Search results for: crow search algorithm

Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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Authors: Awatef Ahmed Ben Ramadan, Aqsa Arshad

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Background: There is a huge Muslim migration movement to North America and Europe for several reasons, primarily refuge from war areas and partly to search for better work and educational chances. There are always concerns regarding first-Generation Immigrant women's health and computer literacy, an adequate understanding of the health systems, and the use of the existing healthcare technology and services effectively and efficiently. Language proficiency level, preference for cultural and traditional remedies, socioeconomic factors, fear of stereotyping, limited accessibility to health services, and general unfamiliarity with the existing health services and resources are familiar variables among these women. Aims: The current study aims to assess the health and digital literacy of first-generation Muslim women in Atlanta city. Also, the study aims to examine how the COVID-19 pandemic has encouraged the use of health information technology and increased technology awareness among the targeted women. Methods: The study design is cross-sectional correlational research. The study will be conducted to produce preliminary results that the investigators want to have to supplement an NIH grant application about leveraging information technology to reduce the health inequalities amongst the first-generation immigrant Muslim women in Atlanta City. The investigators will collect the study data in two phases using different tools. Phase one was conducted in June 2022; the investigators used tools to measure health and digital literacy amongst 42 first-generation immigrant Muslim women. Phase two was conducted in November 2022; the investigators measured the Knowledge, Attitude, and Practice (KAP) of using health information technology such as telehealth from a sample of 45 first-generation Muslim immigrant women in Atlanta; in addition, the investigators measured how the current pandemic has affected their KAP to use telemedicine and telehealth services. Both phases' study participants were recruited using convenience sampling methodology. The investigators collected around 40 of 18 years old or older first-generation Muslim immigrant women for both study phases. The study excluded Immigrants who hold work visas and second-generation immigrants. Results: At the point of submitting this abstract, the investigators are still analyzing the study data to produce preliminary results to apply for an NIH grant entitled "Leveraging Health Information Technology (Health IT) to Address and Reduce Health Care Disparities (R01 Clinical Trial Optional)". This research will be the first step of a comprehensive research project to assess and measure health and digital literacy amongst a vulnerable community group. The targeted group might have different points of view from the U.S.-born inhabitants on how to: promote their health, gain healthy lifestyles and habits, screen for diseases, adhere to health treatment and follow-up plans, perceive the importance of using available and affordable technology to communicate with their providers and improve their health, and help in making serious decisions for their health. The investigators aim to develop an educational and instructional health mobile application considering the language and cultural factors that affect immigrants' ability to access different health and social support sources, know their health rights and obligations in their communities, and improve their health behavior and behavior lifestyles.

Keywords: first-generation immigrant Muslim women, telehealth, COVID-19 pandemic, health information technology, health and digital literacy

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Authors: Tatiana Butkova, Nikolai Kibrik, Kristina Malsagova, Alexander Izotov, Alexander Stepanov, Anna Kaysheva

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Relevance. The genetic risk of developing schizophrenia is determined by two factors: single nucleotide polymorphisms and gene copy number variations. The search for serological markers for early diagnosis of schizophrenia is driven by the fact that the first five years of the disease are accompanied by significant biological, psychological, and social changes. It is during this period that pathological processes are most amenable to correction. The aim of this study was to analyze single nucleotide polymorphisms (SNPs) that are hypothesized to potentially influence the onset and development of the endogenous process. Materials and Methods It was analyzed 73 single nucleotide polymorphism variants. The study included 48 patients undergoing inpatient treatment at "Psychiatric Clinical Hospital No. 1" in Moscow, comprising 23 females and 25 males. Inclusion criteria: - Patients aged 18 and above. - Diagnosis according to ICD-10: F20.0, F20.2, F20.8, F21.8, F25.1, F25.2. - Voluntary informed consent from patients. Exclusion criteria included: - The presence of concurrent somatic or neurological pathology, neuroinfections, epilepsy, organic central nervous system damage of any etiology, and regular use of medication. - Substance abuse and alcohol dependence. - Women who were pregnant or breastfeeding. Clinical and psychopathological assessment was complemented by psychometric evaluation using the PANSS scale at the beginning and end of treatment. The duration of observation during therapy was 4-6 weeks. Total DNA extraction was performed using QIAamp DNA. Blood samples were processed on Illumina HiScan and genotyped for 652,297 markers on the Infinium Global Chips Screening Array-24v2.0 using the IMPUTE2 program with parameters Ne=20,000 and k=90. Additional filtration was performed based on INFO>0.5 and genotype probability>0.5. Quality control of the obtained DNA was conducted using agarose gel electrophoresis, with each tested sample having a volume of 100 µL. Results. It was observed that several SNPs exhibited gender dependence. We identified groups of single nucleotide polymorphisms with a membership of 80% or more in either the female or male gender. These SNPs included rs2661319, rs2842030, rs4606, rs11868035, rs518147, rs5993883, and rs6269.Another noteworthy finding was the limited combination of SNPs sufficient to manifest clinical symptoms leading to hospitalization. Among all 48 patients, each of whom was analyzed for deviations in 73 SNPs, it was discovered that the combination of involved SNPs in the manifestation of pronounced clinical symptoms of schizophrenia was 19±3 out of 73 possible. In study, the frequency of occurrence of single nucleotide polymorphisms also varied. The most frequently observed SNPs were rs4849127 (in 90% of cases), rs1150226 (86%), rs1414334 (75%), rs10170310 (73%), rs2857657, and rs4436578 (71%). Conclusion. Thus, the results of this study provide additional evidence that these genes may be associated with the development of schizophrenia spectrum disorders. However, it's impossible cannot rule out the hypothesis that these polymorphisms may be in linkage disequilibrium with other functionally significant polymorphisms that may actually be involved in schizophrenia spectrum disorders. It has been shown that missense SNPs by themselves are likely not causative of the disease but are in strong linkage disequilibrium with non-functional SNPs that may indeed contribute to disease predisposition.

Keywords: gene polymorphisms, genotyping, single nucleotide polymorphisms, schizophrenia.

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Authors: Ana C. R. P. Ferreira, Adriano H. P. Pereira

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Given the frequent variation of mineral properties throughout the Araxá pyrochlore deposit, even if a good homogenization work has been carried out before feeding the processing plants, an operation with quality and performance’s high variety standard is expected. These results could be improved and standardized if the blend composition parameters that most influence the processing route are determined, and then the types of raw materials are grouped by them, finally presenting a great reference with operational settings for each group. Associating the physical and chemical parameters of a unit operation through benchmarking or even an optimal reference of metallurgical recovery and product quality reflects in the reduction of the production costs, optimization of the mineral resource, and guarantee of greater stability in the subsequent processes of the production chain that uses the mineral of interest. Conducting a comprehensive exploratory data analysis to identify which characteristics of the ore are most relevant to the process route, associated with the use of Machine Learning algorithms for grouping the raw material (ore) and associating these with reference variables in the process’ benchmark is a reasonable alternative for the standardization and improvement of mineral processing units. Clustering methods through Decision Tree and K-Means were employed, associated with algorithms based on the theory of benchmarking, with criteria defined by the process team in order to reference the best adjustments for processing the ore piles of each cluster. A clean user interface was created to obtain the outputs of the created algorithm. The results were measured through the average time of adjustment and stabilization of the process after a new pile of homogenized ore enters the plant, as well as the average time needed to achieve the best processing result. Direct gains from the metallurgical recovery of the process were also measured. The results were promising, with a reduction in the adjustment time and stabilization when starting the processing of a new ore pile, as well as reaching the benchmark. Also noteworthy are the gains in metallurgical recovery, which reflect a significant saving in ore consumption and a consequent reduction in production costs, hence a more rational use of the tailings dams and life optimization of the mineral deposit.

Keywords: mineral clustering, machine learning, process optimization, pyrochlore processing

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Authors: Melanie Grier, Adam Murry

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Objective: Worldwide, Indigenous Peoples experience underemployment and poverty at disproportionately higher rates than non-Indigenous people, despite similar rates of employment seeking. Euro-colonial conquest and genocidal assimilation policies are implicated as perpetuating poverty, which research consistently links to health and wellbeing disparities. Many of the contributors to poverty, such as inadequate income and lack of access to medical care, can be directly or indirectly linked to underemployment. Calls have been made to prioritize Indigenous perspectives in Industrial-Organizational (I/O) psychology research, yet the literature on Indigenous employment remains scarce. What does exist is disciplinarily diverse, topically scattered, and lacking evidence of community-based participatory research (CBPR) practices, a research project approach which prioritizes community leadership, partnership, and betterment and reduces the potential for harm. Due to the harmful colonial legacy of extractive scientific inquiry "on" rather than "with" Indigenous groups, Indigenous leaders and research funding agencies advocate for academic researchers to adopt reparative research methodologies such as CBPR to be used when studying issues pertaining to Indigenous Peoples or individuals. However, the frequency and consistency of CBPR implementation within scholarly discourse are unknown. Therefore, this project’s goal is two-fold: (1) to understand what comprises CBPR in Indigenous research and (2) to determine if CBPR has been historically used in Indigenous employment research. Method: Using a systematic literature review process, sixteen articles about CBPR use with Indigenous groups were selected, and content was analyzed to identify key components comprising CBPR usage. An Indigenous CBPR components framework was constructed and subsequently utilized to analyze the Indigenous employment empirical literature. A similar systematic literature review process was followed to search for relevant empirical articles on Indigenous employment. A total of 120 articles were identified in six global regions: Australia, New Zealand, Canada, America, the Pacific Islands, and Greenland/Norway. Each empirical study was procedurally examined and coded for criteria inclusion using content analysis directives. Results: Analysis revealed that, in total, CBPR elements were used 14% of the time in Indigenous employment research. Most studies (n=69; 58%) neglected to mention using any CBPR components, while just two studies discussed implementing all sixteen (2%). The most significant determinant of overall CBPR use was community member partnership (CP) in the research process. Studies from New Zealand were most likely to use CBPR components, followed by Canada, Australia, and America. While CBPR use did increase slowly over time, meaningful temporal trends were not found. Further, CBPR use did not directly correspond with the total number of topical articles published that year. Conclusions: Community-initiated and engaged research approaches must be better utilized in employment studies involving Indigenous Peoples. Future research efforts must be particularly attentive to community-driven objectives and research protocols, emphasizing specific areas of concern relevant to the field of I/O psychology, such as organizational support, recruitment, and selection.

Keywords: community-based participatory research, content analysis, employment, indigenous research, international, reconciliation, recruitment, reparative research, selection, systematic literature review

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Authors: Palak V. Patel, Jessica Pixley, Steven R. Feldman

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Introduction: Basal cell carcinoma (BCC) is the most common skin cancer worldwide and requires substantial resources to treat. When choosing between indicated therapies, providers consider their associated adverse effects, efficacy, cosmesis, and function preservation. The patient’s tumor burden, infiltrative risk, and risk of tumor recurrence are also considered. Treatment cost is often left out of these discussions. This can lead to financial toxicity, which describes the harm and quality of life reductions inflicted by high care costs. Methods: We studied the guidelines set forth by the American Academy of Dermatology for the treatment of BCC. A PubMed literature search was conducted to identify the costs of each recommended therapy. We discuss costs alongside treatment efficacy and side-effect profile. Results: Surgical treatment for BCC can be cost-effective if the appropriate treatment is selected for the presenting tumor. Curettage and electrodesiccation can be used in low-grade, low-recurrence tumors in aesthetically unimportant areas. The benefits of cost-conscious care are not likely to be outweighed by the risks of poor cosmesis or tumor return ($471 BCC of the cheek). When tumor burden is limited, MMS offers better cure rates and lower recurrence rates than surgical excision, and with comparable costs (MMS $1263; SE $949). Surgical excision with permanent sections may be indicated when tumor burden is more extensive or if molecular testing is necessary. The utility of surgical excision with frozen sections, which costs substantially more than MMS without comparable outcomes, is less clear (SE with frozen sections $2334-$3085). Less data exists on non-surgical treatments for BCC. These techniques cost less, but recurrence-risk is high. Side-effects of nonsurgical treatment are limited to local skin reactions, and cosmesis is good. Cryotherapy, 5-FU, and MAL-PDT are all more affordable than surgery, but high recurrence rates increase risk of secondary financial and psychosocial burden (recurrence rates 21-39%; cost $100-270). Radiation therapy offers better clearance rates than other nonsurgical treatments but is associated with similar recurrence rates and a significantly larger financial burden ($2591-$3460 BCC of the cheek). Treatments for advanced or metastatic BCC are extremely costly, but few patients require their use, and the societal cost burden remains low. Vismodegib and sonidegib have good response rates but substantial side effects, and therapy should be combined with multidisciplinary care and palliative measures. Expert-review has found sonidegib to be the less expensive and more efficacious option (vismodegib $128,358; sonidegib $122,579). Platinum therapy, while not FDA-approved, is also effective but expensive (~91,435). Immunotherapy offers a new line of treatment in patients intolerant of hedgehog inhibitors ($683,061). Conclusion: Dermatologists working within resource-compressed practices and with resource-limited patients must prudently manage the healthcare dollar. Surgical therapies for BCC offer the lowest risk of recurrence at the most reasonable cost. Non-surgical therapies are more affordable, but high recurrence rates increase the risk of secondary financial and psychosocial burdens. Treatments for advanced BCC are incredibly costly, but the low incidence means the overall cost to the system is low.

Keywords: nonmelanoma skin cancer, basal cell skin cancer, squamous cell skin cancer, cost of care

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Authors: Nilki Upathissa, Torin Wirasinghe

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This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Samuel O. Alamu, Seong W. Lee, Blaise Kalmia, Marc J. Louise Caballes, Xuejun Qian

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The application of combustion technologies for thermal conversion of biomass and solid wastes to energy has been a major solution to the effective handling of wastes over a long period of time. Lab-scale biomass combustion systems have been observed to be economically viable and socially acceptable, but major concerns are the environmental impacts of the process and deviation of temperature distribution within the combustion chamber. Both high and low combustion chamber temperature may affect the overall combustion efficiency and gaseous emissions. Therefore, there is an urgent need to develop a control system which measures the deviations of chamber temperature from set target values, sends these deviations (which generates disturbances in the system) in the form of feedback signal (as input), and control operating conditions for correcting the errors. In this research study, major components of the feedback control system were determined, assembled, and tested. In addition, control algorithms were developed to actuate operating conditions (e.g., air velocity, fuel feeding rate) using ladder logic functions embedded in the Programmable Logic Controller (PLC). The developed control algorithm having chamber temperature as a feedback signal is integrated into the lab-scale swirling fluidized bed combustor (SFBC) to investigate the temperature distribution at different heights of the combustion chamber based on various operating conditions. The air blower rates and the fuel feeding rates obtained from automatic control operations were correlated with manual inputs. There was no observable difference in the correlated results, thus indicating that the written PLC program functions were adequate in designing the experimental study of the lab-scale SFBC. The experimental results were analyzed to study the effect of air velocity operating at 222-273 ft/min and fuel feeding rate of 60-90 rpm on the chamber temperature. The developed temperature-based feedback control system was shown to be adequate in controlling the airflow and the fuel feeding rate for the overall biomass combustion process as it helps to minimize the steady-state error.

Keywords: air flow, biomass combustion, feedback control signal, fuel feeding, ladder logic, programmable logic controller, temperature

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Authors: Bukra Kalayci, Geraldine Brennan

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Introduction: Over the last decade, a proliferation of literature related to textile and fashion business in the context of sustainable production and consumption has emerged. However, the economic and environmental benefits of solid waste recovery have not been comprehensively searched. Therefore at the end-of-life or end-of-use textile waste management remains a gap. Solid textile waste reuse and recycling principles of the circular economy need to be developed to close the disposal stage of the textile supply chain. The environmental problems associated with the over-production and –consumption of textile products arise. Together with growing population and fast fashion culture the share of solid textile waste in municipal waste is increasing. Focusing on post-consumer textile waste literature, this research explores the opportunities, obstacles and enablers or success factors associated with closed-loop textile business systems. Methodology: A systematic literature review was conducted in order to identify best practices and gaps from the existing body of knowledge related to closed-loop post-consumer textile waste initiatives over the last decade. Selected keywords namely: ‘cradle-to-cradle ‘, ‘circular* economy* ‘, ‘closed-loop* ‘, ‘end-of-life* ‘, ‘reverse* logistic* ‘, ‘take-back* ‘, ‘remanufacture* ‘, ‘upcycle* ‘ with the combination of (and) ‘fashion* ‘, ‘garment* ‘, ‘textile* ‘, ‘apparel* ‘, clothing* ‘ were used and the time frame of the review was set between 2005 to 2017. In order to obtain a broad coverage, Web of Knowledge and Science Direct databases were used, and peer-reviewed journal articles were chosen. The keyword search identified 299 number of papers which was further refined into 54 relevant papers that form the basis of the in-depth thematic analysis. Preliminary findings: A key finding was that the existing literature is predominantly conceptual rather than applied or empirical work. Moreover, the enablers or success factors, obstacles and opportunities to implement closed-loop systems in the textile industry were not clearly articulated and the following considerations were also largely overlooked in the literature. While the circular economy suggests multiple cycles of discarded products, components or materials, most research has to date tended to focus on a single cycle. Thus the calculations of environmental and economic benefits of closed-loop systems are limited to one cycle which does not adequately explore the feasibility or potential benefits of multiple cycles. Additionally, the time period textile products spend between point of sale, and end-of-use/end-of-life return is a crucial factor. Despite past efforts to study closed-loop textile systems a clear gap in the literature is the lack of a clear evaluation framework which enables manufacturers to clarify the reusability potential of textile products through consideration of indicators related too: quality, design, lifetime, length of time between manufacture and product return, volume of collected disposed products, material properties, and brand segment considerations (e.g. fast fashion versus luxury brands).

Keywords: circular fashion, closed loop business, product service systems, solid textile waste elimination

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Authors: Adeline Stals

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The transformation of architectural design practices has been underway for almost forty years due to the development and democratization of computer technology. New and more efficient tools are constantly being proposed to architects, amplifying a technological wave that sometimes stimulates them, sometimes overwhelms them, depending essentially on their digital culture and the context (socio-economic, structural, organizational) in which they work on a daily basis. Our focus is on VR, AR, and MR technologies dedicated to architecture. The commercialization of affordable headsets like the Oculus Rift, the HTC Vive or more low-tech like the Google CardBoard, makes it more accessible to benefit from these technologies. In that regard, researchers report the growing interest of these tools for architects, given the new perspectives they open up in terms of workflow, representation, collaboration, and client’s involvement. However, studies rarely mention the consequences of the sample studied on results. Our research provides an overview of VR, AR, and MR researches among a corpus of papers selected from conferences and journals. A closer look at the sample of these research projects highlights the necessity to take into consideration the context of studies in order to develop tools truly dedicated to the real practices of specific architect profiles. This literature review formalizes milestones for future challenges to address. The methodology applied is based on a systematic review of two sources of publications. The first one is the Cumincad database, which regroups publications from conferences exclusively about digital in architecture. Additionally, the second part of the corpus is based on journal publications. Journals have been selected considering their ranking on Scimago. Among the journals in the predefined category ‘architecture’ and in Quartile 1 for 2018 (last update when consulted), we have retained the ones related to the architectural design process: Design Studies, CoDesign, Architectural Science Review, Frontiers of Architectural Research and Archnet-IJAR. Beside those journals, IJAC, not classified in the ‘architecture’ category, is selected by the author for its adequacy with architecture and computing. For all requests, the search terms were ‘virtual reality’, ‘augmented reality’, and ‘mixed reality’ in title and/or keywords for papers published between 2015 and 2019 (included). This frame time is defined considering the fast evolution of these technologies in the past few years. Accordingly, the systematic review covers 202 publications. The literature review on studies about XR technologies establishes the state of the art of the current situation. It highlights that studies are mostly based on experimental contexts with controlled conditions (pedagogical, e.g.) or on practices established in large architectural offices of international renown. However, few studies focus on the strategies and practices developed by offices of smaller size, which represent the largest part of the market. Indeed, a European survey studying the architectural profession in Europe in 2018 reveals that 99% of offices are composed of less than ten people, and 71% of only one person. The study also showed that the number of medium-sized offices is continuously decreasing in favour of smaller structures. In doing so, a frontier seems to remain between the worlds of research and practice, especially for the majority of small architectural practices having a modest use of technology. This paper constitutes a reference for the next step of the research and for further worldwide researches by facilitating their contextualization.

Keywords: architectural design, literature review, SME, XR technologies

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Authors: Peter Falanga

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During the process of de-Stalinization that began in 1956 with the Twentieth Congress of the Soviet Communist Party, many filmmakers in Hungary chose to explore their country’s political discomforts by using Socialist Realism as a negative model against which they could react to the dominating ideology. A renewed national film industry and a more permissive political regime would allow filmmakers to take to task the plight of the preceding generation who had experienced the fatal political turmoil of both World Wars and the purges of Stalin. What follows is no longer the multigenerational unity found in Socialist Realism wherein both the old and the young embrace Stalin’s revolutionary optimism; instead, the protagonists are parentless, and thus their connection to the previous generation is partially severed. In these films, violent historical forces leave one generation to search for both a connection with their family’s past, and for moral guidance to direct their future. István Szabó’s Father (1966), Márta Mészáros Diary for My Children (1984), and Pál Gábor’s Angi Vera (1978) each consider the fraught relationship between successive generations through the lens of postwar youth. A characteristic each of their protagonist’s share is that they are all missing one or both parents, and cope with familial loss either through recalling memories of their parents in dream-like sequences, or, in the case of Angi Vera, through embracing the surrogate paternalism that the Communist Party promises to provide. This paper considers the argument these films present about the progress of Hungarian history, and how this topic is explored in more recent films that similarly focus on the transmission of generational values. Scholars such as László Strausz and John Cunningham have written on the continuous concern with the transmission of generational values in more recent films such as István Szabó’s Sunshine (1999), Béla Tarr’s Werckmeister Harmonies (2000), György Pálfi’s Taxidermia (2006), Ágnes Kocsis’ Pál Adrienn (2010), and Kornél Mundruczó’s Evolution (2021). These films, they argue, make intimate portrayals of the various sweeping political changes in Hungary’s history and question how these epochs or events have impacted Hungarian identities. If these films attempt to personalize historical shifts of Hungary, then what is the significance of featuring characters who have lost one or both parents? An attempt to understand this coherent trend in Hungarian cinema will profit from examining the earlier, celebrated films of Szabó, Mészáros, and Gábor, who inaugurated this preoccupation with generational values. The pervasive interplay of dreams and memory in their films invites an additional element to their argument concerning historical progression. This paper incorporates Richard Teniman’s notion of the “dialectics of memory” in which memory is in a constant process of negation and reinvention to explain why these Directors prefer to explore Hungarian identity through the disarranged form of psychological realism over the linear causality structure of historical realism.

Keywords: film theory, Eastern European Studies, film history, Eastern European History

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Authors: Kiran Datta, Daniella Holland-Hart, Anthony Byrne

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Introduction: Oesophago-gastric (OG) cancers are the fifth commonest in the UK, accounting for over 12,000 deaths each year. Most patients will present at later stages of the disease, with only 21% of patients with stage 4 disease surviving longer than a year. As a result, many patients are unsuitable for curative surgery and instead receive palliative treatment to improve prognosis and symptom burden. However, palliative chemotherapy can result in significant toxicity: almost half of the patients are unable to complete their chemotherapy regimen, with this proportion rising significantly in older and frailer patients. In addition, clinical trials often exclude older and frailer patients due to strict inclusion criteria, meaning there is limited evidence to guide which patients are most likely to benefit from palliative chemotherapy. Inappropriate chemotherapy administration is at odds with the goals of palliative treatment and care, which are to improve quality of life, and this also represents a significant resource expenditure. This literature review aimed to examine and appraise evidence regarding treatment resilience in order to guide clinicians in identifying the most suitable candidates for palliative chemotherapy. Factors influencing treatment resilience were assessed, as measured by completion rates, dose reductions, and toxicities. Methods: This literature review was conducted using rapid review methodology, utilising modified systematic methods. A literature search was performed across the MEDLINE, EMBASE, and Cochrane Library databases, with results limited to papers within the last 15 years and available in English. Key inclusion criteria included: 1) participants with either oesophageal, gastro-oesophageal junction, or gastric cancers; 2) patients treated with palliative chemotherapy; 3) available data evaluating the association between baseline participant characteristics and treatment resilience. Results: Of the 2326 papers returned, 11 reports of 10 studies were included in this review after excluding duplicates and irrelevant papers. Treatment resilience factors that were assessed included: age, performance status, frailty, inflammatory markers, and sarcopenia. Age was generally a poor predictor for how well patients would tolerate chemotherapy, while poor performance status was a better indicator of the need for dose reduction and treatment non-completion. Frailty was assessed across one cohort using multiple screening tools and was an effective marker of the risk of toxicity and the requirement for dose reduction. Inflammatory markers included lymphopenia and the Glasgow Prognostic Score, which assessed inflammation and hypoalbuminaemia. Although quick to obtain and interpret, these findings appeared less reliable due to the inclusion of patients treated with palliative radiotherapy. Sarcopenia and body composition were often associated with chemotherapy toxicity but not the rate of regimen completion. Conclusion: This review demonstrates that there are numerous measures that can estimate the ability of patients with oesophago-gastric cancer to tolerate palliative chemotherapy, and these should be incorporated into clinical assessments to promote personalised decision-making around treatment. Age should not be a barrier to receiving chemotherapy and older and frailer patients should be included in future clinical trials to better represent typical patients with oesophago-gastric cancers. Decisions regarding palliative treatment should be guided by these factors identified as well as patient preference.

Keywords: frailty, oesophago-gastric cancer, palliative chemotherapy, treatment resilience

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Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Ugur Fidan, Naim Karasekreter

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Healthy and clean water should not contain disease-causing micro-organisms and toxic chemicals and must contain the necessary minerals in a balanced manner. Today, water resources have a limited and strategic importance, necessitating the management of water reserves. Water tanks meet the water needs of people and should be regularly chlorinated to prevent waterborne diseases. For this purpose, automatic chlorination systems placed in water tanks for killing bacteria. However, the regular operation of automatic chlorination systems depends on refilling the chlorine tank when it is empty. For this reason, there is a need for a stock control system, in which chlorine levels are regularly monitored and supplied. It has become imperative to take urgent measures against epidemics caused by the fact that most of our country is not aware of the end of chlorine. The aim of this work is to rehabilitate existing water tanks and to provide a method for a modern water storage system in which chlorination is digitally monitored by turning the newly established water tanks into a closed system. A sensor network structure using GSM/GPRS communication infrastructure has been developed in the study. The system consists of two basic units: hardware and software. The hardware includes a chlorine level sensor, an RFID interlock system for authorized personnel entry into water tank, a motion sensor for animals and other elements, and a camera system to ensure process safety. It transmits the data from the hardware sensors to the host server software via the TCP/IP protocol. The main server software processes the incoming data through the security algorithm and informs the relevant unit responsible (Security forces, Chlorine supply unit, Public health, Local Administrator) by e-mail and SMS. Since the software is developed base on the web, authorized personnel are also able to monitor drinking water tank and report data on the internet. When the findings and user feedback obtained as a result of the study are evaluated, it is shown that closed drinking water tanks are built with GRP type material, and continuous monitoring in digital environment is vital for sustainable health water supply for people.

Keywords: wireless sensor networks (WSN), monitoring, chlorine, water tank, security

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Authors: Bharti Jain, Rajeev Jain, Abuzar Kabir, Torki Zughaibi, Shweta Sharma

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Drug-facilitated crimes (DFCs) entail the use of a single drug or a mixture of drugs to render a victim unable. Traditionally, biological samples have been gathered from victims and conducted analysis to establish evidence of drug administration. Nevertheless, the rapid metabolism of various drugs and delays in analysis can impede the identification of such substances. For this, the present article describes a rapid, sustainable, highly efficient and miniaturized protocol for the identification and quantification of three sedative-hypnotic drugs, namely diazepam, chlordiazepoxide and ketamine in alcoholic beverages and complex food samples (cream of biscuit, flavored milk, juice, cake, tea, sweets and chocolate). The methodology involves utilizing fabric phase sorptive extraction (FPSE) to extract diazepam (DZ), chlordiazepoxide (CDP), and ketamine (KET). Subsequently, the extracted samples are subjected to analysis using gas chromatography-mass spectrometry (GC-MS). Several parameters, including the type of membrane, pH, agitation time and speed, ionic strength, sample volume, elution volume and time, and type of elution solvent, were screened and thoroughly optimized. Sol-gel Carbowax 20M (CW-20M) has demonstrated the most effective extraction efficiency for the target analytes among all evaluated membranes. Under optimal conditions, the method displayed linearity within the range of 0.3–10 µg mL–¹ (or µg g–¹), exhibiting a coefficient of determination (R2) ranging from 0.996–0.999. The limits of detection (LODs) and limits of quantification (LOQs) for liquid samples range between 0.020-0.069 µg mL-¹ and 0.066-0.22 µg mL-¹, respectively. Correspondingly, the LODs for solid samples ranged from 0.056-0.090 µg g-¹, while the LOQs ranged from 0.18-0.29 µg g-¹. Notably, the method showcased better precision, with repeatability and reproducibility both below 5% and 10%, respectively. Furthermore, the FPSE-GC-MS method proved effective in determining diazepam (DZ) in forensic food samples connected to drug-facilitated crimes (DFCs). Additionally, the proposed method underwent evaluation for its whiteness using the RGB12 algorithm.

Keywords: drug facilitated crime, fabric phase sorptive extraction, food forensics, white analytical chemistry

Procedia PDF Downloads 70

Authors: Artem Kim, Clara Savary, Christele Dubourg, Wilfrid Carre, Houda Hamdi-Roze, Valerie Dupé, Sylvie Odent, Marie De Tayrac, Veronique David

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Holoprosencephaly (HPE) is a rare congenital brain malformation resulting from the incomplete separation of the two cerebral hemispheres. It is characterized by a wide phenotypic spectrum and a high degree of locus heterogeneity. Genetic defects in 16 genes have already been implicated in HPE, but account for only 30% of cases, suggesting that a large part of genetic factors remains to be discovered. HPE has been recently redefined as a complex multigenic disorder, requiring the joint effect of multiple mutational events in genes belonging to one or several developmental pathways. The onset of HPE may result from accumulation of the effects of multiple rare variants in functionally-related genes, each conferring a moderate increase in the risk of HPE onset. In order to decipher the genetic basis of HPE, unconventional patterns of inheritance involving multiple genetic factors need to be considered. The primary objective of this study was to uncover possible disease causing combinations of multiple rare variants underlying HPE by performing trio-based Whole Exome Sequencing (WES) of familial cases where no molecular diagnosis could be established. 39 families were selected with no fully-penetrant causal mutation in known HPE gene, no chromosomic aberrations/copy number variants and without any implication of environmental factors. As the main challenge was to identify disease-related variants among a large number of nonpathogenic polymorphisms detected by WES classical scheme, a novel variant prioritization approach was established. It combined WES filtering with complementary gene-level approaches: transcriptome-driven (RNA-Seq data) and clinically-driven (public clinical data) strategies. Briefly, a filtering approach was performed to select variants compatible with disease segregation, population frequency and pathogenicity prediction to identify an exhaustive list of rare deleterious variants. The exome search space was then reduced by restricting the analysis to candidate genes identified by either transcriptome-driven strategy (genes sharing highly similar expression patterns with known HPE genes during cerebral development) or clinically-driven strategy (genes associated to phenotypes of interest overlapping with HPE). Deeper analyses of candidate variants were then performed on a family-by-family basis. These included the exploration of clinical information, expression studies, variant characteristics, recurrence of mutated genes and available biological knowledge. A novel bioinformatics pipeline was designed. Applied to the 39 families, this final integrated workflow identified an average of 11 candidate variants per family. Most of candidate variants were inherited from asymptomatic parents suggesting a multigenic inheritance pattern requiring the association of multiple mutational events. The manual analysis highlighted 5 new strong HPE candidate genes showing recurrences in distinct families. Functional validations of these genes are foreseen.

Keywords: complex genetic disorder, holoprosencephaly, multiple rare variants, whole exome sequencing

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Authors: Shuo Li, Yannick Coffinier, Chann Lagadec, Fabrizio Cleri, Katsuhiko Nishiguchi, Akira Fujiwara, Soo Hyeon Kim, Nicolas Clément

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The need for single cell detection and analysis techniques has increased in the past decades because of the heterogeneity of individual living cells, which increases the complexity of the pathogenesis of malignant tumors. In the search for early cancer detection, high-precision medicine and therapy, the technologies most used today for sensitive detection of target analytes and monitoring the variation of these species are mainly including two types. One is based on the identification of molecular differences at the single-cell level, such as flow cytometry, fluorescence-activated cell sorting, next generation proteomics, lipidomic studies, another is based on capturing or detecting single tumor cells from fresh or fixed primary tumors and metastatic tissues, and rare circulating tumors cells (CTCs) from blood or bone marrow, for example, dielectrophoresis technique, microfluidic based microposts chip, electrochemical (EC) approach. Compared to other methods, EC sensors have the merits of easy operation, high sensitivity, and portability. However, despite various demonstrations of low limits of detection (LOD), including aptamer sensors, arrayed EC sensors for detecting single-cell have not been demonstrated. In this work, a new technique based on 20-nm-thick nanopillars array to support cells and keep them at ideal recognition distance for redox-labeled aptamers grafted on the surface. The key advantages of this technology are not only to suppress the false positive signal arising from the pressure exerted by all (including non-target) cells pushing on the aptamers by downward force but also to stabilize the aptamer at the ideal hairpin configuration thanks to a confinement effect. With the first implementation of this technique, a LOD of 13 cells (with5.4 μL of cell suspension) was estimated. In further, the nanosupported cell technology using redox-labeled aptasensors has been pushed forward and fully integrated into a single-cell electrochemical aptasensor array. To reach this goal, the LOD has been reduced by more than one order of magnitude by suppressing parasitic capacitive electrochemical signals by minimizing the sensor area and localizing the cells. Statistical analysis at the single-cell level is demonstrated for the recognition of cancer cells. The future of this technology is discussed, and the potential for scaling over millions of electrodes, thus pushing further integration at sub-cellular level, is highlighted. Despite several demonstrations of electrochemical devices with LOD of 1 cell/mL, the implementation of single-cell bioelectrochemical sensor arrays has remained elusive due to their challenging implementation at a large scale. Here, the introduced nanopillar array technology combined with redox-labeled aptamers targeting epithelial cell adhesion molecule (EpCAM) is perfectly suited for such implementation. Combining nanopillar arrays with microwells determined for single cell trapping directly on the sensor surface, single target cells are successfully detected and analyzed. This first implementation of a single-cell electrochemical aptasensor array based on Brownian-fluctuating redox species opens new opportunities for large-scale implementation and statistical analysis of early cancer diagnosis and cancer therapy in clinical settings.

Keywords: bioelectrochemistry, aptasensors, single-cell, nanopillars

Procedia PDF Downloads 117

Authors: Yahoel Van Essen, Gordon Ho, Brett Russell, Hans-Georg Worms, Xiao Lin Long, Edward David Cooper, Avner Bachar

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Weeds reduce farm and forest productivity, invade crops, smother pastures and some can harm livestock. Farmers need to spend a significant amount of money to control weeds by means of biological, chemical, cultural, and physical methods. To solve the global agricultural labor shortage and remove poisonous chemicals, a fully autonomous, eco-friendly, and sustainable weeding technology is developed. This takes the form of a weeding robot, ‘Roboweeder’. Roboweeder includes a four-wheel-drive self-driving vehicle, a 4-DOF robotic arm which is mounted on top of the vehicle, an electromagnetic wave generator (magnetron) which is mounted on the “wrist” of the robotic arm, 48V battery packs, and a control/communication system. Cameras are mounted on the front and two sides of the vehicle. Using image processing and recognition, distinguish types of weeds are detected before being eliminated. The electromagnetic wave technology is applied to heat the individual weeds and clusters dielectrically causing them to wilt and die. The 4-DOF robotic arm was modeled mathematically based on its structure/mechanics, each joint’s load, brushless DC motor and worm gear’ characteristics, forward kinematics, and inverse kinematics. The Proportional-Integral-Differential control algorithm is used to control the robotic arm’s motion to ensure the waveguide aperture pointing to the detected weeds. GPS and machine vision are used to traverse the farm and avoid obstacles without the need of supervision. A Roboweeder prototype has been built. Multiple test trials show that Roboweeder is able to detect, point, and kill the pre-defined weeds successfully although further improvements are needed, such as reducing the “weeds killing” time and developing a new waveguide with a smaller waveguide aperture to avoid killing crops surrounded. This technology changes the tedious, time consuming and expensive weeding processes, and allows farmers to grow more, go organic, and eliminate operational headaches. A patent of this technology is pending.

Keywords: autonomous navigation, machine vision, precision heating, sustainable and eco-friendly

Procedia PDF Downloads 252

Authors: Shreya Srivastava, Sagnik Dey

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Aerosol direct radiative forcing (ADRF) refers to the alteration of the Earth's energy balance from the scattering and absorption of solar radiation by aerosol particles. India experiences substantial ADRF due to high aerosol loading from various sources. These aerosols' radiative impact depends on their physical characteristics (such as size, shape, and composition) and atmospheric distribution. Quantifying ADRF is crucial for understanding aerosols’ impact on the regional climate and the Earth's radiative budget. In this study, we have taken radiation data from Clouds and the Earth’s Radiant Energy System (CERES, spatial resolution=1ox1o) for 22 years (2000-2021) over the Indian subcontinent. Except for a few locations, the short-wave DARF exhibits aerosol cooling at the TOA (values ranging from +2.5 W/m2 to -22.5W/m2). Cooling due to aerosols is more pronounced in the absence of clouds. Being an aerosol hotspot, higher negative ADRF is observed over the Indo-Gangetic Plain (IGP). Aerosol Forcing Efficiency (AFE) shows a decreasing seasonal trend in winter (DJF) over the entire study region while an increasing trend over IGP and western south India during the post-monsoon season (SON) in clear-sky conditions. Analysing atmospheric heating and AOD trends, we found that only the aerosol loading is not governing the change in atmospheric heating but also the aerosol composition and/or their vertical profile. We used a Multi-angle Imaging Spectro-Radiometer (MISR) Level-2 Version 23 aerosol products to look into aerosol composition. MISR incorporates 74 aerosol mixtures in its retrieval algorithm based on size, shape, and absorbing properties. This aerosol mixture information was used for analysing long-term changes in aerosol composition and dominating aerosol species corresponding to the aerosol forcing value. Further, ADRF derived from this method is compared with around 35 studies across India, where a plane parallel Radiative transfer model was used, and the model inputs were taken from the OPAC (Optical Properties of Aerosols and Clouds) utilizing only limited aerosol parameter measurements. The result shows a large overestimation of TOA warming by the latter (i.e., Model-based method).

Keywords: aerosol radiative forcing (ARF), aerosol composition, MISR, CERES, SBDART

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Authors: Thiago Ribeiro de Alencar, Jacyro Gramulia Junior, Patricia Teixeira Leite

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The area of the electricity sector that deals with energy needs by the hydroelectric in a coordinated manner is called Operation Planning of Hydrothermal Power Systems (OPHPS). The purpose of this is to find a political operative to provide electrical power to the system in a given period, with reliability and minimal cost. Therefore, it is necessary to determine an optimal schedule of generation for each hydroelectric, each range, so that the system meets the demand reliably, avoiding rationing in years of severe drought, and that minimizes the expected cost of operation during the planning, defining an appropriate strategy for thermal complementation. Several optimization algorithms specifically applied to this problem have been developed and are used. Although providing solutions to various problems encountered, these algorithms have some weaknesses, difficulties in convergence, simplification of the original formulation of the problem, or owing to the complexity of the objective function. An alternative to these challenges is the development of techniques for simulation optimization and more sophisticated and reliable, it can assist the planning of the operation. Thus, this paper presents the development of a computational tool, namely Hydro-IA for solving optimization problem identified and to provide the User an easy handling. Adopted as intelligent optimization technique is Genetic Algorithm (GA) and programming language is Java. First made the modeling of the chromosomes, then implemented the function assessment of the problem and the operators involved, and finally the drafting of the graphical interfaces for access to the User. The results with the Genetic Algorithms were compared with the optimization technique nonlinear programming (NLP). Tests were conducted with seven hydroelectric plants interconnected hydraulically with historical stream flow from 1953 to 1955. The results of comparison between the GA and NLP techniques shows that the cost of operating the GA becomes increasingly smaller than the NLP when the number of hydroelectric plants interconnected increases. The program has managed to relate a coherent performance in problem resolution without the need for simplification of the calculations together with the ease of manipulating the parameters of simulation and visualization of output results.

Keywords: energy, optimization, hydrothermal power systems, artificial intelligence and genetic algorithms

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Authors: Andrew Read, Alice Parry, Kate Woods

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Since the advent of the SARS-CoV-2 pandemic, the search for medications that can reduce mortality and morbidity has been a global research priority. Several multi-center trials have recently demonstrated improved mortality associated with the use of Tocilizumab, an interleukin-6 receptor antagonist, in patients with severe SARS-CoV-2 pneumonia. Initial data supported the administration in patients requiring respiratory support (non-invasive or invasive ventilation), but more recent data has shown benefit in all hypoxic patients. At the height of the second wave of COVID-19 infections in London, our hospital introduced the use of Tocilizumab for patients with severe COVID-19. Tocilizumab is licensed for use in chronic inflammatory conditions and has been associated with an increased risk of severe bacterial and fungal infections, as well as reactivation of chronic viral infections (e.g., hepatitis B). It is a specialist drug that suppresses the formation of C-reactive protein (CRP) for 6 – 12 weeks. It is not widely used by the general medical community. We aimed to assess Tocilizumab use in our hospital and to implement changes to the protocol as required to ensure administration was safe and appropriate. A retrospective study design was used to assess prescriptions over an initial 3-week period in both intensive care and on the medical wards. This amounted to a total of 13 patients. The initial data collection identified four key areas of concern: adherence to national and local inclusion & exclusion criteria; a collection of appropriate screening blood prior to administration; documentation of informed consent or best interest decision and documentation of Tocilizumab administration on patient discharge information, to alert future healthcare providers that typical measures of inflammation and infection, such as CRP, are unreliable for up to 3-months. Data were collected from electronic notes, blood results and observation charts, and cross referenced with pharmacy data. Initial results showed that all four key areas were completed in approximately 50% of cases. Of particular concern was adherence to exclusion criteria, such as current evidence of bacterial infection, and ensuring the correct screening blood was sent to exclude infections such as hepatitis. To remedy this and improve patient safety, the initial data was presented to relevant healthcare professionals. Subsequently, three interventions were introduced and education on each provided to hospital staff. An electronic ‘order set’ collating the appropriate screening blood was created simplifying the screening process. Pre-formed electronic documentation which can be inserted into the notes was created to provide a framework for consent discussions and reduce the time needed for junior doctors to complete this task. Additionally, a ‘Tocilizumab’ administration card was created and administered via pharmacy. This was distributed to each patient on discharge to ensure future healthcare professionals were aware of the potential effects of Tocilizumab administration, including suppression of CRP. Following these changes, repeat data collection over two months illustrated that each of the 4 safety aspects was met with a 100% success rate in every patient. Although this demonstrates good progress and effective interventions the challenge will be to maintain this progress. The audit data collection is ongoing

Keywords: education, patient safety , SARS-CoV-2, Tocilizumab

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Authors: Chen Wang, Jared Evans, Yan Asmann

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With the quick evolvement of next-generation sequencing techniques, whole-exome or exome-panel data have become a cost-effective way for detection of small exonic mutations, but there has been a growing desire to accurately detect copy number variations (CNVs) as well. In order to address this research and clinical needs, we developed a sequencing coverage pattern-based method not only for copy number detections, data integrity checks, CNV calling, and visualization reports. The developed methodologies include complete automation to increase usability, genome content-coverage bias correction, CNV segmentation, data quality reports, and publication quality images. Automatic identification and removal of poor quality outlier samples were made automatically. Multiple experimental batches were routinely detected and further reduced for a clean subset of samples before analysis. Algorithm improvements were also made to improve somatic CNV detection as well as germline CNV detection in trio family. Additionally, a set of utilities was included to facilitate users for producing CNV plots in focused genes of interest. We demonstrate the somatic CNV enhancements by accurately detecting CNVs in whole exome-wide data from the cancer genome atlas cancer samples and a lymphoma case study with paired tumor and normal samples. We also showed our efficient reuses of existing exome sequencing data, for improved germline CNV calling in a family of the trio from the phase-III study of 1000 Genome to detect CNVs with various modes of inheritance. The performance of the developed method is evaluated by comparing CNV calling results with results from other orthogonal copy number platforms. Through our case studies, reuses of exome sequencing data for calling CNVs have several noticeable functionalities, including a better quality control for exome sequencing data, improved joint analysis with single nucleotide variant calls, and novel genomic discovery of under-utilized existing whole exome and custom exome panel data.

Keywords: bioinformatics, computational genetics, copy number variations, data reuse, exome sequencing, next generation sequencing

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Authors: Abdulaziz Alakeel, Abdulrahman Alomair, Mohammed Fouda

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Introduction: Methotrexate (MTX) is an antimetabolite used to treat multiple conditions, including neoplastic diseases, severe psoriasis, and rheumatoid arthritis. Skin cancer is the out-of-control growth of abnormal cells in the epidermis, the outermost skin layer, caused by unrepaired DNA damage that triggers mutations. These mutations lead the skin cells to multiply rapidly and form malignant tumors. The aim of this review is to evaluate the risk of skin cancer associated with the use of methotrexate and to suggest regulatory recommendations if required. Methodology: Signal Detection team at Saudi Food and Drug Authority (SFDA) performed a safety review using National Pharmacovigilance Center (NPC) database as well as the World Health Organization (WHO) VigiBase, alongside with literature screening to retrieve related information for assessing the causality between skin cancer and methotrexate. The search conducted in July 2020. Results: Four published articles support the association seen while searching in literature, a recent randomized control trial published in 2020 revealed a statistically significant increase in skin cancer among MTX users. Another study mentioned methotrexate increases the risk of non-melanoma skin cancer when used in combination with immunosuppressant and biologic agents. In addition, the incidence of melanoma for methotrexate users was 3-fold more than the general population in a cohort study of rheumatoid arthritis patients. The last article estimated the risk of cutaneous malignant melanoma (CMM) in a cohort study shows a statistically significant risk increase for CMM was observed in MTX exposed patients. The WHO database (VigiBase) searched for individual case safety reports (ICSRs) reported for “Skin Cancer” and 'Methotrexate' use, which yielded 121 ICSRs. The initial review revealed that 106 cases are insufficiently documented for proper medical assessment. However, the remaining fifteen cases have extensively evaluated by applying the WHO criteria of causality assessment. As a result, 30 percent of the cases showed that MTX could possibly cause skin cancer; five cases provide unlikely association and five un-assessable cases due to lack of information. The Saudi NPC database searched to retrieve any reported cases for the combined terms methotrexate/skin cancer; however, no local cases reported up to date. The data mining of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by the WHO Uppsala Monitoring Centre to measure the reporting ratio. Positive IC reflects higher statistical association, while negative values translated as a less statistical association, considering the null value equal to zero. Results showed that a combination of 'Methotrexate' and 'Skin cancer' observed more than expected when compared to other medications in the WHO database (IC value is 1.2). Conclusion: The weighted cumulative pieces of evidence identified from global cases, data mining, and published literature are sufficient to support a causal association between the risk of skin cancer and methotrexate. Therefore, health care professionals should be aware of this possible risk and may consider monitoring any signs or symptoms of skin cancer in patients treated with methotrexate.

Keywords: methotrexate, skin cancer, signal detection, pharmacovigilance

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Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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a systematic observation from a defined time of origin up to certain failure or censor is known as survival data. Survival analysis is a major area of interest in biostatistics and biomedical researches. At the heart of understanding, the most scientific and medical research inquiries lie for a causality analysis. Thus, the main concern of this study is to investigate the causal effect of treatment on survival time conditional to the possibly time-varying covariates. The theory of causality often differs from the simple association between the response variable and predictors. A causal estimation is a scientific concept to compare a pragmatic effect between two or more experimental arms. To evaluate an average treatment effect on survival outcome, the estimating equation was adjusted for time-varying covariates under the semi-parametric transformation models. The proposed model intuitively obtained the consistent estimators for unknown parameters and unspecified monotone transformation functions. In this article, the proposed method estimated an unbiased average causal effect of treatment on survival time of interest. The modified estimating equations of semiparametric transformation models have the advantage to include the time-varying effect in the model. Finally, the finite sample performance characteristics of the estimators proved through the simulation and Stanford heart transplant real data. To this end, the average effect of a treatment on survival time estimated after adjusting for biases raised due to the high correlation of the left-truncation and possibly time-varying covariates. The bias in covariates was restored, by estimating density function for left-truncation. Besides, to relax the independence assumption between failure time and truncation time, the model incorporated the left-truncation variable as a covariate. Moreover, the expectation-maximization (EM) algorithm iteratively obtained unknown parameters and unspecified monotone transformation functions. To summarize idea, the ratio of cumulative hazards functions between the treated and untreated experimental group has a sense of the average causal effect for the entire population.

Keywords: a modified estimation equation, causal effect, semiparametric transformation models, survival analysis, time-varying covariate

Procedia PDF Downloads 175

Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

Procedia PDF Downloads 398

Authors: Melissa de Zeeuw, Peter Smolianov, Arnold Bohl

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This study was designed to help and improve international performance as well increase swimming participation in the Netherlands. Over 200 sources of literature on sport delivery systems from 28 Australasian, North and South American, Western and Eastern European countries were analyzed to construct a globally applicable model of high performance swimming integrated with mass participation, comprising of the following seven elements and three levels: Micro level (operations, processes, and methodologies for development of individual athletes): 1. Talent search and development, 2. Advanced athlete support. Meso level (infrastructures, personnel, and services enabling sport programs): 3. Training centers, 4. Competition systems, 5. Intellectual services. Macro level (socio-economic, cultural, legislative, and organizational): 6. Partnerships with supporting agencies, 7. Balanced and integrated funding and structures of mass and elite sport. This model emerged from the integration of instruments that have been used to analyse and compare national sport systems. The model has received scholarly validation and showed to be a framework for program analysis that is not culturally bound. It has recently been accepted as a model for further understanding North American sport systems, including (in chronological order of publications) US rugby, tennis, soccer, swimming and volleyball. The above model was used to design a questionnaire of 42 statements reflecting desired practices. The statements were validated by 12 international experts, including executives from sport governing bodies, academics who published on high performance and sport development, and swimming coaches and administrators. In this study both a highly structured and open ended qualitative analysis tools were used. This included a survey of swim coaches where open responses accompanied structured questions. After collection of the surveys, semi-structured discussions with Federation coaches were conducted to add triangulation to the findings. Lastly, a content analysis of Dutch Swimming’s website and organizational documentation was conducted. A representative sample of 1,600 Dutch Swim coaches and administrators was collected via email addresses from Royal Dutch Swimming Federation' database. Fully completed questionnaires were returned by 122 coaches from all key country’s regions for a response rate of 7,63% - higher than the response rate of the previously mentioned US studies which used the same model and method. Results suggest possible enhancements at macro level (e.g., greater public and corporate support to prepare and hire more coaches and to address the lack of facilities, monies and publicity at mass participation level in order to make swimming affordable for all), at meso level (e.g., comprehensive education for all coaches and full spectrum of swimming pools particularly 50 meters long), and at micro level (e.g., better preparation of athletes for a future outside swimming and better use of swimmers to stimulate swimming development). Best Dutch swimming management practices (e.g., comprehensive support to most talented swimmers who win Olympic medals) as well as relevant international practices available for transfer to the Netherlands (e.g., high school competitions) are discussed.

Keywords: sport development, high performance, mass participation, swimming

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Authors: Pedro M. Rodrigues, Claudia Raposo, Denise Schrama, Marco Cerqueira

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Farmed fish welfare is a very recent concept, widely discussed among the scientific community. Consumers’ interest regarding farmed animal welfare standards has significantly increased in the last years posing a huge challenge to producers in order to maintain an equilibrium between good welfare principles and productivity, while simultaneously achieve public acceptance. The major bottleneck of standard aquaculture is to impair considerably fish welfare throughout the production cycle and with this, the quality of fish protein. Welfare assessment in farmed fish is undertaken through the evaluation of fish stress responses. Primary and secondary stress responses include release of cortisol and glucose and lactate to the blood stream, respectively, which are currently the most commonly used indicators of stress exposure. However, the reliability of these indicators is highly dubious, due to a high variability of fish responses to an acute stress and the adaptation of the animal to a repetitive chronic stress. Our objective is to use comparative proteomics to identify and validate a fingerprint of proteins that can present an more reliable alternative to the already established welfare indicators. In this way, the culture conditions will improve and there will be a higher perception of mechanisms and metabolic pathway involved in the produced organism’s welfare. Due to its high economical importance in Portuguese aquaculture Gilthead seabream will be the elected species for this study. Protein extracts from Gilthead Seabream fish muscle, liver and plasma, reared for a 3 month period under optimized culture conditions (control) and induced stress conditions (Handling, high densities, and Hipoxia) are collected and used to identify a putative fish welfare protein markers fingerprint using a proteomics approach. Three tanks per condition and 3 biological replicates per tank are used for each analisys. Briefly, proteins from target tissue/fluid are extracted using standard established protocols. Protein extracts are then separated using 2D-DIGE (Difference gel electrophoresis). Proteins differentially expressed between control and induced stress conditions will be identified by mass spectrometry (LC-Ms/Ms) using NCBInr (taxonomic level - Actinopterygii) databank and Mascot search engine. The statistical analysis is performed using the R software environment, having used a one-tailed Mann-Whitney U-test (p < 0.05) to assess which proteins were differentially expressed in a statistically significant way. Validation of these proteins will be done by comparison of the RT-qPCR (Quantitative reverse transcription polymerase chain reaction) expressed genes pattern with the proteomic profile. Cortisol, glucose, and lactate are also measured in order to confirm or refute the reliability of these indicators. The identified liver proteins under handling and high densities induced stress conditions are responsible and involved in several metabolic pathways like primary metabolism (i.e. glycolysis, gluconeogenesis), ammonia metabolism, cytoskeleton proteins, signalizing proteins, lipid transport. Validition of these proteins as well as identical analysis in muscle and plasma are underway. Proteomics is a promising high-throughput technique that can be successfully applied to identify putative welfare protein biomarkers in farmed fish.

Keywords: aquaculture, fish welfare, proteomics, welfare biomarkers

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Authors: Kirill Filianin, Satu-Pia Reinikainen, Tuomo Sainio

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Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.

Keywords: abnormal process behavior, failure detection, principal component analysis, solvent extraction

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Authors: Jalil ur Rehman, Ramesh C, Tailor, Isa Khan, Jahanzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott

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The purpose of this analysis is to estimate secondary cancer risks after VMAT compared to other modalities of head and neck radiotherapy (IMRT, 3DCRT). Computer tomography (CT) scans of Radiological Physics Center (RPC) head and neck phantom were acquired with CT scanner and exported via DICOM to the treatment planning system (TPS). Treatment planning was done using four arc (182-178 and 180-184, clockwise and anticlockwise) for volumetric modulated arc therapy (VMAT) , Nine fields (200, 240, 280, 320,0,40,80,120 and 160), which has been commonly used at MD Anderson Cancer Center Houston for intensity modulated radiation therapy (IMRT) and four fields for three dimensional radiation therapy (3DCRT) were used. True beam linear accelerator of 6MV photon energy was used for dose delivery, and dose calculation was done with CC convolution algorithm with prescription dose of 6.6 Gy. Primary Target Volume (PTV) coverage, mean and maximal doses, DVHs and volumes receiving more than 2 Gy and 3.8 Gy of OARs were calculated and compared. Absolute point dose and planar dose were measured with thermoluminescent dosimeters (TLDs) and GafChromic EBT2 film, respectively. Quality Assurance of VMAT and IMRT were performed by using ArcCHECK method with gamma index criteria of 3%/3mm dose difference to distance to agreement (DD/DTA). PTV coverage was found 90.80 %, 95.80 % and 95.82 % for 3DCRT, IMRT and VMAT respectively. VMAT delivered the lowest maximal doses to esophagus (2.3 Gy), brain (4.0 Gy) and thyroid (2.3 Gy) compared to all other studied techniques. In comparison, maximal doses for 3DCRT were found higher than VMAT for all studied OARs. Whereas, IMRT delivered maximal higher doses 26%, 5% and 26% for esophagus, normal brain and thyroid, respectively, compared to VMAT. It was noted that esophagus volume receiving more than 2 Gy was 3.6 % for VMAT, 23.6 % for IMRT and up to 100 % for 3DCRT. Good agreement was observed between measured doses and those calculated with TPS. The averages relative standard errors (RSE) of three deliveries within eight TLD capsule locations were, 0.9%, 0.8% and 0.6% for 3DCRT, IMRT and VMAT, respectively. The gamma analysis for all plans met the ±5%/3 mm criteria (over 90% passed) and results of QA were greater than 98%. The calculations for maximal doses and volumes of OARs suggest that the estimated risk of secondary cancer induction after VMAT is considerably lower than IMRT and 3DCRT.

Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD

Procedia PDF Downloads 507