Sustainability: A new imperative in contaminated land remediation

Land is not only a critical component of the earth's life support system, but also a precious resource and an important factor of production in economic systems. However, historical industrial operations have resulted in large areas of contaminated land that are only slowly being remediated. In recent years, sustainability has drawn increasing attention in the environmental remediation field. In Europe, there has been a movement towards sustainable land management; and in the US, there is an urge for green remediation. Based on a questionnaire survey and a review of existing theories and empirical evidence, this paper suggests the expanding emphasis on sustainable remediation is driven by three general factors: (1) increased recognition of secondary environmental impacts (e.g., life-cycle greenhouse gas emissions, air pollution, energy consumption, and waste production) from remediation operations, (2) stakeholders' demand for economically sustainable brownfield remediation and "green" practices, and (3) institutional pressures (e.g., social norm and public policy) that promote sustainable practices (e.g., renewable energy, green building, and waste recycling). This paper further argues that the rise of the "sustainable remediation" concept represents a critical intervention point from where the remediation field will be reshaped and new norms and standards will be established for practitioners to follow in future years. This paper presents a holistic view of sustainability considerations in remediation, and an integrated framework for sustainability assessment and decision making. The paper concludes that "sustainability" is becoming a new imperative in the environmental remediation field, with important implications for regulators, liability owners, consultants, contractors, and technology vendors. © 2014 Elsevier Ltd.

宏观经济的决策支持系统

本文介绍了一个用于宏观经济决策的决策支持系统的内容和特点。论述了处理经济系统时建立知识库系统的必要性。着重介绍了用于宏观经济的知识库系统以及建立在这个系统基础之上的整个决策支持系统的结构。最后简述了这个系统的发展方向。

Context-aware real-time assistant architecture for pervasive healthcare

The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems.

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Knowledge-based IMRT treatment planning for prostate cancer.

PURPOSE: To demonstrate the feasibility of using a knowledge base of prior treatment plans to generate new prostate intensity modulated radiation therapy (IMRT) plans. Each new case would be matched against others in the knowledge base. Once the best match is identified, that clinically approved plan is used to generate the new plan. METHODS: A database of 100 prostate IMRT treatment plans was assembled into an information-theoretic system. An algorithm based on mutual information was implemented to identify similar patient cases by matching 2D beam's eye view projections of contours. Ten randomly selected query cases were each matched with the most similar case from the database of prior clinically approved plans. Treatment parameters from the matched case were used to develop new treatment plans. A comparison of the differences in the dose-volume histograms between the new and the original treatment plans were analyzed. RESULTS: On average, the new knowledge-based plan is capable of achieving very comparable planning target volume coverage as the original plan, to within 2% as evaluated for D98, D95, and D1. Similarly, the dose to the rectum and dose to the bladder are also comparable to the original plan. For the rectum, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are 1.8% +/- 8.5%, -2.5% +/- 13.9%, and -13.9% +/- 23.6%, respectively. For the bladder, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are -5.9% +/- 10.8%, -12.2% +/- 14.6%, and -24.9% +/- 21.2%, respectively. A negative percentage difference indicates that the new plan has greater dose sparing as compared to the original plan. CONCLUSIONS: The authors demonstrate a knowledge-based approach of using prior clinically approved treatment plans to generate clinically acceptable treatment plans of high quality. This semiautomated approach has the potential to improve the efficiency of the treatment planning process while ensuring that high quality plans are developed.

Using decision analysis to improve malaria control policy making.

Malaria and other vector-borne diseases represent a significant and growing burden in many tropical countries. Successfully addressing these threats will require policies that expand access to and use of existing control methods, such as insecticide-treated bed nets (ITNs) and artemesinin combination therapies (ACTs) for malaria, while weighing the costs and benefits of alternative approaches over time. This paper argues that decision analysis provides a valuable framework for formulating such policies and combating the emergence and re-emergence of malaria and other diseases. We outline five challenges that policy makers and practitioners face in the struggle against malaria, and demonstrate how decision analysis can help to address and overcome these challenges. A prototype decision analysis framework for malaria control in Tanzania is presented, highlighting the key components that a decision support tool should include. Developing and applying such a framework can promote stronger and more effective linkages between research and policy, ultimately helping to reduce the burden of malaria and other vector-borne diseases.

Development of a decision aid to inform patients' and families' renal replacement therapy selection decisions.

BACKGROUND: Few educational resources have been developed to inform patients' renal replacement therapy (RRT) selection decisions. Patients progressing toward end stage renal disease (ESRD) must decide among multiple treatment options with varying characteristics. Complex information about treatments must be adequately conveyed to patients with different educational backgrounds and informational needs. Decisions about treatment options also require family input, as families often participate in patients' treatment and support patients' decisions. We describe the development, design, and preliminary evaluation of an informational, evidence-based, and patient-and family-centered decision aid for patients with ESRD and varying levels of health literacy, health numeracy, and cognitive function. METHODS: We designed a decision aid comprising a complementary video and informational handbook. We based our development process on data previously obtained from qualitative focus groups and systematic literature reviews. We simultaneously developed the video and handbook in "stages." For the video, stages included (1) directed interviews with culturally appropriate patients and families and preliminary script development, (2) video production, and (3) screening the video with patients and their families. For the handbook, stages comprised (1) preliminary content design, (2) a mixed-methods pilot study among diverse patients to assess comprehension of handbook material, and (3) screening the handbook with patients and their families. RESULTS: The video and handbook both addressed potential benefits and trade-offs of treatment selections. The 50-minute video consisted of demographically diverse patients and their families describing their positive and negative experiences with selecting a treatment option. The video also incorporated health professionals' testimonials regarding various considerations that might influence patients' and families' treatment selections. The handbook was comprised of written words, pictures of patients and health care providers, and diagrams describing the findings and quality of scientific studies comparing treatments. The handbook text was written at a 4th to 6th grade reading level. Pilot study results demonstrated that a majority of patients could understand information presented in the handbook. Patient and families screening the nearly completed video and handbook reviewed the materials favorably. CONCLUSIONS: This rigorously designed decision aid may help patients and families make informed decisions about their treatment options for RRT that are well aligned with their values.

Real-time decision aid display

Gemstone Team Future Firefighting Advancements

Development and usability testing of a Web-based decision aid for families of patients receiving prolonged mechanical ventilation.

BACKGROUND: Web-based decision aids are increasingly important in medical research and clinical care. However, few have been studied in an intensive care unit setting. The objectives of this study were to develop a Web-based decision aid for family members of patients receiving prolonged mechanical ventilation and to evaluate its usability and acceptability. METHODS: Using an iterative process involving 48 critical illness survivors, family surrogate decision makers, and intensivists, we developed a Web-based decision aid addressing goals of care preferences for surrogate decision makers of patients with prolonged mechanical ventilation that could be either administered by study staff or completed independently by family members (Development Phase). After piloting the decision aid among 13 surrogate decision makers and seven intensivists, we assessed the decision aid's usability in the Evaluation Phase among a cohort of 30 surrogate decision makers using the Systems Usability Scale (SUS). Acceptability was assessed using measures of satisfaction and preference for electronic Collaborative Decision Support (eCODES) versus the original printed decision aid. RESULTS: The final decision aid, termed 'electronic Collaborative Decision Support', provides a framework for shared decision making, elicits relevant values and preferences, incorporates clinical data to personalize prognostic estimates generated from the ProVent prediction model, generates a printable document summarizing the user's interaction with the decision aid, and can digitally archive each user session. Usability was excellent (mean SUS, 80 ± 10) overall, but lower among those 56 years and older (73 ± 7) versus those who were younger (84 ± 9); p = 0.03. A total of 93% of users reported a preference for electronic versus printed versions. CONCLUSIONS: The Web-based decision aid for ICU surrogate decision makers can facilitate highly individualized information sharing with excellent usability and acceptability. Decision aids that employ an electronic format such as eCODES represent a strategy that could enhance patient-clinician collaboration and decision making quality in intensive care.

Cost-effectiveness analysis of the diagnosis of meniscus tears.

BACKGROUND: Diagnostic imaging represents the fastest growing segment of costs in the US health system. This study investigated the cost-effectiveness of alternative diagnostic approaches to meniscus tears of the knee, a highly prevalent disease that traditionally relies on MRI as part of the diagnostic strategy. PURPOSE: To identify the most efficient strategy for the diagnosis of meniscus tears. STUDY DESIGN: Economic and decision analysis; Level of evidence, 1. METHODS: A simple-decision model run as a cost-utility analysis was constructed to assess the value added by MRI in various combinations with patient history and physical examination (H&P). The model examined traumatic and degenerative tears in 2 distinct settings: primary care and orthopaedic sports medicine clinic. Strategies were compared using the incremental cost-effectiveness ratio (ICER). RESULTS: In both practice settings, H&P alone was widely preferred for degenerative meniscus tears. Performing MRI to confirm a positive H&P was preferred for traumatic tears in both practice settings, with a willingness to pay of less than US$50,000 per quality-adjusted life-year. Performing an MRI for all patients was not preferred in any reasonable clinical scenario. The prevalence of a meniscus tear in a clinician's patient population was influential. For traumatic tears, MRI to confirm a positive H&P was preferred when prevalence was less than 46.7%, with H&P preferred above that. For degenerative tears, H&P was preferred until the prevalence reaches 74.2%, and then MRI to confirm a negative was the preferred strategy. In both settings, MRI to confirm positive physical examination led to more than a 10-fold lower rate of unnecessary surgeries than did any other strategy, while MRI to confirm negative physical examination led to a 2.08 and 2.26 higher rate than H&P alone in primary care and orthopaedic clinics, respectively. CONCLUSION: For all practitioners, H&P is the preferred strategy for the suspected degenerative meniscus tear. An MRI to confirm a positive H&P is preferred for traumatic tears for all practitioners. Consideration should be given to implementing alternative diagnostic strategies as well as enhancing provider education in physical examination skills to improve the reliability of H&P as a diagnostic test. CLINICAL RELEVANCE: Alternative diagnostic strategies that do not include the use of MRI may result in decreased health care costs without harm to the patient and could possibly reduce unnecessary procedures.

Las infraestructuras de recarga y el despegue del vehículo eléctrico.

Este trabajo revisa la evolución y estado actual de la automoción eléctrica; analiza las ventajas ambientales, de eficiencia energética y de costes del motor eléctrico frente al de combustión interna; y presenta como limitaciones para el uso del vehículo eléctrico, el desarrollo actual de las baterías recargables y la lenta implantación de electrolineras. Con el objetivo de contribuir al desarrollo de una actividad económica respetuosa con el medio ambiente y basada en nuevas tecnologías, se proyecta, a partir de experiencias previas, una instalación de puntos de recarga para una ciudad de 50.000 habitantes con un parque de 100 vehículos eléctricos que dispone de dos plazas de recarga rápida (poste trifásico 400V CA), siete plazas de recarga lenta (postes monofásicos 230V CA) y de 50 módulos fotovoltaicos que producen diariamente la energía equivalente a la recarga lenta de un vehículo en los meses fríos y de dos en los meses cálidos.

Risk assessment of E-Commerce projects using evidential reasoning

The purpose of this study is to develop a decision making system to evaluate the risks in E-Commerce (EC) projects. Competitive software businesses have the critical task of assessing the risk in the software system development life cycle. This can be conducted on the basis of conventional probabilities, but limited appropriate information is available and so a complete set of probabilities is not available. In such problems, where the analysis is highly subjective and related to vague, incomplete, uncertain or inexact information, the Dempster-Shafer (DS) theory of evidence offers a potential advantage. We use a direct way of reasoning in a single step (i.e., extended DS theory) to develop a decision making system to evaluate the risk in EC projects. This consists of five stages 1) establishing knowledge base and setting rule strengths, 2) collecting evidence and data, 3) determining evidence and rule strength to a mass distribution for each rule; i.e., the first half of a single step reasoning process, 4) combining prior mass and different rules; i.e., the second half of the single step reasoning process, 5) finally, evaluating the belief interval for the best support decision of EC project. We test the system by using potential risk factors associated with EC development and the results indicate that the system is promising way of assisting an EC project manager in identifying potential risk factors and the corresponding project risks.

A streamlined life-cycle assessment and decision tool for used tyres recycling

In order to achieve progress towards sustainable resource management, it is essential to evaluate options for the reuse and recycling of secondary raw materials, in order to provide a robust evidence base for decision makers. This paper presents the research undertaken in the development of a web-based decision-support tool (the used tyres resource efficiency tool) to compare three processing routes for used tyres compared to their existing primary alternatives. Primary data on the energy and material flows for the three routes, and their alternatives were collected and analysed. The methodology used was a streamlined life-cycle assessment (sLCA) approach. Processes included were: car tyre baling against aggregate gabions; car tyre retreading against new car tyres; and car tyre shred used in landfill engineering against primary aggregates. The outputs of the assessment, and web-based tool, were estimates of raw materials used, carbon dioxide emissions and costs. The paper discusses the benefits of carrying out a streamlined LCA and using the outputs of this analysis to develop a decision-support tool. The strengths and weakness of this approach are discussed and future research priorities identified which could facilitate the use of life cycle approaches by designers and practitioners.

Posting Incognito ... Males with Eating Problems: Online Emotional Expression and Support

Qualitative research in the area of eating disorders (eds) has predominantly focused on females,whilst the experiences of males’ remains poorly understood. due to the secretive nature of eating problems/eds it can be difficult to explore the experiences of males with these problems; however, online support groups/message boards, which are common and popular, provide a non-invasive
forum for researchers to conduct research. This study analyzed naturally occurring discussions on an internet message board dedicated to males and eating problems using content analysis. Two major overarching themes of emotional expression (sharing feelings of disturbed eating attitudes and emotions; being secretive) and support (informational and emotional) were identified. The message board provided a vital support system for this group, suggesting that online message boards may be an important avenue for health professionals to provide information, support, and advice.

PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.