Search results for: clinical prediction models

Authors: Kung-Jen Tu, Danny Vernatha

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To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.

Keywords: database, electricity sub-meters, energy anomaly detection, sensor

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Authors: Rahy Farooq, Maria Ahmad Khan, Ayesha Isani Majeed

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Objective: The main aim of this research is to assess the knowledge, attitude and practices of female nursing staff and nursing students regarding breast cancer, to provide a baseline for monitoring trends of breast cancer awareness in them. Background: Healthcare professionals are a direct source of information for the patients and the general public as a whole. It is, therefore, essential that the information they convey be accurate and helps in building additional awareness. However, clinical experience does not influence the knowledge, attitude and practices regarding breast cancer. Nurses, being the prime part of the healthcare professionals, play a significant role and hence, their awareness regarding this pressing issue is pertinent. Lack of awareness regarding common presenting symptoms or breast cancer risk factors translates to poor breast cancer screening practices and late diagnosis. Methodology: A cross-sectional study of 280 female nurses was conducted at a tertiary care hospital in Islamabad, Pakistan. A pre-tested structured questionnaire with additional variables like cultural barriers to seeking medical help was used. The scores for outcome variables including knowledge, attitude and practices were pre-defined. Data was analyzed using SPSSv23. Results: Of the 280 participants with a mean age of 28.99±9.98 years, 142 (50.7%) were married, and 138 (49.3%) were unmarried. Mean scores were computed to be 6.14±2.93 (out of 12), 0.30±0.7 (out of 3) and 9.53±1.92 (out of 16) for knowledge, attitude and practice respectively. Using independent sample T-test, a statistically significant correlation was found when means for the score of Attitude was compared with age. With a p-value of 0.018, 117 nurses of age more than 30 years, faced more practical, financial, emotional and service barriers as compared to 163 women younger than 30 years of age. Knowledge of age-related lifetime risks was also significantly poor more in single women; with a p-value of 0.006 for identification of correct age as a risk factor and a p-value of 0.005 for correct identification of risk for development of breast cancer in the lifetime of women. By application of Chi-square test, there was a significant correlation between marital status and cultural barriers to seeking medical help, showing that single women (58.7%) shy away from talking about breast cancer considering it a taboo (p-value 0.028) whereas, more married nurses (59.2%) were apprehensive that they might be considered at fault by the society, as compared to 40.8% of single nurses. (p-value 0.038). Conclusion: Owing to the scarcity of awareness among nurses, this study recognizes the need for delivering effective information to the female nurses regarding breast cancer. Educating patients is likely to be effective if the female nurses play their part and have correct attitudes towards breast cancer practices. A better understanding of the knowledge and practices regarding breast cancer among the nursing population will enable high-risk patients to be recognized early. Therefore, we recommend arrangement of special courses and seminars for all healthcare professionals including the nursing staff.

Keywords: breast cancer, cultural barriers, kap, nurses

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Authors: Berk Ecer, Ebru Akcapinar Sezer

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Traditional management models of intersections, such as no-light intersections or signalized intersection, are not the most effective way of passing the intersections if the vehicles are intelligent. To this end, Dresner and Stone proposed a new intersection control model called Autonomous Intersection Management (AIM). In the AIM simulation, they were examining the problem from a multi-agent perspective, demonstrating that intelligent intersection control can be made more efficient than existing control mechanisms. In this study, autonomous intersection management has been investigated. We extended their works and added a potential-based lane organization layer. In order to distribute vehicles evenly to each lane, this layer triggers vehicles to analyze near lanes, and they change their lane if other lanes have an advantage. We can observe this behavior in real life, such as drivers, change their lane by considering their intuitions. Basic intuition on selecting the correct lane for traffic is selecting a less crowded lane in order to reduce delay. We model that behavior without any change in the AIM workflow. Experiment results show us that intersection performance is directly connected with the vehicle distribution in lanes of roads of intersections. We see the advantage of handling lane management with a potential approach in performance metrics such as average delay of intersection and average travel time. Therefore, lane management and intersection management are problems that need to be handled together. This study shows us that the lane through which vehicles enter the intersection is an effective parameter for intersection management. Our study draws attention to this parameter and suggested a solution for it. We observed that the regulation of AIM inputs, which are vehicles in lanes, was as effective as contributing to aim intersection management. PLO-AIM model outperforms AIM in evaluation metrics such as average delay of intersection and average travel time for reasonable traffic rates, which is in between 600 vehicle/hour per lane to 1300 vehicle/hour per lane. The proposed model reduced the average travel time reduced in between %0.2 - %17.3 and reduced the average delay of intersection in between %1.6 - %17.1 for 4-lane and 6-lane scenarios.

Keywords: AIM project, autonomous intersection management, lane organization, potential-based approach

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Authors: Roengchai Tansuchat, Woraphon Yamaka, Paravee Maneejuk

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From the past recent, the global financial crisis, economic instability, and large fluctuation in agricultural commodity price have led to increased concerns about the volatility transmission among them. The problem is further exacerbated by commodities volatility caused by other commodity price fluctuations, hence the decision on hedging strategy has become both costly and useless. Thus, this paper is conducted to analysis the volatility spillover effect among major agriculture including corn, soybeans, wheat and rice, to help the commodity suppliers hedge their portfolios, and manage the risk and co-volatility of them. We provide a switching regime approach to analyzing the issue of volatility spillovers in different economic conditions, namely upturn and downturn economic. In particular, we investigate relationships and volatility transmissions between these commodities in different economic conditions. We purposed a Copula-based multivariate Markov Switching GARCH model with two regimes that depend on an economic conditions and perform simulation study to check the accuracy of our proposed model. In this study, the correlation term in the cross-hedge ratio is obtained from six copula families – two elliptical copulas (Gaussian and Student-t) and four Archimedean copulas (Clayton, Gumbel, Frank, and Joe). We use one-step maximum likelihood estimation techniques to estimate our models and compare the performance of these copula using Akaike information criterion (AIC) and Bayesian information criteria (BIC). In the application study of agriculture commodities, the weekly data used are conducted from 4 January 2005 to 1 September 2016, covering 612 observations. The empirical results indicate that the volatility spillover effects among cereal futures are different, as response of different economic condition. In addition, the results of hedge effectiveness will also suggest the optimal cross hedge strategies in different economic condition especially upturn and downturn economic.

Keywords: agricultural commodity futures, cereal, cross-hedge, spillover effect, switching regime approach

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Authors: Michael Campbell, Malaz Elsaddig, Kevin Jones

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Background: Despite encouraging progress, global maternal mortality has remained stubbornly high since the declaration of the Millennium development goals. Sub-Saharan Africa accounts for well over half of maternal deaths with Post-Partum Haemorrhage (PPH) being the lead cause. ‘In house’ simulation training delivered by local doctors may be a sustainable approach for improving emergency obstetric care. The aim of this study was to evaluate the use of a Train-the-Trainer (TtT) model in a rural Ugandan hospital to ascertain whether it can feasibly improve practitioners’ management of PPH. Methods: Three Ugandan doctors underwent a training course to enable them to design and deliver simulation training. These doctors used MamaNatalie® models to simulate PPH scenarios for midwives, nurses and medical students. The main outcome was improvement in participants’ knowledge and confidence, assessed using self-reported scores on a 10-point scale. Results: The TtT model produced significant improvements in the confidence and knowledge scores of the ten participants. The mean confidence score rose significantly (p=0.0005) from 6.4 to 8.6 following the simulation training. There was also a significant increase in the mean knowledge score from 7.2 to 9.0 (p=0.04). Medical students demonstrated the greatest overall increase in confidence scores whilst increases in knowledge scores were largest amongst nurses. Conclusions: This study demonstrates that a TtT model can be used in a low resource setting to improve healthcare professionals’ confidence and knowledge in managing obstetric emergencies. This Train-the-Trainer model represents a sustainable approach to addressing skill deficits in low resource settings. We believe that its expansion across healthcare institutions in Sub-Saharan Africa will help to reduce the region’s high maternal mortality rate and step closer to achieving the ambitions of the Millennium development goals.

Keywords: low resource setting, post-partum haemorrhage, simulation training, train the trainer

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Siwach Sripokangkul, Autthapon Muangming

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The Dao Din group is a gathering of students from the Faculty of Law, Khon Kaen University, a leading university in the northeast of Thailand. The Dao Din group has been one of the most prominent student movements in the past four decades since the bloody massacre of the 6th of October 1976. The group of student is a movement who gather to oppose and protest against different capitalist-run projects that have impacted upon the environment since 2009. The students have become heroes in Thai society and receive support from various groups, especially the middle class who regard the students as role models for the youth. Subsequently, the Dao Din group has received numerous awards between 2011-2013. However, the Dao Din group opposed the military coup d’état of 2014 and the subsequent military junta. Under the military dictatorship regime (2014-present), security officials have hunted, insulted, arrested, and jailed members of the group many times amidst silence from most of the from the middle class. Therefore, this article posits the question of why the Dao Din group which was once the hero and hope of Thai society, has become a political victim in only a few years. The study methods used are the analysis of documentaries, news articles, and interviews with representatives of the Dao Din group. The author argues that Thailand’s middle class previously demonstrated a positive perception of the Dao Din group precisely because that group had earlier opposed policies of the elected Yingluck Shinawatra government, which most of the middle class already despised. However, once the Dao Din group began to protest against the anti-Yingluck military government, then the middle class turned to harshly criticize the Dao Din group. So it can be concluded that the Thai middle class tends to put its partisan interests ahead of a civil society group which has been critical of elected as well as military administrations. This has led the middle class to support the demolishing of Thai democracy. Such a Thai middle-class characteristic not only poses a strong bulwark for the perpetuation of military rule but also destroys a civil society group (composed of young people) who should be the future hope of the nation rather than under the Thai society of darkness.

Keywords: Dao Din student activists, the military coup d’état of 2014, Thai politics, human rights violations

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Authors: Liyu Adhi Kasari Sulung

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Poverty has been a major problem in Indonesia. Islamic micro finance (IMF) named Baitul Maal Wat Tamwil (Bmt) plays a prominent role to eradicate this. Indonesia as the biggest muslim country has many successful applied products such as worldwide adopt group-based lending approach, flexible financing for farmers, and gold pawning. The Problems related to these models are operation risk management and internal control system (ICS). A proper ICS will help an organization in preventing the occurrence of bad financing through detecting error and irregularities in its operation. This study aims to seek a proper risk management scheme of credit system in Bmt and internal control system’s rank for every stage. Risk management variables are obtained at the first In-Depth Interview (IDI) and Focus Group Discussion (FGD) with Shariah supervisory boards, boards of directors, and operational managers. Survey was conducted covering nationwide data; West Java, South Sulawesi, and West Nusa Tenggara. Moreover, Content analysis is employed to build the relationship among these variables. Research Findings shows that risk management Characteristics in Indonesia involves ex ante, credit process, and ex post strategies to deal with risk in credit system. Ex-ante control consists of Shariah compliance, survey, group leader reference, and islamic forming orientation. Then, credit process involves saving, collateral, joint liability, loan repayment, and credit installment controlling. Finally, ex-post control includes shariah evaluation, credit evaluation, grace period and low installment provisions. In addition, internal control order sort three stages by its priority; Credit process as first rank, then ex-post control as second, and ex ante control as the last rank.

Keywords: internal control system, islamic micro finance, poverty, risk management

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Authors: Rajeswari Raguraman, Sowmya Parameswaran, Krishnakumar Subramanian, Jagat Kanwar, Rupinder Kanwar

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Background: Tumor microenvironment has been implicated in several cancers to regulate cell growth, invasion and metastasis culminating in outcome of therapy. Tumor stroma consists of multiple cell types that are in constant cross-talk with the tumor cells to favour a pro-tumorigenic environment. Not much is known about the existence of tumor microenvironment in the pediatric intraocular malignancy, Retinoblastoma (RB). In the present study, we aim to understand the multiple stromal cellular subtypes and tumor stromal interactions expressed in RB tumors. Materials and Methods: Immunohistochemistry for stromal cell markers CD31, CD68, alpha-smooth muscle (α-SMA), vimentin and glial fibrillary acidic protein (GFAP) was performed on formalin fixed paraffin embedded tissues sections of RB (n=12). The differential expression of stromal target molecules; fibroblast activation protein (FAP), tenascin-C (TNC), osteopontin (SPP1), bone marrow stromal antigen 2 (BST2), stromal derived factor 2 and 4 (SDF2 and SDF4) in primary RB tumors (n=20) and normal retina (n=5) was studied by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and Western blotting. The differential expression was correlated with the histopathological features of RB. The interaction between RB cell lines (Weri-Rb-1, NCC-RbC-51) and Bone marrow stromal cells (BMSC) was also studied using direct co-culture and indirect co-culture methods. The functional effect of the co-culture methods on the RB cells was evaluated by invasion and proliferation assays. Global gene expression was studied by using Affymetrix 3’ IVT microarray. Pathway prediction was performed using KEGG and the key molecules were validated using qRT-PCR. Results: The immunohistochemistry revealed the presence of several stromal cell types such as endothelial cells (CD31+;Vim+/-); macrophages (CD68+;Vim+/-); Fibroblasts (Vim+; CD31-;CD68- );myofibroblasts (α-SMA+/ Vim+) and invading retinal astrocytes/ differentiated retinal glia (GFAP+; Vim+). A characteristic distribution of these stromal cell types was observed in the tumor microenvironment, with endothelial cells predominantly seen in blood vessels and macrophages near actively proliferating tumor or necrotic areas. Retinal astrocytes and glia were predominant near the optic nerve regions in invasive tumors with sparse distribution in tumor foci. Fibroblasts were widely distributed with rare evidence of myofibroblasts in the tumor. Both gene and protein expression revealed statistically significant (P<0.05) up-regulation of FAP, TNC and BST2 in primary RB tumors compared to the normal retina. Co-culture of BMSC with RB cells promoted invasion and proliferation of RB cells in direct and indirect contact methods respectively. Direct co-culture of RB cell lines with BMSC resulted in gene expression changes in ECM-receptor interaction, focal adhesion, IL-8 and TGF-β signaling pathways associated with cancer. In contrast, various metabolic pathways such a glucose, fructose and amino acid metabolism were significantly altered under the indirect co-culture condition. Conclusion: The study suggests that the close interaction between RB cells and the stroma might be involved in RB tumor invasion and progression which is likely to be mediated by ECM-receptor interactions and secretory factors. Targeting the tumor stroma would be an attractive option for redesigning treatment strategies for RB.

Keywords: gene expression profiles, retinoblastoma, stromal cells, tumor microenvironment

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Authors: Sousan Arafeh

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Educational systems and schools worldwide are increasingly reliant on information and communication technology (ICT). Unfortunately, most educational leadership development programs do not offer formal curricular and/or field experiences that prepare students for managing ICT resources, personnel, and processes. The result is a steep learning curve for the leader and his/her staff and dissipated organizational energy that compromises desired outcomes. To address this gap in education leaders’ development, Arafeh’s Integrated Technology Leadership Model (AITLM) was created. It is a conceptual model and tool that educational leadership students can use to better understand the ICT ecology that exists within their schools. The AITL Model consists of six 'infrastructure types' where ICT activity takes place: technical infrastructure, communications infrastructure, core business infrastructure, context infrastructure, resources infrastructure, and human infrastructure. These six infrastructures are further divided into 16 key areas that need management attention. The AITL Model was created by critically analyzing existing technology/ICT leadership models and working to make something more authentic and comprehensive regarding school leaders’ purview and experience. The AITL Model then served as a tool when it was distributed to over 150 educational leadership students who were asked to review it and qualitatively share their reactions. Students said the model presented crucial areas of consideration that they had not been exposed to before and that the exercise of reviewing and discussing the AITL Model as a group was useful for identifying areas of growth that they could pursue in the leadership development program and in their professional settings. While development in all infrastructures and key areas was important for students’ understanding of ICT, they noted that they were least aware of the importance of the intangible area of the resources infrastructure. The AITL Model will be presented and session participants will have an opportunity to review and reflect on its impact and utility. Ultimately, the AITL Model is one that could have significant policy and practice implications. At the very least, it might help shape ICT content in educational leadership development programs through curricular and pedagogical updates.

Keywords: education leadership, information and communications technology, ICT, leadership capacity building, leadership development

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Authors: Umberto Di Staso, Marco Soave, Alessio Giori, Federico Prandi, Raffaele De Amicis

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The increasing availability of information about earth surface elevation (Digital Elevation Models DEM) generated from different sources (remote sensing, Aerial Images, Lidar) poses the question about how to integrate and make available to the most than possible audience this huge amount of data. In order to exploit the potential of 3D elevation representation the quality of data management plays a fundamental role. Due to the high acquisition costs and the huge amount of generated data, highresolution terrain surveys tend to be small or medium sized and available on limited portion of earth. Here comes the need to merge large-scale height maps that typically are made available for free at worldwide level, with very specific high resolute datasets. One the other hand, the third dimension increases the user experience and the data representation quality, unlocking new possibilities in data analysis for civil protection, real estate, urban planning, environment monitoring, etc. The open-source 3D virtual globes, which are trending topics in Geovisual Analytics, aim at improving the visualization of geographical data provided by standard web services or with proprietary formats. Typically, 3D Virtual globes like do not offer an open-source tool that allows the generation of a terrain elevation data structure starting from heterogeneous-resolution terrain datasets. This paper describes a technological solution aimed to set up a so-called “Terrain Builder”. This tool is able to merge heterogeneous-resolution datasets, and to provide a multi-resolution worldwide terrain services fully compatible with CesiumJS and therefore accessible via web using traditional browser without any additional plug-in.

Keywords: Terrain Builder, WebGL, Virtual Globe, CesiumJS, Tiled Map Service, TMS, Height-Map, Regular Grid, Geovisual Analytics, DTM

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Authors: Miao Li, Mengyuan Xu

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As a low-carbon travel mode, bicycling has drawn increasing political interest in the contemporary Chinese urban context, and the public sharing bikes have become the most popular ways of bike usage in China now. This research aims to explore the spatial-temporal relationship between sharing bike usage and different characteristics of the urban street built environment. In the research, street segments were used as the analytic unit of the street built environment defined by street intersections. The sharing bike usage data in the research include a total of 2.64 million samples that are the entire sharing bike distribution data recorded in two days in 2018 within a neighborhood of 185.4 hectares in the city of Wuhan, China. And these data are assigned to the 97 urban street segments in this area based on their geographic location. The built environment variables used in this research are categorized into three sections: 1) street design characteristics, such as street width, street greenery, types of bicycle lanes; 2) condition of other public transportation, such as the availability of metro station; 3) Street function characteristics that are described by the categories and density of the point of interest (POI) along the segments. Spatial Lag Models (SLM) were used in order to reveal the relationships of specific urban streets built environment characteristics and the likelihood of sharing bicycling usage in whole and different periods a day. The results show: 1) there is spatial autocorrelation among sharing bicycling usage of urban streets in case area in general, non-working day, working day and each period of a day, which presents a clustering pattern in the street space; 2) a statistically strong association between bike sharing usage and several different built environment characteristics such as POI density, types of bicycle lanes and street width; 3) the pattern that bike sharing usage is influenced by built environment characteristics depends on the period within a day. These findings could be useful for policymakers and urban designers to better understand the factors affecting bike sharing system and thus propose guidance and strategy for urban street planning and design in order to promote the use of sharing bikes.

Keywords: big data, sharing bike usage, spatial statistics, urban street built environment

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Authors: Jiangang Shen

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Olfactory dysfunction is a common symptom companied by anxiety- and depressive-like behaviors in depressive patients. Chronic stress triggers hormone responses and inhibits the proliferation and differentiation of neural stem cells (NSCs) in the hippocampus and subventricular zone (SVZ)-olfactory bulb (OB), contributing to depressive behaviors and olfactory dysfunction. However, the cellular signaling molecules to regulate chronic stress mediated olfactory dysfunction are largely unclear. Adaptor proteins containing the pleckstrin homology domain, phosphotyrosine binding domain, and leucine zipper motif (APPLs) are multifunctional adaptor proteins. Herein, we tested the hypothesis that APPL2 could inhibit hippocampal neurogenesis by affecting glucocorticoid receptor (GR) signaling, subsequently contributing to depressive and anxiety behaviors as well as olfactory dysfunctions. The major discoveries are included: (1) APPL2 Tg mice had enhanced GR phosphorylation under basic conditions but had no different plasma corticosterone (CORT) level and GR phosphorylation under stress stimulation. (2) APPL2 Tg mice had impaired hippocampal neurogenesis and revealed depressive and anxiety behaviors. (3) GR antagonist RU486 reversed the impaired hippocampal neurogenesis in the APPL2 Tg mice. (4) APPL2 Tg mice displayed higher GR activity and less capacity for neurogenesis at the olfactory system with lesser olfactory sensitivity than WT mice. (5) APPL2 negatively regulates olfactory functions by switching fate commitments of NSCs in adult olfactory bulbs via interaction with Notch1 signaling. Furthermore, baicalin, a natural medicinal compound, was found to be a promising agent targeting APPL2/GR signaling and promoting adult neurogenesis in APPL2 Tg mice and chronic corticosterone-induced depression mouse models. Behavioral tests revealed that baicalin had antidepressant and olfactory-improving effects. Taken together, APPL2 is a critical therapeutic target for antidepressant treatment.

Keywords: APPL2, hippocampal neurogenesis, depressive behaviors and olfactory dysfunction, stress

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Authors: Sujing Wang, Song Wang, Jian Zhang, Qiang Xu

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Flaring emissions during abnormal operating conditions such as plant start-ups, shut-downs, and upsets in chemical process industries (CPI) are usually significant. Flare minimization can help to save raw material and energy for CPI plants, and to improve local environmental sustainability. In this paper, a systematic methodology based on plant-wide dynamic simulation is presented for CPI plant flare minimizations under abnormal operating conditions. Since off-specification emission sources are inevitable during abnormal operating conditions, to significantly reduce flaring emission in a CPI plant, they must be either recycled to the upstream process for online reuse, or stored somewhere temporarily for future reprocessing, when the CPI plant manufacturing returns to stable operation. Thus, the off-spec products could be reused instead of being flared. This can be achieved through the identification of viable design and operational strategies during normal and abnormal operations through plant-wide dynamic scheduling, simulation, and optimization. The proposed study includes three stages of simulation works: (i) developing and validating a steady-state model of a CPI plant; (ii) transiting the obtained steady-state plant model to the dynamic modeling environment; and refining and validating the plant dynamic model; and (iii) developing flare minimization strategies for abnormal operating conditions of a CPI plant via a validated plant-wide dynamic model. This cost-effective methodology has two main merits: (i) employing large-scale dynamic modeling and simulations for industrial flare minimization, which involves various unit models for modeling hundreds of CPI plant facilities; (ii) dealing with critical abnormal operating conditions of CPI plants such as plant start-up and shut-down. Two virtual case studies on flare minimizations for start-up operation (over 50% of emission savings) and shut-down operation (over 70% of emission savings) of an ethylene plant have been employed to demonstrate the efficacy of the proposed study.

Keywords: flare minimization, large-scale modeling and simulation, plant shut-down, plant start-up

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Authors: Pranay Mathur, Carlo Michelassi, Simi Karatha, Gilda Pedoto

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Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start has major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these CTQs are measured with 2 metrics, MTBT(Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability. Baker Hughes Trip reduction program structured to reduce these unwanted trip 1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition. 2. Real time alerting system based on analytics available remotely. 3. Remote access to trip logs and alarms from control system to identify the cause of events. 4. Continuous support to field engineers by remotely connecting with subject matter expert. 5. Live tracking of key CTQs 6. Benchmark against fleet 7. Break down to the cause of failure to component level 8. Investigate top contributor, identify design and operational root cause 9. Implement corrective and preventive action 10. Assessing effectiveness of implemented solution using reliability growth models. 11. Develop analytics for predictive maintenance With this approach , Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This Presentation explains these approach while providing successful case studies, in particular where 12nos. of LNG and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT

Keywords: reliability, availability, sustainability, digital infrastructure, weibull, effectiveness, automation, trips, fail start

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Authors: Tomiwa, Akinyemi Clement

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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.

Keywords: remote sensing, precipitation, drop size distribution, micro rain radar

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Authors: S. Hatamikia, A. Biguri, H. Furtado, G. Kronreif, J. Kettenbach, W. Birkfellner

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Nowadays, three dimensional Cone Beam CT (CBCT) has turned into a widespread clinical routine imaging modality for interventional radiology. In conventional CBCT, a circular sourcedetector trajectory is used to acquire a high number of 2D projections in order to reconstruct a 3D volume. However, the accumulated radiation dose due to the repetitive use of CBCT needed for the intraoperative procedure as well as daily pretreatment patient alignment for radiotherapy has become a concern. It is of great importance for both health care providers and patients to decrease the amount of radiation dose required for these interventional images. Thus, it is desirable to find some optimized source-detector trajectories with the reduced number of projections which could therefore lead to dose reduction. In this study we investigate some source-detector trajectories with the optimal arbitrary orientation in the way to maximize performance of the reconstructed image at particular regions of interest. To achieve this approach, we developed a box phantom consisting several small target polytetrafluoroethylene spheres at regular distances through the entire phantom. Each of these spheres serves as a target inside a particular region of interest. We use the 3D Point Spread Function (PSF) as a measure to evaluate the performance of the reconstructed image. We measured the spatial variance in terms of Full-Width-Half-Maximum (FWHM) of the local PSFs each related to a particular target. The lower value of FWHM shows the better spatial resolution of reconstruction results at the target area. One important feature of interventional radiology is that we have very well-known imaging targets as a prior knowledge of patient anatomy (e.g. preoperative CT) is usually available for interventional imaging. Therefore, we use a CT scan from the box phantom as the prior knowledge and consider that as the digital phantom in our simulations to find the optimal trajectory for a specific target. Based on the simulation phase we have the optimal trajectory which can be then applied on the device in real situation. We consider a Philips Allura FD20 Xper C-arm geometry to perform the simulations and real data acquisition. Our experimental results based on both simulation and real data show our proposed optimization scheme has the capacity to find optimized trajectories with minimal number of projections in order to localize the targets. Our results show the proposed optimized trajectories are able to localize the targets as good as a standard circular trajectory while using just 1/3 number of projections. Conclusion: We demonstrate that applying a minimal dedicated set of projections with optimized orientations is sufficient to localize targets, may minimize radiation.

Keywords: CBCT, C-arm, reconstruction, trajectory optimization

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Authors: Ismaila Olabisi Ogundiji, Hakeem Mayowa Olujide, Qasim Usamot

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The main reasons for site-specific management for agricultural inputs are to increase the profitability of crop production, to protect the environment and to improve products’ quality. Information about the variability of different soil attributes within a field is highly essential for the decision-making process. Lack of fast and accurate acquisition of soil characteristics remains one of the biggest limitations of precision agriculture due to being expensive and time-consuming. Adaptive sampling has been proven as an accurate and affordable sampling technique for planning within a field for site-specific management of agricultural inputs. This study employed spatial uncertainty of soil apparent electrical conductivity (ECa) estimates to identify adaptive re-survey areas in the field. The original dataset was grouped into validation and calibration groups where the calibration group was sub-grouped into three sets of different measurements pass intervals. A conditional simulation was performed on the field ECa to evaluate the ECa spatial uncertainty estimates by the use of the geostatistical technique. The grouping of high-uncertainty areas for each set was done using image segmentation in MATLAB, then, high and low area value-separate was identified. Finally, an adaptive re-survey was carried out on those areas of high-uncertainty. Adding adaptive re-surveying significantly minimized the time required for resampling whole field and resulted in ECa with minimal error. For the most spacious transect, the root mean square error (RMSE) yielded from an initial crude sampling survey was minimized after an adaptive re-survey, which was close to that value of the ECa yielded with an all-field re-survey. The estimated sampling time for the adaptive re-survey was found to be 45% lesser than that of all-field re-survey. The results indicate that designing adaptive sampling through spatial uncertainty models significantly mitigates sampling cost, and there was still conformity in the accuracy of the observations.

Keywords: soil electrical conductivity, adaptive sampling, conditional simulation, spatial uncertainty, site-specific management

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Authors: Weber-Lewerenz Bianca

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Digitization is developing fast and has become a powerful tool for digital planning, construction, and operations. Its transformation bears high potentials for companies, is critical for success, and thus, requires responsible handling. This study provides an assessment of calls made in the sustainable development goals by the United Nations (SDGs), White Papers on AI by international institutions, EU-Commission and German Government requesting for the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of artificial intelligence (AI) in construction engineering from an ethical perspective by generating data via conducting case studies and interviewing experts as part of the qualitative method. This research evaluates critically opportunities and risks revolving around corporate digital responsibility (CDR) in the construction industry. To the author's knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to the digitization and AI, to mitigate digital transformation both in large, medium-sized, and small companies. No study addressed the key research question: Where can CDR be allocated, how shall its adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Now is the right timing for constructive approaches and apply ethics-by-design in order to develop and implement a safe and efficient AI. This represents the first study in construction engineering applying a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation, examine benefits of AI and define ethical principles as the key driver for success, resources-cost-time efficiency, and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. Innovative corporate organizations starting new business models are more likely to succeed than those dominated by conservative, traditional attitudes.

Keywords: construction engineering, digitization, digital transformation, artificial intelligence, ethics, corporate digital responsibility, digital innovation

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Authors: Chalachew Gashu, Yoseph Kassa, Habtamu Geremew, Mengestie Mulugeta

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Background and Aims: Severe acute malnutrition remains a significant health challenge, particularly in low‐ and middle‐income countries. The aim of this study was to determine the survival time of under‐five children with severe acute malnutrition. Methods: A retrospective cohort study was conducted at a hospital, focusing on under‐five children with severe acute malnutrition. The study included 322 inpatients admitted to the Chiro hospital in Chiro, Ethiopia, between September 2019 and August 2020, whose data was obtained from medical records. Survival functions were analyzed using Kaplan‒Meier plots and log‐rank tests. The survival time of severe acute malnutrition was further analyzed using the Cox proportional hazards model and Bayesian parametric survival models, employing integrated nested Laplace approximation methods. Results: Among the 322 patients, 118 (36.6%) died as a result of severe acute malnutrition. The estimated median survival time for inpatients was found to be 2 weeks. Model selection criteria favored the Bayesian Weibull accelerated failure time model, which demonstrated that age, body temperature, pulse rate, nasogastric (NG) tube usage, hypoglycemia, anemia, diarrhea, dehydration, malaria, and pneumonia significantly influenced the survival time of severe acute malnutrition. Conclusions: This study revealed that children below 24 months, those with altered body temperature and pulse rate, NG tube usage, hypoglycemia, and comorbidities such as anemia, diarrhea, dehydration, malaria, and pneumonia had a shorter survival time when affected by severe acute malnutrition under the age of five. To reduce the death rate of children under 5 years of age, it is necessary to design community management for acute malnutrition to ensure early detection and improve access to and coverage for children who are malnourished.

Keywords: Bayesian analysis, severe acute malnutrition, survival data analysis, survival time

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Authors: Wenyan Xu, Rundong Wang, Chen Li, Yonghong Hu, Zhonghua Lu

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In the field of quantitative trading, it is common to find patterns in short-term volatile trends of the market. These patterns are known as High-Frequency (HF) risk factors, serving as effective indicators of future stock price volatility. However, in the past, these risk factors were usually generated by traditional financial models, and the validity of these risk factors is heavily based on domain-specific knowledge manually added instead of extensive market data. Inspired by symbolic regression (SR), the task of inferring mathematical laws from existing data, we take the extraction of formulaic risk factors from high-frequency trading (HFT) market data as an SR task. In this paper, we challenge the procedure of manually constructing risk factors and propose an end-to-end methodology, Intraday Risk Factor Transformer (IRFT) to directly predict the full formulaic factors, constants included. Specifically, we utilize a hybrid symbolic-numeric vocabulary where symbolic tokens denote operators/stock features and numeric tokens denote constants. Then, we train a Transformer model on the HFT dataset to directly generate complete formulaic HF risk factors without relying on the skeleton, which is a parametric function using a pre-defined list of operators – typically, the math operations (+, ×, /) and functions(√x, log x, cos x). It determines the general shape of the stock volatility law up to a choice of constants, e.g., f(x) = tan(ax+b) (x is the stock price). We further refine predicted constants(a,b) using the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) as informed guesses to mitigate non-linear issues. Compared to the 10 approaches in SRBench, which is a living benchmark for SR, IRFT gains a 30% excess investment return on the HS300 and S&P500 datasets, with inference times orders of magnitude faster than theirs in HF risk factor mining tasks.

Keywords: transformer, factor-mining language model, highfrequency risk factor collections

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Authors: Ziwen Fang, Jianran Wang, Hongtao Liu, Weiguo Kong, Kefei Wang, Qi Luo, Haifeng Hong

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A 4-stage crash energy management (CEM) system for subway rail vehicles used by Massachusetts Bay Transportation Authority (MBTA) in the USA is developed in this paper. The 4 stages of this new CEM system include 1) energy absorbing coupler (draft gear and shear bolts), 2) primary energy absorbers (aluminum honeycomb structured box), 3) secondary energy absorbers (crush tube), and 4) collision post and corner post. A sliding anti-climber and a fixed anti-climber are designed at the front of the vehicle cooperating with the 4-stage CEM to maximize the energy to be absorbed and minimize the damage to passengers and crews. In order to investigate the effectiveness of this CEM system, both finite element (FE) methods and crashworthiness test have been employed. The whole vehicle consists of 3 married pairs, i.e., six cars. In the FE approach, full-scale railway car models are developed and different collision cases such as a single moving car impacting a rigid wall, two moving cars into a rigid wall, two moving cars into two stationary cars, six moving cars into six stationary cars and so on are investigated. The FE analysis results show that the railway vehicle incorporating this CEM system has a superior crashworthiness performance. In the crashworthiness test, a simplified vehicle front end including the sliding anti-climber, the fixed anti-climber, the primary energy absorbers, the secondary energy absorber, the collision post and the corner post is built and impacted to a rigid wall. The same test model is also analyzed in the FE and the results such as crushing force, stress, and strain of critical components, acceleration and velocity curves are compared and studied. FE results show very good comparison to the test results.

Keywords: railway vehicle collision, crash energy management design, finite element method, crashworthiness test

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Authors: Aziz Ikhlef

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Most of multi-slices Computed Tomography (CT) scanners are built with detectors composed of scintillator - photodiodes arrays. The photodiodes arrays are mainly based on front-illuminated technology for detectors under 64 slices and on back-illuminated photodiode for systems of 64 slices or more. The designs based on back-illuminated photodiodes were being investigated for CT machines to overcome the challenge of the higher number of runs and connection required in front-illuminated diodes. In backlit diodes, the electronic noise has already been improved because of the reduction of the load capacitance due to the routing reduction. This is translated by a better image quality in low signal application, improving low dose imaging in large patient population. With the fast development of multi-detector-rows CT (MDCT) scanners and the increasing number of examinations, the clinical community has raised significant concerns on radiation dose received by the patient in both medical and regulatory community. In order to reduce individual exposure and in response to the recommendations of the International Commission on Radiological Protection (ICRP) which suggests that all exposures should be kept as low as reasonably achievable (ALARA), every manufacturer is trying to implement strategies and solutions to optimize dose efficiency and image quality based on x-ray emission and scanning parameters. The added demands on the CT detector performance also comes from the increased utilization of spectral CT or dual-energy CT in which projection data of two different tube potentials are collected. One of the approaches utilizes a technology called fast-kVp switching in which the tube voltage is switched between 80 kVp and 140 kVp in fraction of a millisecond. To reduce the cross-contamination of signals, the scintillator based detector temporal response has to be extremely fast to minimize the residual signal from previous samples. In addition, this paper will present an overview of detector technologies and image chain improvement which have been investigated in the last few years to improve the signal-noise ratio and the dose efficiency CT scanners in regular examinations and in energy discrimination techniques. Several parameters of the image chain in general and in the detector technology contribute in the optimization of the final image quality. We will go through the properties of the post-patient collimation to improve the scatter-to-primary ratio, the scintillator material properties such as light output, afterglow, primary speed, crosstalk to improve the spectral imaging, the photodiode design characteristics and the data acquisition system (DAS) to optimize for crosstalk, noise and temporal/spatial resolution.

Keywords: computed tomography, X-ray detector, medical imaging, image quality, artifacts

Procedia PDF Downloads 182

Authors: Hamdan Hamood Aldumaini, Amjad Hussein Meqdam, Shamsaldeen kassim Ali, Hamed Mohammed Al-Yousefi, Haron Ahmed Al-Badawi

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Background: Autism Spectrum Disorder (ASD) is a complex developmental challenge characterized by significant impairments in social interaction, communication, and behavioral patterns. Diagnosing ASD is challenging due to the lack of definitive medical tests, making early identification crucial. Therefore, increasing people's awareness about autism leads to early diagnosis and better prognosis. Objective: Our study aims to identify the initial symptoms prompting families to seek medical advice, determine the timeline between symptom onset and formal diagnosis, and explore methods for assessing the severity of ASD. Subjects and Methods: The study design employed was a descriptive cross-sectional design, which was suitable for the nature of the research. The data collection took place from March 5, 2022, to April 5, 2022, in Autism Rehabilitation Centers in Sana'a, Yemen. The study population consisted of all children who were diagnosed with autism and visited Autism rehabilitation centers in Sana'a city. The sample size was determined using Epi info version 7, and a total population of 587 autistic children attending the treatment was calculated, but only 250 children were included in this study (176 were male vs. 74 female). Result: In terms of sociability problems, it was found that a significant proportion of Yemeni children with autism experienced difficulties in this area. Specifically, 39.6% were classified as having severe sociability problems, while 28.4% were classified as having moderate issues. Sensory-cognitive awareness problems were also prevalent among the respondents, with 29.6% exhibiting severe difficulties in this domain. Health and physical problems were identified as significant concerns for Yemeni children with autism. The results indicated that 38.4% of the participants experienced severe health and physical issues. Identifying the first symptoms of autism is crucial for early detection and intervention. According to the study, speech delay was the most commonly observed first abnormality, reported by 71.3% of parents. Communication difficulties with others were the second most noticed abnormality, reported by 54.9% of parents. Repetitive movements were the third most commonly observed abnormality, reported by 18% of parents. Regarding the awareness among parents of ASD, our study showed that a significant portion (62%) of parents lack awareness about Autism Spectrum Disorder (ASD) and its causes. Surprisingly, a majority of these parents (over 80%) believe that autism is a curable condition. Additionally, more than half (51.2%) of the parents surveyed reported insufficient knowledge about medication options available to support therapy and rehabilitation for their autistic children.

Keywords: autism characteristics, rehabilitation centres, yemen, children

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Authors: Mustafa Metin Donma, Orkide Donma

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Vitamin deficiencies are common in obese individuals. Particularly, the status of vitamin B12 and its association with vitamin B9 (folate) and vitamin D is under investigation in recent time. Vitamin B12 is closely related to many vital processes in the body. In clinical studies, its involvement in fat metabolism draws attention from the obesity point of view. Obesity, in its advanced stages and in combination with metabolic syndrome (MetS) findings, may be a life-threatening health problem. Pediatric obesity is particularly important because it may be a predictor of severe chronic diseases during the adulthood period of the child. Due to its role in fat metabolism, vitamin B12 deficiency may disrupt metabolic pathways of the lipid and energy metabolisms in the body. The association of low B12 levels with obesity degree may be an interesting topic to be investigated. Obesity indices may be helpful at this point. Weight- and fat-based indices are available. Of them, body mass index (BMI) is in the first group. Fat mass index (FMI), fat-free mass index (FFMI) and diagnostic obesity notation model assessment-II (D2I) index lie in the latter group. The aim of this study is to clarify possible associations between vitamin B12 status and obesity indices in the pediatric population. The study comprises a total of one hundred and twenty-two children. Thirty-two children were included in the normal body mass index (N-BMI) group. Forty-six and forty-four children constitute groups with morbid obese children without MetS and with MetS, respectively. Informed consent forms and the approval of the institutional ethics committee were obtained. Tables prepared for obesity classification by World Health Organization were used. Metabolic syndrome criteria were defined. Anthropometric and blood pressure measurements were taken. Body mass index, FMI, FFMI, D2I were calculated. Routine laboratory tests were performed. Vitamin B9, B12, D concentrations were determined. Statistical evaluation of the study data was performed. Vitamin B9 and vitamin D levels were reduced in MetS group compared to children with N-BMI (p>0.05). Significantly lower values were observed in vitamin B12 concentrations of MetS group (p<0.01). Upon evaluation of blood pressure as well as triglyceride levels, there exist significant increases in morbid obese children. Significantly decreased concentrations of high density lipoprotein cholesterol were observed. All of the obesity indices and insulin resistance index exhibit increasing tendency with the severity of obesity. Inverse correlations were calculated between vitamin D and insulin resistance index as well as vitamin B12 and D2I in morbid obese groups. In conclusion, a fat-based index, D2I, was the most prominent body index, which shows a strong correlation with vitamin B12 concentrations in the late stage of obesity in children. A negative correlation between these two parameters was a confirmative finding related to the association between vitamin B12 and obesity degree.

Keywords: body mass index, children, D2I index, fat mass index, obesity

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Authors: John Gaber

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Objectives: Complementary and alternative medicine (CAM) is a medicine or health practice that is used alongside conventional practice. Nowadays, CAM is commonly used in North America and other countries, and there is a need for more scientific study to understand its efficacy in different clinical cases. This retrospective study explores the effectiveness and recovery time of CAMs such as cupping, acupuncture, and sotai to treat cases of non-specific low back pain (ANLBP). Methods: We assessed the effectiveness of acupuncture, cupping, and sotai methods on pain and for the treatment of ANLBP. We have compared the magnitude of pain relief using a pain scale assessment method to compare the efficacy of each treatment. The Face Pain Scale assessment was conducted before and 24 hours post-treatment. This retrospective study analyzed 40 patients and categorized them according to the treatment they received. The study included the control group, and the three intervention groups, each with ten patients. Each of the three intervention groups received one of the intervention methods. The first group received the cupping treatment, where cups were placed on the lower back of both sides on points: BL23, BL25, BL26, BL54, BL37, BL40, and BL57. After vacuuming, the cups will stay for 10-15 minutes under infrared light (IR) heating. IR heating is applied by an infrared heat lamp. The second group received the acupuncture treatment, placing needles on points: BL23, BL25, BL26, BL52BL54, GB30, BL37, BL40, BL57, BL59, BL60, and KI3. The needles will be simulated with IR light. The final group received the sotai treatment, a Japanese form of structural realignment that relieves pain, balance, and mobility -moving the body naturally and spontaneously towards a comfortable direction by focusing on the inner feeling and synchronizing with the patient’s breathing. The SPSS statistical software was used to analyze the data using repeated-measures ANOVA. The data collected demonstrates the change in the FPS assessment method value over the course of treatment. p<0.05 was considered statistically significant. Results: In the cupping, acupuncture, and sotai therapy groups, the mean of the FPS value reduced from 8.7±1.2, 8.8±1.2, 9.0±0.8 before the intervention to 3.5±1.4, 4.3±1.4, 3.3±1.3, 24 hours after the intervention, respectively. The data collected shows that the CAM methods included in this study all show improvements in pain relief 24 hours after treatment. Conclusion: Complementary and alternative medicine were developed to treat injuries and illnesses with the whole body in mind, designed to be used in addition to standard treatments. The data above shows that the use of these treatments can have a pain-relieving effect, but more research should be done on the matter, as finding CAM methods that are efficacious is crucial in the landscape of health sciences.

Keywords: acupuncture, cupping, alternative medicine, rehabilitation, acute injury

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Authors: Prabir Patel, Eugene Ming Han Lim

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Introduction: Current literature shows that postoperative pulmonary complications following abdominal surgery may be reduced by using lower than conventional tidal volumes intraoperatively together with moderate levels of positive end expiratory pressure (PEEP). The recent studies demonstrated significant reduction demonstrated significant reduction in major complications in elective abdominal surgery through the use of lower tidal volumes (6-8 ml/kg predicted body weight), PEEP of 5 cmH20 and recruitment manoeuvres compared to higher ‘conventional’ volumes (10-12 mls/kg PBW) without lung recruitment. Our objective was to retrospectively audit current practice for patients undergoing major abdominal surgery in Sir Charles Gairdner Hospital. Methods: Patients over 18 undergoing elective general surgery lasting more than 3 hours and intubated during the duration of procedure were included in this audit. Data was collected over a 6 month period. Patients who had hepatic surgery, procedures necessitating one-lung ventilation, transplant surgery, documented history of pulmonary or intracranial hypertension were excluded. Results: 58 suitable patients were identified and notes were available for 54 patients. Key findings: Average peak airway pressure was 21cmH20 (+4), average peak airway pressure was less than 30 cmH20 in all patients, and less than 25 cmH20 in 80% of the cases. PEEP was used in 81% of the cases. Where PEEP was used, 75% used PEEP more than or equal to 5 cmH20. Average tidal volume per actual body weight was 7.1 ml/kg (+1.6). Average tidal volume per predicted body weight (PBW) was 8.8 ml/kg (+1.5). Average tidal volume was less than 10 ml/kg PBW in 90% of cases; 6-8 ml/kg PBW in 40% of the cases. There was no recorded use of recruitment manoeuvres in any cases. Conclusions: In the vast majority of patients undergoing prolonged abdominal surgery, a lung protective strategy using moderate levels of PEEP, peak airway pressures of less than 30 cmH20 and tidal volumes of less than 10 cmH20/kg PBW was utilised. A recent randomised control trial demonstrated benefit from utilising even lower volumes (6-8 mls/kg) based on findings in critical care patients, but this was compared to volumes of 10-12 ml/kg. Volumes of 6-8 ml/kg PBW were utilised in 40% of cases in this audit. Although theoretically beneficial, clinical benefit of lower volumes than what is currently practiced in this institution remains to be seen. The incidence of pulmonary complications was much lower than in the other cited studies and a larger data set would be required to investigate any benefit from lower tidal volume ventilation. The volumes used are comparable to results from published local and international data but PEEP utilisation was higher in this audit. Strategies that may potentially be implemented to ensure and maintain best practice include pre-operative recording of predicted body weight, adjustment of default ventilator settings and education/updates of current evidence.

Keywords: anaesthesia, intraoperative ventilation, PEEP, tidal volume

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Authors: Vishal Raj, Noorhan Abbas

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Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)

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Authors: Aakriti Chetan Shah, Elle Sein

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Background/purpose: Group B Streptococcus(GBS) is the leading cause of severe early onset infection in newborns, with the incidence of Early Onset Group B Streptococcus (EOGBS) in the UK and Ireland rising from 0.48 to 0.57 per 1000 births from 2000 to 2015. A WHO study conducted in 2017, has shown that 38.5% of cases can result in stillbirth and infant deaths. This is an important problem to consider as 20% of women worldwide have GBS colonisation and can suffer from these detrimental effects. Current Royal College of Obstetricians and Midwives (RCOG) guidelines do not recommend bacteriological screening for pregnant women due to its low sensitivity in antenatal screening correlating with the neonate having GBS but advise a patient information leaflet be given to pregnant women. However, a Healthcare Safety Investigation Branch (HSIB) 2019 learning report found that only 50% of trusts and health boards reported giving GBS information leaflets to all pregnant mothers. Therefore, this audit aimed to assess current practices of information dissemination about GBS at Chelsea & Westminster (C&W) Hospital. Methodology: A quantitative cross-sectional study was carried out using a questionnaire based on the RCOG GBS guidelines and the HSIB Learning report. The study was conducted in antenatal clinics at Chelsea & Westminster Hospital, from 29th January 2021 to 14th February 2021, with twenty-two practicing obstetricians and midwives participating in the survey. The main outcome measure was the proportion of obstetricians and midwives who disseminate information about GBS to pregnant women, and the reasons behind why they do or do not. Results: 22 obstetricians and midwives responded with 18 complete responses. Of which 12 were obstetricians and 6 were midwives. Only 17% of clinical staff routinely inform all pregnant women about GBS, and do so at varying timeframes of the pregnancy, with an equal split in the first, second and third trimester. The primary reason for not informing women about GBS was influenced by three key factors: Deemed relevant only for patients at high risk of GBS, lack of time in clinic appointments and no routine NHS screening available. Interestingly 58% of staff in the antenatal clinic believe it is necessary to inform all women about GBS and its importance. Conclusion: It is vital for obstetricians and midwives to inform all pregnant women about GBS due to the high prevalence of incidental carriers in the population, and the harmful effects it can cause for neonates. Even though most clinicians believe it is important to inform all pregnant women about GBS, most do not. To ensure that RCOG and HSIB recommendations are followed, we recommend that women should be given this information at 28 weeks gestation in the antenatal clinic. Proposed implementations include an information leaflet to be incorporated into the Mum and Baby app, an informative video and end-to-end digital clinic documentation to include this information sharing prompt.

Keywords: group B Streptococcus, early onset sepsis, Antenatal care, Neonatal morbidity, GBS

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Authors: Mustafa M. Donma, Orkide Donma

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Spexin, expressed in central nervous system, has attracted much interest in feeding behavior, obesity, diabetes, energy metabolism and cardiovascular functions. Fetuin A is known as negative acute phase reactant synthesized in the liver. So far, it has become a major concern of many studies in numerous clinical states. The relationship between the concentrations of spexin as well as fetuin A and the risk for cardiovascular diseases (CVDs) were also investigated. Eosinophils, suggested to be associated with the development of CVDs, are introduced as early indicators of cardiometabolic complications. Patients with elevated platelet count, associated with hypercoagulable state in the body, are also more liable to CVDs. In this study, the aim is to examine the profiles of spexin and fetuin A concomitant with the course of variations detected in eosinophil as well as platelet counts in morbid obese children. Thirty-four children with normal-body mass index (N-BMI) and fifty-one morbid obese (MO) children participated in the study. Written-informed consent forms were obtained prior to the study. Institutional ethics committee approved the study protocol. Age- and sex-adjusted BMI percentile tables prepared by World Health Organization were used to classify healthy and obese children. Mean age ± SEM of the children were 9.3 ± 0.6 years and 10.7 ± 0.5 years in N-BMI and MO groups, respectively. Anthropometric measurements of the children were taken. Body mass index values were calculated from weight and height values. Blood samples were obtained after an overnight fasting. Routine hematologic and biochemical tests were performed. Within this context, fasting blood glucose (FBG), insulin (INS), triglycerides (TRG), high density lipoprotein-cholesterol (HDL-C) concentrations were measured. Homeostatic model assessment for insulin resistance (HOMA-IR) values were calculated. Spexin and fetuin A levels were determined by enzyme-linked immunosorbent assay. Data were evaluated from the statistical point of view. Statistically significant differences were found between groups in terms of BMI, fat mass index, INS, HOMA-IR and HDL-C. In MO group, all parameters increased as HDL-C decreased. Elevated concentrations in MO group were detected in eosinophils (p<0.05) and platelets (p>0.05). Fetuin A levels decreased in MO group (p>0.05). However, decrease was statistically significant in spexin levels for this group (p<0.05). In conclusion, these results have suggested that increases in eosinophils and platelets exhibit behavior as cardiovascular risk factors. Decreased fetuin A behaved as a risk factor suitable to increased risk for cardiovascular problems associated with the severity of obesity. Along with increased eosinophils, increased platelets and decreased fetuin A, decreased spexin was the parameter, which reflects best its possible participation in the early development of CVD risk in MO children.

Keywords: cardiovascular diseases , eosinophils , fetuin A , pediatric morbid obesity , platelets , spexin

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