assumptions. Longitudinal magnetization (Mz) is exhaustively used without causing abrupt changes in the measured MR signal, which is a prerequisite for artifact free images. Phantom experiments at 3T verify the expected benefit for total accumulated k-space signal when compared with published flip angle series. CONCLUSIONS: Describing the MR signal collection in MPRAGE sequences as a Bellman problem is a new concept. By means of recursively solving a series of overlapping subproblems, this leads to an elegant solution for the problem of maximizing total available MR signal in k-space. A closed-form expression for flip angle variation avoids the complexity of numerical optimization and eases access to controlled variation in an attempt to identify potential clinical applications.

Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression.

Assessment of "CNS drugs/CNS candidates" classification abilities of the multi-parametric optimization (CNS MPO) approach was performed by logistic regression. It was found that the five out of the six separately used physical-chemical properties (topological polar surface area, number of hydrogen-bonded donor atoms, basicity, lipophilicity of compound in neutral form and at pH = 7.4) provided accuracy of recognition below 60%. Only the descriptor of molecular weight (MW) could correctly classify two-thirds of the studied compounds. Aggregation of all six properties in the MPOscore did not improve the classification, which was worse than the classification using only MW. The results of our study demonstrate the imperfection of the CNS MPO approach; in its current form it is not very useful for computer design of new, effective CNS drugs.