bluuebunny/crossref_metadata_2025_split huggingface dataset:
RSM based expert system development for cutting force prediction during machining of Ti–6Al–4V under minimum quantity lubrication
Authors: Shetty R. , Kumar C. R. Sanjeev , Ravindra M. R. 
Published: December 2021

AbstractIn recent days the manufacturing process have become more precise and cost efficient due to advancement in the field of computer technology. Information technology has been integrated with manufacturing practice and has resulted in time reduction from concept of a product to marketing of the product. Cutting force generated is the main manufacturing issue raised among industries as it clearly affects quality and cost of the final product. Hence using extensive literature and data base knowledge optimum cutting parameters are selected. Therefore, this paper focuses on a response surface methodology (RSM) based expert system that has been developed using JAVA programming with the help of response surface second order model to automatically generate values of cutting force during machining of Ti–6Al–4V alloy under minimum quantity lubrication (MQL) for different process input parameters. From RSM it has been observed that calculated value of F (20.36) was greater than the F-table value (3.02) and hence the model developed can be effectively used for machining of Ti–6Al–4V alloy. Further the developed RSM based expert system model can be successfully used to predict the force generated during cutting process while machining Ti–6Al–4V alloy under MQL conditions.
Intelligent user identity authentication in vehicle security system based on wireless signals
Authors: Zhang Jie , Wang Zhongmin , Yan QingLi 
Published: December 2021

AbstractIntelligent identity authentication in vehicle security systems, as a vital component in anti-theft system and safety driving assist system, has received wide attention. Current vehicle security systems, however, focus the car security on the car keys security, ignore the owner of car keys. Anyone who owns the car keys can operate the car. This paper introduces an intelligent identity authentication method for vehicle security system based on wireless signals. Unlike past work, our approach combines car security with car owners and car keys. The intuition underlying our design is that when a user walks towards the car, the user’s gait information can be leveraged to identify the user. We capture the user’s gait information using wireless devices which can be deployed in the car, and then extract features from the received wireless signals using convolution kernel and apply artificial neural network to identify the user. We built a prototype and experimental results show that our approach can achieve high accuracy and strong robustness.
Combining Rainwater Harvesting and Grass Reseeding to Revegetate Denuded African Semi-arid Landscapes
Authors: Mganga Kevin Z. , Bosma Luwieke , Amollo Kevin O. , Kioko Theophilus , Kadenyi Nancy , Ndathi Aphaxard J. N. , Wambua Stephen M. , Kaindi Eric M. , Musyoki Gilbert K. , Musimba Nashon K. R. , van Steenbergen Frank 
Published: December 2021

AbstractIn African drylands, perennial grasses preferred by grazing livestock are disappearing at an alarming rate. This has led to recurrent livestock feed shortages threatening pastoralist’s livelihoods. Combining native grass reseeding and rainwater harvesting offers a viable and innovative solution to reverse this trend. However, studies to determine how biomass yields are affected by soil moisture availability attributed to in situ rainwater harvesting in African drylands are limited. We investigated how biomass yields of three grasses native to Africa, i.e., Enteropogon macrostachyus (Bush rye grass), Cenchrus ciliaris (African foxtail grass), and Eragrostis superba (Maasai love grass), are affected by soil moisture content in a typical semi-arid landscape. Rainwater harvesting structures included trenches, micro-catchments and furrows. Additionally, rain runoff was diverted from an adjacent road used as a catchment area. Soil moisture was measured between November 2018 and August 2019 using PlantCare Mini-Logger sensors installed at 40 and 50 cm depths and 0, 1, 5 and 15 m away from the trench. Quadrat method was used to determine biomass yields in August 2019. Peaks in soil moisture were observed after rainfall events. Soil moisture content gradually decreased after the rainy season, but was higher closer to the trench. This is attributed to the prolonged rainwater retention in the trenches. Biomass yields were in the order Eragrostis superba > Cenchrus ciliaris > Enteropogon macrostachyus. Biomass production was higher near the trenches for all the studied species. Sensitivity to soil moisture demonstrated by the magnitude to yield reduction during the growing season was in the order Eragrostis superba > Cenchrus ciliaris > Enteropogon macrostachyus. These results suggest that Eragrostis superba is more sensitive to drought stress than Enteropogon macrostachyus that is adapted to a wide range of soil moisture conditions. We demonstrated that in situ rainwater harvesting structures enhanced soil moisture availability and displayed great potential for revegetating denuded natural rangelands in semi-arid African landscapes. Thus, combining rainwater harvesting and reseeding techniques can produce measurable improvements in pastoral livelihoods and should be incorporated in dryland development policies in the region. Ultimately, incorporating such innovative strategies can strengthen the effectiveness of ecological restoration in African drylands to meet the objectives of the UN Decade on Ecosystem Restoration and achieving the UN Sustainable Development Goals.
Graphical abstract
A COLLECTIVE RESERVING MODEL WITH CLAIM OPENNESS
Authors: Lindholm Mathias , Zakrisson Henning 
Published: December 2021

AbstractThe present paper introduces a simple aggregated reserving model based on claim count and payment dynamics, which allows for claim closings and re-openings. The modelling starts off from individual Poisson process claim dynamics in discrete time, keeping track of accident year, reporting year and payment delay. This modelling approach is closely related to the one underpinning the so-called double chain-ladder model, and it allows for producing separate reported but not settled and incurred but not reported reserves. Even though the introduction of claim closings and re-openings will produce new types of dependencies, it is possible to use flexible parametrisations in terms of, for example, generalised linear models (GLM) whose parameters can be estimated based on aggregated data using quasi-likelihood theory. Moreover, it is possible to obtain interpretable and explicit moment calculations, as well as having consistency of normalised reserves when the number of contracts tend to infinity. Further, by having access to simple analytic expressions for moments, it is computationally cheap to bootstrap the mean squared error of prediction for reserves. The performance of the model is illustrated using a flexible GLM parametrisation evaluated on non-trivial simulated claims data. This numerical illustration indicates a clear improvement compared with models not taking claim closings and re-openings into account. The results are also seen to be of comparable quality with machine learning models for aggregated data not taking claim openness into account.
Evaluation of antibiotic escalation in response to nurse-driven inpatient sepsis screen
Authors: Furukawa Daisuke , Dieringer Thomas D. , Wong Mitchell D. , Tong Julia T. , Cader Isa A. , Wisk Lauren E. , Han Maria A. , Gupta Summer M. , Kerbel Russell B. , Uslan Daniel Z. , Graber Christopher J. 
Published: December 2021

Abstract

Objective:
To determine the frequency and predictors of antibiotic escalation in response to the inpatient sepsis screen at our institution.

Design:
Retrospective cohort study.

Setting:
Two affiliated academic medical centers in Los Angeles, California.

Patients:
Hospitalized patients aged 18 years and older who had their first positive sepsis screen between January 1, 2019, and December 31, 2019, on acute-care wards.

Methods:
We described the rate and etiology of antibiotic escalation, and we conducted multivariable regression analyses of predictors of antibiotic escalation.

Results:
Of the 576 cases with a positive sepsis screen, antibiotic escalation occurred in 131 cases (22.7%). New infection was the most documented etiology of escalation, with 76 cases (13.2%), followed by known pre-existing infection, with 26 cases (4.5%). Antibiotics were continued past 3 days in 17 cases (3.0%) in which new or existing infection was not apparent. Abnormal temperature (adjusted odds ratio [aOR], 3.00; 95% confidence interval [CI], 1.91–4.70) and abnormal lactate (aOR, 2.04; 95% CI, 1.28–3.27) were significant predictors of antibiotic escalation. The patient already being on antibiotics (aOR, 0.54; 95% CI, 0.34–0.89) and the positive screen occurred during a nursing shift change (aOR, 0.36; 95% CI, 0.22–0.57) were negative predictors. Pneumonia was the most documented new infection, but only 19 (50%) of 38 pneumonia cases met full clinical diagnostic criteria.

Conclusions:
Inpatient sepsis screening led to a new infectious diagnosis in 13.2% of all positive sepsis screens, and the risk of prolonged antibiotic exposure without a clear infectious source was low. Pneumonia diagnostics and lactate testing are potential targets for future stewardship efforts.
Closing the Accountability Gap in Corporate Supply Chains for Violations of the Trafficking Victims Protection Act
Authors: GREEN Jennifer 
Published: December 2021

AbstractOver 40 million people around the world are victims of modern forms of slavery: forced labour and human trafficking. People are tricked into working under onerous conditions, and unable to leave or return home due to physical, psychological or financial coercion, and many of these trafficking victims produce goods for United States (US) and other multinational corporations that profit by relying on the lower wages earned by workers in their global supply chains. Well-developed legal standards prohibit these practices, and governments, intergovernmental organizations, business associations and non-governmental organizations have developed mechanisms to prevent, detect and provide redress to victims. Some businesses lead or comply with the standards and enforcement mechanisms, but too many do not. US law offers a powerful but under-utilized tool to address trafficking: the 2008 Trafficking Victims Protection Reauthorization Act (TVPRA), which imposes civil liability on those who ‘knew or should have known’ about forced labour or human trafficking in their corporate ventures. Unfortunately, courts have ignored or misinterpreted this standard, at times confusing civil and criminal provisions of the statute. Correct and vigorous legal enforcement is key to addressing the accountability gap between the well-developed standards and the continuing use of forced labour and human trafficking. This article is the first to demonstrate that, with regard to the TVPRA standard, corporations have long been on notice of both the obligation to effectively monitor labour conditions and the mechanisms that would accomplish that task. US courts must enforce the ‘knew or should have known’ standard to protect workers – the most vulnerable people in the supply chain – and to prevent an unfair competitive advantage over companies that have established compliance programmes that actually prevent and punish human trafficking and forced labour.
CRISPR-Cas9 and Food in the European Union: An Organic Solution to an Undetectable Problem for Food Business Operators
Authors: Hughes Shane Michael 
Published: December 2021

AbstractVarious methods of genetic modification have been applied to plant breeding as an integral part of agriculture. This article examines a method of targeted mutagenesis – CRISPR-Cas9 – and its dysregulation in the European Union (EU). It provides clarity for food business operators relating to the traceability and labelling of food products induced using this biotechnology. In addition, it outlines policy recommendations to improve the regulation of such food products in the EU.
Knowledge, attitudes, and behaviours towards schizophrenia, bipolar disorder, and autism: a pilot study
Authors: Mothersill David , Loughnane Gerard , Grasso Gabriela , Hargreaves April 
Published: December 2021

AbstractObjectives:Lack of knowledge and discriminatory attitudes and behaviours towards individuals with mental disorders is a worldwide problem but may be particularly damaging for young people. This pilot study examined knowledge, attitudes and behaviours towards schizophrenia, bipolar disorder and autism within a large sample of adults in Ireland, a country with the youngest population in Europe, in order to better understand public views on these groups.Methods:In a correlational, cross-sectional design, 307 adults in Ireland over the age of 18 completed a questionnaire over Google Forms examining knowledge, attitudes and behaviours towards schizophrenia, bipolar disorder and autism. Responses to questions specifically relating to each diagnosis were compared using trimmed mean ANOVA to examine whether responses to questions differed depending on diagnosis.Results:Results indicate varied knowledge, attitudes and behaviours towards these groups, but a majority believe it should be a research priority. ANOVA and post hoc tests revealed significant differences in knowledge, attitudes and behaviours towards each of schizophrenia, bipolar disorder, and autism (p < 0.005), and reported attitudes and behaviours towards schizophrenia were more negative than either bipolar disorder or autism. A majority of participants (54.8%) felt not informed enough about mental health by the media.Conclusions:In our Irish sample, type and level of stigma varies according to mental health diagnosis. Our sample also report feeling inadequately informed about mental health by the media. Thus future policy and campaigns could consider targeting individual mental health diagnoses, with a focus on increasing familiarity and knowledge.
OP128 Improving Literature Searching For Evidence On Health Apps: The National Institute For Health And Care Excellence (NICE) MEDLINE And Embase (Ovid) Health Apps Search Filters
Authors: Ayiku Lynda , Glover Sarah 
Published: December 2021

IntroductionLiterature searching for evidence on apps in bibliographic databases is challenging because they are often described with inconsistent terminology. Information Specialists from the United Kingdom's National Institute for Health and Care Excellence (NICE) have developed validated search filters for retrieving evidence about apps from MEDLINE and Embase (Ovid) reliably.MethodsMedical informatics journals were hand-searched to create a ‘gold standard’ set of app references. The gold standard set was divided into two sets. The development set provided the search terms for the filters. The filters were validated by calculating their recall against the validation set. Target recall was >90%.A case study was then conducted to compare the number-needed-to-read (NNR) of the filters with previous non-validated MEDLINE and Embase app search strategies used for the ‘MIB214 myCOPD app’ NICE topic. NNR is the number of references screened to find each relevant reference.ResultsThe MEDLINE and Embase filters achieved 98.6 percent and 98.5 percent recall against the validation set, respectively. In the case study they achieved 100 percent recall, reducing NNR from 348 to 147 in MEDLINE and from 456 to 271 in Embase.ConclusionsThe novel NICE health apps search filters retrieve evidence on apps from MEDLINE and Embase effectively and more efficiently than previous non-validated search strategies used at NICE.
OP199 From Pilot Studies To System-Wide Innovation: Challenges And Opportunities For Clinical Decision Support Systems (CDSS) Implementation In Australia
Authors: Laka Mah , Milazzo Adriana , Carter Drew , Merlin Tracy 
Published: December 2021

IntroductionThe clinical data is increasing at a considerably higher rate than the capacity of the healthcare system and clinicians to manage this data. Digital tools such as clinical decision support systems (CDSS) provide opportunities for evidence-based patient care by intelligently filtering and presenting the information required for clinical decision making at the point of care. Despite the success of pilot projects, CDSS have had limited implementation in broader health systems. We aimed to identify challenges faced by policymakers for CDSS implementation and to provide policy recommendations.MethodsWe conducted eleven semi-structured interviews with Australian policymakers from state and national committees involved in digital health activities. The data were analyzed using reflexive thematic analysis to identify policy priorities.ResultsOur findings indicate that fragmentation of care processes and structures in the digital health ecosystem is one of the main impediments to delivering coordinated care using CDSS. Five themes for policy action were identified: (i) establishing a shared conceptual framework for user-centered design of CDSS that is aligned with stakeholders’ priorities, (ii) maintaining the right balance between the customization and standardization of systems, (iii) developing mutually agreed semantic interoperability standards at the local, state and national level, allowing generation and exchange of information across the health system without changing its context and meaning, (iv) reorienting organizational structures to build capacity to foster change, and (v) developing collaborative care models to avoid conflicting interests between stakeholders.ConclusionsFindings highlight the importance of developing system-wide guidance to establish a clear vision for CDSS implementation and alignment of organizational processes across all levels of health care. There is a need to build a shared policy framework for modelling the innovative activities such as CDSS implementation across the digital health landscape which minimizes the operational and strategic fragmentation of different organizations.
OP218 Searching Preprint Repositories For COVID-19 Therapeutics Using A Semi-Automated Text-Mining Tool
Authors: Gonzalez-Moral Sonia Garcia , Al-Assaf Aalya , Pandey Savitri , Ogunbayo Oladapo , Craig Dawn 
Published: December 2021

IntroductionThe COVID-19 pandemic led to a significant surge in clinical research activities in the search for effective and safe treatments. Attempting to disseminate early findings from clinical trials in a bid to accelerate patient access to promising treatments, a rise in the use of preprint repositories was observed. In the UK, NIHR Innovation Observatory (NIHRIO) provided primary horizon-scanning intelligence on global trials to a multi-agency initiative on COVID-19 therapeutics. This intelligence included signals from preliminary results to support the selection, prioritisation and access to promising medicines.MethodsA semi-automated text mining tool in Python3 used trial IDs (identifiers) of ongoing and completed studies selected from major clinical trial registries according to pre-determined criteria. Two sources, BioRxiv and MedRxiv are searched using the IDs as search criteria. Weekly, the tool automatically searches, de-duplicates, excludes reviews, and extracts title, authors, publication date, URL and DOI. The output produced is verified by two reviewers that manually screen and exclude studies that do not report results.ResultsA total of 36,771 publications were uploaded to BioRxiv and MedRxiv between March 3 and November 9 2020. Approximately 20–30 COVID-19 preprints per week were pre-selected by the tool. After manual screening and selection, a total of 123 preprints reporting clinical trial preliminary results were included. Additionally, 50 preprints that presented results of other study types on new vaccines and repurposed medicines for COVID-19 were also reported.ConclusionsUsing text mining for identification of clinical trial preliminary results proved an efficient approach to deal with the great volume of information. Semi-automation of searching increased efficiency allowing the reviewers to focus on relevant papers. More consistency in reporting of trial IDs would support automation. A comparison of accuracy of the tool on screening titles/abstract or full papers may help to support further refinement and increase efficiency gains.This project is funded by the NIHR [(HSRIC-2016-10009)/Innovation Observatory]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
OP236 Evidence Synthesis Of Time-To-Event Outcomes In The Presence Of Non-Proportional Hazards
Authors: Freeman Suzanne , Cooper Nicola , Sutton Alex , Crowther Michael , Carpenter James , Hawkins Neil 
Published: December 2021

IntroductionSynthesis of clinical effectiveness is a well-established component of health technology assessment (HTA) combining data from multiple trials to obtain an overall pooled estimate of clinical effectiveness, which may inform an associated economic evaluation. Time-to-event outcomes are often synthesized using effect measures from Cox proportional hazards models assuming a constant hazard ratio over time. However, where treatment effects vary over time an assumption of proportional hazards is not always valid. Several methods have been proposed for synthesizing time-to-event outcomes in the presence of non-proportional hazards. However, guidance on choosing between these methods and the implications for HTA is lacking.MethodsWe applied five methods for estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption to a network of melanoma trials, reporting overall survival: restricted mean survival time, an accelerated failure time generalized gamma model, piecewise exponential, fractional polynomial and Royston-Parmar models. We conducted a simulation study to compare these five methods. Simulated individual patient data was generated from a mixture Weibull distribution assuming a treatment-time interaction. Each simulated meta-analysis consisted of five trials with varying numbers of patients and length of follow-up across trials. For each model fitted to each dataset, we calculated the restricted mean survival time at the end of observed follow-up and following extrapolation to a 20-year time horizon.ResultsAll models fitted the melanoma data reasonably well with some variation in the treatment rankings and differences in the survival curves. The simulation study demonstrated the potential for different conclusions from different modelling approaches.ConclusionsThe restricted mean survival time, generalized gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can all accommodate non-proportional hazards and differing lengths of trial follow-up within an evidence synthesis of time-to-event outcomes. Further work is needed in this area to extend the simulation study to the network meta-analysis setting and provide guidance on the key considerations for informing model choice for the purposes of HTA.