The high energy X-ray probe (HEX-P): magnetars and other isolated neutron stars

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The hard X-ray emission from magnetars and other isolated neutron stars remains under-explored. An instrument with higher sensitivity to hard X-rays is critical to understanding the physics of neutron star magnetospheres and also the relationship between magnetars and Fast Radio Bursts (FRBs). High sensitivity to hard X-rays is required to determine the number of magnetars with hard X-ray tails, and to track transient non-thermal emission from these sources for years post-outburst. This sensitivity would also enable previously impossible studies of the faint non-thermal emission from middle-aged rotation-powered pulsars (RPPs), and detailed phase-resolved spectroscopic studies of younger, bright RPPs. The High Energy X-ray Probe (HEX-P) is a probe-class mission concept that will combine high spatial resolution X-ray imaging ( < 5 arcsec half-power diameter (HPD) at 0.2–25 keV) and broad spectral coverage (0.2–80 keV) with a sensitivity superior to current facilities (including XMM-Newton and NuSTAR). HEX-P has the required timing resolution to perform follow-up observations of sources identified by other facilities and positively identify candidate pulsating neutron stars. Here we discuss how HEX-P is ideally suited to address important questions about the physics of magnetars and other isolated neutron stars.Peer reviewe

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Optimizing Production and Inventory Decisions for Mixed Make-to-order/Make-to-stock Ready-made Garment Industry

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Multi-Criteria Decision-Making for Machine Selection in Manufacturing and Construction: Recent Trends

As the number of alternative machines has increased and their technology has been continuously developed, the machine selection problem has attracted many researchers. This article reviews recent developments in applying multi-criteria decision-making (MCDM) methods for selecting machines in the manufacturing and construction industries. Selected articles are classified according to the application area and the applied MCDM method. By focusing on the last five years, this paper identifies recent trends in developing and using these methods. Results suggest that there has been a noticeable growth in the utilization of MCDM techniques for machine selection problems in both sectors. It is also noted that several decision-support tools and methods have been developed and successfully applied during this period. Accordingly, needs and directions for future research are discussed

Multi-Criteria Decision-Making for Machine Selection in Manufacturing and Construction: Recent Trends

As the number of alternative machines has increased and their technology has been continuously developed, the machine selection problem has attracted many researchers. This article reviews recent developments in applying multi-criteria decision-making (MCDM) methods for selecting machines in the manufacturing and construction industries. Selected articles are classified according to the application area and the applied MCDM method. By focusing on the last five years, this paper identifies recent trends in developing and using these methods. Results suggest that there has been a noticeable growth in the utilization of MCDM techniques for machine selection problems in both sectors. It is also noted that several decision-support tools and methods have been developed and successfully applied during this period. Accordingly, needs and directions for future research are discussed

The development and validation of an internet-based training package for the management of perineal trauma following childbirth:MaternityPEARLS

Abstract Background Birth-related perineal trauma has a major impact on women's health. Appropriate management of perineal injuries requires clinical knowledge and skill. At present, there is no agreement as to what constitutes an effective clinical training programme, despite the presence of sufficient evidence to support standardised perineal repair techniques. To address this deficiency, we developed and validated an interactive distance learning multi-professional training package called MaternityPEARLS. Method MaternityPEARLS was developed as a comprehensive e-learning package in 2010. The main aim of the MaternityPEARLS project was to develop, refine and validate this multi-professional e-learning tool. The effect of MaternityPEARLS in improving clinical skills and knowledge was compared with two other training models; traditional training (lectures + model-based hands on training) and offline computer lab-based training. Midwives and obstetricians were recruited for each training modality from three maternity units. An analysis of covariance was done to assess the effects of clinical profession and years of experience on scoring within each group. Feedback on MaternityPEARLS was also collected from participants. The project started in January 2010 and was completed in December 2010. Results Thirty-eight participants were included in the study. Pretraining and post-training scores in each group showed considerable improvement in skill scores (p&amp;lt;0.001 in all groups). Mean changes were similar across all three groups for knowledge (3.24 (SD 5.38), 3.00 (SD 3.74), 3.30 (SD 3.73)) and skill (25.34 (SD 8.96), 22.82 (SD 9.24), 20.7 (SD 9.76)) in the traditional, offline computer lab-based and e-learning groups, respectively. There was no evidence of any effect of clinical experience and baseline knowledge on outcomes. Conclusions MaternityPEARLS is the first validated perineal trauma management e-learning package. It provides a level of improvement in skill and knowledge comparable to traditional methods of training. However, as an e-learning system, it has the advantage of ensuring the delivery of a standardised, continuously updated curriculum that has global accessibility. </jats:sec

Types and Sources of Social Support Accessible to University Students with Disabilities in Saudi Arabia during the COVID-19 Pandemic

University students with disabilities face an increased risk of experiencing negative implications in educational, psychological, and social spheres during the COVID-19 pandemic. This study aimed at assessing various dimensions of social support and its sources during the COVID-19 pandemic that availed university students with disabilities. This cross-sectional descriptive study collected data from 53 university students with disabilities. We administered the Social Support Scale (SSC) to assess five dimensions: informational, emotional, esteem, social integration and tangible support, and access to social support from four sources: family, friends, teachers, and colleagues. Multiple regression analysis showed that university students with disabilities mainly relied upon their friends for informational support (β = 0.64; p p p p p p < 0.05). The findings from the current study suggest that students with disabilities primarily sought informational, emotional, and social integration support from their peers. Although teachers were the primary source of informational support, emotional and esteem support were not found to be significantly associated with them. These findings necessitate exploring the underlying factors and how to enhance them during unusual circumstances such as online distance education and social distancing