OT Modeling: The Enterprise Beyond IT

Enterprises are composed of an enormous number of elements (e.g., organizational units, human resources, production processes, and IT systems) typically classified in the business or the IT domain. However, some crucial elements do not belong in either group: they are directly responsible for producing and delivering the company’s goods and services and include all the elements that support day to day operations. Collectively, these elements have been called operational technologies (OT) and have been conspicuously excluded from enterprise modeling (EM) approaches which traditionally have focused on the business and IT dimensions. Evidence of this is the absence of OT elements in languages and metamodels for EM. This is in line with the historical division between IT and OT in organizations that has led to information silos, independent teams, and disparate tech- nologies that only recently have started to be reconciled. Considering that OT is critical to most productive organi- zations, and the benefits that EM brings to its understand- ing and improvement, it makes sense to expand EM to include OT. For that purpose, this paper proposes an extension to ArchiMate 3.0 which includes crucial OT elements. On top of that, this paper also proposes an approach to further expand ArchiMate to address specific industries where more specific OT elements are required. This is illustrated in the paper with an extension for the Oil and Gas case that was validated with experts belonging to five companies in the sector

White paper: Research Challenges at the Intersection of Energy and Equity in the Energy Transition

Summary report from NSF2026: Conference Workshops to Identify Research Challenges at the Intersection of Energy and Equity in the Energy Transitio

Performance-limiting nanoscale trap clusters at grain junctions in halide perovskites.

Halide perovskite materials have promising performance characteristics for low-cost optoelectronic applications. Photovoltaic devices fabricated from perovskite absorbers have reached power conversion efficiencies above 25 per cent in single-junction devices and 28 per cent in tandem devices1,2. This strong performance (albeit below the practical limits of about 30 per cent and 35 per cent, respectively3) is surprising in thin films processed from solution at low-temperature, a method that generally produces abundant crystalline defects4. Although point defects often induce only shallow electronic states in the perovskite bandgap that do not affect performance5, perovskite devices still have many states deep within the bandgap that trap charge carriers and cause them to recombine non-radiatively. These deep trap states thus induce local variations in photoluminescence and limit the device performance6. The origin and distribution of these trap states are unknown, but they have been associated with light-induced halide segregation in mixed-halide perovskite compositions7 and with local strain8, both of which make devices less stable9. Here we use photoemission electron microscopy to image the trap distribution in state-of-the-art halide perovskite films. Instead of a relatively uniform distribution within regions of poor photoluminescence efficiency, we observe discrete, nanoscale trap clusters. By correlating microscopy measurements with scanning electron analytical techniques, we find that these trap clusters appear at the interfaces between crystallographically and compositionally distinct entities. Finally, by generating time-resolved photoemission sequences of the photo-excited carrier trapping process10,11, we reveal a hole-trapping character with the kinetics limited by diffusion of holes to the local trap clusters. Our approach shows that managing structure and composition on the nanoscale will be essential for optimal performance of halide perovskite devices

Age- and region-specific hepatitis B prevalence in Turkey estimated using generalized linear mixed models: a systematic review

Toy M, Önder FO, Wörmann T, et al. Age- and region-specific hepatitis B prevalence in Turkey estimated using generalized linear mixed models: a systematic review. BMC infectious diseases. 2011;11(1): 337.BACKGROUND: To provide a clear picture of the current hepatitis B situation, the authors performed a systematic review to estimate the age- and region-specific prevalence of chronic hepatitis B (CHB) in Turkey. METHODS: A total of 339 studies with original data on the prevalence of hepatitis B surface antigen (HBsAg) in Turkey and published between 1999 and 2009 were identified through a search of electronic databases, by reviewing citations, and by writing to authors. After a critical assessment, the authors included 129 studies, divided into categories: 'age-specific'; 'region-specific'; and 'specific population group'. To account for the differences among the studies, a generalized linear mixed model was used to estimate the overall prevalence across all age groups and regions. For specific population groups, the authors calculated the weighted mean prevalence. RESULTS: The estimated overall population prevalence was 4.57, 95% confidence interval (CI): 3.58, 5.76, and the estimated total number of CHB cases was about 3.3 million. The outcomes of the age-specific groups varied from 2.84, (95% CI: 2.60, 3.10) for the 0-14-year olds to 6.36 (95% CI: 5.83, 6.90) in the 25-34-year-old group. CONCLUSION: There are large age-group and regional differences in CHB prevalence in Turkey, where CHB remains a serious health problem

A Review on the Mechanical Modeling of Composite Manufacturing Processes

© 2016, The Author(s). The increased usage of fiber reinforced polymer composites in load bearing applications requires a detailed understanding of the process induced residual stresses and their effect on the shape distortions. This is utmost necessary in order to have more reliable composite manufacturing since the residual stresses alter the internal stress level of the composite part during the service life and the residual shape distortions may lead to not meeting the desired geometrical tolerances. The occurrence of residual stresses during the manufacturing process inherently contains diverse interactions between the involved physical phenomena mainly related to material flow, heat transfer and polymerization or crystallization. Development of numerical process models is required for virtual design and optimization of the composite manufacturing process which avoids the expensive trial-and-error based approaches. The process models as well as applications focusing on the prediction of residual stresses and shape distortions taking place in composite manufacturing are discussed in this study. The applications on both thermoset and thermoplastic based composites are reviewed in detail

Effects of antibiotic resistance, drug target attainment, bacterial pathogenicity and virulence, and antibiotic access and affordability on outcomes in neonatal sepsis: an international microbiology and drug evaluation prospective substudy (BARNARDS)

Background Sepsis is a major contributor to neonatal mortality, particularly in low-income and middle-income countries (LMICs). WHO advocates ampicillin–gentamicin as first-line therapy for the management of neonatal sepsis. In the BARNARDS observational cohort study of neonatal sepsis and antimicrobial resistance in LMICs, common sepsis pathogens were characterised via whole genome sequencing (WGS) and antimicrobial resistance profiles. In this substudy of BARNARDS, we aimed to assess the use and efficacy of empirical antibiotic therapies commonly used in LMICs for neonatal sepsis. Methods In BARNARDS, consenting mother–neonates aged 0–60 days dyads were enrolled on delivery or neonatal presentation with suspected sepsis at 12 BARNARDS clinical sites in Bangladesh, Ethiopia, India, Pakistan, Nigeria, Rwanda, and South Africa. Stillborn babies were excluded from the study. Blood samples were collected from neonates presenting with clinical signs of sepsis, and WGS and minimum inhibitory concentrations for antibiotic treatment were determined for bacterial isolates from culture-confirmed sepsis. Neonatal outcome data were collected following enrolment until 60 days of life. Antibiotic usage and neonatal outcome data were assessed. Survival analyses were adjusted to take into account potential clinical confounding variables related to the birth and pathogen. Additionally, resistance profiles, pharmacokinetic–pharmacodynamic probability of target attainment, and frequency of resistance (ie, resistance defined by in-vitro growth of isolates when challenged by antibiotics) were assessed. Questionnaires on health structures and antibiotic costs evaluated accessibility and affordability. Findings Between Nov 12, 2015, and Feb 1, 2018, 36 285 neonates were enrolled into the main BARNARDS study, of whom 9874 had clinically diagnosed sepsis and 5749 had available antibiotic data. The four most commonly prescribed antibiotic combinations given to 4451 neonates (77·42%) of 5749 were ampicillin–gentamicin, ceftazidime–amikacin, piperacillin–tazobactam–amikacin, and amoxicillin clavulanate–amikacin. This dataset assessed 476 prescriptions for 442 neonates treated with one of these antibiotic combinations with WGS data (all BARNARDS countries were represented in this subset except India). Multiple pathogens were isolated, totalling 457 isolates. Reported mortality was lower for neonates treated with ceftazidime–amikacin than for neonates treated with ampicillin–gentamicin (hazard ratio [adjusted for clinical variables considered potential confounders to outcomes] 0·32, 95% CI 0·14–0·72; p=0·0060). Of 390 Gram-negative isolates, 379 (97·2%) were resistant to ampicillin and 274 (70·3%) were resistant to gentamicin. Susceptibility of Gram-negative isolates to at least one antibiotic in a treatment combination was noted in 111 (28·5%) to ampicillin–gentamicin; 286 (73·3%) to amoxicillin clavulanate–amikacin; 301 (77·2%) to ceftazidime–amikacin; and 312 (80·0%) to piperacillin–tazobactam–amikacin. A probability of target attainment of 80% or more was noted in 26 neonates (33·7% [SD 0·59]) of 78 with ampicillin–gentamicin; 15 (68·0% [3·84]) of 27 with amoxicillin clavulanate–amikacin; 93 (92·7% [0·24]) of 109 with ceftazidime–amikacin; and 70 (85·3% [0·47]) of 76 with piperacillin–tazobactam–amikacin. However, antibiotic and country effects could not be distinguished. Frequency of resistance was recorded most frequently with fosfomycin (in 78 isolates [68·4%] of 114), followed by colistin (55 isolates [57·3%] of 96), and gentamicin (62 isolates [53·0%] of 117). Sites in six of the seven countries (excluding South Africa) stated that the cost of antibiotics would influence treatment of neonatal sepsis

Can diffusion weighted MRI differentiate between inflammatory-infectious and malignant pleural effusions?

Aim: To assess exudative pleural effusions with diffusion-weighted magnetic resonance imaging (DW-MRI) in order to determine non-invasive differentiation criteria for inflammatory-infectious and malignant effusions. Materials and methods: Thirty-two patients with pleural effusions underwent DW-MRI with 4 different b values (10, 500, 750 and 1000 s/mm2). ADC maps were generated automatically. Signal intensity and ADC values were measured. Following MRI, pleural fluid of 10-15 ml was obtained and analyzed. AUC values were compared for different diffusion levels of ADC and SI measurements. The relationship between ADC values and pleural effusion LDH and total protein levels was examined. Results: The cut-off values obtained from signal intensity and ADC measurements to differentiate exudates with malignant pathology were not found to be statistically significant. In the inflammatory-infectious group, a significant negative correlation was observed between ADC values and pleural fluid LDH measurements in all b values. In the malignant group, a significant positive correlation was observed between ADC values and pleural fluid total protein measurements in b values of 500 and 1000. Conclusion: Infectious/inflammatory and malignant effusions overlap strongly and cannot therefore be differentiated using DW MRI