The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Synthesis of Manufacturing and Facility Data for Sustainability Analysis

This paper discusses data synthesis of production and facility knowledge for sustainability analysis by applying the ISA 95 "Activity Models of Manufacturing Operations Management" (MOM) model. Presently, production and facility management are "silo" operations, which basically function independently of each other. This paper presents the addition of facility activities to the MOM model, in accordance with the needs for attaining a holistic view of sustainability analysis. Historically, production and facility data are represented in various forms, e.g., data bases, CAD, and spreadsheets, without a common unifying representation. This paper addresses the issue by introduced Core Manufacturing Simulation Data (CMSD) Standard. A case study of the data synthesis for a precision sand casting production facility is provided

Suppressing Speckle Noise for Simultaneous Differential Extrasolar Planet Imaging (SDI) at the VLT and MMT

We discuss the instrumental and data reduction techniques used to suppress speckle noise with the Simultaneous Differential Imager (SDI) implemented at the VLT and the MMT. SDI uses a double Wollaston prism and a quad filter to take 4 identical images simultaneously at 3 wavelengths surrounding the 1.62 um methane bandhead found in the spectrum of cool brown dwarfs and gas giants. By performing a difference of images in these filters, speckle noise from the primary can be significantly attenuated, resulting in photon noise limited data past 0.5''. Non-trivial data reduction tools are necessary to pipeline the simultaneous differential imaging. Here we discuss a custom algorithm implemented in IDL to perform this reduction. The script performs basic data reduction tasks but also precisely aligns images taken in each of the filters using a custom shift and subtract routine. In our survey of nearby young stars at the VLT and MMT (see Biller et al., this conference), we achieved H band contrasts >25000 (5 sigma Delta F1(1.575 um) > 10.0 mag, Delta H > 11.5 mag for a T6 spectral type object) at a separation of 0.5" from the primary star. We believe that our SDI images are among the highest contrast astronomical images ever made from ground or space for methane rich companions.Comment: 5 pages, 3 figures, 1 table. Presented at IAU Colloquium 200, Direct Imaging of Exoplanets: Science and Technique

Multidisciplinary management of acromegaly: A consensus.

The 13th Acromegaly Consensus Conference was held in November 2019 in Fort Lauderdale, Florida, and comprised acromegaly experts including endocrinologists and neurosurgeons who considered optimal approaches for multidisciplinary acromegaly management. Focused discussions reviewed techniques, results, and side effects of surgery, radiotherapy, and medical therapy, and how advances in technology and novel techniques have changed the way these modalities are used alone or in combination. Effects of treatment on patient outcomes were considered, along with strategies for optimizing and personalizing therapeutic approaches. Expert consensus recommendations emphasize how best to implement available treatment options as part of a multidisciplinary approach at Pituitary Tumor Centers of Excellence

Prolactinomas, Cushing's disease and acromegaly: debating the role of medical therapy for secretory pituitary adenomas

Pituitary adenomas are associated with a variety of clinical manifestations resulting from excessive hormone secretion and tumor mass effects, and require a multidisciplinary management approach. This article discusses the treatment modalities for the management of patients with a prolactinoma, Cushing's disease and acromegaly, and summarizes the options for medical therapy in these patients

Efficacy and safety of once-monthly pasireotide in Cushing's disease: A 12 month clinical trial

© 2017 Elsevier Ltd. Background: Cushing's disease is a rare debilitating endocrine disorder for which few prospective interventional studies have been done. We report results of the first phase 3 trial assessing long-acting intramuscular pasireotide in patients with Cushing's disease. Methods: In this phase 3 clinical trial we recruited patients aged 18 years or older with persistent, recurrent, or de-novo (non-surgical candidates) Cushing's disease who had a mean urinary free cortisol (mUFC) concentration (from three 24 h samples) of 1·5-5·0 times the upper limit of normal (ULN), a normal or greater than normal morning plasma adrenocorticotropic hormone concentration, and a pituitary source of Cushing's syndrome, from 57 sites across 19 countries. Exclusion criteria included previous pasireotide treatment, mitotane therapy within 6 months, and pituitary irradiation within 10 years. We randomly allocated patients 1:1 (block size of four) using an interactive-response-technology system to intramuscular pasireotide 10 mg or 30 mg every 4 weeks for 12 months (in the core phase). We stratified randomisation by screening mUFC concentration (1·5 to < 2·0 × ULN and 2·0-5·0 × ULN). The dose could be uptitrated (from 10 mg to 30 mg or from 30 mg to 40 mg) at month 4 if the mUFC concentration was greater than 1·5 × ULN, and at month 7, month 9, or month 12 if the mUFC concentration was greater than 1·0 × ULN. Investigators, patients, site personnel, and those assessing outcomes were masked to dose group allocation. The primary endpoint was the proportion of patients in each group with an mUFC concentration of less than or equal to the ULN at month 7. Efficacy analyses were based on intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01374906. Findings: Between Dec 28, 2011, and Dec 9, 2014, we randomly allocated 150 patients to receive pasireotide 10 mg (74 [49%] patients) or 30 mg (76 [51%] patients). The primary efficacy endpoint was met by 31 (41·9% [95% CI 30·5-53·9]) of 74 patients in the 10 mg group and 31 (40·8% [29·7-52·7] ) of 76 in the 30 mg group. The most common adverse events were hyperglycaemia (36 [49%] in the 10 mg group and 36 [47%] in the 30 mg group), diarrhoea (26 [35%] and 33 [43%] ), cholelithiasis (15 [20%] and 34 [45%] ), diabetes mellitus (14 [19%] and 18 [24%] ), and nausea (15 [20%] and 16 [21%] ). Serious adverse events suspected to be study drug related were reported in eight (11%) patients in the 10 mg group and four (5%) in the 30 mg group. Two (3%) patients in the 30 mg group died during the study (pulmonary artery thrombosis and cardiorespiratory failure); neither death was judged to be related to the study drug. Interpretation: Long-acting pasireotide normalised mUFC concentration in about 40% of patients with Cushing's disease at month 7 and had a similar safety profile to that of twice-daily subcutaneous pasireotide. Long-acting pasireotide is an efficacious treatment option for some patients with Cushing's disease who have persistent or recurrent disease after initial surgery or are not surgical candidates, and provides a convenient monthly administration schedule. Funding: Novartis Pharma AG

Implementing Digital Twins That Learn: AI and Simulation Are at the Core

As companies are trying to build more resilient supply chains using digital twins created by smart manufacturing technologies, it is imperative that senior executives and technology providers understand the crucial role of process simulation and AI in quantifying the uncertainties of these complex systems. The resulting digital twins enable users to replay history, gain predictive visibility into the future, and identify corrective actions to optimize future performance. In this article, we define process digital twins and their four foundational elements. We discuss how key digital twin functions and enabling AI and simulation technologies integrate to describe, predict, and optimize supply chains for Industry 4.0 implementations