Search results for: post processing

Authors: Marcela Parra-Vargas, Ana Sandoval-Rodriguez, Roberto Rodriguez-Echevarria, Jose Dominguez-Rosales, Juan Armendariz-Borunda

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Non-alcoholic fatty liver disease (NAFLD) is characterized by an excess of hepatic lipids, and it is to author’s best knowledge, the most prevalent chronic liver disorder. Anthocyanin-rich food consumption is linked to health benefits in metabolic disorders associated with obesity and NAFLD, although the precise functional role of anthocyanidin delphinidin (Dp) has yet to be established. The aim of this study was to investigate the effect of the Dp in NAFLD metabolic alterations by evaluating prevention or amelioration of hepatic lipid accumulation, as well as molecular mechanisms in two experimental obesity-related models of NALFD. In vitro: HepG2 cells were incubated with sodium palmitate (PA, 1 mM) to induce lipotoxic damage, and concomitantly treated with Dp (180 uM) for 24 h. Subsequently, total lipid accumulation was measured by colorimetric staining with Oil Red O, and total intrahepatic triglycerides were determined by an enzymatic assay. To assess molecular mechanisms, cells were pre-treated with PA for 24 h and then exposed to Dp for 1 h. In vivo: four-week-old male C57BL/6Nhsd mice were allocated in two main groups. Mice were fed with standard diet (control) or high-fat and high-carbohydrate diet (45% fat, HFD) for 16 wk to induce NAFLD. Then HFD was divided into subgroups: one treated orally with Dp (15 mg/kg bw, HFD-Dp) every day for 4 wk, while HFD group treated with vehicle (DMSO). Weight and fasting glucose were recorded weekly, while dietary ingestion was measured daily. Insulin tolerance test was performed at the end of treatment. Liver histology was evaluated with H&E and Masson’s trichrome stain. RT-PCR was used to evaluate gene expression and Western Blot to determine levels of protein in both experimental models. Parametric data were analyzed with one-way ANOVA and Tukey’s post-hoc test. Kruskal-Wallis and Mann-Whitney U test for non-parametric data, and P < 0.5 were considered significant. Dp prevented hepatic lipid accumulation by PA in HepG2 hepatocytes. Furthermore, Dp down-regulated gene expression of SREBP1c, FAS, and CPT1a without modifying AMPK phosphorylation levels. In vivo, Dp oral administration did not ameliorate lipid metabolic alterations raised by HFD. Adiposity, dietary ingestion, fasting glucose, and insulin sensitivity after Dp treatment remained similar to HFD group. Histological analysis showed hepatic damage in HFD groups and no differences between HFD and HFD-Dp groups were found. Hepatic gene expression of ACC and FAS were not altered by HFD. SREBP1c was similar in both HFD and HFD-Dp groups. No significant changes were observed in SREBP1c, ACC, and FAS adipose tissue gene expression by HFD or Dp treatment. Additionally, immunoblotting analysis revealed no changes in pathway SIRT1-LKB-AMPK and PPAR alpha by both HFD groups compared to control. In conclusion, the antioxidant Dp may provoke beneficial effects in the prevention of hepatic lipid accumulation. Nevertheless, the oral dose administrated in mice that simulated the total intake of anthocyanins consumed daily by humans has no effect as a treatment on hepatic lipid metabolic alterations and histological abnormalities associated with exposure to chronic HFD. A healthy lifestyle with regular intake of antioxidants such as anthocyanins may prevent metabolic alterations in NAFLD.

Keywords: anthocyanins, antioxidants, delphinidin, non-alcoholic fatty liver disease, obesity

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Authors: S. A. Krutovertsev, A. E. Tarasova, L. S. Krutovertseva, O. M. Ivanova

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The aim of the work was to get stable sensitive films with good sensitivity to ethanolamine (C2H7NO) in air. Ethanolamine is used as adsorbent in different processes of gas purification and separation. Besides it has wide industrial application. Chemical sensors of sorption type are widely used for gas analysis. Their behavior is determined by sensor characteristics of sensitive sorption layer. Forming conditions and characteristics of chemical gas sensors based on nanostructured modified silica films activated by different admixtures have been studied. As additives molybdenum containing polyoxometalates of the eighteen series were incorporated in silica films. The method of hydrolythic polycondensation from tetraethyl orthosilicate solutions was used for forming such films in this work. The method’s advantage is a possibility to introduce active additives directly into an initial solution. This method enables to obtain sensitive thin films with high specific surface at room temperature. Particular properties make polyoxometalates attractive as active additives for forming of gas-sensitive films. As catalyst of different redox processes, they can either accelerate the reaction of the matrix with analyzed gas or interact with it, and it results in changes of matrix’s electrical properties Polyoxometalates based films were deposited on the test structures manufactured by microelectronic planar technology with interdigitated electrodes. Modified silica films were deposited by a casting method from solutions based on tetraethyl orthosilicate and polyoxometalates. Polyoxometalates were directly incorporated into initial solutions. Composite nanostructured films were deposited by drop casting method on test structures with a pair of interdigital metal electrodes formed at their surface. The sensor’s active area was 4.0 x 4.0 mm, and electrode gap was egual 0.08 mm. Morphology of the layers surface were studied with Solver-P47 scanning probe microscope (NT-MDT, Russia), the infrared spectra were investigated by a Bruker EQUINOX 55 (Germany). The conditions of film formation varied during the tests. Electrical parameters of the sensors were measured electronically in real-time mode. Films had highly developed surface with value of 450 m2/g and nanoscale pores. Thickness of them was 0,2-0,3 µm. The study shows that the conditions of the environment affect markedly the sensors characteristics, which can be improved by choosing of the right procedure of forming and processing. Addition of polyoxometalate into silica film resulted in stabilization of film mass and changed markedly of electrophysical characteristics. Availability of Mn3P2Mo18O62 into silica film resulted in good sensitivity and selectivity to ethanolamine. Sensitivity maximum was observed at weight content of doping additive in range of 30–50% in matrix. With ethanolamine concentration changing from 0 to 100 ppm films’ conductivity increased by 10-12 times. The increase of sensor’s sensitivity was received owing to complexing reaction of tested substance with cationic part of polyoxometalate. This fact results in intramolecular redox reaction which sharply change electrophysical properties of polyoxometalate. This process is reversible and takes place at room temperature.

Keywords: ethanolamine, gas analysis, polyoxometalate, silica film

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Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

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Authors: Marthinus Conradie

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The conversational dynamics of democratically orientated deliberation continue to stimulate critical scholarship for its potential to bolster robust engagement between different sections of pluralist societies. Several axes of deliberation that have attracted academic attention include face-to-face vs. online interaction, and citizen-to-citizen communication vs. engagement between citizens and political elites. In all these areas, numerous researchers have explored deliberative procedures aimed at achieving instrumental goals such a securing consensus on policy issues, against procedures that prioritise expressive outcomes such as broadening the range of argumentative repertoires that discursively construct and mediate specific political issues. The study that informs this paper, works in the latter stream. Drawing its data from the reader-comments section of a South African broadsheet newspaper, the study investigates online, citizen-to-citizen deliberation by analysing the discursive practices through which competing understandings of social problems are articulated and contested. To advance this agenda, the paper deals specifically with user-generated comments posted in response to news stories on questions of race and racism in South Africa. The analysis works to discern and interpret the various sets of discourse practices that shape how citizens deliberate contentious political issues, especially racism. Since the website in question is designed to encourage the critical comparison of divergent interpretations of news events, without feeding directly into national policymaking, the study adopts an analytic framework that traces how citizens articulate arguments, rather than the instrumental effects that citizen deliberations might exert on policy. The paper starts from the argument that such expressive interactions are particularly crucial to current trends in South African politics, given that the precise nature of race and racism remain contested and uncertain. Centred on a sample of 2358 conversational moves in 814 posts to 18 news stories emanating from issues of race and racism, the analysis proceeds in a two-step fashion. The first stage conducts a qualitative content analysis that offers insights into the levels of reciprocity among commenters (do readers engage with each other or simply post isolated opinions?), as well as the structures of argumentation (do readers support opinions by citing evidence?). The second stage involves a more fine-grained discourse analysis, based on a theorisation of argumentation that delineates it into three components: opinions/conclusions, evidence/data to support opinions/conclusions and warrants that explicate precisely how evidence/data buttress opinions/conclusions. By tracing the manifestation and frequency of specific argumentative practices, this study contributes to the archive of research currently aggregating around the practices that characterise South Africans’ engagement with provocative political questions, especially racism and racial inequity. Additionally, the study also contributes to recent scholarship on the affordances of Web 2.0 software by eschewing a simplistic bifurcation between cyber-optimist vs. pessimism, in favour of a more nuanced and context-specific analysis of the patterns that structure online deliberation.

Keywords: online deliberation, discourse analysis, qualitative content analysis, racism

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Authors: E. Tan, O. McKay, T. Clarnette T., D. Croagh

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Background: Historically with pancreatic trauma, complete disruption of the main pancreatic duct (MPD), classified as Grade IV-V by the American Association for the Surgery of Trauma (AAST), necessitated a damage-control laparotomy. This was to avoid mortality, shorten diet upgrade timeframe, and hence shorter length of stay. However, acute pancreatic resection entailed complications of pancreatic fistulas and leaks. With the advance of imaging-guided interventions, non-operative management such as percutaneous and transpapillary drainage of traumatic peripancreatic collections have been trialled favourably. The aim of this case series is to evaluate the efficacy of endoscopic ultrasound-guided (EUS) transmural drainage in managing traumatic peripancreatic collections as a less invasive alternative to traditional approaches. This study also highlights the importance of anatomical knowledge regarding peripancreatic collection’s common location in the lesser sac, the pancreas relationship to adjacent organs, and the formation of the main pancreatic duct in regards to the feasibility of therapeutic internal drainage. Methodology: A retrospective case series was conducted at a single tertiary endoscopy unit, analysing patient data over a 5-year period. Inclusion criteria outlined patients age 5 to 80-years-old, traumatic pancreatic injury of at least Grade IV and haemodynamic stability. Exclusion criteria involved previous episodes of pancreatitis or abdominal trauma. Patient demographics and clinicopathological characteristics were retrospectively collected. Results: The study identified 7 patients with traumatic pancreatic injuries that were managed from 2018-2022; age ranging from 5 to 34 years old, with majority being female (n=5). Majority of the mechanisms of trauma were a handlebar injury (n=4). Diagnosis was confirmed with an elevated lipase and computerized tomotography (CT) confirmation of proximal pancreatic transection with MPD disruption. All patients sustained an isolated single organ grade IV pancreatic injury, except case 4 and 5 with other intra-abdominal visceral Grade 1 injuries. 6 patients underwent early ERCP-guided transpapillary drainage with 1 being unsuccessful for pancreatic duct stent insertion (case 1) and 1 complication of stent migration (case 2). Surveillance imaging post ERCP showed the stents were unable to bridge the disrupted duct and development of symptomatic collections with an average size of 9.9cm. Hence, all patients proceeded to EUS-guided transmural drainage, with 2/7 patients requiring repeat drainages (case 6 and 7). Majority (n=6) had a cystogastrostomy, whilst 1 (case 6) had a cystoenterostomy due to feasibility of the peripancreatic collection being adjacent to duodenum rather than stomach. However, case 6 subsequently required repeat EUS-guided drainage with cystogastrostomy for ongoing collections. Hence all patients avoided initial laparotomy with an average index length of stay of 11.7 days. Successful transmural drainage was demonstrated, with no long-term complications of pancreatic insufficiency; except for 1 patient requiring a distal pancreatectomy at 2 year follow-up due to chronic pain. Conclusion: The early results of this series support EUS-guided transmural drainage as a viable management option for traumatic peripancreatic collections, showcasing successful outcomes, minimal complications, and long-term efficacy in avoiding surgical interventions. More studies are required before the adoption of this procedure as a less invasive and complication-prone management approach for traumatic peripancreatic collections.

Keywords: endoscopic ultrasound, cystogastrostomy, pancreatic trauma, traumatic peripancreatic collection, transmural drainage

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Authors: Areti Okalidou, Paul D. Hatzigiannakoglou, Aikaterini Vatou, George Kyriafinis

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Young children are nowadays adept at using technology. Hence, computer-based auditory training programs (CBATPs) have become increasingly popular in aural rehabilitation for children with hearing loss and/or with cochlear implants (CI). Yet, their clinical utility for prognostic, diagnostic, and monitoring purposes has not been explored. The purposes of the study were: a) to develop an updated version of the auditory rehabilitation tool for Greek-speaking children with cochlear implants, b) to develop a database for behavioral responses, and c) to compare accuracy rates and reaction times in children differing in hearing status and other medical and demographic characteristics, in order to assess the tool’s clinical utility in prognosis, diagnosis, and progress monitoring. The updated version of the auditory rehabilitation tool was developed on a tablet, retaining the User-Centered Design approach and the elements of the Virtual Reality (VR) serious game. The visual stimuli were farm animals acting in simple game scenarios designed to trigger children’s responses to animal sounds, names, and relevant sentences. Based on an extended version of Erber’s auditory development model, the VR game consisted of six stages, i.e., sound detection, sound discrimination, word discrimination, identification, comprehension of words in a carrier phrase, and comprehension of sentences. A familiarization stage (learning) was set prior to the game. Children’s tactile responses were recorded as correct, false, or impulsive, following a child-dependent set up of a valid delay time after stimulus offset for valid responses. Reaction times were also recorded, and the database was in Εxcel format. The tablet version of the auditory rehabilitation tool was piloted in 22 preschool children with Νormal Ηearing (ΝΗ), which led to improvements. The study took place in clinical settings or at children’s homes. Fifteen children with CI, aged 5;7-12;3 years with post-implantation 0;11-5;1 years used the auditory rehabilitation tool. Eight children with CI were monolingual, two were bilingual and five had additional disabilities. The control groups consisted of 13 children with ΝΗ, aged 2;6-9;11 years. A comparison of both accuracy rates, as percent correct, and reaction times (in sec) was made at each stage, across hearing status, age, and also, within the CI group, based on presence of additional disability and bilingualism. Both monolingual Greek-speaking children with CI with no additional disabilities and hearing peers showed high accuracy rates at all stages, with performances falling above the 3rd quartile. However, children with normal hearing scored higher than the children with CI, especially in the detection and word discrimination tasks. The reaction time differences between the two groups decreased in language-based tasks. Results for children with CI with additional disability or bilingualism varied. Finally, older children scored higher than younger ones in both groups (CI, NH), but larger differences occurred in children with CI. The interactions between familiarization of the software, age, hearing status and demographic characteristics are discussed. Overall, the VR game is a promising tool for tracking the development of auditory skills, as it provides multi-level longitudinal empirical data. Acknowledgment: This work is part of a project that has received funding from the Research Committee of the University of Macedonia under the Basic Research 2020-21 funding programme.

Keywords: VR serious games, auditory rehabilitation, auditory training, children with cochlear implants

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Claire Williams

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Randomised controlled Trials (RCTs) of efficacy are still perceived as the gold standard for the generation of evidence, and whilst advances in data collection methods are well developed, this progress has not been matched for the reporting of adverse events (AEs). Assessment and reporting of AEs in clinical trials are fraught with human error and inefficiency and are extremely time and resource intensive. Recent research conducted into the quality of reporting of AEs during clinical trials concluded it is substandard and reporting is inconsistent. Investigators commonly send reports to sponsors who are incorrectly categorised and lacking in critical information, which can complicate the detection of valid safety signals. In our presentation, we will describe an electronic data capture system, which has been designed to support clinical trial processes by reducing the resource burden on investigators, improving overall trial efficiencies, and making trials safer for patients. This proprietary technology was developed using expertise proven in the delivery of the world’s first prospective, phase 3b real-world trial, ‘The Salford Lung Study, ’ which enabled robust safety monitoring and reporting processes to be accomplished by the remote monitoring of patients’ EHRs. This technology enables safety alerts that are pre-defined by the protocol to be detected from the data extracted directly from the patients EHR. Based on study-specific criteria, which are created from the standard definition of a serious adverse event (SAE) and the safety profile of the medicinal product, the system alerts the investigator or study team to the safety alert. Each safety alert will require a clinical review by the investigator or delegate; examples of the types of alerts include hospital admission, death, hepatotoxicity, neutropenia, and acute renal failure. This is achieved in near real-time; safety alerts can be reviewed along with any additional information available to determine whether they meet the protocol-defined criteria for reporting or withdrawal. This active surveillance technology helps reduce the resource burden of the more traditional methods of AE detection for the investigators and study teams and can help eliminate reporting bias. Integration of multiple healthcare data sources enables much more complete and accurate safety data to be collected as part of a trial and can also provide an opportunity to evaluate a drug’s safety profile long-term, in post-trial follow-up. By utilising this robust and proven method for safety monitoring and reporting, a much higher risk of patient cohorts can be enrolled into trials, thus promoting inclusivity and diversity. Broadening eligibility criteria and adopting more inclusive recruitment practices in the later stages of drug development will increase the ability to understand the medicinal products risk-benefit profile across the patient population that is likely to use the product in clinical practice. Furthermore, this ground-breaking approach to AE detection not only provides sponsors with better-quality safety data for their products, but it reduces the resource burden on the investigator and study teams. With the data taken directly from the source, trial costs are reduced, with minimal data validation required and near real-time reporting enables safety concerns and signals to be detected more quickly than in a traditional RCT.

Keywords: more comprehensive and accurate safety data, near real-time safety alerts, reduced resource burden, safer trials

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Authors: Serhat Tüzün, Tufan Demirel

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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

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Authors: Stefano Scippo, Damiana Luzzi, Stefano Cuomo, Maria Ranieri

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Background: In response to the rise in natural disasters linked to climate change, numerous studies on Disaster Risk Reduction Education (DRRE) have emerged since the '90s, mainly using a didactic transmission-based approach. Effective DRRE should align with an interactive, experiential, and participatory educational model, which can be costly and risky. A potential solution is using simulations facilitated by eXtended Reality (XR). Research Question: This study aims to conduct a scoping review to explore educational methodologies that use XR to enhance knowledge among teachers, students, and citizens about environmental risks, natural disasters (including climate-related ones), and their management. Method: A search string of 66 keywords was formulated, spanning three domains: 1) education and target audience, 2) environment and natural hazards, and 3) technologies. On June 21st, 2023, the search string was used across five databases: EBSCOhost, IEEE Xplore, PubMed, Scopus, and Web of Science. After deduplication and removing papers without abstracts, 2,152 abstracts (published between 2013 and 2023) were analyzed and 2,062 papers were excluded, followed by the exclusion of 56 papers after full-text scrutiny. Excluded studies focused on unrelated technologies, non-environmental risks, and lacked educational outcomes or accessible texts. Main Results: The 34 reviewed papers were analyzed for context, risk type, research methodology, learning objectives, XR technology use, outcomes, and educational affordances of XR. Notably, since 2016, there has been a rise in scientific publications, focusing mainly on seismic events (12 studies) and floods (9), with a significant contribution from Asia (18 publications), particularly Japan (7 studies). Methodologically, the studies were categorized into empirical (26) and non-empirical (8). Empirical studies involved user or expert validation of XR tools, while non-empirical studies included systematic reviews and theoretical proposals without experimental validation. Empirical studies were further classified into quantitative, qualitative, or mixed-method approaches. Six qualitative studies involved small groups of users or experts, while 20 quantitative or mixed-method studies used seven different research designs, with most (17) employing a quasi-experimental, one-group post-test design, focusing on XR technology usability over educational effectiveness. Non-experimental studies had methodological limitations, making their results hypothetical and in need of further empirical validation. Educationally, the learning objectives centered on knowledge and skills for surviving natural disaster emergencies. All studies recommended XR technologies for simulations or serious games but did not develop comprehensive educational frameworks around these tools. XR-based tools showed potential superiority over traditional methods in teaching risk and emergency management skills. However, conclusions were more valid in studies with experimental designs; otherwise, they remained hypothetical without empirical evidence. The educational affordances of XR, mainly user engagement, were confirmed by the studies. Authors’ Conclusions: The analyzed literature lacks specific educational frameworks for XR in DRRE, focusing mainly on survival knowledge and skills. There is a need to expand educational approaches to include uncertainty education, developing competencies that encompass knowledge, skills, and attitudes like risk perception.

Keywords: disaster risk reduction education, educational technologies, scoping review, XR technologies

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Authors: Jara Kaye S. Villanueva, M. Rosario Concepcion O. Ang

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For the past few years, Philippines has depended most of its energy source on oil, coal, and fossil fuel. According to the Department of Energy (DOE), the dominance of coal in the energy mix will continue until the year 2020. The expanding energy needs in the country have led to increasing efforts to promote and develop renewable energy. This research is a part of the government initiative in preparation for renewable energy development and expansion in the country. The Philippine Renewable Energy Resource Mapping from Light Detection and Ranging (LiDAR) Surveys is a three-year government project which aims to assess and quantify the renewable energy potential of the country and to put them into usable maps. This study focuses on the site suitability analysis of the four renewable energy sources – biomass (coconut, corn, rice, and sugarcane), hydro, solar, and wind energy. The site assessment is a key component in determining and assessing the most suitable locations for the construction of renewable energy power plants. This method maximizes the use of both the technical methods in resource assessment, as well as taking into account the environmental, social, and accessibility aspect in identifying potential sites by utilizing and integrating two different methods: the Multi-Criteria Decision Analysis (MCDA) method and Geographic Information System (GIS) tools. For the MCDA, Analytical Hierarchy Processing (AHP) is employed to determine the parameters needed for the suitability analysis. To structure these site suitability parameters, various experts from different fields were consulted – scientists, policy makers, environmentalists, and industrialists. The need to have a well-represented group of people to consult with is relevant to avoid bias in the output parameter of hierarchy levels and weight matrices. AHP pairwise matrix computation is utilized to derive weights per level out of the expert’s gathered feedback. Whereas from the threshold values derived from related literature, international studies, and government laws, the output values were then consulted with energy specialists from the DOE. Geospatial analysis using GIS tools translate this decision support outputs into visual maps. Particularly, this study uses Euclidean distance to compute for the distance values of each parameter, Fuzzy Membership algorithm which normalizes the output from the Euclidean Distance, and the Weighted Overlay tool for the aggregation of the layers. Using the Natural Breaks algorithm, the suitability ratings of each of the map are classified into 5 discrete categories of suitability index: (1) not suitable (2) least suitable, (3) suitable, (4) moderately suitable, and (5) highly suitable. In this method, the classes are grouped based on the best groups similar values wherein each subdivision are set from the rest based on the big difference in boundary values. Results show that in the entire Philippine area of responsibility, biomass has the highest suitability rating with rice as the most suitable at 75.76% suitability percentage, whereas wind has the least suitability percentage with score 10.28%. Solar and Hydro fall in the middle of the two, with suitability values 28.77% and 21.27%.

Keywords: site suitability, biomass energy, hydro energy, solar energy, wind energy, GIS

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Authors: Carly Cummings, Maria Soledad Peresin

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Alabama has a long history of racial injustice and unsatisfactory educational performance. In the 1870s Jim Crow laws segregated public schools and disproportionally allocated funding and resources to white institutions across the South. Despite the Supreme Court ruling to integrate schools following Brown vs. the Board of Education in 1954, Alabama’s school system continued to exhibit signs of segregation, compounded by “white flight” and the establishment of exclusive private schools, which still exist today. This discriminatory history has had a lasting impact of the state’s education system, reflected in modern school demographics and achievement data. It is well known that Alabama struggles with education performance, especially in science education. On average, minority groups scored the lowest in science proficiency. In Alabama, minority populations are concentrated in a region known as the Black Belt, which was once home to countless slave plantations and was the epicenter of the Civil Rights Movement. Today the Black Belt is characterized by a high density of woodlands and plays a significant role in Alabama’s leading economic industry-forest products. Given the economic importance of forestry and agriculture to the state, environmental science proficiency is essential to its stability; however, it is neglected in areas where it is needed most. To better understand the inequity of science education within Alabama, our study first investigates how geographic location, demographics and school funding relate to science achievement scores using ArcGIS and Pearson’s correlation coefficient. Additionally, our study explores the implementation of a relevant, problem-based, active learning lesson in schools. Relevant learning engages students by connecting material to their personal experiences. Problem-based active learning involves real-world problem-solving through hands-on experiences. Given Alabama’s significant woodland coverage, educational materials on forest products were developed with consideration of its relevance to students, especially those located in the Black Belt. Furthermore, to incorporate problem solving and active learning, the lesson centered around students using forest products to solve environmental challenges, such as water pollution- an increasing challenge within the state due to climate change. Pre and post assessment surveys were provided to teachers to measure the effectiveness of the lesson. In addition to pedagogical practices, community and mentorship programs are known to positively impact educational achievements. To this end, our work examines the results of surveys measuring educational professionals’ attitudes toward a local mentorship group within the Black Belt and its potential to address environmental and science literacy. Additionally, our study presents survey results from participants who attended an educational community event, gauging its effectiveness in increasing environmental and science proficiency. Our results demonstrate positive improvements in environmental awareness and science literacy with relevant pedagogy, mentorship, and community involvement. Implementing these practices can help provide equitable and inclusive learning environments and can better equip students with the skills and knowledge needed to bridge this historic educational gap within Alabama.

Keywords: equitable education, environmental science, environmental education, science education, racial injustice, sustainability, rural education

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Authors: Wellemans Isabelle, Remmelink Myriam, Foucart Annick, Rusu Stefan, Compère Christophe

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Primary pulmonary meningioma (PPM) is a very rare tumor, and its occurrence has been reported only sporadically. Multiple PPMs are even more exceptional, and herein, we report, to the best of our knowledge, the fourth case, focusing on the clinicopathological features of the tumor. Moreover, the possible relationship between the use of progesterone–only contraceptives and the development of these neoplasms will be discussed. Case Report: We report a case of a 51-year-old female presenting three solid pulmonary nodules, with the following localizations: right upper lobe, middle lobe, and left lower lobe, described as incidental findings on computed tomography (CT) during a pre-bariatric surgery check-up. The patient revealed no drinking or smoking history. The physical exam was unremarkable except for the obesity. The lesions ranged in size between 6 and 24 mm and presented as solid nodules with lobulated contours. The largest lesion situated in the middle lobe had mild fluorodeoxyglucose (FDG) uptake on F-18 FDG positron emission tomography (PET)/CT, highly suggestive of primary lung neoplasm. For pathological assessment, video-assisted thoracoscopic middle lobectomy and wedge resection of the right upper nodule was performed. Histological examination revealed relatively well-circumscribed solid proliferation of bland meningothelial cells growing in whorls and lobular nests, presenting intranuclear pseudo-inclusions and psammoma bodies. No signs of anaplasia were observed. The meningothelial cells expressed diffusely Vimentin, focally Progesterone receptors and were negative for epithelial (cytokeratin (CK) AE1/AE3, CK7, CK20, Epithelial Membrane Antigen (EMA)), neuroendocrine markers (Synaptophysin, Chromogranin, CD56) and Estrogenic receptors. The proliferation labelling index Ki-67 was low (<5%). Metastatic meningioma was ruled out by brain and spine magnetic resonance imaging (MRI) scans. The third lesion localized in the left lower lobe was followed-up and resected three years later because of its slow but significant growth (14 mm to 16 mm), alongside two new infra centimetric lesions. Those three lesions showed a morphological and immunohistochemical profile similar to previously resected lesions. The patient was disease-free one year post-last surgery. Discussion: Although PPMs are mostly benign and slow-growing tumors with an excellent prognosis, they do not present specific radiological characteristics, and it is difficult to differentiate it from other lung tumors, histopathologic examination being essential. Aggressive behavior is associated with atypical or anaplastic features (WHO grades II–III) The etiology is still uncertain and different mechanisms have been proposed. A causal connection between sexual hormones and meningothelial proliferation has long been suspected and few studies examining progesterone only contraception and meningioma risk have all suggested an association. In line with this, our patient was treated with Levonorgestrel, a progesterone agonist, intra-uterine device (IUD). Conclusions: PPM, defined by the typical histological and immunohistochemical features of meningioma in the lungs and the absence of central nervous system lesions, is an extremely rare neoplasm, mainly solitary and associating, and indolent growth. Because of the unspecific radiologic findings, it should always be considered in the differential diagnosis of lung neoplasms. Regarding multiple PPM, only three cases are reported in the literature, and this is the first described in a woman treated by a progesterone-only IUD to the best of our knowledge.

Keywords: pulmonary meningioma, multiple meningioma, meningioma, pulmonary nodules

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Authors: Heather Mroz

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Geothermal fluids can provide a nearly unlimited source of renewable energy but are often highly corrosive due to dissolved carbon dioxide (CO2), hydrogen sulphide (H2S), Ammonia (NH3) and chloride ions. The corrosive environment drives material selection for many components, including piping, heat exchangers and pressure vessels, to higher alloys of stainless steel, nickel-based alloys and titanium. The use of these alloys is cost-prohibitive and does not offer the pressure rating of carbon steel. One solution, explosion cladding, has been proven to reduce the capital cost of the geothermal equipment while retaining the mechanical and corrosion properties of both the base metal and the cladded surface metal. Explosion cladding is a solid-state welding process that uses precision explosions to bond two dissimilar metals while retaining the mechanical, electrical and corrosion properties. The process is commonly used to clad steel with a thin layer of corrosion-resistant alloy metal, such as stainless steel, brass, nickel, silver, titanium, or zirconium. Additionally, explosion welding can join a wider array of compatible and non-compatible metals with more than 260 metal combinations possible. The explosion weld is achieved in milliseconds; therefore, no bulk heating occurs, and the metals experience no dilution. By adhering to a strict set of manufacturing requirements, both the shear strength and tensile strength of the bond will exceed the strength of the weaker metal, ensuring the reliability of the bond. For over 50 years, explosion cladding has been used in the oil and gas and chemical processing industries and has provided significant economic benefit in reduced maintenance and lower capital costs over solid construction. The focus of this paper will be on the many benefits of the use of explosion clad in process equipment instead of more expensive solid alloy construction. The method of clad-plate production with explosion welding as well as the methods employed to ensure sound bonding of the metals. It will also include the origins of explosion cladding as well as recent technological developments. Traditionally explosion clad plate was formed into vessels, tube sheets and heads but recent advances include explosion welded piping. The final portion of the paper will give examples of the use of explosion-clad metals in geothermal energy recovery. The classes of materials used for geothermal brine will be discussed, including stainless steels, nickel alloys and titanium. These examples will include heat exchangers (tube sheets), high pressure and horizontal separators, standard pressure crystallizers, piping and well casings. It is important to educate engineers and designers on material options as they develop equipment for geothermal resources. Explosion cladding is a niche technology that can be successful in many situations, like geothermal energy recovery, where high temperature, high pressure and corrosive environments are typical. Applications for explosion clad metals include vessel and heat exchanger components as well as piping.

Keywords: clad metal, explosion welding, separator material, well casing material, piping material

Procedia PDF Downloads 154

Authors: Abdoulaye Maihatchi Ahamed, Zubin Arora, Benjamin Swobada, Jean-yves Lansot, Alexandre Chagnes

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Lithium-ion battery (LiB) is the technology of choice in the development of electric vehicles. But there are still many challenges, including the development of positive electrode materials exhibiting high cycle ability, high energy density, and low environmental impact. For this latter, LiBs must be manufactured in a circular approach by developing the appropriate strategies to reuse and recycle them. Presently, the recycling of LiBs is carried out by the pyrometallurgical route, but more and more processes implement or will implement the hydrometallurgical route or a combination of pyrometallurgical and hydrometallurgical operations. After producing the black mass by mineral processing, the hydrometallurgical process consists in leaching the black mass in order to uptake the metals contained in the cathodic material. Then, these metals are extracted selectively by liquid-liquid extraction, solid-liquid extraction, and/or precipitation stages. However, liquid-liquid extraction combined with precipitation/crystallization steps is the most implemented operation in the LiB recycling process to selectively extract copper, aluminum, cobalt, nickel, manganese, and lithium from the leaching solution and precipitate these metals as high-grade sulfate or carbonate salts. Liquid-liquid extraction consists in contacting an organic solvent and an aqueous feed solution containing several metals, including the targeted metal(s) to extract. The organic phase is non-miscible with the aqueous phase. It is composed of an extractant to extract the target metals and a diluent, which is usually aliphatic kerosene produced from the petroleum industry. Sometimes, a phase modifier is added in the formulation of the extraction solvent to avoid the third phase formation. The extraction properties of the diluent do not depend only on the chemical structure of the extractant, but it may also depend on the nature of the diluent. Indeed, the interactions between the diluent can influence more or less the interactions between extractant molecules besides the extractant-diluent interactions. Only a few studies in the literature addressed the influence of the diluent on the extraction properties, while many studies focused on the effect of the extractants. Recently, new low-carbon footprint aliphatic diluents were produced by catalytic dearomatisation and distillation of bio-based oil. This study aims at investigating the influence of the nature of the diluent on the extraction properties of three extractants towards cobalt, nickel, manganese, copper, aluminum, and lithium: Cyanex®272 for nickel-cobalt separation, DEHPA for manganese extraction, and Acorga M5640 for copper extraction. The diluents used in the formulation of the extraction solvents are (i) low-odor aliphatic kerosene produced from the petroleum industry (ELIXORE 180, ELIXORE 230, ELIXORE 205, and ISANE IP 175) and (ii) bio-sourced aliphatic diluents (DEV 2138, DEV 2139, DEV 1763, DEV 2160, DEV 2161 and DEV 2063). After discussing the effect of the diluents on the extraction properties, this conference will address the development of a low carbon footprint process based on the use of the best bio-sourced diluent for the production of high-grade cobalt sulfate, nickel sulfate, manganese sulfate, and lithium carbonate, as well as metal copper.

Keywords: diluent, hydrometallurgy, lithium-ion battery, recycling

Procedia PDF Downloads 88

Authors: Elsie M. Nolte, Annie M. Joubert, Roy Lakier, Maryke Etsebeth, Jolene M. Helena, Marcel Verwey, Laurence Lafanechere, Anne E. Theron

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Treatment regimens for breast- and lung cancers may include both radiation- and chemotherapy. Ideally, a pharmaceutical agent which selectively sensitizes cancer cells to gamma (γ)-radiation would allow administration of lower doses of each modality, yielding synergistic anti-cancer benefits and lower metastasis occurrence, in addition to decreasing the side-effect profiles. A range of 2-methoxyestradiol (2-ME) analogues, namely 2-ethyl-3-O-sulphamoyl-estra-1,3,5 (10) 15-tetraene-3-ol-17one (ESE-15-one), 2-ethyl-3-O-sulphamoyl-estra-1,3,5(10),15-tetraen-17-ol (ESE-15-ol) and 2-ethyl-3-O-sulphamoyl-estra-1,3,5(10)16-tetraene (ESE-16) were in silico-designed by our laboratory, with the aim of improving the parent compound’s bioavailability in vivo. The main effect of these compounds is the disruption of microtubule dynamics with a resultant mitotic accumulation and induction of programmed cell death in various cancer cell lines. This in vitro study aimed to determine the cellular responses involved in the radiation sensitization effects of these analogues at low doses in breast- and lung cancer cell lines. The oestrogen receptor positive MCF-7-, oestrogen receptor negative MDA-MB-231- and triple negative BT-20 breast cancer cell lines as well as the A549 lung cancer cell line were used. The minimal compound- and radiation doses able to induce apoptosis were determined using annexin-V and cell cycle progression markers. These doses (cell line dependent) were used to pre-sensitize the cancer cells 24 hours prior to 6 gray (Gy) radiation. Experiments were conducted on samples exposed to the individual- as well as the combination treatment conditions in order to determine whether the combination treatment yielded an additive cell death response. Morphological studies included light-, fluorescence- and transmission electron microscopy. Apoptosis induction was determined by flow cytometry employing annexin V, cell cycle analysis, B-cell lymphoma 2 (Bcl-2) signalling, as well as reactive oxygen species (ROS) production. Clonogenic studies were performed by allowing colony formation for 10 days post radiation. Deoxyribonucleic acid (DNA) damage was quantified via γ-H2AX foci and micronuclei quantification. Amplification of the p53 signalling pathway was determined by western blot. Results indicated that exposing breast- and lung cancer cells to nanomolar concentrations of these analogues 24 hours prior to γ-radiation induced more cell death than the compound- and radiation treatments alone. Hypercondensed chromatin, decreased cell density, a damaged cytoskeleton and an increase in apoptotic body formation were observed in cells exposed to the combination treatment condition. An increased number of cells present in the sub-G1 phase as well as increased annexin-V staining, elevation of ROS formation and decreased Bcl-2 signalling confirmed the additive effect of the combination treatment. In addition, colony formation decreased significantly. p53 signalling pathways were significantly amplified in cells exposed to the analogues 24 hours prior to radiation, as was the amount of DNA damage. In conclusion, our results indicated that pre-treatment of breast- and lung cancer cells with low doses of 2-ME analogues sensitized breast- and lung cancer cells to γ-radiation and induced apoptosis more so than the individual treatments alone. Future studies will focus on the effect of the combination treatment on non-malignant cellular counterparts.

Keywords: cancer, microtubule dynamics, radiation therapy, radiosensitization

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Authors: Zdravko Trivic, Kelvin E. Y. Low, Darko Radovic, Raymond Lucas

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Our understanding and experience of the built environment are primarily shaped by multi‐sensory, emotional and symbolic modes of exchange with spaces. Associated sensory and cognitive declines that come with ageing substantially affect the overall quality of life of the elderly citizens and the ways they perceive and use urban environment. Reduced mobility and increased risk of falls, problems with spatial orientation and communication, lower confidence and independence levels, decreased willingness to go out and social withdrawal are some of the major consequences of sensory declines that challenge almost all segments of the seniors’ everyday living. However, contemporary urban environments are often either sensory overwhelming or depleting, resulting in physical, mental and emotional stress. Moreover, the design and planning of housing neighbourhoods hardly go beyond the passive 'do-no-harm' and universal design principles, and the limited provision of often non-integrated eldercare and inter-generational facilities. This paper explores and discusses the largely neglected relationships between the 'hard' and 'soft' aspects of housing neighbourhoods and urban experience, focusing on seniors’ perception and multi-sensory experience as vehicles for design and planning of high-density housing neighbourhoods that are inclusive and empathetic yet build senior residents’ physical and mental abilities at different stages of ageing. The paper outlines methods and key findings from research conducted in two high-density housing neighbourhoods in Singapore with aims to capture and evaluate multi-sensorial qualities of two neighbourhoods from the perspective of senior residents. Research methods employed included: on-site sensory recordings of 'objective' quantitative sensory data (air temperature and humidity, sound level and luminance) using multi-function environment meter, spatial mapping of patterns of elderly users’ transient and stationary activity, socio-sensory perception surveys and sensorial journeys with local residents using eye-tracking glasses, and supplemented by walk-along or post-walk interviews. The paper develops a multi-sensory framework to synthetize, cross-reference, and visualise the activity and spatio-sensory rhythms and patterns and distill key issues pertinent to ageing-friendly and health-supportive neighbourhood design. Key findings show senior residents’ concerns with walkability, safety, and wayfinding, overall aesthetic qualities, cleanliness, smell, noise, and crowdedness in their neighbourhoods, as well as the lack of design support for all-day use in the context of Singaporean tropical climate and for inter-generational social interaction. The (ongoing) analysis of eye-tracking data reveals the spatial elements of senior residents’ look at and interact with the most frequently, with the visual range often directed towards the ground. With capacities to meaningfully combine quantitative and qualitative, measured and experienced sensory data, multi-sensory framework shows to be fruitful for distilling key design opportunities based on often ignored aspects of subjective and often taken-for-granted interactions with the familiar outdoor environment. It offers an alternative way of leveraging the potentials of housing neighbourhoods to take a more active role in enabling healthful living at all stages of ageing.

Keywords: ageing-friendly neighbourhoods, eye-tracking, high-density environment, multi-sensory approach, perception

Procedia PDF Downloads 154

Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

Procedia PDF Downloads 91

Authors: Clorinda Hogan, Shaun Abbott, Mark Halaki, Marcela Torres Catiglioni, Goshi Yamauchi, Lachlan Mitchell, James Salter, Michael Romann, Stephen Cobley

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Introduction: Common annual age-group competition structures unintentionally introduce participation inequalities, performance (dis)advantages and selection biases due to the effect of maturational variation between youth swimmers. On this basis, there are implications for improving performance evaluation strategies. Therefore the aim was to: (1) To determine maturity timing distributions in female youth swimming; (2) quantify the relationship between maturation status and 100-m FC performance; (3) apply Maturational-based Corrective Adjustment Procedures (Mat-CAPs) for removal of maturational status performance influences. Methods: (1) Cross-sectional analysis of 663 female (10-15 years) swimmers who underwent assessment of anthropometrics (mass, height and sitting height) and estimations of maturity timing and offset. (2) 100-m front-crawl performance (seconds) was assessed at Australian regional, state, and national-level competitions between 2016-2020. To determine the relationship between maturation status and 100-m front-crawl performance, MO was plotted against 100-m FC performance time. The expected maturity status - performance relationship for females aged 10-15 years of age was obtained through a quadratic function (y = ax2 + bx + c) from unstandardized coefficients. The regression equation was subsequently used for Mat-CAPs. (3) Participants aged 10-13 years were categorised into maturity-offset categories. Maturity offset distributions for Raw (‘All’, ‘Top 50%’ & ‘Top 25%’) and Correctively Adjusted swim times were examined. Chi-square, Cramer’s V and ORs determined the occurrence of maturation biases for each age group and selection level. Results—: (1) Maturity timing distributions illustrated overrepresentation of ‘normative’ maturing swimmers (11.82 ± 0.40 years), with a descriptive shift toward the early maturing relative to the normative population. (2) A curvilinear relationship between maturity-offset and swim performance was identified (R2 = 0.53, P < 0.001) and subsequently utilised for Mat-CAPs. (3) Raw maturity offset categories identified partial maturation status skewing towards biologically older swimmers at 10/11 and 12 years, with effect magnitudes increasing in the ‘Top 50%’ and ‘25%’ of performance times. Following Mat-CAPs application, maturity offset biases were removed in similar age groups and selection levels. When adjusting performance times for maturity offset, Mat-CAPs was successful in mitigating against maturational biases until approximately 1-year post Peak Height Velocity. The overrepresentation of ‘normative’ maturing female swimmers contrasted with the substantial overrepresentation of ‘early’ maturing male swimmers found previously in 100-m front-crawl. These findings suggest early maturational timing is not advantageous in females, but findings associated with Aim 2, highlight how advanced maturational status remained beneficial to performance. Observed differences between female and male maturational biases may relate to the differential impact of physiological development during pubertal years. Females experience greater increases of fat mass and potentially differing changes in body shape which can negatively affect swim performance. Conclusions: Transient maturation status-based participation and performance advantages were apparent within a large sample of Australian female youth 100-m FC swimmers. By removing maturity status performance biases within female youth swimming, Mat-CAPs could help improve participation experiences and the accuracy of identifying genuinely skilled female youth swimmers.

Keywords: athlete development, long-term sport participation, performance evaluation, talent identification, youth competition

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Authors: Sarah Niklas, Lynne Chester, Mark Diesendorf

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Geopolitical tensions, climate change and recent movements favouring a transformative shift in institutional power structures have influenced the economics of conventional energy supply for decades. This study takes a multi-dimensional approach to illustrate the potential of renewable energy (RE) technology to provide a pathway to a low-carbon economy driven by ecologically sustainable, independent and socially just energy. This comparative analysis identifies economic, political and social drivers that shaped the adoption of RE policy in two significantly different economies, Germany and Australia, with strong and weak commitments to RE respectively. Two complementary political-economy theories frame the document-based analysis. Régulation Theory, inspired by Marxist ideas and strongly influenced by contemporary economic problems, provides the background to explore the social relationships contributing the adoption of RE within the macro-economy. Varieties of Capitalism theory, a more recently developed micro-economic approach, examines the nature of state-firm relationships. Together these approaches provide a comprehensive lens of analysis. Germany’s energy policy transformed substantially over the second half of the last century. The development is characterised by the coordination of societal, environmental and industrial demands throughout the advancement of capitalist regimes. In the Fordist regime, mass production based on coal drove Germany’s astounding economic recovery during the post-war period. Economic depression and the instability of institutional arrangements necessitated the impulsive seeking of national security and energy independence. During the postwar Flexi-Fordist period, quality-based production, innovation and technology-based competition schemes, particularly with regard to political power structures in and across Europe, favoured the adoption of RE. Innovation, knowledge and education were institutionalized, leading to the legislation of environmental concerns. Lastly the establishment of government-industry-based coordinative programs supported the phase out of nuclear power and the increased adoption of RE during the last decade. Australia’s energy policy is shaped by the country’s richness in mineral resources. Energy policy largely served coal mining, historically and currently one of the most capital-intense industry. Assisted by the macro-economic dimensions of institutional arrangements, social and financial capital is orientated towards the export-led and strongly demand-oriented economy. Here energy policy serves the maintenance of capital accumulation in the mining sector and the emerging Asian economies. The adoption of supportive renewable energy policy would challenge the distinct role of the mining industry within the (neo)-liberal market economy. The state’s protective role of the mining sector has resulted in weak commitment to RE policy and investment uncertainty in the energy sector. Recent developments, driven by strong public support for RE, emphasize the sense of community in urban and rural areas and the emergence of a bottom-up approach to adopt renewables. Thus, political economy frameworks on both the macro-economic (Regulation Theory) and micro-economic (Varieties of Capitalism theory) scales can together explain the strong commitment to RE in Germany vis-à-vis the weak commitment in Australia.

Keywords: political economy, regulation theory, renewable energy, social relationships, energy transitions

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Authors: Yujie Yuan, Yiyang Fan, Hong Fan

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Objective: The RNA methylation recognition protein Aly/REF export factor (ALYREF) is considered one type of “reader” protein acting as a recognition protein of m5C, has been reported involved in several biological progresses including cancer initiation and progression. 5-methylcytosine (m5C) is a conserved and prevalent RNA modification in all species, as accumulating evidence suggests its role in the promotion of tumorigenesis. It has been claimed that ALYREF mediates nuclear export of mRNA with m5C modification and regulates biological effects of cancer cells. However, the systematical regulatory pathways of ALYREF in cancer tissues have not been clarified, yet. Methods: The expression level of ALYREF in pan-cancer and their normal tissues was compared through the data acquired from The Cancer Genome Atlas (TCGA). The University of Alabama at Birmingham Cancer data analysis Portal UALCAN was used to analyze the relationship between ALYREF and clinical pathological features. The relationship between the expression level of ALYREF and prognosis of pan-cancer, and the correlation genes of ALYREF were figured out by using Gene Expression Correlation Analysis database GEPIA. Immune related genes were obtained from TISIDB (an integrated repository portal for tumor-immune system interactions). Immune-related research was conducted by using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and TIMER. Results: Based on the data acquired from TCGA, ALYREF has an obviously higher-level expression in various types of cancers compared with relevant normal tissues excluding thyroid carcinoma and kidney chromophobe. The immunohistochemical images on The Human Protein Atlas showed that ALYREF can be detected in cytoplasm, membrane, but mainly located in nuclear. In addition, a higher expression level of ALYREF in tumor tissue generates a poor prognosis in majority of cancers. According to the above results, cancers with a higher expression level of ALYREF compared with normal tissues and a significant correlation between ALYREF and prognosis were selected for further analysis. By using TISIDB, we found that portion of ALYREF co-expression genes (such as BIRC5, H2AFZ, CCDC137, TK1, and PPM1G) with high Pearson correlation coefficient (PCC) were involved in anti-tumor immunity or affect resistance or sensitivity to T cell-mediated killing. Furthermore, based on the results acquired from GEPIA, there was significant correlation between ALYREF and PD-L1. It was exposed that there is a negative correlation between the expression level of ALYREF and ESTIMATE score. Conclusion: The present study indicated that ALYREF plays a vital and universal role in cancer initiation and progression of pan-cancer through regulating mitotic progression, DNA synthesis and metabolic process, and RNA processing. The correlation between ALYREF and PD-L1 implied ALYREF may affect the therapeutic effect of immunotherapy of tumor. More evidence revealed that ALYREF may play an important role in tumor immunomodulation. The correlation between ALYREF and immune cell infiltration level indicated that ALYREF can be a potential therapeutic target. Exploring the regulatory mechanism of ALYREF in tumor tissues may expose the reason for poor efficacy of immunotherapy and offer more directions of tumor treatment.

Keywords: ALYREF, pan-cancer, immunotherapy, PD-L1

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Authors: Simrat Kaur, Fatema Diwan, Brad Reddersen

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The unsustainable and injudicious use of natural renewable resources beyond the self-replenishment limits of our planet has proved catastrophic. Most of the Earth’s resources, including land, water, minerals, and biodiversity, have been overexploited. Owing to this, there is a steep rise in the global events of natural calamities of contrasting nature, such as torrential rains, storms, heat waves, rising sea levels, and megadroughts. These are all interconnected through common elements, namely oceanic currents and land’s the green cover. The deforestation fueled by the ‘economic elites’ or the global players have already cleared massive forests and ecological biomes in every region of the globe, including the Amazon. These were the natural carbon sinks prevailing and performing CO2 sequestration for millions of years. The forest biomes have been turned into mono cultivation farms to produce feedstock crops such as soybean, maize, and sugarcane; which are one of the biggest green house gas emitters. Such unsustainable agriculture practices only provide feedstock for livestock and food processing industries with huge carbon and water footprints. These are two main factors that have ‘cause and effect’ relationships in the context of climate change. In contrast to organic and sustainable farming, the mono-cultivation practices to produce food, fuel, and feedstock using chemicals devoid of the soil of its fertility, abstract surface, and ground waters beyond the limits of replenishment, emit green house gases, and destroy biodiversity. There are numerous cases across the planet where due to overuse; the levels of surface water reservoir such as the Lake Mead in Southwestern USA and ground water such as in Punjab, India, have deeply shrunk. Unlike the rain fed food production system on which the poor communities of the world relies; the blue water (surface and ground water) dependent mono-cropping for industrial and processed food create water deficit which put the burden on the domestic users. Excessive abstraction of both surface and ground waters for high water demanding feedstock (soybean, maize, sugarcane), cereal crops (wheat, rice), and cash crops (cotton) have a dual and synergistic impact on the global green house gas emissions and prevalence of megadroughts. Both these factors have elevated global temperatures, which caused cascading events such as soil water deficits, flash fires, and unprecedented burning of the woods, creating megafires in multiple continents, namely USA, South America, Europe, and Australia. Therefore, it is imperative to reduce the green and blue water footprints of agriculture and industrial sectors through recycling of black and gray waters. This paper explores various opportunities for successful implementation of wastewater management for drought preparedness in high risk communities.

Keywords: wastewater, drought, biodiversity, water footprint, nutrient recovery, algae

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Authors: John Doe

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This paper presents the research project “Escape Through Culture”, which is co-funded by the European Union and national resources through the Operational Programme “Competitiveness, Entrepreneurship and Innovation” 2014-2020 and the Single RTDI State Aid Action "RESEARCH - CREATE - INNOVATE". The project implementation is assumed by three partners, (1) the Computer Technology Institute and Press "Diophantus" (CTI), experienced with the design and implementation of serious games, natural language processing and ICT in education, (2) the Laboratory of Environmental Communication and Audiovisual Documentation (LECAD), part of the University of Thessaly, Department of Architecture, which is experienced with the study of creative transformation and reframing of the urban and environmental multimodal experiences through the use of AR and VR technologies, and (3) “Apoplou”, an IT Company with experience in the implementation of interactive digital applications. The research project proposes the design of innovative infrastructure of digital educational escape games for mobile devices and computers, with the use of Virtual Reality and Augmented Reality for the promotion of Greek cultural heritage in Greece and abroad. In particular, the project advocates the combination of Greek cultural heritage and literature, digital technologies advancements and the implementation of innovative gamifying practices. The cultural experience of the players will take place in 3 layers: (1) In space: the digital games produced are going to utilize the dual character of the space as a cultural landscape (the real space - landscape but also the space - landscape as presented with the technologies of augmented reality and virtual reality). (2) In literary texts: the selected texts of Greek writers will support the sense of place and the multi-sensory involvement of the user, through the context of space-time, language and cultural characteristics. (3) In the philosophy of the "escape game" tool: whether played in a computer environment, indoors or outdoors, the spatial experience is one of the key components of escape games. The innovation of the project lies both in the junction of Augmented/Virtual Reality with the promotion of cultural points of interest, as well as in the interactive, gamified practices of literary texts. The digital escape game infrastructure will be highly interactive, integrating the projection of Greek landscape cultural elements and digital literary text analysis, supporting the creation of escape games, establishing and highlighting new playful ways of experiencing iconic cultural places, such as Elefsina, Skiathos etc. The literary texts’ content will relate to specific elements of the Greek cultural heritage depicted by prominent Greek writers and poets. The majority of the texts will originate from Greek educational content available in digital libraries and repositories developed and maintained by CTI. The escape games produced will be available for use during educational field trips, thematic tourism holidays, etc. In this paper, the methodology adopted for infrastructure development will be presented. The research is based on theories of place, gamification, gaming development, making use of corpus linguistics concepts and digital humanities practices for the compilation and the analysis of literary texts.

Keywords: escape games, cultural landscapes, gamification, digital humanities, literature

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Authors: Alexandra Papagianni, George Filis, Panagiotis Papadopoulos

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The liberalization of the energy market and the increasing penetration of fluctuating renewables (e.g., wind and solar power) have heightened the importance of the spot market for ensuring efficient electricity supply. This is further emphasized by the EU’s goal of achieving net-zero emissions by 2050. The day-ahead market (DAM) plays a key role in European energy trading, accounting for 80-90% of spot transactions and providing critical insights for next-day pricing. Therefore, short-term electricity price forecasting (EPF) within the DAM is crucial for market participants to make informed decisions and improve their market positioning. Existing literature highlights out-of-sample performance as a key factor in assessing EPF accuracy, with influencing factors such as predictors, forecast horizon, model selection, and strategy. Several studies indicate that electricity demand is a primary price determinant, while renewable energy sources (RES) like wind and solar significantly impact price dynamics, often lowering prices. Additionally, incorporating data from neighboring countries, due to market coupling, further improves forecast accuracy. Most studies predict up to 24 steps ahead using hourly data, while some extend forecasts using higher-frequency data (e.g., half-hourly or quarter-hourly). Short-term EPF methods fall into two main categories: statistical and computational intelligence (CI) methods, with hybrid models combining both. While many studies use advanced statistical methods, particularly through different versions of traditional AR-type models, others apply computational techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). Recent research combines multiple methods to enhance forecasting performance. Despite extensive research on EPF accuracy, a gap remains in understanding how forecasting strategy affects prediction outcomes. While iterated strategies are commonly used, they are often chosen without justification. This paper contributes by examining whether the choice of forecasting strategy impacts the quality of day-ahead price predictions, especially for multi-step forecasts. We evaluate both iterated and direct methods, exploring alternative ways of conducting iterated forecasts on benchmark and state-of-the-art forecasting frameworks. The goal is to assess whether these factors should be considered by end-users to improve forecast quality. We focus on the Greek DAM using data from July 1, 2021, to March 31, 2022. This period is chosen due to significant price volatility in Greece, driven by its dependence on natural gas and limited interconnection capacity with larger European grids. The analysis covers two phases: pre-conflict (January 1, 2022, to February 23, 2022) and post-conflict (February 24, 2022, to March 31, 2022), following the Russian-Ukraine conflict that initiated an energy crisis. We use the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) for evaluation, as well as the Direction of Change (DoC) measure to assess the accuracy of price movement predictions. Our findings suggest that forecasters need to apply all strategies across different horizons and models. Different strategies may be required for different horizons to optimize both accuracy and directional predictions, ensuring more reliable forecasts.

Keywords: short-term electricity price forecast, forecast strategies, forecast horizons, recursive strategy, direct strategy

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Mohiuddin Ahsanul Kabir Chowdhury, Nafisa Lira Huq, Afroza Khanom, Rafiqul Islam, Abdullah Nurus Salam Khan, Farhana Karim, Nabila Zaka, Shams El Arifeen, Sk. Masum Billah

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After having astounding achievements in reducing maternal mortality and achieving the target for Millennium Development Goal (MDG) 5, the Government of Bangladesh has set new target to reduce Maternal Mortality Ratio (MMR) to 70 per 100,000 live births aligning with targets of Sustainable Development Goals (SDGs). Aversion of deaths from maternal complication by ensuring quality health care could be an important path to accelerate the rate of reduction of MMR. This formative research was aimed at exploring the provision of quality maternal health services at different level of health facilities. The study was conducted in 1 district hospital (DH) and 4 Upazila health complexes (UHC) of Kurigram district of Bangladesh, utilizing both quantitative and qualitative research methods. We conducted 14 key informant interviews with facility managers and 20 in-depth interviews with health care providers and support staff. Besides, we observed 387 normal deliveries from which we found 17 cases of post partum haemorrhage (PPH) and 2 cases of eclampsia during the data collection period extended from July-September 2016. The quantitative data were analyzed by using descriptive statistics, and the qualitative component underwent thematic analysis with the broad themes of facility readiness for maternal complication management, and management of complications. Inadequacy in human resources has been identified as the most important bottleneck to provide quality care to manage maternal complications. The DH had a particular paucity of human resources in medical officer cadre where about 61% posts were unfilled. On the other hand, in the UHCs the positions mostly empty were obstetricians (75%, paediatricians (75%), staff nurses (65%), and anaesthetists (100%). The workload on the existing staff is increased because of the persistence of vacant posts. Unavailability of anesthetists and consultants does not permit the health care providers (HCP) of lower cadres to perform emergency operative procedures and forces them to refer the patients although referral system is not well organized in rural Bangladesh. Insufficient bed capacity, inadequate training, shortage of emergency medicines etc. are other hindrance factors for facility readiness. Among the 387 observed delivery case, 17 (4.4%) were identified as PPH cases, and only 2 cases were found as eclampsia/pre-eclampsia. The majority of the patients were treated with uterine message (16 out of 17, 94.1%) and injectable Oxytocin (14 out of 17, 82.4%). The providers of DH mentioned that they can manage the PPH because of having provision for diagnostic and blood transfusion services, although not as 24/7 services. Regarding management of eclampsia/pre-eclampsia, HCPs provided Diazepam, MgSO4, and other anti-hypertensives. The UHCs did not have MgSO4 at stock even, and one facility manager admitted that they treat eclampsia with Diazepam only. The nurses of the UHCs were found to be afraid to handle eclampsia cases. The upcoming interventions must ensure refresher training of service providers, continuous availability of essential medicine and equipment needed for complication management, availability of skilled health workforce, availability of functioning blood transfusion unit and pairing of consultants and anaesthetists to reach the newly set targets altogether.

Keywords: Bangladesh, health facilities, maternal complications, quality of care

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Authors: Kimberly Samaha

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In order to attract new types of investors into the emerging Bio-Economy, new methodologies to analyze investment risk are needed. The Bio-Hub Ecosystem model was developed to address a critical area of concern within the global energy market regarding the use of biomass as a feedstock for power plants. This study looked at repurposing existing biomass-energy plants into Circular Zero-Waste Bio-Hub Ecosystems. A Bio-Hub model that first targets a ‘whole-tree’ approach and then looks at the circular economics of co-hosting diverse industries (wood processing, aquaculture, agriculture) in the vicinity of the Biomass Power Plants facilities. This study modeled the economics and risk strategies of cradle-to-cradle linkages to incorporate the value-chain effects on capital/operational expenditures and investment risk reductions using a proprietary techno-economic model that incorporates investment risk scenarios utilizing the Monte Carlo methodology. The study calculated the sequential increases in profitability for each additional co-host on an operating forestry-based biomass energy plant in West Enfield, Maine. Phase I starts with the base-line of forestry biomass to electricity only and was built up in stages to include co-hosts of a greenhouse and a land-based shrimp farm. Phase I incorporates CO2 and heat waste streams from the operating power plant in an analysis of lowering and stabilizing the operating costs of the agriculture and aquaculture co-hosts. Phase II analysis incorporated a jet-fuel biorefinery and its secondary slip-stream of biochar which would be developed into two additional bio-products: 1) A soil amendment compost for agriculture and 2) A biochar effluent filter for the aquaculture. The second part of the study applied the Monte Carlo risk methodology to illustrate how co-location derisks investment in an integrated Bio-Hub versus individual investments in stand-alone projects of energy, agriculture or aquaculture. The analyzed scenarios compared reductions in both Capital and Operating Expenditures, which stabilizes profits and reduces the investment risk associated with projects in energy, agriculture, and aquaculture. The major findings of this techno-economic modeling using the Monte Carlo technique resulted in the masterplan for the first Bio-Hub to be built in West Enfield, Maine. In 2018, the site was designated as an economic opportunity zone as part of a Federal Program, which allows for Capital Gains tax benefits for investments on the site. Bioenergy facilities are currently at a critical juncture where they have an opportunity to be repurposed into efficient, profitable and socially responsible investments, or be idled and scrapped. The Bio-hub Ecosystems techno-economic analysis model is a critical model to expedite new standards for investments in circular zero-waste projects. Profitable projects will expedite adoption and advance the critical transition from the current ‘take-make-dispose’ paradigm inherent in the energy, forestry and food industries to a more sustainable Bio-Economy paradigm that supports local and rural communities.

Keywords: bio-economy, investment risk, circular design, economic modelling

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Authors: V. V. Fomin, A. P. Mikhailovich, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Ground-level landscape photos can be used as a source of objective data on woody vegetation and vegetation dynamics. We proposed a method for processing, analyzing, and presenting ground photographs, which has the following advantages: 1) researcher has to form holistic representation of the study area in form of a set of interlapping ground-level landscape photographs; 2) it is necessary to define or obtain characteristics of the landscape, objects, and phenomena present on the photographs; 3) it is necessary to create new or supplement existing textual descriptions and annotations for the ground-level landscape photographs; 4) single or multiple ground-level landscape photographs can be used to develop specialized geoinformation layers, schematic maps or thematic maps; 5) it is necessary to determine quantitative data that describes both images as a whole, and displayed objects and phenomena, using algorithms for automated image analysis. It is suggested to match each photo with a polygonal geoinformation layer, which is a sector consisting of areas corresponding with parts of the landscape visible in the photos. Calculation of visibility areas is performed in a geoinformation system within a sector using a digital model of a study area relief and visibility analysis functions. Superposition of the visibility sectors corresponding with various camera viewpoints allows matching landscape photos with each other to create a complete and wholesome representation of the space in question. It is suggested to user-defined data or phenomenons on the images with the following superposition over the visibility sector in the form of map symbols. The technology of geoinformation layers’ spatial superposition over the visibility sector creates opportunities for image geotagging using quantitative data obtained from raster or vector layers within the sector with the ability to generate annotations in natural language. The proposed method has proven itself well for relatively open and clearly visible areas with well-defined relief, for example, in mountainous areas in the treeline ecotone. When the polygonal layers of visibility sectors for a large number of different points of photography are topologically superimposed, a layer of visibility of sections of the entire study area is formed, which is displayed in the photographs. Also, as a result of this overlapping of sectors, areas that did not appear in the photo will be assessed as gaps. According to the results of this procedure, it becomes possible to obtain information about the photos that display a specific area and from which points of photography it is visible. This information may be obtained either as a query on the map or as a query for the attribute table of the layer. The method was tested using repeated photos taken from forty camera viewpoints located on Ray-Iz mountain massif (Polar Urals, Russia) from 1960 until 2023. It has been successfully used in combination with other ground-based and remote sensing methods of studying the climate-driven dynamics of woody vegetation in the Polar Urals. Acknowledgment: This research was collaboratively funded by the Russian Ministry for Science and Education project No. FEUG-2023-0002 (image representation) and Russian Science Foundation project No. 24-24-00235 (automated textual description).

Keywords: woody, vegetation, repeated, photographs

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Authors: Dean Sinković, Tea Golja, Morena Paulišić

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Upon the end of the vicious war in former Yugoslavia in 1995, international development community widely promoted microfinance as the key development framework to eradicate poverty, create jobs, increase income. Widespread claims were made that microfinance institutions would play vital role in creating a bedrock for sustainable ‘bottom-up’ economic development trajectory, thus, helping newly formed states to find proper way from economic post-war depression. This uplifting neoliberal narrative has no empirical support in the Republic of Croatia. Firstly, the type of enterprises created via microfinance sector are small, unskilled, labor intensive, no technology and with huge debt burden. This results in extremely high failure rates of microenterprises and poor individuals plunging into even deeper poverty, acute indebtedness and social marginalization. Secondly, evidence shows that microcredit is exact reflection of dangerous and destructive sub-prime lending model with ‘boom-to-bust’ scenarios in which benefits are solely extracted by the tiny financial and political elite working around the microfinance sector. We argue that microcredit providers are not proper financial structures through which developing countries should look way out of underdevelopment and poverty. In order to achieve sustainable long-term growth goals, public policy needs to focus on creating, supporting and facilitating the small and mid-size enterprises development. These enterprises should be technically sophisticated, capable of creating new capabilities and innovations, with managerial expertise (skills formation) and inter-connected with other organizations (i.e. clusters, networks, supply chains, etc.). Evidence from South-East Europe suggest that such structures are not created via microfinance model but can be fostered through various forms of social enterprises. Various legal entities may operate as social enterprises: limited liability private company, limited liability public company, cooperative, associations, foundations, institutions, Mutual Insurances and Credit union. Our main hypothesis is that cooperatives are potential agents of social and economic transformation and community development in the region. Financial cooperatives are structures that can foster more efficient allocation of financial resources involving deeper democratic arrangements and more socially just outcomes. In Croatia, pioneers of the first social enterprises were civil society organizations whilst forming a separated legal entity. (i.e. cooperatives, associations, commercial companies working on the principles of returning the investment to the founder). Ever since 1995 cooperatives in Croatia have not grown by pursuing their own internal growth but mostly by relying on external financial support. The greater part of today’s registered cooperatives tend to be agricultural (39%), followed by war veterans cooperatives (38%) and others. There are no financial cooperatives in Croatia. Due to the above mentioned we look at the historical developments and the prevailing social enterprises forms and discuss their advantages and disadvantages as potential agents for social and economic transformation and community development in the region. There is an evident lack of understanding of this business model and of its potential for social and economic development followed by an unfavorable institutional environment. Thus, we discuss the role of governance and management in the formation of social enterprises in Croatia, stressing the challenges for the governance of the country’s social enterprise movement.

Keywords: financial cooperatives, governance and management models, microfinance institutions, social enterprises

Procedia PDF Downloads 276