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We compare multiple machine learning algorithms and develop models to predict future hospitalization among Home- and Community-Based Services (HCBS) Users. Furthermore, we calculate feature importance, the score of input variables based on their importance to predict the outcome, to identify the most relevant variables to predict hospitalization. We use the 2012 national Medicaid Analytic eXtract data and Medicare Provider Analysis and Review data. Predicting any hospitalization, Random Forest appears to be the most robust approach, though XGBoost achieved similar predictive performance. While the importance of features varies by algorithm, chronic conditions, previous hospitalizations, as well as use of services for ambulance, personal care, and durable medical equipment were generally found to be important predictors of hospitalization. Utilizing prediction models to identify those who are prone to hospitalization could be useful in developing early interventions to improve outcomes among HCBS users.

BackgroundAutoimmune hepatitis (AIH) can result in end-stage liver disease that requires inpatient treatment of the hepatic complications. Given this phenomenon, it is important to analyse the impact of gender and race on the outcomes of patients who are admitted with AIH using a national hospital registry.MethodsThe 2012–2017 National Inpatient Sample database was used to select patients with AIH, who were stratified using gender and race (Hispanics and blacks as cases and whites as reference). Propensity score matching was employed to match the controls with cases and compare mortality, length of stay and hepatic complications.ResultsAfter matching, there were 4609 females and 4609 males, as well as 3688 blacks and 3173 Hispanics with equal numbers of whites, respectively. In multivariate analysis, females were less likely to develop complications, with lower rates of cirrhosis, ascites, variceal bleeding, hepatorenal syndrome, encephalopathy and acute liver failure (ALF); they also exhibited lower length of stay (adjusted OR, aOR 0.96 95% CI 0.94 to 0.97). When comparing races, blacks (compared with whites) had higher rates of ALF and hepatorenal syndrome related to ALF, but had lower rates of cirrhosis-related encephalopathy; in multivariate analysis, blacks had longer length of stay (aOR 1.071, 95% CI 1.050 to 1.092). Hispanics also exhibited higher rates of hepatic complications, including ascites, varices, variceal bleeding, spontaneous bacterial peritonitis and encephalopathy.ConclusionMales and minorities are at a greater risk of developing hepatic complications and having increased hospital costs when admitted with AIH.

This study investigates the effect of instruction and micro-social factors (social distance and power) on learners’ production of L2 Spanish apologies during a 6-week immersion program in Spain. It also explores whether learners are able to apply semantic formulas learned for one speech act (apologies) to the context of another (refusals). Thirty-one learners completed an oral discourse completion task before and after study abroad. Results show that learners approximate native-like norms for apologies after study abroad and instruction, and that they are able to apply this knowledge to the context of refusals. Learners also show differences in apology formula usage across situations of varying social distance and power. They are sensitive to situational differences in both factors after study abroad.

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios.

Purpose This paper aims to discover why such a public partnership project had been successful with a non-profit third-party alliance such as a smart city consortium (SCC) promoting smart city development. Design/methodology/approach This descriptive case study is primarily based on analysing data collected from various texts, public statements, media interviews and three semi-structured interviews with key members involved in the Covid-19 dashboard project. Findings The data and analysis reviews that both interpersonal and interorganisational trust, dedication and proactiveness of the leaders at SCC were major contributing factors to why SCC was able to partner with the Hong Kong Government in the Covid-19 dashboard in the first place and that the success was also a direct outcome of effective mass collaborative knowledge management activities. Research limitations/implications The research in leadership attributes and activities in the non-profit alliance has been few and this collaborative partnership between the alliance and the government is an example of the importance of further research in smart city leadership. Practical implications In deploying projects for mass collaboration and knowledge sharing in smart city development (which is multi-disciplinary in nature). there are still many new and evolving organisational practices and leadership matters that many business leaders and city managers can learn from. Social implications Smart city development projects involve the notion of sharing data in an open environment enabled by software and mediating tools. Successful projects such as this Hong Kong Covid-19 dashboard which serves a diverse audience can further promote the importance of an open data policy regime for the benefit of the public. Originality/value This case study covers a highly original and unique case study with the leaders at the SCC and representatives from the Hong Kong Government.

Patients with autoimmune hepatitis (AIH) may co-present with features of primary biliary cholangitis (PBC) or primary sclerosing cholangitis (PSC). Using a national transplant registry, the outcomes of patients with these autoimmune liver conditions were compared.The UNOS-STAR registry was used to select a study population of AIH, PSC, and PBC liver transplant (LT) patients. Living and multi-organ transplant cases were excluded. Using the UNOS-registered diagnoses, the study population was subdivided into those with nonoverlapping autoimmune liver diseases and those with overlapping forms (e.g., AIH-PBC). Outcomes were compared, using endpoints such as all-cause mortality, graft failure, and organ-system specific causes of death.The main analysis featured 2048 entries, with 1927 entries having nonoverlapping AIH, 52 entries having PSC overlap, and 69 entries having PBC overlap. Patients with PBC overlap were more likely to have graft failure (adjusted hazard ratio [aHR] 3.46 95% CI 1.70-7.05), mortality secondary to respiratory causes (aHR 3.57 95% CI 1.23-10.43), and mortality secondary to recurrent disease (aHR 9.53 95% CI 1.85-49.09). Case incidence rates reflected these findings, expressed in events per 1000 person-years. For patients with PBC overlap and nonoverlapping AIH cases, respectively. Graft failure: 28.87 events vs. 9.42 events, mortality secondary to respiratory causes: 12.83 deaths vs. 3.77 deaths, mortality secondary to recurrent disease: 6.42 deaths vs. 1.26 deaths. Those with AIH-PSC overlap experienced a higher risk of death from graft infection (aHR 10.43 95% CI 1.08-100.37; case-incidence rate: 3.89 vs. 0.31 mortalities per 1000 person-years). Supplementary analysis showed similar findings, in which overlapping autoimmune conditions were associated with higher adverse outcome rates.Patients with AIH-PBC overlap have higher risk of mortality due to recurrent liver disease and respiratory causes, and patients with AIH-PSC overlap have higher risk of mortality due to graft infection. While further prospective studies are needed to clarify the underlying mechanisms related to these findings, our study characterizes the prognostic implications of AIH overlap on post-LT mortality and graft failure risks.

Considering the importance of social and structural support and resources in recovering health, where people reside could lead to differences in health outcome in Medicare home health care. We used the 2019 Outcome and Assessment Information Set and Area Deprivation Index to examine the association between neighborhood context and successful discharge to community among older Medicare home health care users. Based on the multivariable logistic regression (OR: 0.84; 95% CI, 0.83–0.85) and conditional logistic regression models stratified by home health agency (OR: 0.95; 95% CI, 0.94–0.95), patients living in the most disadvantaged neighborhoods were less likely to experience successful discharge to community than others. Furthermore, the predicted probability of successful discharge to community decreased as the percentage of patients from the most disadvantaged neighborhoods within a home health agency increased. Policymakers should consider using area-level interventions and supports to reduce disparities in Medicare home health care.

We consider a swarm of n autonomous mobile robots, distributed on a 2-dimensional grid. A basic task for such a swarm is the gathering process: All robots have to gather at one (not predefined) place. A common local model for extremely simple robots is the following: The robots do not have a common compass, only have a constant viewing radius, are autonomous and indistinguishable, can move at most a constant distance in each step, cannot communicate, are oblivious and do not have flags or states. The only gathering algorithm under this robot model, with known runtime bounds, needs \(\mathcal {O}(n^2)\) rounds and works in the Euclidean plane. The underlying time model for the algorithm is the fully synchronous \(\mathcal {FSYNC}\) model. On the other side, in the case of the 2-dimensional grid, the only known gathering algorithms for the same time and a similar local model additionally require a constant memory, states and “flags” to communicate these states to neighbors in viewing range. They gather in time \(\mathcal {O}(n)\).

When optimising the vehicle trajectory and powertrain energy management of hybrid electric vehicles, it is important to include look-ahead information such as road conditions and other traffic. One method for doing so is dynamic programming, but the execution time of such an algorithm on a general purpose CPU is too slow for it to be useable in real time. Significant improvements in execution time can be achieved by utilising parallel computations, for example, using a Field-Programmable Gate Array (FPGA). A tool for automatically converting a vehicle model written in C++ into code that can executed on an FPGA which can be used for dynamic programming-based control is presented in this paper. A vehicle model with a mild-hybrid powertrain is used as a case study to evaluate the developed tool and the output quality and execution time of the resulting hardware.

A solid phase adsorption method for selective isolation of hyaluronan (HA) from biological samples is presented. Following enzymatic degradation of protein, HA can be separated from sulfated glycosaminoglycans, other unsulfated glycosaminoglycans, nucleic acids, and proteolytic fragments by adsorption to amorphous silica at specific salt concentrations. The adsorbed HA can be released from silica using neutral and basic aqueous solutions. HA ranging in size from ∼9 kDa to MDa polymers has been purified by this method from human serum and conditioned medium of cultured cells.

Fault diagnosis of a certain class of hybrid systems referred to as structurally reconfigurable (SR) systems is complicated. This is because SR systems tend to switch their configuration, which may or may not be faulty. It is important to identify the mode of the SR system along with the corresponding fault if any, in order to facilitate a fault tolerant action. This paper combines discrete fault diagnosis with mode identification for SR systems to achieve two main objectives: Sensor selection for fault detection, isolation and mode identification, and residual selection for mode identification. The framework is built using a structural analysis-based approach to meet these objectives. This framework is demonstrated for a 10-speed Automatic Transmission, which is an illustrative example of SR systems.

Design of fault diagnosis systems is complicated by limited training data and inaccuracies in physical-based models when designing fault classifiers. A hybrid fault diagnosis approach is proposed using model-based residuals as input to a set of data-driven fault classifiers. As a case study, sensor data from an internal combustion engine test bed is used where faults have been injected into the system and a physical-based mathematical model of the air flow through the engine is available. First, a feature selection algorithm is applied to find a minimal set of residuals that is able to separate the different fault modes. Then, two different fault classification approaches are discussed, Random Forests and one-class Support Vector Machines. A set of one-class Support Vector Machines is used to model data from each fault mode separately. The case study illustrates an advantage of using one-class classifiers, which makes it possible to detect unknown faults by identifying samples not belonging to any known fault mode.

In model-based diagnosis there are often more candidate residual generators than what is needed and residual selection is therefore an important step in the design of model-based diagnosis systems. The availability of computer-aided tools for automatic generation of residual generators have made it easier to generate a large set of candidate residual generators for fault detection and isolation. Fault detection performance varies significantly between different candidates due to the impact of model uncertainties and measurement noise. Thus, to achieve satisfactory fault detection and isolation performance, these factors must be taken into consideration when formulating the residual selection problem. Here, a convex optimization problem is formulated as a residual selection approach, utilizing both structural information about the different residuals and training data from different fault scenarios. The optimal solution corresponds to a minimal set of residual generators with guaranteed performance. Measurement data and residual generators from an internal combustion engine test-bed is used as a case study to illustrate the usefulness of the proposed method.

Most kidney stones (KS) are composed of calcium oxalate, and small increases in urine oxalate affect the stone risk. Intestinal oxalate secretion mediated by anion exchanger SLC26A6 (PAT1) plays a crucial role in limiting net absorption of ingested oxalate, thereby preventing hyperoxaluria and related KS, reflecting the importance of understanding regulation of intestinal oxalate transport. We previously showed that ATP and UTP inhibit oxalate transport by human intestinal Caco2-BBE cells (C2). Since ATP is rapidly degraded to adenosine (ADO), we examined whether intestinal oxalate transport is regulated by ADO. We measured [14C]oxalate uptake in the presence of an outward Cl gradient as an assay of Cl-oxalate exchange activity, ≥49% of which is PAT1-mediated in C2 cells. We found that ADO significantly inhibited oxalate transport by C2 cells, an effect completely blocked by the nonselective ADO receptor antagonist 8- p-sulfophenyltheophylline. ADO also significantly inhibited oxalate efflux by C2 cells, which is important since PAT1 mediates oxalate efflux in vivo. Using pharmacological antagonists and A2Badenosine receptor (A2BAR) siRNA knockdown studies, we observed that ADO inhibits oxalate transport through the A2BAR, phospholipase C, and PKC. ADO inhibits oxalate transport by reducing PAT1 surface expression as shown by biotinylation studies. We conclude that ADO inhibits oxalate transport by lowering PAT1 surface expression in C2 cells through signaling pathways including the A2BAR, PKC, and phospholipase C. Given higher ADO levels and overexpression of the A2BAR in inflammatory bowel disease (IBD), our findings have potential relevance to pathophysiology of IBD-associated hyperoxaluria and related KS.

We focus on the following question about Gathering of n autonomous, mobile robots in the Euclidean plane: Is it possible to solve Gathering of robots that do not agree on their coordinate systems (disoriented) and see other robots only up to a constant distance (limited visibility) in linear time? Up to now, such a result is only known for robots on a two-dimensional grid [1, 8]. We answer the question positively for robots that are connected in one closed chain (like [1]), i.e., every robot is connected to exactly two other robots, and the connections form a cycle. We show that these robots can be gathered by asynchronous robots (\(\mathcal {A}\) sync) in \(\varTheta \left( n\right) \) epochs assuming the \(\mathcal {LUMI}\) model [12] that equips the robots with locally visible lights like in [1, 8]. The lights are used to initiate and perform so-called runs along the chain, which are essential for the linear runtime. Starting of runs is done by determining locally unique robots (based on geometric shapes of neighborhoods). In contrast to the grid [1], this is not possible in every configuration in the Euclidean plane. Based on the theory of isogonal polygons by Grunbaum [18], we identify the class of isogonal configurations in which, due to a high symmetry, no locally unique robots can be identified. Our solution consists of two algorithms that might be executed in parallel: The first one gathers isogonal configurations without any lights. The second one works for non-isogonal configurations; it is based on the concept of runs using a constant number of lights.

Structural analysis is a useful tool for fault diagnosability analysis to handle systems that are described by a large set of non-linear differential algebraic equations. Distributed fault diagnosis is an attractive approach for complex systems to reduce computational complexity by partitioning the system into a set of smaller subsystems and perform fault diagnosis of each subsystem. Defining these subsystems requires methods to understand how fault diagnosis properties of each subsystem relates to the properties of the whole system. Another related problem is that large and complex systems are likely to be developed by several companies where each company is developing different subsystems that can be used in different system configurations. In these situations, each subsystem will have limited model information about the other subsystems, which complicates performing structural analysis of the whole system. The main contribution in this work is extending some of the existing results in structural analysis for one system model to a distributed set of connected subsystems. The results show the relationship between structural fault diagnosis properties of the whole system and properties of the set of individual subsystems.

This study systematically evaluated the implications of advanced age on post-surgical outcomes following gastrectomy for gastric cancer using a national database.The 2011-2017 National Inpatient Sample was used to isolate patients who underwent gastrectomy for gastric cancer. From this, the population was stratified into those belonging to the younger age cohort (18-59 years), sexagenarians, septuagenarians, and octogenarians. The younger cohort and each advanced age category were compared in terms of the following endpoints: mortality following surgery, length of hospital stay, charges, and surgical complications.This study included a total of 5,213 patients: 1,366 sexagenarians, 1,490 septuagenarians, 743 octogenarians, and 1,614 under 60 years of age. Between the younger cohort and sexagenarians, there was no difference in mortality (2.27 vs. 1.67%; P=0.30; odds ratio [OR], 1.36; 95% confidence interval [CI], 0.81-2.30), length of stay (11.0 vs. 11.1 days; P=0.86), or charges ($123,557 vs. $124,425; P=0.79). Compared to the younger cohort, septuagenarians had higher rates of in-hospital mortality (4.30% vs. 1.67%; P0.01; OR, 2.64; 95% CI, 1.67-4.16), length of stay (12.1 vs. 11.1 days; P0.01), and charges ($139,200 vs. $124,425; P0.01). In the multivariate analysis, septuagenarians had higher mortality (P=0.01; adjusted odds ratio [aOR], 2.01; 95% CI, 1.18-3.43). Similarly, compared to the younger cohort, octogenarians had a higher rate of mortality (7.67% vs. 1.67%; P0.001; OR, 4.88; 95% CI, 3.06-7.79), length of stay (12.3 vs. 11.1 days; P0.01), and charges ($131,330 vs. $124,425; P0.01). In the multivariate analysis, octogenarians had higher mortality (P0.001; aOR, 4.03; 95% CI, 2.28-7.11).Advanced age (70 years) is an independent risk factor for postoperative death in patients with gastric cancer undergoing gastrectomy.

By running simulation models on FPGAs, their execution speed can be significantly improved, at the cost of increased development effort. This paper describes a project to develop a tool which converts simulation models written in high level languages into fast FPGA hardware. The tool currently converts code written using custom C++ data types into Verilog. A model of a hybrid electric vehicle is used as a case study, and the resulting hardware runs significantly faster than on a general purpose CPU.

Individual differences (IDs) have long been considered one of the most important factors explaining variable rates and outcomes in second language acquisition (Dewaele, 2013). While traditional operationalizations of IDs have, explicitly or implicitly, assumed that IDs are static traits that are stable through time, more recent research inspired by complex dynamic systems theory (Larsen-Freeman, 1997, 2020) demonstrates that many IDs are dynamic and variable through time and across contexts, a theme echoed throughout the current issue. This study reports the initial semester of a diachronic project investigating the dynamicity of four learner IDs: motivation, personality, learning and cognitive styles, and working memory. In the initial semester, data from 323 participants in their first year of university-level Spanish were collected and analyzed to determine what type of variability may be present across learners with respect to the four IDs studied at one time point and to discern possible learner profiles in the data or patterns via which the data may be otherwise meaningfully described. The results revealed four types of learner profiles present in the dataset.

This work focuses on the following question related to the Gathering problem of n autonomous, mobile robots in the Euclidean plane: Is it possible to solve Gathering of disoriented robots with limited visibility in \(o(n^2)\) fully synchronous rounds ( Open image in new window )? The best known algorithm considering the \(\mathcal {OBLOT}\) model (oblivious robots) needs \(\varTheta \left( n^2\right) \) rounds [6]. The lower bound for this algorithm even holds in a simplified closed chain model, where each robot has exactly two neighbors and the chain connections form a cycle. The only existing algorithms achieving a linear number of rounds for disoriented robots assume robots that are located on a two dimensional grid [1] and [5]. Both algorithms consider the \(\mathcal {LUMINOUS}\) model.

Most kidney stones are composed of calcium oxalate, and minor changes in urine oxalate affect the stone risk. Obesity is a risk factor for kidney stones and a positive correlation of unknown etiology between increased body size, and elevated urinary oxalate excretion has been reported. Here, we used obese ob/ob ( ob ) mice to elucidate the pathogenesis of obesity-associated hyperoxaluria. These ob mice have significant hyperoxaluria (3.3-fold) compared with control mice, which is not due to overeating as shown by pair-feeding studies. Dietary oxalate removal greatly ameliorated this hyperoxaluria, confirming that it is largely enteric in origin. Transporter SLC26A6 (A6) plays an essential role in active transcellular intestinal oxalate secretion, and ob mice have significantly reduced jejunal A6 mRNA (- 80%) and total protein (- 62%) expression. While net oxalate secretion was observed in control jejunal tissues mounted in Ussing chambers, net absorption was seen in ob tissues, due to significantly reduced secretion. We hypothesized that the obesity-associated increase in intestinal and systemic inflammation, as reflected by elevated proinflammatory cytokines, suppresses A6-mediated intestinal oxalate secretion and contributes to obesity-associated hyperoxaluria. Indeed, proinflammatory cytokines (elevated in ob mice) significantly decreased intestinal oxalate transport in vitro by reducing A6 mRNA and total protein expression. Proinflammatory cytokines also significantly reduced active mouse jejunal oxalate secretion, converting oxalate transport from net secretion in vehicle-treated tissues to net absorption in proinflammatory cytokines-treated tissues. Thus, reduced active intestinal oxalate secretion, likely secondary to local and systemic inflammation, contributes to the pathogenesis of obesity-associated hyperoxaluria. Hence, proinflammatory cytokines represent potential therapeutic targets.

The absence of human senses and experience in autonomous systems pose a variety of unforeseen challenges. One of these challenges is the effective health monitoring of autonomous systems. This paper proposes a comprehensive active fault management framework. The proposed framework works on the measured signal and control inputs of the system. The set of residuals and isolation tests, which is part of the passive fault diagnosis system, have the capability of adapting to new and unforeseen scenarios. If the fault is not isolable or detectable in magnitude, it will be excited by manipulating the inputs of the system in a controlled fashion. Once the fault is confirmed, it will be mitigated to minimize the performance degradation and damage to the system. The later part of the framework on active fault diagnosis (sensitivity analysis based fault excitation and mitigation) has been demonstrated for a powertrain of an autonomous electric vehicle. The simulation results confirm the effectiveness of the proposed active fault management framework.

Hydrogen sulfide (H2S) represents one of the main odorant gases emitted from sewer networks. A mathematical model can be a fast and low-cost tool for estimating its emission. This study investigates two approaches to modeling H2S gas transfer at a waterfall in a discharge manhole. The first approach is based on an adaptation of oxygen models for H2S emission at a waterfall and the second consists of a new model. An experimental set-up and a statistical data analysis allowed the main factors affecting H2S emission to be studied. A new model of the emission kinetics was developed using linear regression and taking into account H2S liquid concentration, waterfall height and fluid velocity at the outlet pipe of a rising main. Its prediction interval was estimated by the residual standard deviation (15.6%) up to a rate of 2.3 g H2S·h−1. Finally, data coming from four sampling campaigns on sewer networks were used to perform simulations and compare predictions of all developed models.

Detecting changes in residuals is important for fault detection and is commonly performed by thresholding the residual using, for example, a CUSUM test. However, detecting variations in the residua ...

To facilitate the use of advanced fault diagnosis analysis and design techniques to industrial sized systems, there is a need for computer support. This paper describes a Matlab toolbox and evaluat ...

Localization of unknown faults in industrial systems is a difficult task for data-driven diagnosis methods. The classification performance of many machine learning methods relies on the quality of training data. Unknown faults, for example faults not represented in training data, can be detected using, for example, anomaly classifiers. However, mapping these unknown faults to an actual location in the real system is a non-trivial problem. In model-based diagnosis, physical-based models are used to create residuals that isolate faults by mapping model equations to faulty system components. Developing sufficiently accurate physical-based models can be a time-consuming process. Hybrid modeling methods combining physical-based methods and machine learning is one solution to design data-driven residuals for fault isolation. In this work, a set of neural network-based residuals are designed by incorporating physical insights about the system behavior in the residual model structure. The residuals are trained using only fault-free data and a simulation case study shows that they can be used to perform fault isolation and localization of unknown faults in the system., Comment: 8 pages, 7 figures, If citing this paper please use: In: Proceedings of the Annual Conference of the PHM Society, Scottsdale, Arizona, USA (2019)

Routing is a challenging problem for wireless ad hoc networks, especially when the nodes are mobile and spread so widely that in most cases multiple hops are needed to route a message from one node to another. In fact, it is known that any online routing protocol has a poor performance in the worst case, in a sense that there is a distribution of nodes resulting in bad routing paths for that protocol, even if the nodes know their geographic positions and the geographic position of the destination of a message is known. The reason for that is that radio holes in the ad hoc network may require messages to take long detours in order to get to a destination, which are hard to find in an online fashion.

Predictive maintenance of systems and their components in technical systems is a promising approach to optimize system usage and reduce system downtime. Various sensor data are logged during system operation for different purposes, but sometimes not directly related to the degradation of a specific component. Variable selection algorithms are necessary to reduce model complexity and improve interpretability of diagnostic and prognostic algorithms. This paper presents a forest-based variable selection algorithm that analyzes the distribution of a variable in the decision tree structure, called Variable Depth Distribution, to measure its importance. The proposed variable selection algorithm is developed for datasets with correlated variables that pose problems for existing forest-based variable selection methods. The proposed variable selection method is evaluated and analyzed using three case studies: survival analysis of lead–acid batteries in heavy-duty vehicles, engine misfire detection, and a simulated prognostics dataset. The results show the usefulness of the proposed algorithm, with respect to existing forest-based methods, and its ability to identify important variables in different applications. As an example, the battery prognostics case study shows that similar predictive performance is achieved when only 17% percent of the variables are used compared to all measured signals.

The scaled boundary finite element method (SBFEM) is a semi-analytical method in which only the boundary is discretized. The results on the boundary are scaled into the domain with respect to a scaling center which must be “visible” from the whole boundary. For beam-like problems the scaling center can be selected at infinity and only the cross-section is discretized. The current work is devoted to the development of some SBFEM elements for thin-walled beams on the basis of the first order shear deformation theory. The beam sections are considered to be multilayered laminate plates with arbitrary layup. The arbitrary cross-section is discretized with beam-like elements of Timoshenko type. Using the virtual work principle gives a system of differential equations of a gyroscopic type. The solution is given using the matrix exponential function. Knowing the deformation of the beam, the stresses, strains and curvatures can be calculated and a failure criterion can be used to assess the laminate. The SBFEM has been tested and compared with a finite element model and it gives good results.

A commonly used signal for engine misfire detection is the crankshaft angular velocity measured at the flywheel. However, flywheel manufacturing errors result in vehicle-to-vehicle variations in th ...

Predicting lead-acid battery failure is important for heavy-duty trucks to avoid unplanned stops by the road. There are large amount of data from trucks in operation, however, data is not closely r ...

Experimental data are presented showing maximum carbon C6+ ion energies obtained from nm-scaled targets in the relativistic transparent regime for laser intensities between 9 × 1019 and 2 × 1021 W/cm2. When combined with two-dimensional particle-in-cell simulations, these results show a steep linear scaling for carbon ions with the normalized laser amplitude a0 ($a_0 \propto \sqrt ( I)$). The results are in good agreement with a semi-analytic model that allows one to calculate the optimum thickness and the maximum ion energies as functions of a0 and the laser pulse duration τλ for ion acceleration in the relativistic-induced transparency regime. Following our results, ion energies exceeding 100 MeV/amu may be accessible with currently available laser systems.

Dynamic programming is widely used to benchmark the performance of a hybrid electric vehicle. It is also well documented that it is a very computationally heavy procedure depending on the number of states and control inputs in the problem formulation. In this paper we investigate the possibility of reduction in the computational time by splitting the number of states and control inputs between two models and applying dynamic programming individually, using the output of one as an input to the other and hence cascading the two models. A range extended hybrid electric vehicle powertrain architecture is modeled with four states and four control inputs, which is considered as the full model. Further, the states and control inputs of the battery and engine are separated from the other states, splitting them between the two new DP models. The vehicle performance estimated from this ‘cascaded models approach’ is compared with that from the full model. Initial comparisons show a very good match with minor differences in performance and considerable a reduction in computation time from around 6 hours to around a minute.

Study abroad has been proposed as a crucial aspect to acquire pragmatics in a second language, under the assumption that learners receive more access to authentic input than is available in the classroom. Recent trends indicate a rise in the frequency of short-term study abroad programs (less than 3 months, Allen, 2010), although research has shown that learners may need closer to 9 months to approximate native-like norms without instruction (FÉLIX-BRASDEFER, 2004). This raises the question of how much pragmatic development can be seen in short-term programs, and how to maximize this development. The current study analyzed the development of two expressive speech acts, compliments and apologies, in students who completed a five-week study abroad program in Mérida, Mexico. During the program, learners received instruction on compliments, but not apologies. Speech act data was collected via a 24-item oral discourse completion task administered at both the beginning and end of the program and was further analyzed in SPSS. Results indicate that only some learners developed their production of apologies, while almost all learners showed development in their production of compliments, operationalized by an increased repertoire of strategies available. These results suggest the need for pragmatic instruction during short-term study abroad, and question the utility of native-speaker norms to measure pragmatic development during short-term programs. Study abroad has been proposed as a crucial aspect to acquire pragmatics in a second language, under the assumption that learners receive more access to authentic input than is available in the classroom. Recent trends indicate a rise in the frequency of short-term study abroad programs (less than 3 months, Allen, 2010), although research has shown that learners may need closer to 9 months to approximate native-like norms without instruction (FÉLIX-BRASDEFER, 2004). This raises the question of how much pragmatic development can be seen in short-term programs, and how to maximize this development. The current study analyzed the development of two expressive speech acts, compliments and apologies, in students who completed a five-week study abroad program in Mérida, Mexico. During the program, learners received instruction on compliments, but not apologies. Speech act data was collected via a 24-item oral discourse completion task administered at both the beginning and end of the program and was further analyzed in SPSS. Results indicate that only some learners developed their production of apologies, while almost all learners showed development in their production of compliments, operationalized by an increased repertoire of strategies available. These results suggest the need for pragmatic instruction during short-term study abroad, and question the utility of native-speaker norms to measure pragmatic development during short-term programs.***Uma pesquisa sobre os efeitos da instrução pragmática: uma comparação de elogios e desculpas em espanhol como segunda língua durante um programa de intercâmbio de curta duração***O intercâmbio estudantil foi proposto como um aspecto crucial para adquirir competência pragmática em um segundo idioma, sob a suposição de que os alunos recebem mais acesso à linguagem autêntica do que é oferecido em sala de aula. As tendências recentes indicam um aumento na frequência de programas de intercâmbio de curto prazo no exterior (menos de três meses, Allen, 2010), contudo outras pesquisas têm mostrado que os alunos podem precisar de mais de nove meses para aproximar-se das normas nativas sem instrução (FÉLIX-BRASDEFER, 2004). Isso levanta a questão de quanto o desenvolvimento pragmático pode ser visto em programas de intercâmbio de curto prazo, e como maximizar esse desenvolvimento. O presente estudo analisou o desenvolvimento de dois atos de fala expressivos, elogios e desculpas, em estudantes que completaram um programa de intercâmbio de cinco semanas em Mérida, México. Durante o programa, os alunos receberam instrução sobre expressões de elogios, mas não sobre pedidos de desculpas. Os dados do ato de fala foram coletados por meio de uma tarefa de conclusão do discurso oral de 24 itens, administrada no início e no final do programa e analisados no SPSS. Os resultados indicam que apenas alguns dos estudantes desenvolveram seus atos de pedir desculpas, enquanto quase todos os alunos mostraram desenvolvimento em sua produção de elogios, operacionalizado como um repertório crescente de estratégias disponíveis. Esses resultados sugerem a necessidade da instrução pragmática durante os programas de intercâmbio estudantil de curto prazo e questionam a utilidade das normas dos falantes nativos para medir o desenvolvimento pragmático durante programas de curto prazo.

Routing is a challenging problem for wireless ad hoc networks, especially when the nodes are mobile and spread so widely that in most cases multiple hops are needed to route a message from one node to another. In fact, it is known that any online routing protocol has a poor performance in the worst case, in a sense that there is a distribution of nodes resulting in bad routing paths for that protocol, even if the nodes know their geographic positions and the geographic position of the destination of a message is known. The reason for that is that radio holes in the ad hoc network may require messages to take long detours in order to get to a destination, which are hard to find in an online fashion. In this short paper, we assume that the wireless ad hoc network can make limited use of long-range links provided by a global communication infrastructure like a cellular infrastructure or a satellite in order to compute an abstraction of the wireless ad hoc network that allows the messages to be sent along near-shortest paths in the ad hoc network. We present distributed algorithms that compute an abstraction of the ad hoc network in $\mathcalO left(log ^2 n\right)$ time using long-range links, which results in c -competitive routing paths between any two nodes of the ad hoc network for some constant c if the convex hulls of the radio holes do not intersect.

Fault diagnosis of structurally re-configurable systems is complicated as the system structure changes when the system operates in different modes. It is important that faults can be detected and isolated in each operating mode. In model-based diagnosis, faults are detected and isolated by detecting inconsistencies between model predictions and sensor data. Thus, determining where to mount sensors is an important task to be able to detect and isolate faults, especially when faults can result in unexpected system re-configuration. For structurally re-configurable systems this means selecting a set of sensors that fulfills requirements in multiple models describing the different system modes. A sensor selection algorithm is proposed for structurally re-configurable systems which computes minimal sensor sets that make faults in all modes detectable and isolable. As a case study, the sensor selection algorithm is applied to determine sensor locations in an eight-speed automatic transmission.

Design space exploration of hybrid electric vehicles is an important multi-objective global optimization problem. One of the main objectives is to minimize fuel consumption while maintaining satisfactory driveability performance and vehicle cost. The design problem often includes multiple design options, including different driveline architectures and component sizes, where different candidates have various trade-offs between different, in many cases contradictory, performance requirements. Thus, there is no global optimum but a set of Pareto-optimal solutions to be explored. The objective functions can be expensive to evaluate, due to time-consuming simulations, which requires careful selection of which candidates to evaluate. A design space exploration algorithm is proposed for finding the set of Pareto-optimal solutions when the design search space includes multiple design options. As a case study, powertrain optimization is performed for a medium-sized series hybrid electric delivery truck.