A note on: an empirical comparison of forgetting models

In the above paper, Nembhard and Osothsilp (2001) empirically compared several forgetting models against empirical data on production breaks. Among the models compared was the learn–forget curve model (LFCM) developed by Jaber and Bonney(1996). In previous research, several studies have shown that the LFCM is advantageous to some of the models being investigated, however, Nembhard and Osothsilp (2001) found that the LFCM showed the largest deviation from empirical data. In this commentary, we demonstrate that the poor performance of the LFCM in the study of Nembhard and Osothsilp (2001) might be attributed to an error on their part when fitting the LFCM to their empirical data

Fully-automated Runtime Enforcement of Component-based Systems with Formal and Sound Recovery

International audienceWe introduce runtime enforcement of specifications on component-based systems (CBS) modeled in the BIP (Behavior, Interaction and Priority) framework. Runtime enforcement is an increasingly popular and effective dynamic validation technique aiming to ensure the correct runtime behavior (w.r.t. a formal specification) of a system using a so-called enforcement monitor. BIP is a powerful and expressive component-based framework for the formal construction of heterogeneous systems. Because of BIP expressiveness however , it is difficult to enforce complex behavioral properties at design-time. We first introduce a theoretical runtime enforcement framework for component-based systems where we delineate a hierarchy of enforceable properties (i.e., properties that can be enforced) according to the number of observational steps a system is allowed to deviate from the property (i.e., the notion of k-step enforceability). To ensure the observational equivalence between the correct executions of the initial system and the monitored system, we show that i) only stutter-invariant properties should be enforced on CBS with our monitors, and ii) safety properties are 1-step enforceable. Second, given an abstract enforcement monitor for some 1-step enforceable property, we define a series of formal transformations to instrument (at relevant locations) a CBS described in the BIP framework to integrate the monitor. At runtime, the monitor observes and automatically avoids any error in the behavior of the system w.r.t. the property. Third, our approach is fully implemented in RE-BIP, an available tool integrated in the BIP tool suite. Fourth, to validate our approach, we use RE-BIP to i) enforce deadlock-freedom on a dining philosophers benchmark, and ii) ensure the correct placement of robots on a map

Partial discharge detection using low cost RTL-SDR model for wideband spectrum sensing

Partial discharge (PD) is one of the predominant factors to be controlled to ensure reliability and undisrupted functions of power generators, motors, Gas Insulated Switchgear (GIS) and grid connected power distribution equipment, especially in the future smart grid. The emergence of wireless technology has provided numerous opportunities to optimise remote monitoring and control facilities that can play a significant role in ensuring swift control and restoration of HV plant equipment. In order to monitor PD, several approaches have been employed, however, the existing schemes do not provide an optimal approach for PD signal analysis, and are very costly. In this paper an RTL-SDR (Software Defined Radio) based spectrum analyser has been proposed in order to provide a potentially low cost solution for PD detection and monitoring. Initially, a portable spectrum analyser has been used for PD detection that was later replaced by an RTL-SDR device. The proposed schemes exhibit promising results for spectral detection within the VHF and UHF band

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

A numerical comparison of three potential learning and forgetting models

Researchers from various disciplines have been pursuing better understanding of learning and forgetting processes. A range of mathematical models, sometimes based on empirical data, has been developed to measure the effect of production breaks on the learning process. Thus far, researchers and practitioners have not resolved the issue of how learning and forgetting interacts. However, some of the developed models seem promising. This paper investigates and discusses three such potential models; namely, the learn-forget curve model (LFCM), the recency model (RC), and the power integration diffusion (PID) with their similarities and differences addressed. Results showed that for a moderate learning scenario, where the learning rate classifies a task as being more cognitive than motor, the three models produced very close predictions to one another for all values of production breaks and initial processing times. Furthermore, the PID and RC models, and the PID and the LFCM models, could best be differentiated for cases characterised by high initial processing times, long production breaks, and for tasks that identified as being more motor than cognitive. Numerical results for the PID and LFCM suggested that as learning becomes slower forgetting becomes faster. This result is inconsistent with that of the RC model, which suggests that fast (slow) learners forget faster (slower)

Interference-adjusted power learning curve model with forgetting

Funding Information: This work was supported by the Finnish Work Environment Fund (grant number 190242) (J. Peltokorpi); and the Social Sciences and Humanities Research Council (SSHRC) , Canada – Insight Grant Program (grant number 435-2020-0628) (M. Y. Jaber). Publisher Copyright: © 2021 The AuthorsResearchers in production and operations management have studied the effect of worker learning and forgetting on system performance for decades. It remains an active research topic. Those studies have assumed that production interruptions (or production breaks) cause forgetting, which deteriorates performance. Research on human working memory provides enough evidence that continuous forgetting, precisely cognitive interference, results from overloading the memory with information. Despite the evidence, few studies have incorporated it into learning curve models. This paper presents an enhanced version of the power learning curve that accounts for a variable degree of interference when moving from a production cycle to the next. It adopts the concept of memory trace decay to measure the residual (interference-adjusted), not the nominal (maximum) cumulative experience. We test the developed model against learning data from manual assembly and inspection tasks, with varying numbers of repetitions and breaks. We also test three alternative power-form learning and forgetting curve models from the literature. The results show that the interference-adjusted model fits the data very well. The proposed learning and forgetting model and its individualized cumulative metrics can help identify struggling workers early and release precocious learners earlier than expected. As such, the model gives insights for managers on the occurrence of interference to enable individual learning support.Peer reviewe