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The growing use of digital technologies (DTs) has a myriad of implications to socio-technical systems, which are not yet fully recognised. This paper investigates the contributions and drawbacks of DTs to resilient performance (RP), an aspect that so far has received less attention in comparison to others such as efficiency. To this end, a survey questionnaire was applied to 79 academics and practitioners linked to resilience engineering. Data analysis involved descriptive statistics and a thematic analysis of the open-text responses to the survey. Mixed impacts were identified, with 10 themes related to contributions and 16 to drawbacks. Regarding the contributions, the results highlighted the use of DTs for monitoring and anticipating system performance. Machine learning seems to be the most promising approach for this purpose. A key drawback is the need for developing new skills across the workforce so that they can make sense of the outputs of DTs and are aware of their strengths and weaknesses. The human role is expected to remain crucial for RP, which makes the current coordination difficulties with DTs even more important to address. A research agenda composed of five topics is proposed, encompassing description, prescription, and assessment. The agenda emphasizes the need for mapping the attributes or functionalities of DTs onto resilience concepts, models, and frameworks. [ABSTRACT FROM AUTHOR]

• Only 50% of the published trials were registered. • Unregistered trials may have collected other measures that remain unreported. • Many additional outcomes not appearing in trial registries were published. • Some reported positive findings are likely spurious due to unadjusted multiple comparisons. Sedentary behaviour research is a relatively new field, much of which has emerged since the widespread acceptance of clinical trial registration. The aim of this study was to investigate the trial registration and related issues in studies investigating the effect of frequent activity interruptions to prolonged sitting-time. Secondary analysis of a scoping review including systematic searches of databases and trial registries. We included experimental studies investigating the effects of frequent activity interruptions to prolonged sitting-time. We identified 32 trials published in 45 papers. Only 16 (50%) trials were registered, with all 16 trials being completed and published. Of the unregistered trials, we identified three (19%) for which similarities in the sample size and participant demographics across papers was suggestive of duplicate publication. Identification of potential duplicate publications was difficult for the remaining 13 (81%). Results from 53 (76%) of the 70 registered outcomes were published, but 11 (69%) registered trials reported results from additional outcomes not prospectively registered. A total of 46 different outcomes (out of 53 reported outcome measures, similar measures were collated) were reported across all trials, 31 (67%) of which were collected in ≤2 trials. We found direct evidence of trial registration issues in experimental trials of breaking up sitting-time. The lack of prospective registration of all trials, and the large number of outcomes measured per trial are key considerations for future research in this field. These issues are unlikely to be confined to the field of sedentary behaviour research. [ABSTRACT FROM AUTHOR]

• Physical therapists from dermatology report having knowledge to be able to implement EBP. • Physical therapists from dermatology believe having skills to be able to implement EBP. • There were inconsistencies on some answers about knowledge and skills related to EBP. Dermatology is a relatively new subdiscipline of physical therapy with growth potential. Therefore, it is important to identify whether professionals from this area have the knowledge and skills required to offer the best available service based on evidence-based practice. To describe the self-reported behavior, knowledge, skills, opinion, and barriers related to the evidence-based practice of Brazilian physical therapists from the dermatology subdiscipline. An adapted electronic questionnaire was sent by the Brazilian Association of Dermatology Physical Therapy via email to all registered members. The data were analyzed descriptively. The response rate was 40.4% (101/250). Brazilian physical therapists from the dermatology subdiscipline reported that they update themselves equally through scientific papers and courses, and access preferentially databases that offer scientific papers in the Portuguese language. Respondents believe they have sufficient knowledge to use evidence-based practice, inform patients about treatment options and consider their choices in the decision-making process. However, there were inconsistencies in responses regarding the experience with evidence-based practice during undergraduate or postgraduate degree, as well as having discussions about evidence-based practice in the workplace. The barriers most frequently reported were difficulty to obtain full-text papers, lack of quality of the scientific papers, applicability of the findings into clinical practice, lack of evidence-based practice training and difficulty to understand the statistics. Brazilian physical therapists from the dermatology subdiscipline have positive perceived behavior, believe that they have sufficient knowledge and skills, and have favorable opinion related to evidence-based practice. However, there are inconsistencies related to some aspects of knowledge and skills set. [ABSTRACT FROM AUTHOR]

• The way to facilitate access to scientific articles is share the results of their studies over the internet. • In order to measure the attention attracted online, a new score named Altmetric was created. • There are no studies that have analysed associated factors of Altmetric score for systematic reviews and clinical practice guidelines. Although Altmetric has been widely used by researchers to monitor the audience of their articles, there are no studies that have analysed factors associated with Altmetric score for systematic reviews and clinical practice guidelines. 1) To analyse factors that could be associated with Altmetric scores for low back pain systematic reviews and clinical practice guidelines. 2) To describe the characteristics of these articles and their Altmetric scores. We searched for all low back pain systematic reviews and guidelines indexed on the Physiotherapy Evidence Database published between 2015 and 2017. We extracted data related to the published paper, the publishing journal, and Altmetric scores. A total of 66 systematic reviews and 5 guidelines were included. The variable impact factor (independent variable) was associated with Altmetric mentioned score (dependent variable) with a β coefficient of 15.4 (95% CI: 0.97, 29.7) ajusted to all remaining variables. The variable number of citations normalized by year of publication (independent variable) was associated with Altmetric reader score (dependent variable) with a β coefficient of 6.4 (95% CI: 4.03, 8.72) ajusted to all remaining variables. We also found that the majority of the systematic reviews and guidelines were published in English, had a descriptive title, were published as open access, included multicenter studies, and had media release generated by the publishing journal. Metrics related to the number of citations, such as the impact factor are associated with Altmetric scores. [ABSTRACT FROM AUTHOR]

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed. [ABSTRACT FROM AUTHOR]

The present research investigates the contribution of environmental innovation to firms' competitive advantage through the case of medium and large-sized firms operating in Greece. Due to the growing trend of environmental innovation in Greece and a consequent increase in the research interest, this paper fills the existing gap in the relevant literature as there is not enough evidence concerning Greek firms' environmental innovation practices. To meet the research goal, a questionnaire was distributed to 892 medium and large-sized firms operating in Greece; 225 questionnaires were finally responded. The data is based on firms' self-assessment concerning environmental innovation and competitive advantage and are analyzed using both descriptive and inductive statistics, including principal components analysis and multiple linear regression. The research results show that the examined firms implement environmental innovation practices at a moderate level. The most common practices of environmental innovation are the implementation of ISO 14001 management systems and the toxic substances usage reduction. The percentage of ISO 14001 implementation is increased compared to the previous years. Furthermore, the regression analysis revealed that the components concerning environmental process innovation and environmental product innovation have a positive impact on the examined firms' competitive capability. The relationship between open innovation and environmental innovation and the contribution of open innovation dynamics are discussed as well. [ABSTRACT FROM AUTHOR]

• A single cryolipolysis session has no effect on abdominal adipose tissue in healthy women. • Randomized controlled trial with blind assessor. • Ultrassonography was used as a method of evaluating the outcome. To analyze the effects of cryolipolysis on the fat thickness of the lower abdomen of healthy women and patient's satisfaction. Design and setting : a randomized controlled trial, with concealed allocation and blinded assessor. Participants: 34 healthy women between 18 and 48 years, skinfold in the lower abdomen ≥3 cm, BMI between 18.5 and 27 kg/m2, low level of physical activity, and no contraindication to cryolipolysis were allocated to intervention group (IG, n = 17) or control group (CG, n = 17). Interventions: The IG received one session of cryolipolysis with −10 °C of temperature for 50 min. The CG was not submitted to any kind of intervention. Both groups did the evaluation protocols at baseline, 30, 60 and 90 days after the intervention. Main outcome measures: fat thickness was measured by ultrasonography (US), skinfold (SF) and abdominal circumference (AC1 and AC2). No significant differences between the IG and CG were demonstrated at any evaluation at any time of follow up for the variables US (30 days: 0.05 cm (95%CI: −0.12; 0.22), 60 days: 0.05 cm (95%CI: −0.11; 0.20) and 90 days: 0.04 cm (95%CI: −0.7; 0.25)), SF (30 days: −0.09 cm (95%CI: −0.25; 0.08), 60 days: −0.14 cm (95%CI: −0.36; 0.09) and 90 days: −0.001 cm (95%CI: −0.237; 0.234)), AC1 (30 days: 0.42 cm (95%CI: −1.1; 1.9), 60 days: −0.1 cm (95%CI: −1.74; 1.54) and 90 days: −0.007 cm (−1.9; 1.9)) and AC2 (30 days: 0.183 cm (95%CI: −0.84; 1.20), 60 days: −0.13 cm (95%CI: −1.61; 1.35) and 90 days: −0.31 cm (95%CI: −1.61; 1.00)). The current study showed that a single application of the utilized protocol of cryolipolysis does not produce any significant effect on fat thickness of the lower abdomen of healthy women. Clinical Trial Registration number: NCT03160976 (https://clinicaltrials.gov/ct2/show/NCT03160976). Contribution of the Paper : the study is one of the first studies in the literature with methodological rigor to report an unfavorable result for localized abdominal fat treatment with a single session of cryolipolysis. [ABSTRACT FROM AUTHOR]

• We will monitor compliance with the intervention and adverse events. • Data will be analyzed following intention-to-treat principles. • A cost-effectiveness analysis will be performed. This a priori statistical analysis plan describes the methods of analysis for the Trial Of Prevention Strategies for low back pain (TOPS). TOPS aimed to investigate the effectiveness and cost-effectiveness of exercise and education classes compared with a minimal intervention control in preventing recurrence of low back pain (LBP) in people who have recently recovered from an episode of LBP. TOPS is a superiority, pragmatic, parallel-group randomized controlled trial with allocation concealment, blinded outcome assessors, and intention-to-treat analysis. Participants were randomized to a physical therapist-led exercise and education program for 12 weeks or minimal intervention. The primary outcome was days to recurrence of an episode of LBP. The three key secondary outcomes were days to recurrence of an episode of LBP resulting in (1) activity limitation, (2) care seeking for LBP; and (3) work absence of at least 1 day. Differences in survival curves for the primary (days to recurrence) and secondary outcome (days to LBP with activity limitation, days to care seeking due to LBP, and days to work absence due to LBP) will be compared using Cox regression. Hazard ratios (HRs) and median survival times with 95% confidence intervals (CI) will be calculated. The number of adverse events, including serious adverse events will be reported and the proportion of adverse events between groups will be compared using a Chi-squared test. This paper will provide a detailed description of the planned analyses for the TOPS trial. Australian New Zealand Clinical Trials Registry (ACTRN12615000939594). [ABSTRACT FROM AUTHOR]

Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. [ABSTRACT FROM AUTHOR]

The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker’s preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences. [ABSTRACT FROM AUTHOR]

The vast majority of supply-chain literature has focused on supply chain management, innovation and sustainability, separately. However, little supply-chain research focuses on innovation under the supply chain context, which is expected to deliver a sustainable outcome. Is it a great research opportunity to explore or a subject unworthy of studying? This paper offers a systematic literature review considering 107 related papers published from 1996 to 2014. In this review, both descriptive and thematic analyses demonstrate it to be a great research opportunity worthy of exploring. A conceptual framework containing the definition of sustainable supply chain innovation (SSCI) and its distinctive characteristics are proposed and identified. Furthermore, some opportunities for the research in future, such as antecedents to SSCI, are suggested and discussed in this paper. Both academics and practitioners in companies might find this review useful as it stimulates further research and guides sustainable supply chain innovation. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We used an indicator-based approach to assess river landscape aesthetic quality. • We provide maps and distribution curves for Germany. • Results are compared with other landscape types. • Insights can inform river landscape management and planning. Abstract River ecosystems are widely considered to be of high value for nature conservation. This judgment has been validated for biodiversity and regulating ecosystem services. However, little is known about the role of landscape aesthetic quality (LAQ) of river landscapes compared to other landscapes. The objective of this paper is to assess river landscape aesthetic quality (RLAQ), with an indicator-based approach, and to investigate whether river landscapes deserve special protection because of their attractiveness. We compare RLAQ with the aesthetic quality of other landscapes and explore specific indicators of RLAQ by using GIS and descriptive statistics. The results demonstrate that river courses and adjacent recent floodplains achieve higher LAQ scores than other landscapes of Germany, whereas LAQ of historical riparian zones is rated lower than in other landscapes. This is, mainly, due to the large modification of historical riparian zones. Areas with high RLAQ scores are mostly characterized by rare land cover types and, particularly, high relief diversity. In contrast, low RLAQ scores are found in agglomerations and in intensively cultivated historical riparian zones. RLAQ is an important contribution to multifunctional river landscape development as it supports the public acceptance of renaturation measures. [ABSTRACT FROM AUTHOR]

Highlights • A solution of context integration for a process with data production variability is proposed. • The solution focuses on context integration by either substitution or enhancement. • Both integrations are based on an extension of the model driven architecture approach. • Substitution or enhancement rules conforming to this extended approach are specified. • Modelling and rules coding of both integration cases show automatization feasibility. Abstract Existent process models can hardly cope with the emerging issue of modelling exponential variable data volumes in systems' workflow, from specifications to operation. Given the strong relation between data context and data variability, this paper considers the automated integration of contextual models for processes with data variability. The proposed approach extends methodologically a platform independent model process, using a contextual data model, to obtain automatically the corresponding platform specific model. Contextual data are thus integrated to a process as a model, within a process. Two particular cases of contextual data models are studied in detail: substitution, when the contextual data model defines generated code, and enhancement, when learned data descriptions constitute the contextual data model. The feasibility and value of integrating a contextual model into a process to handle data variability are shown in detail describing these two use cases. Contextual model integration by substitution to include automatically variable ready to use application services to generate code, and contextual model integration by enhancement applied to supervised image classification based on variable descriptors. Results show that relating data variability and its context by means of automated integration of a designed system component model, simplifies variable data processing of system process models. [ABSTRACT FROM AUTHOR]

The exploration problem has been extensively studied in unsafe networks containing malicious hosts of a highly harmful nature, called black holes , which completely destroy mobile agents that visit them. In a recent work, Královič and Miklík (SIROCCO 2010, LNCS 6058, pp. 157–167) [20] considered various types of malicious host behavior in the context of the Periodic Data Retrieval problem in asynchronous ring networks with exactly one malicious host. In this problem, a team of initially co-located agents must report data from all safe nodes of the network to the homebase, infinitely often. The malicious host can choose whether to kill visiting agents or allow them to pass through (gray hole). In another variation of the model, the malicious host can, in addition, alter its whiteboard contents in order to deceive visiting agents. The goal is to design a protocol for Periodic Data Retrieval using as few agents as possible. In this paper, we present the first nontrivial lower bounds on the number of agents for Periodic Data Retrieval in asynchronous ring networks. Specifically, we show that at least 4 agents are needed when the malicious host is a gray hole, and at least 5 agents are needed when the malicious host whiteboard is unreliable. This improves the previous lower bound of 3 in both cases and answers an open question posed in the aforementioned paper. On the positive side, we propose an optimal protocol for Periodic Data Retrieval in asynchronous rings with a gray hole, which solves the problem with only 4 agents. This improves the previous upper bound of 9 agents and settles the question of the optimal number of agents in the gray-hole case. Finally, we propose a protocol with 7 agents when the whiteboard of the malicious host is unreliable, significantly improving the previously known upper bound of 27 agents. Along the way, we set forth a detailed framework for studying networks with malicious hosts of varying capabilities. [ABSTRACT FROM AUTHOR]

Background: Evidence-Based Practice (EBP) has been widely used by health professionals. However, no study in Brazil has investigated the data regarding the knowledge and difficulties related to EBP from a representative sample of physical therapists. Objective: To identify behavior, knowledge, skills, resources, opinions and perceived barriers of Brazilian physical therapists from the state of São Paulo regarding EBP. Method: A customized questionnaire about behavior, knowledge, skills, resources, opinions and perceived barriers regarding EBP was sent by email to a sample of 490 physical therapists registered by the Registration Board of São Paulo, Brazil. Physical therapists who did not respond to the questionnaire were contacted by telephone and/or letter. The data were analyzed descriptively. Results: The final response rate was 64.4% (316/490). Because 60 physical therapists were no longer practicing, 256 answers were analyzed. The physical therapists reported that they routinely read scientific papers (89.5%) as a resource for professional development, followed by continuing education courses (88.3%) and books (86.3%). Approximately 35% of the respondents reported a clear understanding of the implementation of research findings in their practice; approximately 37% reported no difficulties in critically appraising scientific papers; and 67.2% strongly agreed that EBP is important for their practice. The most commonly reported barriers were related to difficulties in obtaining full-text papers (80.1%), using EBP may represent higher cost (80.1%) and the language of publication of the papers (70.3%). Conclusion: Physical therapists from São Paulo state believe that they have knowledge and skills to use EBP. Although they have favorable opinions regarding its implementation, they still encounter difficulties in implementing EBP successfully. [ABSTRACT FROM AUTHOR]

With the ever increasing data collected from the process, the era of big data has arrived in the process industry. Therefore, the computational effort for data modeling and analytics in standalone modes has become increasingly demanding, particularly for large-scale processes. In this paper, a distributed parallel process modeling approach is presented based on a MapReduce framework for big data quality prediction. Firstly, the architecture for distributed parallel data modeling is formulated under the MapReduce framework. Secondly, a big data quality prediction scheme is developed based on the distributed parallel data modeling approach. As an example, the basic Semi-Supervised Probabilistic Principal Component Regression (SSPPCR) model is deployed to concurrently train a set of local models with split datasets. Meanwhile, Bayesian rule is utilized in a MapReduce way to integrate local models based on their predictive abilities. Two case studies demonstrate the effectiveness of the proposed method for big data quality prediction. [ABSTRACT FROM AUTHOR]

Classification is an important task in various areas. In many real-world applications, class imbalance and overlapping problems have been reported as major issues in the application of traditional classification algorithms. An imbalance problem occurs when training data contain considerably more representatives of one class than of other classes. Class overlap occurs when a region in the data space contains a similar number of data for each class. When a class overlap occurs in imbalanced data sets, classification becomes even more complicated. Although various approaches have been proposed to deal separately with class imbalance and overlapping problems, only a few studies have attempted to address both problems simultaneously. In this paper, we propose an overlap-sensitive margin (OSM) classifier based on a modified fuzzy support vector machine and k -nearest neighbor algorithm to address imbalanced and overlapping data sets. The main idea of the proposed OSM classifier is to separate the data space into soft- and hard-overlap regions using the modified fuzzy support vector machine algorithm. The separated spaces are then classified using the decision boundaries of the support vector machine and 1-nearest neighbor algorithms. Furthermore, by separating a data set into soft- and hard-overlap regions, one can determine which part of the data is to be examined more closely for classification in real-world situations. Experiments using synthetic and real-world data sets demonstrated that the proposed OSM classifier outperformed existing methods for imbalanced and overlapping situations. [ABSTRACT FROM AUTHOR]

Classifiers operating in a dynamic, real world environment, are vulnerable to adversarial activity, which causes the data distribution to change over time. These changes are traditionally referred to as concept drift, and several approaches have been developed in literature to deal with the problem of drift detection and handling. However, most concept drift handling techniques approach it as a domain independent task, to make them applicable to a wide gamut of reactive systems. These techniques are developed from an adversarial agnostic perspective, where they naively assume that adversarial activity is like any other change to the data, which can be fixed by retraining the models. However, this is not the case when a malicious agent is trying to evade the deployed classification system. In such an environment, the properties of concept drift are unique, as the drift is intended to degrade the system and at the same time designed to avoid detection by traditional concept drift detection techniques. This special category of drift is termed as adversarial drift, and this paper analyzes its characteristics and impact in a streaming environment. A novel framework for dealing with adversarial concept drift is proposed, called the Predict-Detect streaming framework. This framework uses adversarial forethought and incorporates the context of classification into the drift detection task, to provide leverage in dynamic-adversarial domains. Experimental evaluation of the framework, on generated adversarial drifting data streams, demonstrates that this framework is able to provide early and reliable unsupervised indication of drift, and is able to recover from drifts swiftly. While traditional drift detectors can be evaded by intelligent adversaries, the proposed framework is especially designed to capture adversaries by misdirecting them into revealing themselves. In addition, the framework is designed to work on imbalanced and sparsely labeled data streams, as a limited-memory, incremental algorithm. The generic design and domain independent nature of the framework makes it applicable as a blueprint for developers wanting to implement reactive security to their classification based systems. [ABSTRACT FROM AUTHOR]

Green building rating systems (GBRSs) address a variety of areas of protection to solve current environmental problems. This is more relevant to decision making but not as scientifically reliable as following the midpoint and endpoint approaches. The purpose of this paper is to pinpoint the lack of a standard base for carrying environmental assessment and show the interrelations between eleven most widely used GBRSs in these terms through a descriptive statistical analysis. The study adopts a qualitative and quantitative methodology through credits' classification method, quantitative assessment and comparison on the level of midpoint and endpoint impact categories. The first step requires expanding the discussion to include different methods and levels of incorporating Life Cycle Assessment (LCA) to support credits’ requirements. For other non-LCA based credits, a quantification index is used to express the contribution of each credit to the respective quantifiable inventory of midpoint impact categories. The analysis shows that the total weighted sum of midpoint and endpoint scores differ from one GBRS to another. Nevertheless, the correlation matrix indicates interrelations of some GBRSs which adopt similar approaches to address environmental problems. The study concludes that existing GBRSs although they have similar targets, they exhibit discrepancies in the base of their environmental assessment. This does not provide a robust base for comparing the outcome results and indicates the need for restructuring credits according to the midpoint and endpoint approaches. This investigation adds to the existing body of literature to demonstrate with evidence the need for using both approaches in a consistent framework for modelling environmental assessment. This allows optimum use of the benefits and overcomes existing gaps and limitations of both of them. This can assist GBRSs plan for future development in correspondence with national targets and international environmental initiatives. [ABSTRACT FROM AUTHOR]

Nowadays, multiclass classification is considered as the leading technique on the issue of condition monitoring in induction motor which can be performed accurately and efficiently by support vectors machines. The standard multiclass SVM (MSVM) approaches consists in constructing an optimal decision hyperplane maximizing the margin of separation between the training data. However, relatively small number of outliers can obviously reduce the performance of the classical MSVMs and would have an impact on the decision boundary and the margin calculation. Support Vector Data Description (SVDD) based on hyper-spheres decision boundary is often performed to overcome this drawback. The originality of this paper is to introduce a new extension of the MSVM for broken rotor bar fault diagnosis by embedding the SVDD in the classical multiclass SVM. The experimental results prove the merit of the proposed approach with a classification rate of 100%, which is higher than any accuracy rate achieved by standard MSVM approaches. [ABSTRACT FROM AUTHOR]

Background Current treatment for adults with chronic pain often includes Pain Neuroscience Education (PNE) to make people understand the nature underlying their pain and thus provides a clear rational for a biopsychosocial approach. Despite recommendations to use Pain Neuroscience Education as well in children with chronic pain, a specific program, tailored to children aged 6–12 years is lacking. Objectives The aim of this study was to develop a Pain Neuroscience Education program for children with chronic pain and test its feasibility. Methods First the internet and scientific literature was searched for sources (e.g., books, videos, etc.) that might be supportive in teaching children about the neurophysiology of pain. Based on this content, we developed a Pain Neuroscience Education program for children, ‘PNE4Kids’, which was tested for feasibility in three groups of healthy children ( n = 18; 9 girls and 9 boys) aged between 6 and 12 years old. Results and conclusions This paper provides both scientists and clinicians with a specific program to explain the neurophysiology of pain to children with chronic pain, since it is past high time to use a modern neuroscience approach in this vulnerable population. Further research should examine the effectiveness of this developed PNE4Kids program on pain-related outcomes in children with chronic pain. Registration number: NCT02880332 ( https://clinicaltrials.gov/ct2/show/NCT02880332 ). [ABSTRACT FROM AUTHOR]

Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modern manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description-based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing. [ABSTRACT FROM AUTHOR]

Input and output data, under uncertainty, must be taken into account as an essential part of data envelopment analysis (DEA) models in practice. Many researchers have dealt with this kind of problem using fuzzy approaches, DEA models with interval data or probabilistic models. This paper presents an approach to scenario-based robust optimization for conventional DEA models. To consider the uncertainty in DEA models, different scenarios are formulated with a specified probability for input and output data instead of using point estimates. The robust DEA model proposed is aimed at ranking decision-making units (DMUs) based on their sensitivity analysis within the given set of scenarios, considering both feasibility and optimality factors in the objective function. The model is based on the technique proposed by Mulvey et al. (1995) for solving stochastic optimization problems. The effect of DMUs on the product possibility set is calculated using the Monte Carlo method in order to extract weights for feasibility and optimality factors in the goal programming model. The approach proposed is illustrated and verified by a case study of an engineering company. [ABSTRACT FROM AUTHOR]

Anomaly (or outlier) detection is well researched objective in data mining due to its importance and inherent challenges. An outlier could be the key discovery to be made from large datasets and the insights gathered from them could be of significance in a wide variety of domains like information security, business intelligence, clinical decision support, financial monitoring etc. Recently, Support Vector Data Description (SVDD) driven approaches are shown as having good predictive accuracy. This paper proposes a novel low-complexity anomaly detection algorithm based on Support Vector Data Description (SVDD). The proposed algorithm reduces the complexity by avoiding the calculation of Lagrange multipliers of an objective function, instead locates an approximate pre-image of the SVDD sphere's center, within the input space itself. The crux of the training algorithm is a gradient descent of the primal objective function using Simultaneous Perturbation Stochastic Approximation (SPSA). Experiments using datasets obtained from UCI machine learning repository have demonstrated that the accuracies of the proposed approach are comparable while the training time is much lesser than Classical SVDD. [ABSTRACT FROM AUTHOR]

Background: Physical exercise has been used to mitigate the metabolic effects of diabetes mellitus. Objective: To evaluate the effect of resistance exercise when compared to aerobic exercise without insulin therapy on metabolic and clinical outcomes in patients with type 2 diabetes mellitus. Methods: Papers were searched on the databases MEDLINE/PubMed, CINAHL, SPORTDiscus, LILACS, and SCIELO, without language or date of publication limits. Clinical trials that compared resistance exercise to aerobic exercise in adults with type 2 diabetes mellitus who did not use insulin therapy were included. The quality of evidence and risk of bias were assessed using the GRADE system and the Cochrane Risk of Bias tool, respectively. Meta-analysis was also used, whenever possible. Two reviewers extracted the data independently. Eight eligible articles were included in this study, with a total of 336 individuals, with a mean age of 48-58 years. The protocols of aerobic and resistance exercise varied in duration from eight to 22 weeks, 30-60 min/day, three to five times/week. Results: Overall the available evidence came from a very low quality of evidence and there was an increase in Maximal oxygen consumption (mean difference: -2.86; 95% CI: -3.90 to -1.81; random effect) for the resistance exercise and no difference was found in Glycated hemoglobin, Body mass index, High-density lipoprotein cholesterol, Low-density lipoprotein cholesterol, triglycerides, and total cholesterol. Conclusions: Resistance exercise appears to be more effective in promoting an increase in Maximal oxygen consumption in protocols longer than 12 weeks and there is no difference in the control of glycemic and lipid levels between the two types of exercise. [ABSTRACT FROM AUTHOR]

Background: Statistical analysis plans increase the transparency of decisions made in the analysis of clinical trial results. The purpose of this paper is to detail the planned analyses for the PREVENT trial, a randomized, placebo-controlled trial of patient education for acute low back pain. Results: We report the pre-specified principles, methods, and procedures to be adhered to in the main analysis of the PREVENT trial data. The primary outcome analysis will be based on Mixed Models for Repeated Measures (MMRM), which can test treatment effects at specific time points, and the assumptions of this analysis are outlined. We also outline the treatment of secondary outcomes and planned sensitivity analyses. We provide decisions regarding the treatment of missing data, handling of descriptive and process measure data, and blinded review procedures. Conclusions: Making public the pre-specified statistical analysis plan for the PREVENT trial minimizes the potential for bias in the analysis of trial data, and in the interpretation and reporting of trial results. [ABSTRACT FROM AUTHOR]

The preprocessing stage in knowledge discovery projects is costly, normally taking between 50% and 80% of the total project time. It is in this stage that data in a relational database are transformed for applying a data mining technique. This stage is a complex task that demands from database designers a strong interaction with experts having a broad knowledge about the application domain. Frameworks aiming to systemize this stage have significant limitations when applied to Credit Behavioral Scoring solutions. This paper proposes a framework based on the Model Driven Development approach to systemize the mentioned stage. This work has three main contributions: 1) improving the discriminant power of data mining techniques by means of the construction of new input variables which embed temporal knowledge for the technique; 2) reducing the time of data transformation using automatic code generation, and 3) allowing artificial intelligence and statistics modelers to perform the data transformation without the help of database experts. In order to validate the proposed framework, two comparative studies were conducted. Experiments showed that the proposed framework delivers a performance equivalent or superior to those of existing frameworks and reduces the time of data transformation with a confidence level of 95%. [ABSTRACT FROM AUTHOR]

In challenging economic times, the ability to monitor trends and shifts in the job market would be hugely valuable to job-seekers, employers, policy makers and investors. To analyze the job market, researchers are increasingly turning to data science and related techniques which are able to extract underlying patterns from large collections of data. One database which is of particular relevance in the presence context is O*NET, which is one of the most comprehensive publicly accessible databases of occupational requirements for skills, abilities and knowledge. However, by itself the information in O * NET is not enough to characterize the distribution of occupations required in a given market or region. In this paper, we suggest a data mining based approach for identifying the most in-demand occupations in the modern job market. To achieve this, a Latent Semantic Indexing (LSI) model was developed that is capable of matching job advertisement extracted from the Web with occupation description data in the O * NET database. The findings of this study demonstrate the general usefulness and applicability of the proposed method for highlighting job trends in different industries and geographical areas, identifying occupational clusters, studying the changes in jobs context over time and for various other research embodiments. [ABSTRACT FROM AUTHOR]

Entity resolution constitutes a crucial task for many applications, but has an inherently quadratic complexity. In order to enable entity resolution to scale to large volumes of data, blocking is typically employed: it clusters similar entities into (overlapping) blocks so that it suffices to perform comparisons only within each block. To further increase efficiency, Meta-blocking is being used to clean the overlapping blocks from unnecessary comparisons, increasing precision by orders of magnitude at a small cost in recall. Despite its high time efficiency though, using Meta-blocking in practice to solve entity resolution problem on very large datasets is still challenging: applying it to 7.4 million entities takes (almost) 8 full days on a modern high-end server. In this paper, we introduce scalable algorithms for Meta-blocking, exploiting the MapReduce framework. Specifically, we describe a strategy for parallel execution that explicitly targets the core concept of Meta-blocking, the blocking graph. Furthermore, we propose two more advanced strategies, aiming to reduce the overhead of data exchange. The comparison-based strategy creates the blocking graph implicitly, while the entity-based strategy is independent of the blocking graph, employing fewer MapReduce jobs with a more elaborate processing. We also introduce a load balancing algorithm that distributes the computationally intensive workload evenly among the available compute nodes. Our experimental analysis verifies the feasibility and superiority of our advanced strategies, and demonstrates their scalability to very large datasets. [ABSTRACT FROM AUTHOR]

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia which is often challenging to diagnose because patients may be asymptomatic, and episodes are usually intermittent. In this paper we describe a method for identifying patients with a history of PAF using electrocardiogram (ECG) recordings during sinus rhythm. We analyzed, on a beat-to-beat basis, the P-waves in the sinus rhythm ECGs of 69 patients with a history of PAF and 59 healthy individuals. From each subject's P-waves, we calculated key electrocardiographic metrics, including some which are proposed here for the first time. Using means testing and feature selection methods, we discerned the features which were most useful for classification, and trained a Random Forest which identified PAF patients. Our approach achieved a 93.45% accuracy, sensitivity of 95.21%, and specificity of 91.40% using P-wave integral and novel amplitude and slope-based features which ranked highest in importance compared to other metrics from the literature. In particular, descriptive statistics of P-wave amplitudes, slopes, and integrals, were effective for identifying subjects with PAF history vs. healthy individuals. Our method has a high sensitivity and discrimination ability, with an AUC=0.9669 which is superior to others', and can thus be potentially valuable for the early identification of patients who are prone to episodes of PAF, even as part of the standard cardiological checkup that most adults undergo periodically. • Early identification of PAF during sinus rhythm with high accuracy. • P-wave signal metrics on a beat-to-beat basis are proposed. • P-wave amplitude, integral and slope features were ranked as the most important. • A RF classifier distinguishes PAF from healthy subjects with a state-of-the-art AUC. [ABSTRACT FROM AUTHOR]

• The first review article summarizes 30 years of the TSLP studies. • A comprehensive review of the problem literature with a new classification system. • Detailed descriptive statistics for the literature. • New directions for future research are given. Traffic flow data is a decisive element in transportation planning and traffic management. Over time, traffic sensors have been recognized as sources of such data. Despite their outstanding capabilities in measuring different traffic flow information types, they are not practical to apply across all transportation network streets or intersections. Thus, the traffic sensor location problem (TSLP) has emerged to answer two typical questions: how many sensors are needed, and what are the best locations for their deployment. This paper reviews the TSLP classes that have been extensively examined in the literature over the last three decades. This study tries to fulfill two major gaps in the existing literature. First, this is the only review article that summarizes the contributions made toward solving the TSLP spanning nearly 30 years. Second, it presents a comprehensive review and analysis of most TSLP studies with a new categorization system. This contribution clarifies the progress made and provides recommendations for further research. [ABSTRACT FROM AUTHOR]

Due to the European Union energy reduction strategy, many in-use buildings will be energetically monitored in the coming years to obtain their main thermal characteristics to improve or prove their energy efficiency. This is difficult to do with a reduced set of sensors and a robust data analysis methodology. This paper is focused on proposing some modifications on the existing ISO 9869 method and co-heating method to make them usable with basic energy monitoring data of in-use buildings and obtain their main thermal characteristics: the Heat Loss Coefficient (HLC considers heat losses through envelope plus infiltration) and the solar aperture (S a or gA-value) of the whole building. Under the FP7 project A2PBEER an occupied big office building has been energetically monitored. This monitoring system has been designed and installed while the building was in operation. Using this monitored data a modified ISO 9869 method has been applied to some specifically selected cloudy and cold winter periods to obtain the HLC of the building. Taking this HLC as a reference, a modified co-heating method has been used to estimate both the HLC again and the S a of the whole building. Although monitoring was carried out under very difficult conditions since the building was occupied, the proposed modifications on those two existing methods have delivered very reliable results with these two Key Performance Indicators (HLC and S a ) of the building under real operation conditions. [ABSTRACT FROM AUTHOR]

Two contrasting approaches are widely used to derive population dynamics as an emergent property deriving from the physiology and behavior of individual organisms. “Individual-based models” (IBMs) are computer simulations where the “state” (e.g., age, size) of each individual in a population is followed explicitly along with changes in its environment. Population properties (e.g., density, age- or size-structure) emerge from simple bookkeeping and descriptive statistics. Physiologically structured population models (PPSMs) have an identical philosophy, but assume a very large (formally infinite) population and that all individuals in a given state have an identical response to any given environment. These assumptions allow the bookkeeping to proceed through a series of mathematical steps that lead to partial differential or integral equations describing the population dynamics. There is software for both approaches that handles the bookkeeping, with the modeler specifying solely the individual model using stylized files, thereby eliminating the need for technical expertise in either complex computer simulations or advanced calculus. Each approach has its advantages and disadvantages. IBMs are easier to formulate and to explain to people with limited mathematical experience than PSPMs, but PSPMs allow for more extensive mapping of possible dynamic attractors. IBMs alone can reveal the population level effects of demographic stochasticity and of differences among individuals. Formal equilibrium analysis of PSPMs show possible stable states (size distributions) of the populations that include unstable steady states from which slightly perturbed populations may start cycling. The equilibrium size structure at these unstable states can serve as an initial condition for IBMs, thereby facilitating study of the cycles. We illustrated the interconnections and contrasting insights from the two approaches using a food-chain model for which the PSPM was previously studied by De Roos and Persson (Proc. Nat. Acad. Sci. USA: 99, 12907-12912, 2002). Future general population ecology theory requires work with model populations that are both physiologically structured and distributed in space. We describe concepts from spatially explicit IBMs with identical individuals that, in combination with the results in this paper, may point to a way forward. [ABSTRACT FROM AUTHOR]

This paper proposes a new independent and related variable monitoring based on kernel principal component analysis (KPCA) and support vector data description (SVDD) algorithm. Some process variables are considered independent from other variables and the monitoring of independent and related variables should be performed separately. First, an independent variable division strategy based on mutual information is presented. Second, SVDD and KPCA methods are adopted to monitor independent variable space and related variable space, respectively. Finally, a general statistic is built according to the monitoring results of SVDD and KPCA. The proposed KPCA–SVDD method considers the related and independent characters of variables. This method combines the advantages of KPCA in managing nonlinear related variables and those of SVDD in handling independent variables. A numerical system and the Tennessee Eastman process are used to examine the efficiency of the proposed method. Simulation results have proved the superiority of KPCA–SVDD method. [ABSTRACT FROM AUTHOR]

The cost of construction project is a fundamental input for decision making process set by owner during procurement stage. The paper identifies the cost drivers that are used in parametric cost estimation model for water treatment plants. Cost estimation at planning stage of projects is important for the success of the next stages in the projects. It is also very useful at the design stage of a project when information is incomplete and detailed designs are limited in such an early stage. Literature has been reviewed and interviews were conducted with experts and officials in water treatment plants to explore all variables that influence the construction cost of water treatment plants. A questionnaire survey was then conducted to assess the impact of the identified factors on construction costs of water treatment plants. Datasets that consist of 160 water treatment plant projects in Egypt were collected. Construction cost drivers have been nominated through two different procedures. The first technique is descriptive statistics ranking of variables by evaluating Mean Score and Relative Importance Index based on respondents' feedback in conducting questionnaire. The second technique utilizes exploratory factor analysis on the collected dataset. These cost drivers are used to construct two predictive models for estimating the construction cost of water treatment plants models using artificial neural networks. Analysis of results was performed to validate the models and demonstrate their effectiveness. The proposed methodology aids public authorities to perform comparative analysis and evaluate the different alternatives of water treatment plant projects. [ABSTRACT FROM AUTHOR]

Background: Analyzing the historical and social path of an occupation using the sociology of professions and the perspective of scientific knowledge promotes an understanding of the origin of physical therapy in Brazil and of discussions of the profession in its contemporary context. Objective: The aim of this paper was to discuss the professionalization process of physical therapy in São Paulo. The authors tried to analyze bath therapy, massage therapy, and physical therapy as occupations involving distinct expertise and as part of the group of occupations that evolved into the profession of physiotherapy in the first half of the twentieth century. Method: The analysis undertaken was a qualitative study based on an analysis of historical documents. Eighty-six professional records from the Service of Inspection of Professional Practice in the state of São Paulo and healthcare legislation from the 1930s and 1940s were analyzed. Results: The distinction between physical therapy practitioner and profession of physiotherapy can be seen by examining registration requirements for rank-and-file nurses with expertise in interactions; this distinction suggests the emergence of specialized expertise that was clearly a part of neither medicine nor nursing and contributed to expertise in physical therapy since the 1950s. Conclusion: The regulation of physiotherapy practices, the recognition of expertise, the accreditation of practical nurses by the State, and the institutionalization of a course for physical therapy practitioners in 1951 are key elements of the professionalization process for the physical therapy profession in Brazil [ABSTRACT FROM AUTHOR]

Public street lighting system consists of devices distributed in points of light and a supervision and control application. The system architecture is modular and expandable. In developing the work the C# language is adopted to develop the operation and monitoring via standard CyberOPC and XML file types are applied to the device description and definition of the network topology. This paper describes the validation proposed and the results obtained attests that this applied methodology is feasible and can be applied to other public lighting systems. [ABSTRACT FROM AUTHOR]

• Annually, there are up to 5 million visits to EDs across the country of patients with a primary complaint of "Alcohol Intoxication". • ED Health Care Providers are charged with initial assessment, serial evaluations and then deciding when these patients are clinically improved to the point when they are ready for disposition. • Previously, there was no widely accepted tool for providers to use to perform a standardized, formal examination of these patients. • Hack's Impairment Index (HII Score) is a clinical tool currently being used at the bedside by nurses at select institutions across the United States using paper prompts. • An App version of the HII Score for use on mobile devices was developed. • This study evaluated the effects on nursing performance with the tool when paper charting was replaced. There is no formal assessment to determine level of disability in the millions of patients with alcohol-induced impairment who present to emergency departments annually. Hack's Impairment Index (HII) is a standardized, serializable clinical tool designed to quantify ability. Nursing staff members at this center perform the HII and determine a score using paper prompts. We developed an HII electronic application and investigated whether or not an electronic version on a mobile device would affect nursing performance. A chart review-based quality improvement project compared the number, repetitions, and completeness of HII score documentation performed by nurses over 6.5 months. Group 1: paper-based HII scores for the 90-day period before intervention; group 2: iPad-based HII scores for the 90 days after intervention. There was a 2-week period for staff training and electronic version feedback between groups. Informal, ad hoc interviews were performed with nurses at investigation termination. Group 1: 476 emergency department patients with alcohol-induced impairment had HII scores ordered; 339 (71.2%; 95% CI, 67.1, 75.3%) had HII assessments with a total of 539 HII scores documented. An average (SD) 1.60 (0.01) serial assessments occurred per patient, 5 (1.1%; 95% CI, (0, 2.2%) scores were incomplete. Group 2: 569 alcohol-induced impairment emergency department patients were seen and had HII scores ordered; 420 (73.8%; 95% CI, (70.2, 77.4%) had HII assessments with a total of 639 HII scores documented. An average (SD) 1.52 (0.03) serial assessments occurred per patient, 4 (0.9%; 95% CI, (0.81, 0.99%) had incomplete HII scores. Although our study took place at 1 center, was a chart review, and not directly observed, we found that the mobile device-based HII application to determine a score did not interfere with nursing performance. Specifically, the repetition and completeness of nursing assessments of emergency department patients with impairment from alcohol use was not altered when comparing paper chart documentation with electronic format documentation. (Curr Ther Res Clin Exp. 2021; 82:XXX–XXX) [ABSTRACT FROM AUTHOR]

Ecological degradation has become an important constraint to global socio-economic development. Therefore, ecological protection and restoration has received increasing attention. The important goal and the most direct effect of ecological protection and restoration are the vegetation restoration, which emphasizes the maximum or minimum range of NDVI of different vegetation types under specific terrain conditions. Based on the data of NDVI and topographic factors in a stable ecosystem, this paper comprehensively used descriptive statistics, multiple regression with interaction terms and threshold regression to analyze the spatial distribution of vegetation and its relationship with elevation, slope and aspect in the Wanhuigou catchment on the Loess Plateau. It showed that the NDVI values were high in the northwest and low in the southeast, and were generally consistent with the distribution of elevation. The change of NDVI with the slope was large, but the change with the aspect was not obvious. Meanwhile, the effect of elevation on NDVI would be limited by slope, but would not be affected by aspect; the positive effect of slope on NDVI would also be limited by the elevation and aspect. In addition, the influence of topographic factors on NDVI was non-linear, i.e. there was a threshold effect and different types of vegetation require different topographic factor conditions. Therefore, by rationally arranging the growth areas of different types of vegetation in accordance with the laws of topographic factor conditions, ecological protection and restoration could be more targeted to help build a stable ecosystem in areas with severe soil erosion such as the Wanhuigou catchment. Unlabelled Image • There are interactive effects on NDVI between different topographic factors. • The influence of terrain factors on NDVI is non-linear and has threshold effects. • Suitable topographic factor conditions for vegetation growth were found out. • This paper provides a reference for ecological protection and restoration. [ABSTRACT FROM AUTHOR]

Background: The Vestibular Disorders Activities of Daily Living Scale (VADL) is considered an important subjective assessment to evaluate patients suffering from dizziness and imbalance. Although frequently used, its metric characteristics still require further investigation. Objective: This paper aims to analyze the psychometric properties of the Brazilian version of the VADL in an elderly population. Method: The sample comprises patients (≥65 years old) with chronic dizziness resulting from vestibular disorders. For discriminant analysis, patients were compared to healthy subjects. All subjects answered the VADL-Brazil by interview. To examine the VADL validity, patients filled out the Dizziness Handicap Inventory (DHI) and the ABC scale and were tested on the Dynamic Gait Index (DGI). To evaluate the VADL responsiveness, 20 patients were submitted to rehabilitation. Results: Patients (n=140) had a VADL total score of 4.1±1.6 points. Healthy subjects scored significantly less than patients in all the subscales and in the VADL total score. The VADL-Brazil was weakly correlated with the DHI and moderately to the ABC scale and the DGI. Instead of the original 3 subscales, factor analysis resulted in 6 factors. The VADL was capable of detecting changes after rehabilitation, which means that the instrument has responsiveness. Conclusions: This study provided more data about the psychometric properties and usefulness of the VADL-Brazil. The use of such a reliable and valid instrument increases the knowledge about disability in patients with vestibular disorders. [ABSTRACT FROM AUTHOR]

Abstract: The paper addresses the validation issue for the Lingua Cosmica system: have the results of sequences of reasoning (i.e. conclusions) a universal validity in some sense? LINCOS verifications of a selected set of representative statements are considered. They are compared with proofs of the same statements obtained by means of an existing proof system in computer science. The treatment indicates how formalised descriptive logic reasoning can be transposed from one system to the other. As this kind of reasoning is not bound to a specific system, it possesses an aspect of unversality. [Copyright &y& Elsevier]

This paper reports the final results from a prespecified descriptive analysis (conducted 30 months after the last patient was enrolled) that confirms the PFS benefit with panitumumab + FOLFIRI versus FOLFIRI alone in previously treated patients with wild-type KRAS metastatic colorectal cancer. New analyses in this manuscript include efficacy by skin toxicity severity and by prior bevacizumab and oxaliplatin therapy, of which this is the first report of this type. The results by prior therapy show that the addition of panitumumab in patients treated with prior oxaliplatin–bevacizumab improved PFS (median 6.4 versus 3.7 months; HR 0.58 [95% CI 0.37 0.90]; P = 0.014) with trends to improved survival (median 16.1 versus 12.1 months; HR 0.69 [95% CI 0.45, 1.08]; P = 0.10); to our knowledge, the first observation of the efficacy of an anti-EGFR in combination with chemotherapy in the second-line compared with chemotherapy alone following a bevacizumab-containing regimen in the first-line treatment in patients with wild-type KRAS mCRC.Background The study 20050181 demonstrated significant improvements in progression-free survival (PFS), objective response, and a nonsignificant trend toward increased overall survival (OS) with panitumumab–FOLFIRI versus FOLFIRI alone for second-line wild-type (WT) KRAS metastatic colorectal cancer (mCRC). Updated long-term data from a prespecified descriptive analysis are reported. Patients and methods Patients receiving one prior mCRC treatment were randomly assigned (1:1) to panitumumab (6.0 mg/kg)–FOLFIRI versus FOLFIRI every 2 weeks. Co-primary end points (PFS and OS) were prospectively analyzed by tumor KRAS status. Results One thousand one hundred and eighty-six patients were randomly assigned. In patients with WT KRAS tumors, panitumumab–FOLFIRI significantly improved PFS versus FOLFIRI [median 6.7 versus 4.9 months; hazard ratio (HR) 0.82 [95% confidence interval (CI) 0.69, 0.97]; P = 0.023]. A trend toward longer OS was observed (median 14.5 versus 12.5 months; HR 0.92 [95% CI 0.78, 1.10]; P = 0.37). Response rates improved from 10% to 36% (P < 0.0001). From post hoc analyses in patients receiving prior oxaliplatin–bevacizumab, panitumumab–FOLFIRI improved PFS (median 6.4 versus 3.7 months; HR 0.58 [95% CI 0.37, 0.90]; P = 0.014). PFS and OS appeared longer for worst-grade skin toxicity of 2–4, versus 0–1 or FOLFIRI. Safety results were as previously reported and consistent with the known toxicities with anti-epidermal growth factor receptor therapy. Conclusions These data confirm the primary efficacy and safety findings of this trial and support panitumumab–FOLFIRI as a second-line treatment of WT KRAS mCRC. [ABSTRACT FROM AUTHOR]

This paper summarizes findings about papers involving supplier selection and evaluation using Data Envelopment Analysis (DEA) published between 2009 and 2018. It reviews how DEA , one of the most frequently used methods, supports the supplier selection and evaluation process and related management decisions. Its novelty stems from the fact that such a type of review has only been carried out in a much broader context, thus the present approach can reveal potentially new opportunities for development and application. A systematic literature review was completed involving 54 papers that propose the use of some form of DEA for supporting supplier-management-related decisions. The paper uses descriptive and multivariate statistics to cluster the reviewed papers. Although a large number of papers were published in the period under examination, most of them still focus on supplier selection. However, a limited number of papers were identified which are more practice oriented and support strategic decision making in supplier management. Papers considering sustainability tend to focus only on green factors, but in most cases it means an additional criterion in the evaluation. Sustainability problems do not generate substantially new DEA model versions. • 35% of processed articles address sustainability issues in supplier evaluation. • DEA is suitable for measuring efficiency, so it can be used to support purchasing and supply management decisions. • DEA is mostly used for ranking, but literature also suggests DEA to support qualification, segmentation, or supplier development. • Methodology-oriented papers and purchasing and supply management literature are not well interconnected. [ABSTRACT FROM AUTHOR]

Since its inception, the Pradhan Mantri Ujjwala Yojana (PMUY) of the Government of India has provided subsidized liquefied petroleum gas (LPG) connections to many poor households for enhancing use of LPG as a cooking fuel. However, along with such large LPG connections, it is also necessary to understand how the beneficiaries have adopted the same. This paper examines beneficiary households' response to the PMUY in respect of actual use of LPG and identifies the underlying factors in rural areas of the Indian state of Odisha. Using descriptive statistics and estimating limited dependent variable models the paper finds significant positive impact of household heads' education and amount of subsidy on actual use of LPG, whereas general category households have lower adoption of the same. Further, greater availability of kerosene also limits adoption of LPG. Importantly, household income does not influence adoption of this cleaner fuel. The findings of the paper, therefore, have significant implications for fine tuning of policies and institutions towards cleaner energy transition in rural India, particularly in respect of enhancing relative price and awareness about benefits of LPG and its delivery mechanisms. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes three mathematical approaches that can be used to study chemical atmospheric data related to air quality: a) Descriptive statistical analysis, b) Fourier analysis and c) Wavelet analysis. These techniques are used to improve understanding of SO2 pollution in the city of Talcahuano (Chile). The results show high SO2 dispersion values, an annual average standard deviation of 63.5 μg m−3 and a Pearson coefficient of variation of 1.59. The asymmetry and kurtosis coefficients (3.1 and 14.3, respectively) confirm the “non-Gaussianity” of the SO2 concentrations. The main fluctuations of the data studied are due to seasonal and daily cyclical components. Important annual irregularities are detected by the wavelet analysis of daily cycles, especially in winter. This cyclical component is fundamentally due to meteorological factors (because the zonal industrial emissions, mainly emitted by Oil Refining Plant, are roughly constant). Non-negligible correlations, between SO2 concentration and several meteorological variables (vertical wind, temperature, solar radiation, horizontal wind), were also verified (0.49, 0.40, 0.36 and 0.34, respectively). [Copyright &y& Elsevier]