Dual diagnosis: a treatment model for substance abuse and major mental illness

The treatment of "dual diagnosis", co-occurring substance abuse and mental illness, calls for addressing two serious and often confounding problems. The authors introduce an expanded version of the transtheoretical model of change as formulated by J.O. Prochaska and C.C. DiClemente, and suggest that this new version offers a pragmatic approach to the conceptualization and treatment of dual diagnosis. The potential utility of the treatment model is presented through the authors' experiences in working with inner-city, chronic mentally ill individuals with substance abuse problems. Practical guidelines for dual diagnosis group therapy are discussed.     

Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

A correlation-based ant miner for classification rule discovery

In recent years, a few sequential covering algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed. This paper proposes a new ACO-based classification algorithm called AntMiner-C. Its main feature is a heuristic function based on the correlation among the attributes. Other highlights include the manner in which class labels are assigned to the rules prior to their discovery, a strategy for dynamically stopping the addition of terms in a rule’s antecedent part, and a strategy for pruning redundant rules from the rule set. We study the performance of our proposed approach for twelve commonly used data sets and compare it with the original AntMiner algorithm, decision tree builder C4.5, Ripper, logistic regression technique, and a SVM. Experimental results show that the accuracy rate obtained by AntMiner-C is better than that of the compared algorithms. However, the average number of rules and average terms per rule are higher.     

Simultaneously determination of methyl and inorganic mercury in fish species by cold vapour generation atomic absorption spectrometry

A simple and rapid non-chromatographic method was developed to determine methylmercury (MeHg) and inorganic mercury (iHg) levels in muscles tissues of 10 freshwater fish species. The MeHg and iHg were determined by cold vapour atomic absorption spectrometry after alkaline wet digestion of samples. The digested samples were reduced sequentially with stannous chloride and sodium tetrahydroborate for iHg and MeHg, respectively. Parameters such as carrier gas flow rate (argon), volume of oxidizing and potassium persulphate solutions were investigated in detail. The accuracy of the technique was evaluated by using certified reference material (DORM-2) and spiking the both Hg species in muscles tissue of a fish. The limits of detection were 0.117 and 0.133 μg kg⁻¹ for MeHg and iHg, respectively. The concentrations of MeHg and iHg in muscles tissues of ten fish species were found in the range of (28.4–56.3) and (3.01–8.11) μg kg⁻¹, respectively.     

Non-uniform illumination in concentrating solar cells

After a gap of more than two decades, Concentrator Photovoltaics (CPV) technology is once again under spotlight for making use of the best available solar cell technologies and improving the overall performance. CPV finds its use in a number of applications ranging from building integration to huge power generation units. Although the principles of solar concentration are well understood, many practical design, operation, control issues require further understanding and research. A particular issue for CPV technology is the non-uniformity of the incident flux which tends to cause hot spots, current mismatch and reduce the overall efficiency of the system. Understanding of this effect requires further research, and shall help to employ the most successful means of using solar concentrators. This study reviews the causes and effects of the non-uniformity in the CPV systems. It highlights the importance of this issue in solar cell design and reviews the methods for the solar cell characterization under non-uniform flux conditions. Finally, it puts forward a few methods of improving the CPV performance by reducing the non-uniformity effect on the concentrator solar cells.     

Comparison of electrothermal and hydride generation atomic absorption spectrometry for the determination of total arsenic in broiler chicken

The purpose of this study was to estimate total arsenic concentration in different tissues (leg, breast, liver and heart) of broiler chicken by hydride generation atomic absorption spectrometry (HGAAS) and graphite furnace atomic absorption spectrometry (GFAAS), prior to microwave assisted acid digestion. The accuracy of the techniques was evaluated by using certified reference material DORM-2. The percentage recoveries of total As were observed as 100.6% and 99.4% for HGAAS and GFAAS, respectively. The precision of the techniques, expressed as relative standard deviation, was observed as 1.71% and 4.18% for HGAAS and GFAAS measurements, respectively. The limits of detection for HGAAS and GFAAS were 0.025 μg/g and 0.052 μg/g, respectively. The concentrations of total arsenic in different tissues of broiler chicken were found in the range of 2.19–5.28, 2.15–5.22, 2.97–7.17 and 2.68–6.36 μg/g for leg, breast, liver and heart tissues, respectively. At a mean level of chicken consumption (60 g/person/day), people may ingest in the range of 72.0–85.1 μg arsenic/person/day from chicken alone.     

Molecular Pathogenesis of Glioblastoma in Adults and Future Perspectives: A Systematic Review

Glioblastoma (GBM) is the most common and malignant tumour of the central nervous system. Recent appreciation of the heterogeneity amongst these tumours not only changed the WHO classification approach, but also created the need for developing novel and personalised therapies. This systematic review aims to highlight recent advancements in understanding the molecular pathogenesis of the GBM and discuss related novel treatment targets. A systematic search of the literature in the PubMed library was performed following the PRISMA guidelines for molecular pathogenesis and therapeutic advances. Original and meta-analyses studies from the last ten years were reviewed using pre-determined search terms. The results included articles relevant to GBM development focusing on the aberrancy in cell signaling pathways and intracellular events. Theragnostic targets and vaccination to treat GBM were also explored. The molecular pathophysiology of GBM is complex. Our systematic review suggests targeting therapy at the stemness, p53 mediated pathways and immune modulation. Exciting novel immune therapy involving dendritic cell vaccines, B-cell vaccines and viral vectors may be the future of treating GBM.     

The design of consumer packaging: Effects of manipulations of shape,orientation, and alignment of graphical forms on consumers’ assessments

On-package graphics have the potential to influence consumers’ product-related attitudes and behaviours. In the reported study graphics designs on the labels of two products (water and vodka) were manipulated with respect to shape angularity, orientation, and left–right alignment. Participants’ evaluations indicated a preference for rounded shapes that could not be accounted for by differences in design typicality; and preference for upward shape orientation. An interaction between these response variables for ratings of purchase likelihood suggested that congruence between graphical and product form (droplet shape) may be advantageous. Effects of alignment were not consistent with existing theories, with right-aligned graphics being preferred. An explanation that distinguishes processing efficiency and hemispheric efficiency is proposed. Finally, as predicted, a halo effect was apparent, such that effects of aesthetic manipulations extended to ratings of product attributes that were not experienced. Theoretical and practical implications of these results are discussed.     

Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach

The rapid growth of vehicular pollution; mostly running on the diesel engine, emissions emerging are the concerns of the day. Owing to clean burn characteristics features, Hydrogen (H₂) as a fuel is the paradigm of the researcher. Extensive research presented in the literature on H₂ dual fueled diesel engine reveals, the significant role of H₂ in reducing emissions and enhancing the performance of a dual fueled diesel engine. With meager qualitative experiment data, the feasibility to develop an efficient Artificial Neural Network (ANN) model is investigated, the developed model can be utilized as a tool to investigate the H₂ dual fueled diesel engine further. In the process of developing an ANN model, engine load and H₂ flow rate are varied to register performance and emission characteristics. The creditability of the experiment is ascertained with uncertainty analysis of measurable and computed parameters. Leave-out-one method is adopted with 16 data sets; seven training algorithms are explored with eight transfer function combinations to evolve a competent ANN model. The efficacy of the developed model is adjudged with standard benchmark statistic indices. ANN model trained with Broyden, Fletcher, Goldfarb, & Shanno (BFGS) quasi-Newton backpropagation (trainbfg) stand out the best among other algorithms with regression coefficient ranging between 0.9869 and 0.9996.