Article about financial problems and debt as predictive factors for recidivism.
DOCUMENT
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
DOCUMENT
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.
DOCUMENT
The Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) is a risk assessment instrument for adolescents that estimates the risk of multiple adverse outcomes. Prior research into its predictive validity is limited to a handful of studies conducted with the START:AV pilot version and often by the instrument’s developers. The present study examines the START:AV’s field validity in a secure youth care sample in the Netherlands. Using a prospective design, we investigated whether the total scores, lifetime history, and the final risk judgments of 106 START:AVs predicted inpatient incidents during a 4-month follow-up. Final risk judgments and lifetime history predicted multiple adverse outcomes, including physical aggression, institutional violations, substance use, self-injury, and victimization. The predictive validity of the total scores was significant only for physical aggression and institutional violations. Hence, the short-term predictive validity of the START:AV for inpatient incidents in a residential youth care setting was partially demonstrated and the START:AV final risk judgments can be used to guide treatment planning and decision-making regarding furlough or discharge in this setting.
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In this paper we research the following question: What motivational factors relate, in which degree, to intentions on compliance to ISP and how could these insights be utilized to promote endusers compliance within a given organization? The goal of this research is to provide more insight in the motivational factors applicable to ISP and their influence on end-user behavior, thereby broadening knowledge regarding information systems security behaviors in organizations from the viewpoint of non-malicious abuse and offer a theoretical explanation and empirical support. The outcomes are also useful for practitioners to complement their security training and awareness programs, in the end helping enterprises better effectuate their information security policies. In this study an instrument is developed that can be used in practice to measure an organizational context on the effects of six motivational factors recognized. These applicable motivational factors are determined from literature and subsequently evaluated and refined by subject matter experts. A survey is developed, tested in a pilot, refined and conducted within four organizations. From the statistical analysis, findings are reported and conclusions on the hypothesis are drawn. Recommended Citation Straver, Peter and Ravesteyn, Pascal (2018) "End-users Compliance to the Information Security Policy: A Comparison of Motivational Factors," Communications of the IIMA: Vol. 16 : Iss. 4 , Article 1. Available at: https://scholarworks.lib.csusb.edu/ciima/vol16/iss4/1
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Most violence risk assessment tools have been validated predominantly in males. In this multicenter study, the Historical, Clinical, Risk Management–20 (HCR-20), Historical, Clinical, Risk Management–20 Version 3 (HCR-20V3), Female Additional Manual (FAM), Short-Term Assessment of Risk and Treatability (START), Structured Assessment of Protective Factors for violence risk (SAPROF), and Psychopathy Checklist–Revised (PCL-R) were coded on file information of 78 female forensic psychiatric patients discharged between 1993 and 2012 with a mean follow-up period of 11.8 years from one of four Dutch forensic psychiatric hospitals. Notable was the high rate of mortality (17.9%) and readmission to psychiatric settings (11.5%) after discharge. Official reconviction data could be retrieved from the Ministry of Justice and Security for 71 women. Twenty-four women (33.8%) were reconvicted after discharge, including 13 for violent offenses (18.3%). Overall, predictive validity was moderate for all types of recidivism, but low for violence. The START Vulnerability scores, HCR-20V3, and FAM showed the highest predictive accuracy for all recidivism. With respect to violent recidivism, only the START Vulnerability scores and the Clinical scale of the HCR-20V3 demonstrated significant predictive accuracy.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
MULTIFILE
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.
MULTIFILE
Background: More knowledge about characteristics of children and adolescents who need intensive levels of psychiatric treatment is important to improve treatment approaches. These characteristics were investigated in those who need youth Assertive Community Treatment (youth-ACT). Method: A cross-sectional study among children/adolescents and their parents treated in either a regular outpatient clinic or a youth-ACT setting in a specialized mental health treatment center in the Netherlands. Results: Child, parent and family/social context factors were associated with treatment intensification from regular outpatient care to youth-ACT. The combination of the child, parent, and family/social context factors adds substantially to the predictive power of the model (Nagelkerke R2 increasing from 36 to 45% for the three domains separately, to 61% when all domains are combined). The strongest predictors are the severity of psychiatric disorders of the child, parental stress, and domestic violence. Conclusions: Using a wide variety of variables that are potentially associated with treatment intensification from regular outpatient clinic to youth-ACT, we constructed a regression model illustrating a relatively strong relation between the predictor variables and the outcome (Nagelkerke R2 = 0.61), with three strong predictors, i.e. severity of psychiatric disorders of the child, parental stress, and domestic violence. This emphasizes the importance of a system-oriented approach with primary attention for problem solving and stress reduction within the system, in addition to the psychiatric treatment of the child, and possibly also the parents. Auteurs: Vijverberg, R., Ferdinand, R., Beekman, A., & van Meijel B.
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