Open-pit block sequencing optimization: A mathematical model and solution technique

This study presents a comprehensive mathematical formulation model for a short-term open-pit mine block sequencing problem, which considers nearly all relevant technical aspects in open-pit mining. The proposed model aims to obtain the optimum extraction sequences of the original-size (smallest) blocks over short time intervals and in the presence of real-life constraints, including precedence relationship, machine capacity, grade requirements, processing demands and stockpile management. A hybrid branch-and-bound and simulated annealing algorithm is developed to solve the problem. Computational experiments show that the proposed methodology is a promising way to provide quantitative recommendations for mine planning and scheduling engineers.

Optimisation of in-cell accelerated solvent extraction technique for the determination of organochlorine pesticides in river sediments

Organochlorine pesticides (OCPs) are ubiquitous environmental contaminants with adverse impacts on aquatic biota, wildlife and human health even at low concentrations. However, conventional methods for their determination in river sediments are resource intensive. This paper presents an approach that is rapid and also reliable for the detection of OCPs. Accelerated Solvent Extraction (ASE) with in-cell silica gel clean-up followed by Triple Quadrupole Gas Chromatograph Mass Spectrometry (GCMS/MS) was used to recover OCPs from sediment samples. Variables such as temperature, solvent ratio, adsorbent mass and extraction cycle were evaluated and optimised for the extraction. With the exception of Aldrin, which was unaffected by any of the variables evaluated, the recovery of OCPs from sediment samples was largely influenced by solvent ratio and adsorbent mass and, to some extent, the number of cycles and temperature. The optimised conditions for OCPs extraction in sediment with good recoveries were determined to be 4 cycles, 4.5 g of silica gel, 105 ᴼC, and 4:3 v/v DCM: hexane mixture. With the exception of two compounds (α-BHC and Aldrin) whose recoveries were low (59.73 and 47.66 % respectively), the recovery of the other pesticides were in the range 85.35 – 117.97% with precision < 10 % RSD. The method developed significantly reduces sample preparation time, the amount of solvent used, matrix interference, and is highly sensitive and selective.

Absolute quantification of human tear lactoferrin using multiple reaction monitoring technique with stable-isotopic labeling

The mass spectrometry technique of multiple reaction monitoring (MRM) was used to quantify and compare the expression level of lactoferrin in tear films among control, prostate cancer (CaP), and benign prostate hyperplasia (BPH) groups. Tear samples from 14 men with CaP, 15 men with BPH, and 14 controls were analyzed in the study. Collected tears (2 μl) of each sample were digested with trypsin overnight at 37 °C without any pretreatment, and tear lactoferrin was quantified using a lactoferrin-specific peptide, VPSHAVVAR, both using natural/light and isotopic-labeled/heavy peptides with MRM. The average tear lactoferrin concentration was 1.01 ± 0.07 μg/μl in control samples, 0.96 ± 0.07 μg/μl in the BPH group, and 0.98 ± 0.07 μg/μl in the CaP group. Our study is the first to quantify tear proteins using a total of 43 individual (non-pooled) tear samples and showed that direct digestion of tear samples is suitable for MRM studies. The calculated average lactoferrin concentration in the control group matched that in the published range of human tear lactoferrin concentration measured by enzyme-linked immunosorbent assay (ELISA). Moreover, the lactoferrin was stably expressed across all of the samples, with no significant differences being observed among the control, BPH, and CaP groups.

Evaluation of material crack using acoustic emission technique

Cracks in civil structures can result in premature failure due to material degradation and can result in both financial loss and environmental consequences. This thesis reports an effective technique using Acoustic Emission (AE) technique to assess the severity of the crack propagation in steel structures. The outcome of this work confirms that combination of AE parametric analysis and signal processing techniques can be used to evaluate crack propagation under different loading configurations. The technique has potential application to assess and monitor the condition of civil structures.

Characterization of anomalous relaxation using the time-fractional Bloch equation and multiple echo T2 *-weighted magnetic resonance imaging at 7T

PURPOSE To study the utility of fractional calculus in modeling gradient-recalled echo MRI signal decay in the normal human brain. METHODS We solved analytically the extended time-fractional Bloch equations resulting in five model parameters, namely, the amplitude, relaxation rate, order of the time-fractional derivative, frequency shift, and constant offset. Voxel-level temporal fitting of the MRI signal was performed using the classical monoexponential model, a previously developed anomalous relaxation model, and using our extended time-fractional relaxation model. Nine brain regions segmented from multiple echo gradient-recalled echo 7 Tesla MRI data acquired from five participants were then used to investigate the characteristics of the extended time-fractional model parameters. RESULTS We found that the extended time-fractional model is able to fit the experimental data with smaller mean squared error than the classical monoexponential relaxation model and the anomalous relaxation model, which do not account for frequency shift. CONCLUSIONS We were able to fit multiple echo time MRI data with high accuracy using the developed model. Parameters of the model likely capture information on microstructural and susceptibility-induced changes in the human brain.

Polysaccharopeptide enhanced the anti-cancer effect of gammatocotrienol through activation of AMPK

Prostate cancer (PCa) frequently relapses after hormone ablation therapy. Unfortunately, once progressed to the castration resistant stage, the disease is regarded as incurable as prostate tumours are highly resistant to conventional chemotherapy. Therefore, an effective treatment strategy is urgently needed for improving the treatment outcome of the patients.

Impact of exercise selection on hamstring muscle activation

Objective: To determine the extent to which different strength training exercises selectively activate the commonly injured biceps femoris long head (BFLH) muscle. Methods: This two-part observational study recruited 24 recreationally active males. Part 1 explored the amplitudes and the ratios of lateral to medial hamstring (BF/MH) normalised electromyography (nEMG) during the concentric and eccentric phases of 10 common strength training exercises. Part 2 used functional magnetic resonance imaging (fMRI) to determine the spatial patterns of hamstring activation during two exercises which i) most selectively, and ii) least selectively activated the BF in part 1. Results: Eccentrically, the largest BF/MH nEMG ratio was observed in the 45° hip extension exercise and the lowest was observed in the Nordic hamstring (NHE) and bent-knee bridge exercises. Concentrically, the highest BF/MH nEMG ratio was observed during the lunge and 45° hip extension and the lowest was observed for the leg curl and bent-knee bridge. fMRI revealed a greater BFLH to semitendinosus activation ratio in the 45° hip extension than the NHE (p<0.001). The T2 increase after hip extension for BFLH, semitendinosus and semimembranosus muscles were greater than that for BFSH (p<0.001). During the NHE, the T2 increase was greater for the semitendinosus than for the other hamstrings (p≤0.002). Conclusion: This investigation highlights the non-uniformity of hamstring activation patterns in different tasks and suggests that hip extension exercise more selectively activates the BFLH while the NHE preferentially recruits the semitendinosus. These findings have implications for strength training interventions aimed at preventing hamstring injury.

Modelling Cued-Target Recall using Quantum Inspired Models of Target Activation

This article presents and evaluates Quantum Inspired models of Target Activation using Cued-Target Recall Memory Modelling over multiple sources of Free Association data. Two components were evaluated: Whether Quantum Inspired models of Target Activation would provide a better framework than their classical psychological counterparts and how robust these models are across the different sources of Free Association data. In previous work, a formal model of cued-target recall did not exist and as such Target Activation was unable to be assessed directly. Further to that, the data source used was suspected of suffering from temporal and geographical bias. As a consequence, Target Activation was measured against cued-target recall data as an approximation of performance. Since then, a formal model of cued-target recall (PIER3) has been developed [10] with alternative sources of data also becoming available. This allowed us to directly model target activation in cued-target recall with human cued-target recall pairs and use multiply sources of Free Association Data. Featural Characteristics known to be important to Target Activation were measured for each of the data sources to identify any major differences that may explain variations in performance for each of the models. Each of the activation models were used in the PIER3 memory model for each of the data sources and was benchmarked against cued-target recall pairs provided by the University of South Florida (USF). Two methods where used to evaluate performance. The first involved measuring the divergence between the sets of results using the Kullback Leibler (KL) divergence with the second utilizing a previous statistical analysis of the errors [9]. Of the three sources of data, two were sourced from human subjects being the USF Free Association Norms and the University of Leuven (UL) Free Association Networks. The third was sourced from a new method put forward by Galea and Bruza, 2015 in which pseudo Free Association Networks (Corpus Based Association Networks - CANs) are built using co-occurrence statistics on large text corpus. It was found that the Quantum Inspired Models of Target Activation not only outperformed the classical psychological model but was more robust across a variety of data sources.