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AbstractDue to the recent advances and diversity of the subject of modeling and identification of multivariable systems and its important applications in many different traditional and new areas, a need arises for a unification of the existing literature on this subject. In this paper, most of the algorithms which have been proposed for identifying linear discrete‐time multivariable systems from input‐output data will be studied and classified according to the model used. Also, different types of applications and related topics to the subject of multivariable system identification and the features of some identification algorithms when implemented on dedicated computers with limited word length will be discussed.

Abstract. Flood loss models are one important source of uncertainty in flood risk
assessments. Many countries experience sparseness or absence of comprehensive
high-quality flood loss data, which is often rooted in a lack of protocols and
reference procedures for compiling loss datasets after flood events. Such
data are an important reference for developing and validating flood loss
models. We consider the Secchia River flood event of January 2014, when a
sudden levee breach caused the inundation of nearly 52 km2 in northern
Italy. After this event local authorities collected a comprehensive flood
loss dataset of affected private households including building footprints and
structures and damages to buildings and contents. The dataset was enriched with
further information compiled by us, including economic building values,
maximum water depths, velocities and flood durations for each building. By
analyzing this dataset we tackle the problem of flood damage estimation in
Emilia-Romagna (Italy) by identifying empirical uni- and multivariable loss
models for residential buildings and contents. The accuracy of the proposed
models is compared with that of several flood damage models reported in the
literature, providing additional insights into the transferability of the
models among different contexts. Our results show that (1) even simple
univariable damage models based on local data are significantly more
accurate than literature models derived for different contexts;
(2) multivariable models that consider several explanatory variables
outperform univariable models, which use only water depth. However,
multivariable models can only be effectively developed and applied if
sufficient and detailed information is available.

[EN] In this work, the parametric identification of a cooling system in a PEM (proton exchange membrane) fuel cell is carried out. This system is multivariable and nonlinear. In this type of system there are different objectives and the unmodeled dynamics cause conflicting objectives (prediction errors in each output). For this reason, resolution is proposed using a multi-objective optimization approach. Nearly optimal alternatives can exist in any optimization problem. Among them, the nearly optimal solutions that are significantly different (that we call nearly optimal solutions nondominated in their neighborhood) are potentially useful solutions. In identification problems, two situations arise for consideration: 1) aggregation in the design objectives (when considering the prediction error throughout the identification test). When an aggregation occurs in the design objectives, interesting non-neighboring (significantly different) multimodal and nearly optimal alternatives appear. These alternatives have different trade-offs in the aggregated objectives; 2) new objectives in decision making appear. Some models can, with similar performance in the design objectives, obtain a significant improvement in new objectives not included in the optimization phase. A typical case of additional objectives are the validation objectives. In these situations, nearly optimal solutions nondominated in their neighborhood play a key role. These alternatives allow the designer to make the final decision with more valuable information. Therefore, this work highlights, as a novelty, the relevance of considering nearly optimal models nondominated in their neighborhood in problems of parametric identification of multivariable nonlinear systems and shows an application in a complex problem. ; This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096904-B-I00, and in part by the Generalitat Valenciana Regional Government under Project AICO/2019/055. ; Pajares-Ferrando, ...

Purpose: In Cambodia, Village Animal Health Workers (VAHW) have been trained by NGOs or by the government to provide animal health services (treatment, husbandry advice, vaccination) to their communities' farmers. This system is characterised by a high variability of skills because of non-harmonised training plans, poor sustainability with a large number of VAHWs dropping their activities after five years and no harmonised tools for their evaluation. The objective of the study was to assess the work skills of VAHW. Methods: We applied a scoring grid composed of five categories (sustainability, treatment, production, vaccination, reporting). Linked to several evaluation criteria, specific questions were defined to assess if the criteria were fulfilled by the VAHW. An additional questionnaire, with 31 explanatory variables, was developed in order to collect data about factors that could influence the VAHW's score. The study was implemented in three provinces bordering Vietnam (Kampong Cham, Prey Veng and Takeo). A total of 367 villages were selected using a proportional random sampling, g method. We applied a multivariable linear regression model to determine factors associated with high scores for the VAHW evaluation. Results: In the population studied, 23% of the villages did not have a VAHW. According to our scoring system, 23.6% of the VAHW interviewed were in a situation of inactivity. From our multivariable analysis, six factors were significantly associated with a high score in the evaluation of the VAHW once they were active: selecting a VAHW from a village with at least 100 heads of cattle, using practical activities during training, having a training duration longer than 30 days, organising refresher courses, being a member of association and having regular contact with the district veterinarian. Conclusions: These results demonstrate the need of constant networking activities in the surveillance system to ensure that field staffs do not feel isolated. Relevance: Some of these findings could be used as a prerequisite for continued participation in refresher training activities done by the Cambodian government. (Texte intégral)

Abstract Background Cholesterol is an important contributor to morbidity and mortality risks due to its association with obesity, cardiovascular disease, and cancer. A system of mandatory military conscription is a useful tool for disease-risk monitoring in a given male population. Swiss military conscription data are representative for more than 90 % of a given male birth cohort (with Swiss citizenship). The medical examination also includes voluntary laboratory testing, for which approximately 65 % of the young men present at conscription give consent. Methods Here we present the temporal and subgroup analyses of total serum cholesterol levels (TCL) among Swiss conscripts from 2006 to 2012 (N = 174,872; mean age = 19.75 years). The voluntary blood samples were tested by a central laboratory (Viollier AG) with identical measurement standards and strict quality control. To test differences in TCL by socioeconomic occupational status, sports test performance, Body Mass Index (BMI), age, and place of residence of the conscripts we used a multivariable regression model with TCL as dependent variable. Results Mean TCL decreased significantly, by 0.125 mmol/l (95 % CI 0.108–0.142, p < 0.001) from 4.225 mmol/l (95 % CI 4.210–4.240) in 2006 to 4.100 mmol/l (95 % CI 4.091–4.109) in 2012. Similarly, the prevalence of conscripts with an elevated TCL ≥ 5.17 mmol/l decreased from ≥10.2 % prior to 2011 to 6.9 % in 2011 and 8.2 % in 2012. Multivariate regression showed an association between elevated TCL and lower socioeconomic occupational status, lower sports test performance, higher BMI, higher age, and area of residence. There was no longer a significant increase in mean TCL among the three grades of obesity (BMI ≥ 30.0 kg/m2) as defined by the WHO. Within the BMI categories of normal weight and overweight, TCL was stratified by sports performance (better sports performance = lower TCL). Conclusion Decreasing TCL in 2011 and 2012 fits the known pattern of conscripted persons' stabilizing BMI and sports test performance of the conscripts in recent years. However, small temporal drifts within the laboratory analyses cannot be ruled out as confounding factors. In conclusion, identifying subgroups with unfavorable lipid profiles will contribute to the continuing success of intensified public health programs.

Der wachsende Bedarf an Wandlungsfähigkeit führt zu einer höheren Frequenz in der Umplanung von Montagesystemen und erfordert eine kontinuierliche Überprüfung und Anpassung des Automatisierungsgrades. Um auch die komplexen Umgebungsbedingungen abzubilden, sollen nicht-monetäre Faktoren in den Entscheidungsprozess eingebunden werden. Um die Entscheidung zu unterstützen, stellt dieser Beitrag ein Tool zur Identifizierung und Bewertung von Automatisierungsszenarien mittels einer Nutzwert-Aufwand-Analyse vor.
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The increasing need for adaptability in assembly leads to a higher planning frequency of the system and requires continuous checks and adaptations of the appropriate level of automation. To account for the complex environmental conditions, non-monetary factors are included in the decision-making process. This paper presents a decision support tool to identify and evaluate automation scenarios by means of cost and benefit evaluation.

With legislation restricting emissions and customers increasing their demands, the air system of modern diesel engines has evolved into a complex system. Several coupled actuating and controlled variables have to be considered simultaneously in mathematical modelling and control. This thesis investigates the use of a linear-quadratic regulator (LQR) as a model-based, optimal, multivariable control for a diesel engine's air system. This technique is validated by results of simulation studies and on-board tests. The challenge of determining appropriate regulator parameters is addressed by a new method, which calculates optimal regulator parameters as the solution of suitable optimal control problems.

This paper presents a stationary and dynamic study of the advantages of using a regulating valve for the cathode outlet flow in combination with the compressor motor voltage as manipulated variables in a fuel cell system. At a given load current, the cathode input and output flowrate determine the cathode pressure and stoichiometry, and consequently determine the oxygen partial pressure, the generated voltage and the compressor power consumption. In order to maintain a high efficiency during operation, the cathode output regulating valve has to be adjusted to the operating conditions, specially marked by the current drawn from the stack. Besides, the appropriate valve manipulation produces an improvement in the transient response of the system. The influence of this input variable is exploited by implementing a predictive control strategy based on dynamic matrix control (DMC), using the compressor voltage and the cathode output regulating valve as manipulated variables. The objectives of this control strategy are to regulate both the fuel cell voltage and oxygen excess ratio in the cathode, and thus, to improve the system performance. All the simulation results have been obtained using the MATLAB-Simulink environment. ; This work was supported by the project 'Diseño de controladores para el proceso electroquímico en pilas de combustible de tipo PEM' (4796). This work has been funded partially by the project CICYT DPI2004-06871-C02-01 of the Spanish Government, and the support of the Department of Universities, Investigation and Society of Information of the Generalitat de Catalunya. ; Peer Reviewed

OBJECTIVES: Patient reported outcomes (PROs) serve as a means of measuring improvement and quality of care. Legacy PROs rely on a list of questions that have had to demonstrate accuracy, responsiveness, and validity in testing for intended measurements. While certain legacy PROs such as the International Knee Documentation Committee (IKDC) survey have demonstrated these properties well, a lengthy PRO creates a time burden on patients, making patient adherence and completion a challenge. In recent years, PROs such as the Patient Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) and Pain Interference (PI) surveys have been developed which leverage computer adaptive testing that produce equivalent accuracy, responsiveness, and validity of legacy PROs, but use only 4-12 questions per survey. This results in significant reduction in time to complete. As these new PROs are now being adopted, the ability to compare outcomes to prior studies that relied on legacy PROs is necessary. While prior studies have examined correlation between legacy PROs and PROMIS computer adaptive tests, no studies to date have developed effective prediction models utilizing PROMIS surveys to create an IKDC index score. The objective of this study was to develop a predictive model utilizing PROMIS PF and PI to effectively recreate IKDC survey scores. METHODS: The Military Orthopaedics Tracking Injuries and Outcomes Network (MOTION) database is a prospectively collected repository of patient reported outcomes and intraoperative variables. As part of inclusion in MOTION, research patients who underwent knee surgery were asked to complete the IKDC as well as the PROMIS PF and PROMIS PI at varying time points. This cohort of patients that completed both IKDC and PROMIS scores were included in the present analysis. Nonlinear multivariable predictive models using both Gaussian and beta distributions were created to establish an IKDC index score, which was then validated using leave-one-out techniques and minimal ...

International audience ; — Legislation concerning pollutant emissions of diesel passenger cars is becoming increasingly restrictive, especially for nitrogen oxide (NOx) and particulate matter (PM). This article proposes to apply a CRONE control design methodology on a diesel engine in order to adapt the air-path and fuel-path of the engine and minimize NOx emissions. As the multivariable CRONE control strategies need a nominal transfer function and some frequency response of the system (G(s)), several test-bench experiments and a linear identification of the system were performed. The aim of this approach was to find a decoupling and stabilizing controller for the combustion engine that minimized NOx emissions at each operating point considered and during transient of torque/engine speed. The system is a square multivariable system with three inputs: Exhaust gas recirculation valve (EGR), variable geometry turbine (VGT), and start of injection (SOI); and three outputs: mass air flow (MAF), boost pressure (Pboost) and NOx level (NOx). The CRONE control approach developed for multivariable square plants is based on the third generation scalar CRONE methodology. Fractional order transfer functions were used to define all the components of the diagonal open-loop transfer matrix, β. Optimisation gave the best fractional open-loop transfer matrix and finally, frequency domain system identification was used to find a robust controller. Performances of the proposed control structure were tested and validated with a number of experiments on a high dynamic test bed (NEDC driving cycle).

International audience ; — Legislation concerning pollutant emissions of diesel passenger cars is becoming increasingly restrictive, especially for nitrogen oxide (NOx) and particulate matter (PM). This article proposes to apply a CRONE control design methodology on a diesel engine in order to adapt the air-path and fuel-path of the engine and minimize NOx emissions. As the multivariable CRONE control strategies need a nominal transfer function and some frequency response of the system (G(s)), several test-bench experiments and a linear identification of the system were performed. The aim of this approach was to find a decoupling and stabilizing controller for the combustion engine that minimized NOx emissions at each operating point considered and during transient of torque/engine speed. The system is a square multivariable system with three inputs: Exhaust gas recirculation valve (EGR), variable geometry turbine (VGT), and start of injection (SOI); and three outputs: mass air flow (MAF), boost pressure (Pboost) and NOx level (NOx). The CRONE control approach developed for multivariable square plants is based on the third generation scalar CRONE methodology. Fractional order transfer functions were used to define all the components of the diagonal open-loop transfer matrix, β. Optimisation gave the best fractional open-loop transfer matrix and finally, frequency domain system identification was used to find a robust controller. Performances of the proposed control structure were tested and validated with a number of experiments on a high dynamic test bed (NEDC driving cycle).

OBJECTIVE: The mortality rate for Black women with endometrial cancer (EC) is double that of White women, although the incidence rate is lower among Black women. Unequal access to care may contribute to this racial disparity. This study aimed to assess whether survival varied between non-Hispanic Black (NHB) and non-Hispanic White (NHW) women with EC in the Military Health System (MHS) which provides equal access care to its beneficiaries despite racial/ethnic background. METHODS: The study was conducted using data from the U.S. Department of Defense's (DoD) Automated Central Tumor Registry (ACTUR). Study subjects included NHB and NHW women with histologically confirmed and surgically managed EC diagnosed between 1988 and 2013. The study outcome was all-cause death. Overall survival between NHB and NHW women was compared using multivariable Cox modeling. RESULTS: The study included 144 NHB and 1,439 NHW women with EC. Kaplan-Meier curves showed NHB women had worse survival than NHW women (log-rank P<0.0001). The disparity in survival between NHB and NHW women persisted after adjusting for age, diagnosis period, tumor stage, tumor histology/grade, and adjuvant treatment (HR=1.64, 95% CI=1.19 to 2.27). Multivariable analyses stratified by tumor features or treatment showed that the racial disparity was confined to women with low-risk features (stage I/II disease or low-grade EC) or no adjuvant treatment. CONCLUSION: There were racial differences in overall survival between NHB and NHW women with EC in the MHS equal access healthcare system, suggesting that factors other than access to care may be related to this racial disparity.

Abstract. INSYDE is a multivariable, synthetic model for flood damage assessment to
dwellings. The analysis and use of this model highlighted some weaknesses,
linked to its complexity, that can undermine its usability and correct
implementation. This study proposes a simplified version of INSYDE which
maintains its multivariable and synthetic nature but has simpler
mathematical formulations permitting easier use and a direct analysis of
the relation between damage and its explanatory variables.

Abstract. Flood risk management generally relies on economic assessments performed by
using flood loss models of different complexity, ranging from simple
univariable models to more complex multivariable models. The latter account for a
large number of hazard, exposure and vulnerability factors, being
potentially more robust when extensive input information is available. We
collected a comprehensive data set related to three recent major flood events
in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including
flood hazard features (depth, velocity and duration), building
characteristics (size, type, quality, economic value) and reported losses.
The objective of this study is to compare the performances of expert-based
and empirical (both uni- and multivariable) damage models for estimating the
potential economic costs of flood events to residential buildings. The
performances of four literature flood damage models of different natures and
complexities are compared with those of univariable, bivariable and
multivariable models trained and tested by using empirical records from
Italy. The uni- and bivariable models are developed by using linear,
logarithmic and square root regression, whereas multivariable models are
based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the
damage modelling approach for operational disaster risk management. Our
findings suggest that multivariable models have better potential for
producing reliable damage estimates when extensive ancillary data for flood
event characterisation are available, while univariable models can be
adequate if data are scarce. The analysis also highlights that expert-based
synthetic models are likely better suited for transferability to other areas
compared to empirically based flood damage models.