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In the status forecasting problem, classification models such as logistic regression with input variables such as physiological, diagnostic, and treatment variables are typical ways of modeling. However, the parameter value and model performance differ among individuals with different baseline information. To cope with these difficulties, a subgroup analysis is conducted, in which models’ ANOVA and rpart are proposed to explore the influence of baseline information on the parameters and model performance. The results show that the logistic regression model achieves satisfactory performance, which is generally higher than 0.95 in AUC and around 0.9 in F1 and balanced accuracy. The subgroup analysis presents the prior parameter values for monitoring variables including SpO2, milrinone, non-opioid analgesics and dobutamine. The proposed method can be used to explore variables that are and are not medically related to the baseline variables.

Marine protected areas (MPAs) are increasingly implemented as tools to conserve and manage fisheries and target species. Because there are opportunity costs to conservation, there is a need for science-based assessment of MPAs. Here, we present one of the northernmost documentations of MPA effects to date, demonstrated by a replicated before–after control-impact (BACI) approach. In 2006, MPAs were implemented along the Norwegian Skagerrak coast offering complete protection to shellfish and partial protection to fish. By 2010, European lobster (Homarus gammarus) catch-per-unit-effort (CPUE) had increased by 245 per cent in MPAs, whereas CPUE in control areas had increased by 87 per cent. Mean size of lobsters increased by 13 per cent in MPAs, whereas increase in control areas was negligible. Furthermore, MPA-responses and population development in control areas varied significantly among regions. This illustrates the importance of a replicated BACI design for reaching robust conclusions and management decisions. Partial protection of Atlantic cod (Gadus morhua) was followed by an increase in population density and body size compared with control areas. By 2010, MPA cod were on average 5 cm longer than in any of the control areas. MPAs can be useful management tools in rebuilding and conserving portions of depleted lobster populations in northern temperate waters, and even for a mobile temperate fish species such as the Atlantic cod.

In the status forecasting problem, classification models such as logistic regression with input variables such as physiological, diagnostic, and treatment variables are typical ways of modeling. However, the parameter value and model performance differ among individuals with different baseline information. To cope with these difficulties, a subgroup analysis is conducted, in which models’ ANOVA and rpart are proposed to explore the influence of baseline information on the parameters and model performance. The results show that the logistic regression model achieves satisfactory performance, which is generally higher than 0.95 in AUC and around 0.9 in F1 and balanced accuracy. The subgroup analysis presents the prior parameter values for monitoring variables including SpO[sub.2], milrinone, non-opioid analgesics and dobutamine. The proposed method can be used to explore variables that are and are not medically related to the baseline variables.

Objective To explore the risk factors of ketoacidosis (DKA) in elderly patients with diabetes and to construct a nomogram prediction model to guide clinical practice. Methods Baseline, glycolipid metabolism, and related data were collected. Risk factors were screened by multifactor logistic regression analysis to construct a model. The effectiveness of the model was evaluated by Receiver Operating Characteristiv (ROC) curve, calibration curve analysis, and decision curve analysis (DCA). Results Logistic regression analysis showed that age, duration of diabetes, FBG, 2hPG, HbA1c, TG, TC, and C peptide level were the independent risk factors for DKA in elderly diabetic patients (P < 0.05). The nomogram prediction model constructed based on these factors showed good prediction performance in both the training set and the verification set, with the C‐index indexes being 0.880 and 0.918, respectively, and the average absolute errors of coincidence between the predicted value and the true value being 0.102 and 0.075, respectively. The results of the Hosmer–Lemeshow test were χ2 = 12.750, P = 0.120 and χ2 = 8.325, P = 0.402, respectively. The ROC curve showed that the AUC of the nomogram model in the training set and the verification set for predicting the occurrence of DKA in elderly diabetic patients was 0.866 and 0.879, respectively. Conclusion Nomogram prediction model based on baseline data and glucose and lipid metabolism indicators showed good prediction efficiency in both the training set and the verification set. Age, diabetes duration, FBG, 2hPG, HbA1c, TG, TC, and C peptide levels were independent risk factors for DKA in elderly diabetic patients. This study identifies risk factors for DKA in elderly diabetics and develops a predictive nomogram model. Key factors include age, diabetes duration, FBG, HbA1c, and lipid levels. The model demonstrated strong predictive performance with high AUC values, indicating its clinical utility.

This article aims to present formal governance practices of information technology adapted to the general context of Moroccan universities. The study consists of two main phases: the conceptualization phase and the operationalization phase. During the conceptualization phase, the authors reviewed relevant literature on best practices and their associated frameworks in higher education institutions (HEIs). The results revealed that universities had varying levels of maturity in terms of good practices and often used multiple information system frameworks, which can cause organizational and technical problems. In order to find a solution to this situation, the authors conducted in-depth interviews with chief information officers (CIOs) and university officials from four Moroccan universities during the operationalization phase. These interviews enabled them to propose an effective baseline of best practices and an algorithmic approach to assist managers in choosing between two combinations of frameworks that cover all the mechanisms of the baseline. This solution would enable optimal, agile, and easy-to-implement information technology governance in Moroccan universities while avoiding the multiplicity of frameworks.

Global acknowledgement of ecological restoration, as an important tool to complement conservation efforts, requires an effort to increase the effectiveness of restoration interventions. Evidence-based practice is purported to promote effectiveness. A central tenet of this approach is decision making that is based on evidence, not intuition. Evidence can be generated experimentally and in practice but needs to be linked to baseline information collection, clear goals and monitoring of impact. In this paper, we report on a survey conducted to assess practitioners’ perceptions of the evidence generated in restoration practice in South Africa, as well as challenges encountered in building this evidence base. Contrary to a recent assessment of this evidence base which found weaknesses, respondents viewed it as adequate and cited few obstacles to its development. Obstacles cited were mostly associated with planning and resource availability. We suggest that the disparity between practitioners’ perceptions and observed weaknesses in the evidence base could be a challenge in advancing evidence-based restoration. We explore opportunities to overcome this disparity as well as the obstacles listed by practitioners. These opportunities involve a shift from practitioners as users of scientific knowledge and evidence, to practitioners involved in the co-production of evidence needed to increase the effectiveness of restoration interventions.

Pretest–posttest studies are an important and popular method for assessing treatment effects or the effectiveness of an intervention in many areas of scientific research. There are two distinct features for this type of study: availability of baseline information for all subjects in the study and missingness by design of measures of the responses. Important recent research advances on this topic include Leon et al. (2003) on efficient estimation of the treatment effect, and Huang et al. (2008) on a semi-parametric estimation procedure based on empirical likelihood (EL) where the mean responses for the treatment group and the control group are handled separately. EL ratio confidence intervals or tests for the treatment effect, however, cannot be constructed under the approach used by Huang et al. (2008). In this paper, we use an alternative EL formulation, which directly involves the parameter of interest, i.e., the treatment effect, and incorporates baseline information through an imputation approach. Our focus is to derive the EL ratio confidence intervals and tests for the treatment effect under the proposed imputation-based framework. Theoretical results are developed, and finite sample performances of the proposed methods with comparison to existing approaches are investigated through simulation studies. An application to a real data set is also presented. Les études prétest/post-test représentent une méthode populaire et importante pour l'évaluation de l'effet d'un traitement ou de l'efficacité d'une intervention dans plusieurs domaines de recherche scientifique. La disponibilité d'information de référence pour tous les sujets et la présence de valeurs manquantes dues à la méthode de mesure de la variable réponse constituent deux caractéristiques propres à ces études. Récemment, des avancées importantes ont été accomplies par Leon et coll. (2003) au sujet de l'estimation efficace de l'effet thérapeutique, et par Huang et coll. (2008) à propos d'une procédure d'estimation semi-paramétrique basée sur la vraisemblance empirique où les réponses moyennes des groupes expérimental et témoin sont considérées séparément. Les tests et intervalles de confiance basés sur la vraisemblance empirique ne peuvent toutefois pas être construits dans ce cadre. Les auteurs utilisent une formulation différente de la vraisemblance empirique qui contient le paramètre d'intérêt, soit l'effet thérapeutique, et qui tient compte de l'information de référence par une méthode d'imputation. Leur objectif consiste à dériver du rapport de vraisemblance empirique des tests et intervalles de confiance pour l'effet thérapeutique sous le modèle proposé. Ils développent des résultats théoriques et évaluent la performance de leur méthode par rapport aux méthodes existantes sur des échantillons finis à l'aide de simulations. Finalement, les auteurs appliquent leur méthode à l'analyse d'un jeu de données réelles.

Detecting anomalies accurately is fundamental to rapid diagnosis and repair of problems. This paper proposes a novel Anomaly detection system based on the comparison of real traffic and DSNS (Digital Signature of Network Segment), generated by BLGBA (Baseline for Automatic Backbone Management) model, within a hysteresis interval using the residual mean and on the correlation of the detected deviations. Extensive experimental results on real network servers confirmed that our system is able to detect anomalies on the monitored devices, avoiding the high false alarms rate. [PUBLICATION ABSTRACT]

This chapter contains sections titled: Introduction Reasons for undertaking monitoring References and further reading

This chapter contains sections titled: Introduction Changes to the Prices Changes to the Completion Date and any Key Dates Project Manager's assumptions Other related matters Practical issues