A brief overview and introduction to artificial neural networks

Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity

In order to meet the needs of refined landslide risk management, the extended correlation framework of dynamic susceptibility modeling desiderates to be further explored. This work considered the Wanzhou channel of the Three Gorges Reservoir Area as the experimental site, with a transportation channel with significant economic value to carry out innovative research in two stages. (i) Five machine learning models logistic regression (LR), multilayer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), and decision tree (DT) were used to explore landslide susceptibility distribution based on detailed landslide boundaries. (ii) Based on the PS-InSAR technology, the dynamic factor of deformation intensity was obtained. Subsequently, the dynamic factor was combined with proposed static factors (topography conditions, geological conditions, hydrological conditions, and human activities) to generate dynamic landslide susceptibility mapping (DLSM). The receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1 score were proposed as evaluation metrics. Compared with ignoring the dynamic factor, the predictive accuracy of some models was further improved when considering the dynamic factor. Especially the DT model, the area under the curve of ROC (AUC) value increased by 2%, and obtained the highest AUC value (93.1%). The susceptibility results of introducing the dynamic factor are more in line with the spatial distribution of actual landslides. The research framework proposed in this study has important reference significance for the dynamic management and prevention of landslide disasters in the study area.

Susceptibility Analysis of Land Subsidence along the Transmission Line in the Salt Lake Area Based on Remote Sensing Interpretation

As the influence of extreme climate and human engineering activities intensifies, land subsidence frequently occurs in the Salt Lake area of Qinghai Province, China, which seriously threatens the stability of the UHV transmission line crossing the area. Current susceptibility analyses of land subsidence disasters have mostly focused on the classification of land subsidence susceptibility and have ignored the differentiation of susceptibility among different land subsidence intensities. Therefore, the land subsidence susceptibility map does not meet the operation and maintenance management needs of the UHV transmission line, let alone planning and designing of new lines in the Salt Lake area. Therefore, in this study, we proposed a susceptibility analysis of different land subsidence intensities along the transmission line in the Salt Lake area. The small baseline integrated aperture radar interferometry (SBAS-InSAR) method was used to obtain the land subsidence along the transmission line based on 67 Sentinel-1 remote sensing interpretation datasets from 2017 to 2021. Based on a combination of K-means clustering and the transmission line specifications, four annual land subsidence intensity grades were identified as 0~−2 mm/year, −2~−10 mm/year, −10~−20 mm/year, and <−20 mm/year. In addition, eight geological environmental factors were analyzed, and a multi-layer perceptron neural network (MLPNN) model was used to calculate the susceptibility of the different land subsidence intensities. The area under the curve (AUC) and practical examples were used to verify the reliability of the different land subsidence intensities susceptibility mapping. The AUC values of the four subsidence intensity grades showed that the results were accurate: the <−20 mm/year grade produced the largest AUC (0.951), with the −10~−20 mm/year, −2~−10 mm/year and 0~−2 mm/year grades producing AUCs of 0.926, 0.812, 0.879, respectively. At the same time, the susceptibility classification results of different land subsidence intensities were consistent with the interpretation and site tower deformation. The results of this study provided the distribution of land subsidence susceptibility along the transmission line, distinguished the susceptibility of different land subsidence intensities, and provided more detailed subsidence information for each transmission tower. The results provide important information for transmission line tower planning, design, protection, and operation management.

Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions

Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future.

Modelling of multilinear gradient retention time of bio-sweetener rebaudioside A in HPLC analysis

Artificial neural network (ANN), as one of the artificial intelligence methods, has been widely using in HPLC studies for modelling purposes. Stevia rebaudiana is an important industrial plant due to its bio-sweetener molecule, rebaudioside-a, in its leaves. Although rebaudioside-a is up to 300-fold sweeter than sucrose, its calorie is almost zero. In this study, HPLC optimization of rebaudioside-a was studied and the optimization data based on multilinear gradient retention times were modelled by ANN. The input parameters were selected as concentrations, column temperatures, initial acetonitrile percentage for the first step of gradient elution, initial acetonitrile percentage for the second step of gradient elution, slope of acetonitrile, wavelengths, flow rates. The retention time was the output. Also, dried S. rebaudiana leaves were extracted and the concentrations were evaluated by HPLC. According to the ANN results, the most effective parameters on the prediction of non-linear gradient retention time for rebaudioside-a were found as flow rate and initial acetonitrile percentage for the second step of gradient. The best back propagation was selected as Levenberg-Marquardt algorithm. The highest rebaudioside-a level was found as 96.53 ± 6.36 μg mL^-1. ANN modelling methods can be used in preparative HPLC applications to estimate the retention time of steviol glycosides.

Multiparameter prediction control of in vitro drug delivery into mycobacterium smegmatis induced by microbubble-enhanced sonoporation

The antibacterial method induced by microbubble-enhanced sonoporation has shown its great potential in facilitating drug delivery into thallus. The enhanced drug delivery induced by microbubble-enhanced sonoporation is a complex event which can be affected by various physical parameters. How to determine the correlation between experimental parameters and the drug delivery efficiency to give the instruction on reasonably choosing the parameters and achieve the control of drug delivery efficiency is impeding further investigations for this complex biophysical process. In the present work, we have explored a number of key parameters affecting the drug delivery efficiency induced by microbubble-enhanced sonoporation using multivariate biological experiments. To achieve the control of the drug delivery efficiency, a multiparameter prediction control method based on modified artificial neural network is presented in this paper. This method is a new modeling method based on combined back-propagation neural network and the multiple model idea to establish quantitative relationship between experimental parameters and drug delivery efficiency. By analyzing the experimental samples, a mapping relationship expression can be deduced to determine the input and output variables of artificial neural network models. Experimental samples were divided into training and test samples. We trained models based on back-propagation neural network to establish their quantitative relationship. In this model, the multiple model idea was introduced into the selection of training samples to modify the traditional back-propagation neural network model to avoid model mismatch caused by poor training sample selection. Numerical experiments results have shown that compared with the traditional model, the identification results obtained by modified model are more closed to experimental results. It is elucidated that an appropriately trained network can act as a good alternative for costly and time-consuming experiments. The findings of this study indicate that this approach can realize the prediction of drug delivery efficiency induced by microbubble-enhanced sonoporation under different experimental parameters, and then achieve the control of drug delivery efficiency through reasonable parameter selection, finally achieve the purpose of efficiently killing bacteria.

Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.

Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke

In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted - DWI, T₁-weighted – T1WI, T₂-weighted-T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).

Family roles in homes with alcohol-dependent parents: an evidence-based review

The behavioral health care field has seen attempts to understand the functioning of families in which a parent is dependent on alcohol as a set of roles into which the other family members fall. The most popular of these classifications taught in the United States includes five roles (enabler, hero, lost child, mascot, and scapegoat) that are used to conceptualize families and individuals in treatment and support group settings, as well as in popular self-help literature. Attempts to operationalize and measure these roles have, however, been fraught with difficulties. The resulting research base has seen conflicting evidence for the support of such roles, as well as little work on diverse families. The evidence against such well-defined family roles, the questions surrounding their development, and the difficulties of applying such constructs in real-life situations (with numerous confounding factors and unknown associated conditions) may indicate that their clinical utility does not win out over the problems inherent with this manner of classification.

Subjective responses to alcohol consumption as endophenotypes: advancing behavioral genetics in etiological and treatment models of alcoholism

Individual differences in subjective responses to alcohol consumption represent genetically mediated biobehavioral mechanisms of alcoholism risk (i.e., endophenotype). The objective of this review is three-fold: (1) to provide a critical review the literature on subjective response to alcohol and to discuss the rationale for its conceptualization as an endophenotype for alcoholism; (2) to examine the literature on the neurobiological substrates and associated genetic factors subserving individual differences in subjective response to alcohol; and (3) to discuss the treatment implications of this approach and to propose a framework for conceptualizing, and systematically integrating, endophenotypes into alcoholism treatment.

Contemporary psychotherapy research: implications for substance misuse treatment and research

This article provides an overview of the major findings of psychotherapy research and discusses the possible implications of these findings for substance user treatment researchers and practitioners. While the centrality of relationship for human change processes was historically understood, twentieth century research tended to see relationship and person variables as secondary to operationalized "mechanisms of action" unique to particular psychotherapies. Interestingly, recent meta-analytic investigations have uncovered the weakness of randomized controlled trials (RCT) comparison investigations that have, until recently, represented the "gold standard" for the field. There has been a resurgent interest in the "common factors" that appear to be important across many effective psychotherapies. In addition, psychiatric anthropologists have contributed important information about the problems of client noncompliance with mental health treatment that parallel quantitative investigations. Substance misuse researchers have also found that client characteristics, especially clients' readiness to engage treatment, are important to investigate. The importance of the "therapeutic alliance" and the characteristics of clients and clinicians have become central areas for study, rather than variables to be controlled or excluded. Emphasis on these factors may represent the future for research in psychotherapy and substance user treatment, especially if researchers and community practitioners can join together to overcome methodological feasibility and dissemination problems that plague effectiveness research. However, the continued attractiveness of comparative studies and treatment efficacy studies may represent longstanding epistemological assumptions and responses to economic incentives that will be difficult to challenge.

Identification of fuel samples from the Prestige wreckage by pattern recognition methods

A set of 34 worldwide crude oils, 12 distilled products (kerosene, gas oils, and fuel oils) and 45 oil samples taken from several Galician beaches (NW Spain) after the wreckage of the Prestige tanker off the Galician coast was studied. Gas chromatography with flame ionization detection was combined with chemometric multivariate pattern recognition methods (principal components analysis, cluster analysis and Kohonen neural networks) to differentiate and characterize the Prestige fuel oil. All multivariate studies differentiated between several groups of crude oils, fuel oils, distilled products, and samples belonging to the Prestige's wreck and samples from other illegal discharges. In addition, a reduced set of 13 n-alkanes out of 36, were statistically selected by Procrustes Rotation to cope with the main patterns in the datasets. These variables retained the most important characteristics of the data set and lead to a fast and cheap analytical screening methodology.

Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis

AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients.

METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease.

RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS.

CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.

The "rut metaphor": a conceptualization of attractor-shaping properties of addictive drugs

The development of nonlinear models might yield better insight into the dynamics of substance use-related disorders than linear models. Nonlinear modelizations are, however, not always easily intelligible. A metaphor is presented illustrating a nonlinear conceptualization of the development of drug addiction based on recent findings on neural plasticity. Ruts are described as correlates of especially strong mnesic traces, which function as attractors, and hegemonize cognitions and behavior toward drug use. Dopaminergic activity of addictive drugs is proposed to represent the weight of vehicles tracing ruts.

Mother-daughter and father-daughter attachment of college student ACOAs

This 2005 study compared parent-child attachment in 89 American female Adult Children of Alcoholics (ACOAs) as compared to 201 non-ACOAs. Women attended a large university in the southeastern United States. Participants categorized as ACOA on the Children of Alcoholics Screen Test (CAST; Jones, 1983) reported significantly more negative affect and less support from their fathers as indicated on the Parental Attachment Questionnaire (Kenney, 1987). When results were examined by the gender of the alcohol-abusing(1) parent, participants who suspected their fathers were problem drinkers did not differ from non-ACOAs in their attachment to either parent. As compared to non-ACOAs, women who self-identified as daughters of problem-drinking mothers reported poorer attachment both to mothers and fathers.

Evaluation of a therapeutic community treatment program: a long-term follow-up study in Spain

The aims of this study is to carry out a long-term follow-up evaluation of a well-established therapeutic community treatment for addictions in Navarre (Spain) and to make a comparison between the program completers and the dropouts, as well as between relapsing and nonrelapsing patients, on a broad set of variables. A long-term follow-up design (mean of 6 years after leaving treatment) was used to analyze the outcomes of the therapeutic program. The sample consisted of 155 subjects (113 completers and 42 dropouts). A personal interview was carried out with each one of the located subjects. The interviews took place between September 2000 and September 2004. Treatment "dropouts" manifested a higher and earlier rate both of relapses, and of new treatments for their drug addiction than the completion group. The program was also effective in reducing criminal behavior and improving the state of health. Significant differences were found across outcome variables when comparison was made between treatment completers and "dropouts." All subjects improved on outcome variables after receiving the treatment. When relapsing and nonrelapsing patients were compared, significant outcome differences were also found between groups. The study's limitations are noted and future needed research is suggested.

Technology transfer from the science of medicine to the real world: the potential role played by artificial adaptive systems

The author describes a refiguration of medical thought that originates from nonlinear dynamics and chaos theory. The coupling of computer science and these new theoretical bases coming from complex systems mathematics allows the creation of "intelligent" agents capable of adapting themselves dynamically to problems of high complexity: the artificial neural networks (ANNs). ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on an individual basis and not as average trends. These tools can allow a more efficient technology transfer from the science of medicine to the real world, overcoming many obstacles responsible for the present translational failure. They also contribute to a new holistic vision of the human subject person, contrasting the statistical reductionism that tends to squeeze or even delete the single subject, sacrificing him to his group of belongingness. A remarkable contribution to this individual approach comes from fuzzy logic, according to which there are no sharp limits between opposite things, such as wealth and disease. This approach allows one to partially escape from the probability theory trap in situations where it is fundamental to express a judgement based on a single case and favor a novel humanism directed to the management of the patient as an individual subject person.

Correlates of alcohol consumption and tobacco use among tea industry workers of Assam

An epidemiological study on alcohol and tobacco (smoking and nonsmoked tobacco) use was carried out in tea garden population of Assam, one of the largest agroindustries of India. A total sample of 2,264 individuals (male, 1,033; female, 1,231) aged 15 years and older was interviewed in 2002-2003 to collect information about alcohol and tobacco use using a predesigned and pretested questionnaire. Age-adjusted prevalence of alcohol consumption was 59.2% (male, 69.3%; female, 54%). Smoking was more common among males (13.2%) than females (2%). However, use of nonsmoked tobacco was almost as popular among female (71.9%) as among males (75.3%). More than half of the respondents (54.7%) were multiple users of alcohol and tobacco. Prevalence of alcohol consumption, nonsmoked tobacco use, and smoking among the young age group (15-24 years) was 32.2%, 52.5%, and 2.2%, respectively. Prevalence of smoking increased with age, and more than a quarter of males above 54 years were smokers. Similar age trends in the prevalence of alcohol and nonsmoked tobacco was not observed. Sociodemographic correlates, like education, occupation, and marital status, emerged as important predictors of substance uses irrespective of sex. Association of income with substance use was weak in this study, perhaps due to homogeneity of income level. Users of alcohol and tobacco were mostly illiterate, manual workers, and widows/widowers. However, smokers were more common among sedentary worker. Not withstanding the limitations of the study, the findings of the study are useful for planning interventional strategy to control alcohol and tobacco use for better health outcome.

Traditional healers' perceptions of smokeless tobacco use and health in the Limpopo Province of South Africa

Traditional healers (THs) exert a significant influence in indigenous South African communities, where smokeless tobacco (SLT) use and dependence is common among women. This study was conducted during 2002. It sought to explore THs' beliefs about SLT use and its health effects. In-depth interviews were conducted with 28--mostly female (68%)--registered THs, with a mean age of 55 years and with an average of 17 years of practice experience. These listed THs were randomly selected from two culturally diverse regions of the (largely rural) Limpopo Province in South Africa. The THs perceive the ritual (external) use of SLT as an absolute necessity in divination, but 32% have also prescribed its 'internal' use to their clients, usually following a "directive from the ancestors." Almost all the THs who themselves regularly consume SLT condemned the recreational use of SLT and believe that SLT is addictive. However, 39% of them claimed to be able to treat addiction resulting from tobacco use not sanctioned by the ancestors. This study has identified opportunities for enlisting THs' collaboration in future community-based tobacco dependence interventions.

Non drug use- and drug use-specific spirituality as one-year predictors of drug use among high-risk youth

The present article explored two different dimensions of spirituality that might tap negative and positive relations with adolescent drug use over a 1-year period. Non-drug-use-specific spirituality measured how spiritual the person believes he or she is, participation in spiritual groups, and engagement in spiritual practices such as prayer, whereas drug-use-specific spirituality measured using drugs as a spiritual practice. Self-report questionnaire data were collected during 1997-1999 from a sample of 501 adolescents in 18 continuation high schools across southern California. Participants ranged in age from 14 to 19 and were 57% male, with an ethnic distribution of 34% White, 49% Latino, 5% African American, 7% Asian, and 5% other. A series of general linear model analyses were conducted to identify whether or not two different spirituality variables predict drug use (cigarettes, alcohol, marijuana, hallucinogens, and stimulants) at 1-year follow-up. After controlling for baseline drug use, non-drug-use-specific spirituality was negatively predictive of alcohol, marijuana, and stimulant use, whereas drug-use-specific spirituality failed to be found predictive of these variables one year later. Conversely, drug-use-specific spirituality was positively predictive of cigarette smoking and hallucinogen use, whereas non-drug-use spirituality failed to be found predictive of these variables. Our results provide new evidence that suggests that spirituality may have an effect on drug use among adolescents. The drug-use-specific measure of spirituality showed "risk effects" on drug use, whereas the other measure resulted in "protective effects," as found in previous research. Knowledge of the risk and protective patterns and mechanisms of spirituality may be translated into future drug use prevention intervention programs.

Prediction of burn healing time using artificial neural networks and reflectance spectrometer

BACKGROUND

Burn depth assessment is important as early excision and grafting is the treatment of choice for deep dermal burn. Inaccurate assessment causes prolonged hospital stay, increased medical expenses and morbidity. Based on reflected burn spectra, we have developed an artificial neural network to predict the burn healing time.

PURPOSE

Our study is to develop a non-invasive objective method to predict burn-healing time.

METHODS AND MATERIALS

Burns less than 20% TBSA was included. Burn spectra taken on the third postburn day using reflectance spectrometer were analyzed by an artificial neural network system.

RESULTS

Forty-one spectra were collected. With the newly developed method, the predictive accuracy of burns healed in less than 14 days was 96%, and that in more than 14 days was 75%.

CONCLUSIONS

Using reflectance spectrometer, we have developed an artificial neural network to determine the burn healing time with 86% overall predictive accuracy.

Injection risk behavior among women syringe exchangers in San Francisco

Women who inject drugs in cities where syringe exchange programs (SEPs) are well established may have different risks for HIV infection. In 1997, we interviewed 149 female syringe exchangers in San Francisco, CA, a city with high rates of injection drug use that is home to one of the largest and oldest SEPs in the United States. In this report, we describe their sociodemographics, health, and risk behavior, and we examine factors associated with recent syringe sharing. Fifty percent of respondents were women of color and the median age was 38 years. Most (86%) injected heroin and nearly half were currently homeless or had recently been incarcerated. One-third of all women reported needle sharing in the prior month. This was higher than the rate of needle sharing reported by a mixed gender sample of San Francisco exchangers in 1993, although it resembled the rate reported by a mixed gender sample in 1992. In a multivariate analysis, syringe sharing was associated with age, housing status, and sexual partnerships. Syringe sharers were more likely to be young, homeless, or have a sexual partner who was also an injection drug user. While wide access to sterile syringes is an important strategy to reduce HIV transmission among injection drug users (IDU), syringe exchange alone cannot eradicate risky injection by female IDU. Additional efforts to reduce risky injection practices should focus on younger and homeless female IDU, as well as address selective risk taking between sexual partners.

Changes in reasons given for adolescent smoking, 1984-1999

Adolescent smoking has been widely studied, but surprisingly little research has been done about the reasons given for smoking by adolescents themselves. This study examines the reasons given by Finnish adolescents for their own smoking and the reasons that they perceive for smoking by others. It reports on how these reasons have changed over a period of 15 years. In 1984, a questionnaire about reasons for smoking was administered to a sample of adolescents aged 14-16 (N = 396). The questionnaire was administered again to a similar sample (N = 488) in 1999, when Finland adopted strict new tobacco legislation. It was found that the reasons given (i.e., attributions) had changed considerably, and that the attributions for the adolescents' own behavior were quite different from the attributions for smoking by others. The attributions were only weakly influenced by the participants' gender or by their smoking habits, either in 1984 or 1999. In relation to participants' own smoking, the later questionnaire elicited inner subjective experiences involving "good feelings." In relation to the perceived reasons for other people's smoking, it elicited more responses connected with the notion of "belonging." The limitations of the study and suggestions for further research are discussed.

Are educational aspirations associated with the risk of alcohol use and alcohol use-related problems among adolescents?

OBJECTIVE

Using prospective data, we hypothesized that public middle school students with high educational aspirations would report less alcohol use, and alcohol use-related problems in the subsequent year.

METHODS

The participants for these analyses included students, ages 11 to 14 years old, participating in a longitudinal study in an urban sample of public schools (n = 1229). As part of the prospective annual assessments of the students, in 1992 (to) and 1993 (t1), data on educational aspirations and on alcohol use, and alcohol use-related problems were gathered. Latent variable modeling was used to assess the relationship between educational aspirations at baseline (to) and subsequent year drinking behavior (t1) in two separate models, one to examine the relationship of educational aspirations with self-reported alcohol use (model 1), and another to examine the association with alcohol use-related problems (model 2). Potential confounding by age, sex, race-ethnicity, alcohol use by peers, self-reported school performance, and neighborhood environment was held constant in each model. In addition, each model took into account the prior year report of alcohol use and alcohol use-related problems, respectively.

RESULTS

The evidence indicated that students with high aspirations were no more nor less likely to report subsequent alcohol use [beta = 0.15, 95% confidence interval (CI) = -0.19, 0.49; p = 0.38] nor alcohol use-related problems (beta = -0.009, CI = -0.07, 0.06; p = 0.80). Other characteristics were associated with alcohol use at follow-up and included race-ethnicity (being non-Black), neighborhood environment, and having friends who drink alcohol. Characteristics associated with alcohol use-related problems at the time of the follow-up interview also included race-ethnicity, peer drinking, neighborhood environment, as well as older age.

CONCLUSIONS

Findings from the current study do not support the hypothesis that educational aspirations have significant influences on alcohol consumption or drinking problems in this study population of urban, predominantly Black students. As such, this work helps to advance our understanding of suspected relationships between educational aspirations, as well as factors associated with resilience to alcohol use and the occurrence of alcohol use-related problems.

Parent/child transactional processes predictive of resilience or vulnerability to "substance abuse disorders"

This article discusses implications of a theoretical model of resilience--the Resilience Framework, including the impact of parent/child transactional processes in moderating or mediating a child's biological or environmental risks and later substance misuse. Research is presented on behavioral and emotional precursors of substance abuse disorders in children of substance users. Detrimental processes within dysfunctional family environments are presented followed by a listing of strategies for increasing resilience in youth by improving family dynamics. The value in elucidating these interactive processes is to increase our understanding of ways to reduce the impact of risk factors. Prevention providers should use these strategies as benchmarks for selecting or developing effective family-focused prevention programs. Resources are presented for finding effective family interventions as well as an example of a family intervention based on resilience principles, namely the Strengthening Families Program. Recommendations are made for future research and better dissemination of evidence-based family interventions.

Neurologically plausible distinctions in cognition relevant to drug use etiology and prevention

This article outlines several distinctions in cognition and related topics in emotion that receive support from work in cognitive neuroscience and have important implications for prevention: implicit cognition, working memory, nonverbal memory, and neurobiological systems of habit. These distinctions have not been widely acknowledged or applied in drug use prevention research, despite their neural plausibility and the availability of methods to make this link. The authors briefly review the basis for the distinctions and indicate general implications and assessment possibilities for prevention researchers conducting large-scale field trials. Subse-quently, the article outlines a connectionist framework for specific applications in prevention interventions. These possibilities begin the attempt to derive useful fusions of normally distinct areas of prevention and cognitive neuroscience, in the spirit of a transdisciplinary approach.

Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status

Data from several studies have pointed out the existence of a strong correlation between Alzheimer's disease (AD) neuropathology and cognitive state. However, because of their highly complex and nonlinear relationship, it has been difficult to develop a predictive model for individual patient classification through traditional statistical approaches. When exposed to complex data sets, artificial neural networks (ANNs) can recognize patterns, learn the relationship of different variables, and address classification tasks. To predict the results of postmortem brain examinations, we applied ANNs to the Nun Study data set, a longitudinal epidemiological study, which includes annual cognitive and functional evaluation. One hundred seventeen subjects from the study participated in this analysis. We determined how demographic data and the cognitive and functional variables of each subject during the last year of her life could predict the presence of brain pathology expressed as Braak stages, neurofibrillary tangles (NFTs) and neuritic plaques (NPs) count in the neocortex and hippocampus, and brain atrophy. The result of this analysis was then compared with traditional statistical models. ANNs proved to be better predictors than Linear Discriminant Analysis in all experimentations (+ approximately 10% in overall accuracy), especially when assembled in Artificial Organisms (+ approximately 20% in overall accuracy). Demographic, cognitive, and clinical variables were better predictors of tangles count in the neocortex and in the hippocampus when compared to NPs count. These findings strengthen the hypothesis that neurofibrillary pathology may represent the major anatomic substrate of the cognitive impairment found in AD.

Artificial neural networks for adolescent marijuana use and clinical features of marijuana dependence

This article compares the performance of multiple logistic regression (MLR) with feed-forward, artificial neural network (ANN) models for the assessment of adolescent marijuana use and clinical features of dependence based on self-evaluation from recent National Household Surveys on Drug Abuse (NHSDA). The effect of training and testing the neural networks with randomly selected data was compared to data selected as a function of survey year. The technical aim of the study was to account for adolescent marijuana use and features of marijuana dependence based on experiences with alcohol and tobacco. Similarities observed in MLR and ANN model performance may indicate no major complex or nonlinear relationships in cross-sectional epidemiological data selected to model adolescent drug use and dependence in this specific application. We concluded that ANNs should be further studied in future longitudinal research, perhaps with modeling of recursive networks, allowing feedback from drug dependence to levels of marijuana use. The ANN models also have the potential to model drug use and dependence based on input parameters with no obvious direct link to drug involvement--e.g., polymorphisms associated with "openness to experience" or other personality traits hypothesized to function as distal antecedents, and could thus be implemented to identify higher risk youths using assessments indirectly related or nonlinearly associated to adolescent drug use and dependence but less sensitive to survey-related response tendencies.

The impact of an outpatient program for women with substance use-related disorders on retention

Meeting the needs of women manifesting substance-use disorders is a goal in developing treatment programs for this population. As retention in treatment is positively related to treatment outcome, the length of stay in outpatient treatment of alcohol- and other drug-dependent women in Brazil was compared between two programs. Data were analyzed from 181 women entering a Mixed-Gender Program from 1986 to 1996 and from 80 women entering a Women-Only Program from 1997 to 1998. A greater 3-month retention rate was observed in the Women-Only as opposed to the Mixed-Gender Program. Moreover, the impact was more significant among the alcohol-dependent women. This finding suggests that the heterogeneity of women with substance-use disorders has to be taken into account when developing appropriate treatment strategies.

Family-based interventions for substance use and misuse prevention

Because "substance abuse" is a "family disease" of lifestyle, including both genetic and family environmental causes, effective family strengthening prevention programs should be included in all comprehensive substance abuse prevention activities. This article presents reviews of causal models of substance use and evidence-based practices. National searches by the authors suggest that there is sufficient research evidence to support broad dissemination of five highly effective family strengthening approaches (e.g., behavioral parent training, family skills training, in-home family support, brief family therapy, and family education). Additionally, family approaches have average effect sizes two to nine time larger than child-only prevention approaches. Comprehensive prevention programs combining both approaches produced much larger effect sizes. The Strengthening Families Program (SFP) is the only one of these programs that has been replicated with positive results by independent researchers with different cultural groups and with different ages of children. Few research-based programs have been adopted by practitioners, partly because of technology transfer issues. Overall, research on ways to improve dissemination, marketing, training, and funding is needed to improve adoption of effective prevention programs.