Makespan estimation and order acceptance in batch process industries when processing times are uncertain

Batch process industries are characterized by complex precedence relationships between operations, which renders the estimation of an acceptable workload very difficult. A detailed schedule based model can be used for this purpose, but for large problems this may require a prohibitive large amount of computation time. We propose a regression based model to estimate the makespan of a set of jobs. We extend earlier work based on deterministic processing times by considering Erlang-distributed processing times in our model. This regression-based model is used to support customer order acceptance. Three order acceptance policies are compared by means of simulation experiments: a scheduling policy, a workload policy and a regression policy. The results indicate that the performance of the regression policy can compete with the performance of the scheduling policy in situations with high variety in the job mix and high uncertainty in the processing times. Correspondence to: C.V. Ivanescu     

Ein stochastisches,schadensmechanisches Versagensmodell für Komposite

A stochastic damage mechanics failure model of composites This paper focuses on the theoretical simulation of strength and failure of specimens with nonlocal damage. A concept is proposed for quantitative interpretation and prediction of nonlinear. nominal stress-strain curves of damaged materials like fine ceramics or intermetallic alloys. For that reason methods of damage mechanics and of probability theory are combined.     

R curves for energy dissipative materials

This paper focuses on the theoretical simulation of fracture and stable crack growth of specimens with non-local damage. The first law of thermodynamics allows the identification or definition of appropriate crack-driving forces. The results are compared with recent ideas on defining tearing resistance for uncontained yield through the energy dissipation rate. A hypothesis regarding the conversion of mechanical into thermal energies within the non-local damage region is formulated to model the fracture behaviour of energy dissipative materials with rising crack resistance characteristics. The material's capacity to develop non-local damage is assumed to decrease with the actual damage level. This decrease relates linearly with the remaining resources of the material in dissipating energy. The hypothesis, which proposes a square root function for theoretical J-R curves, is verified by the regression analysis of experimental data regarding a European round-robin test of different steels.     

R-Kurven von energiedissipativen Werkstoffen mit Schädigungszone Teil 2: Experimentelle Ermittlung von J-R Kurven

R-curves of energy dissipative materials Part 2: Experimental evaluation of J-R curves A hypothesis regarding the conversion of mechanical into thermal energies within the nonlocal damage region is formulated to model the fracture behaviour of energy dissipative materials with rising crack resistance characteristics. The material's capacity to develop nonlocal damage beyond blunting is assumed to decrease with the actual damage level. This decrease relates linearly with the remaining resources of the material in dissipating energy. The hypothesis, wich proposes a square root function for theoretical J-R curves, is verified by the regression analysis of experimental data regarding a European round robin test of different steels.     

NIST 0:45 reflectometer

The NIST 0:45 reflectometer measures the spectral reflectance factor at an influx angle of 0° and an efflux angle of 45° of colored, nonfluorescent specimens at room temperature, with widths ranging from 3 to 10 cm and heights from 3 to 20 cm and with an uncertainty of less than 0.5 in color difference units. Published in 2008 by John Wiley & Sons, Inc. Col Res Appl, 33, 94–99, 2008     

Distribution and Fate of Escherichia coli in Lake Michigan Following Contamination with Urban Stormwater and Combined Sewer Overflows

Escherichia coli distribution and persistence in nearshore Lake Michigan were assessed following heavy rains and sanitary sewer overflow (SSO) and combined sewer overflow (CSO) events over a 5-year period, including an 18-day period following 25.4 cm of rainfall in which intensive studies were conducted following multiple CSO and SSO events. E. coli levels in the Milwaukee estuary and harbor following SSO and CSO events ranged from 10⁴ to nearly 10⁵ CFU/100 mL, which were significantly higher (p ≤ 0.05) than levels following rainfall alone. Sites outside of the breakwall but within the contamination plume (e.g., within 2 km of the harbor) were an order of magnitude lower. Locations 2–5 km from the harbor ranged from below detection limits, of < 1 to 5 CFU/100 mL. E. coli levels corrected for dilution based on specific conductivity measurements were lower than what would be expected for loss due to dilution alone, suggesting a combination of die-off and dilution, were responsible for the rapid disappearance of these organisms outside of the harbor. E. coli and fecal coliforms measured concurrently demonstrated that fecal coliforms could be recovered longer than E. coli in the open waters of the lake. E. coli isolated directly from sewage treatment plant influent were found to have a marked increase in antibiotic resistance traits for ten antibiotics commonly used in the human population compared with isolates from two animal sources of fecal pollution. However, E. coli obtained from sewage impacted water (n = 2,513) and from stormwater impacted water (n = 1,465) collected the previous year when there were no sewage overflows, were found to have no significant difference (p < 0.05) in the frequency of resistance when comparing the two conditions. E. coli survival characteristics and population dynamics are most likely influenced by multiple factors in complex systems such as the watershed/estuarine/lake environments of the Great Lakes.     

Quality of care problems among Medicare and Medicaid patients

The purpose of this study was to characterize quality of care problems among Medicare and Medicaid inpatients in New York State. The patients selected for this study comprised 1991 and 1992 Medicare and all 1992 Medicaid inpatients in whom quality of care problems with actual or potential adverse effects were found. The patients in this study were drawn from public, proprietary, voluntary and teaching hospitals. A total of 1000 quality of care problems with either actual or potential adverse effects were found in 706 Medicare patients. Two hundred and seventy-five (275) quality of care problems with actual or potential adverse effects were found in 154 Medicaid patients. Premature death occurred in 53 (7.4%) of the 706 Medicare and in 42 (27.2%) of the 154 Medicaid patients. Treatment problems and monitoring failures accounted for the majority of quality of care problems with actual or potential adverse effects for both Medicare (63.0%) and Medicaid (75.7%) patients. Among Medicare patients, the treatment of infections and antibiotic use, fluid and electrolyte management, and inappropriate drug use were among the leading causes of quality of care problems. Attending physicians were associated with the majority of Medicare quality of care problems while house staff and attending physicians were associated with the majority of those among Medicaid patients. The results of this study indicate that there are several leading causes of quality of care problems among Medicare and Medicaid patients. Treatment problems and monitoring failures together comprise the majority of such problems. Among Medicare patients, it was found that most quality of care problems were associated with the treatment of infections and antibiotic use, fluid and electrolyte management, and inappropriate drug use. Most quality of care problems among Medicaid patients were associated with these categories as well as with labor and delivery problems, and poor discharge planning. The results of this study reflect the peer-review process in which providers are given an opportunity to respond to physician-reviewer decisions about the presence of actual or potential adverse effects. Such a process, which permits the presentation of additional data and information by providers, produces fewer final adverse outcome determinations than a process uniquely based on chart review. The quality of care problems observed in this study are amenable to focused educational interventions. Such remedial interventions could yield significant improvements in the quality of care for all patients.     

Evidence of successful river spawning by lake trout (Salvelinus namaycush) in the Lower Niagara River,Lake Ontario

Restoration of a wild-produced lake trout Salvelinus namaycush population in Lake Ontario has not been successful despite the adult population often meeting or exceeding restoration targets. Lack of high-quality spawning habitat in Lake Ontario is suggested as one impediment to recruitment of wild lake trout, although the quantity and location of spawning habitat is poorly understood. If high-quality spawning habitat is limited in Lake Ontario, lake trout may be using uncommon spawning locations such as rivers. Anecdotal angler accounts point to the Niagara River as a lake trout spawning location. To better understand the potential of the Niagara River as a spawning location, egg and juvenile fish collections were conducted 12–14 river kilometers from the mouth of the Niagara River from 2010 to 2012; and mature female lake trout with surgically implanted acoustic tags were monitored from 2015 to 2019. Genetic analyses confirmed 60% of collected eggs and 93% of collected post-hatch juvenile fish in the Niagara River were lake trout. Tagged female lake trout returned to the Niagara River over consecutive years during the spawning season. The short duration of lake trout presence in the river (mean = 56 days/year) suggests female lake trout use the Niagara River primarily for spawning. Diversity in spawning locations may provide lake trout population’s resilience against environmental variability through a portfolio effect. Improved identification of riverine spawning locations, including their overall contribution to wild recruitment, may be a useful tool for managers to restore a wild-produced population of lake trout in Lake Ontario.     

Smoothing: A natural way to detect contour features

This work presents a dominant point detector. The angles of the contour are characterized through local entropy produced by a rotationally symmetric smoothing. The proposed scheme uses a punctual multi-scale approach in which only the candidates are analyzed in higher scales. To preserve the angle-entropy relationship in higher scales, we propose a smoothing kernel which presents special features that ensure its steepness in every scale. It is built from the sum of two Gaussians with different openings resembling center-surround receptive fields. The outputs of the proposed method are confronted to a ground-truth found in the literature, and to popular boundary based corner detectors that used the same set of images. Results reveal that the proposed detector performs extremely well.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.