Search results for: input voltage balancing

Authors: Iman Farasat, Howard M. Salis

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Engineered genetic circuits reprogram cellular behavior to act as living computers with applications in detecting cancer, creating self-controlling artificial tissues, and dynamically regulating metabolic pathways. Phenemenological models are often used to simulate and design genetic circuit behavior towards a desired behavior. While such models assume that each circuit component’s function is modular and independent, even small changes in a circuit (e.g. a new promoter, a change in transcription factor expression level, or even a new media) can have significant effects on the circuit’s function. Here, we use statistical thermodynamics to account for the several factors that control transcriptional regulation in bacteria, and experimentally demonstrate the model’s accuracy across 825 measurements in several genetic contexts and hosts. We then employ our first principles model to design, experimentally construct, and characterize a family of signal amplifying genetic circuits (genetic OpAmps) that expand the dynamic range of cell sensors. To develop these models, we needed a new approach to measuring the in vivo binding free energies of transcription factors (TFs), a key ingredient of statistical thermodynamic models of gene regulation. We developed a new high-throughput assay to measure RNA polymerase and TF binding free energies, requiring the construction and characterization of only a few constructs and data analysis (Figure 1A). We experimentally verified the assay on 6 TetR-homolog repressors and a CRISPR/dCas9 guide RNA. We found that our binding free energy measurements quantitatively explains why changing TF expression levels alters circuit function. Altogether, by combining these measurements with our biophysical model of translation (the RBS Calculator) as well as other measurements (Figure 1B), our model can account for changes in TF binding sites, TF expression levels, circuit copy number, host genome size, and host growth rate (Figure 1C). Model predictions correctly accounted for how these 8 factors control a promoter’s transcription rate (Figure 1D). Using the model, we developed a design framework for engineering multi-promoter genetic circuits that greatly reduces the number of degrees of freedom (8 factors per promoter) to a single dimensionless unit. We propose the Ptashne (Pt) number to encapsulate the 8 co-dependent factors that control transcriptional regulation into a single number. Therefore, a single number controls a promoter’s output rather than these 8 co-dependent factors, and designing a genetic circuit with N promoters requires specification of only N Pt numbers. We demonstrate how to design genetic circuits in Pt number space by constructing and characterizing 15 2-repressor OpAmp circuits that act as signal amplifiers when within an optimal Pt region. We experimentally show that OpAmp circuits using different TFs and TF expression levels will only amplify the dynamic range of input signals when their corresponding Pt numbers are within the optimal region. Thus, the use of the Pt number greatly simplifies the genetic circuit design, particularly important as circuits employ more TFs to perform increasingly complex functions.

Keywords: transcription factor, synthetic biology, genetic circuit, biophysical model, binding energy measurement

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Authors: Molly Parker

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Unintended pregnancy rates in Australia are amongst the highest in the developed world. Women with Substance Use Disorder often have riskier sexual behavior with nil contraceptive use and face disproportionately higher unintended pregnancies and Sexually Transmitted Infections, alongside Substance Use in Pregnancy (SUP) climbing at an alarming rate. In an inner-city Drug and Alcohol (D&A) service, significant barriers to sexual and reproductive health services have been identified, aligning with research. Rapid pathways were created for women seeking D&A treatment to be referred to Sexual and Reproductive Health services for the administration of Long-acting reversible contraception (LARC) and sexual health screening. For clients attending a D&A service, this is an opportunistic time to offer sexual and reproductive health services. Collaboration and multidisciplinary team input between D&A and sexual health and reproductive services are paramount, with rapid referral pathways being identified as the main strategy to improve access to sexual and reproductive health support for this population. With this evidence, a rapid referral pathway was created for women using the D&A service to access LARC, particularly in view of fertility often returning once stable on D&A treatment. A closed-ended survey was used for D&A staff to identify gaps in reproductive health knowledge and views of referral accessibility. Results demonstrated a lack of knowledge of contraception and appropriate referral processes. A closed-ended survey for clients was created to establish the need and access to services and to quantify data. A follow-up data collection will be reviewed to access uptake and satisfaction of the intervention from clients. Sexual health screening access was also identified as a deficit, particularly concerning due to the higher rates of STIs in this cohort. A rapid referral pathway will be undergoing implementation, reducing risks of untreated STIS both pre and post-conception. Similarly, pre and post-intervention structured surveys will be used to identify client satisfaction from the pathway. Although currently in progress, the research and pathway aim to be completed by December 2023. This research and implementation of sexual and reproductive health pathways from the D&A service have significant health and well-being benefits to clients and the wider community, including possible fetal/infancy outcomes. Women now have rapid access to sexual and reproductive health services, with the aim of reducing unplanned pregnancies, poor outcomes associated with SUP, client/staff trauma from termination of pregnancy, and client/staff trauma following the assumption of care of the child due to substance use, the financial cost for out of home care as required, the poor outcomes of untreated STIs to the fetus in pregnancy and the spread of STIs in the wider community. As evidence suggests, the implementation of a streamlined referral process is required between D&A and sexual and reproductive health services and has positive feedback from both clinicians and clients in improving care.

Keywords: substance use in pregnancy, drug and alcohol, substance use disorder, sexual health, reproductive health, contraception, long-acting reversible contraception, neonatal abstinence syndrome, FASD, sexually transmitted infections, sexually transmitted infections pregnancy

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Authors: Marcin Zieliński, Marcin Dębowski, Mirosław Krzemieniewski

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The anaerobic degradation of substrates is limited especially by the rate and effectiveness of the first (hydrolytic) stage of fermentation. This stage may be intensified through pre-treatment of substrate aimed at disintegration of the solid phase and destruction of substrate tissues and cells. The most frequently applied criterion of disintegration outcomes evaluation is the increase in biogas recovery owing to the possibility of its use for energetic purposes and, simultaneously, recovery of input energy consumed for the pre-treatment of substrate before fermentation. Hydrodynamic cavitation is one of the methods for organic substrate disintegration that has a high implementation potential. Cavitation is explained as the phenomenon of the formation of discontinuity cavities filled with vapor or gas in a liquid induced by pressure drop to the critical value. It is induced by a varying field of pressures. A void needs to occur in the flow in which the pressure first drops to the value close to the pressure of saturated vapor and then increases. The process of cavitation conducted under controlled conditions was found to significantly improve the effectiveness of anaerobic conversion of organic substrates having various characteristics. This phenomenon allows effective damage and disintegration of cellular and tissue structures. Disintegration of structures and release of organic compounds to the dissolved phase has a direct effect on the intensification of biogas production in the process of anaerobic fermentation, on reduced dry matter content in the post-fermentation sludge as well as a high degree of its hygienization and its increased susceptibility to dehydration. A device the efficiency of which was confirmed both in laboratory conditions and in systems operating in the technical scale is a hydrodynamic generator of cavitation. Cavitators, agitators and emulsifiers constructed and tested worldwide so far have been characterized by low efficiency and high energy demand. Many of them proved effective under laboratory conditions but failed under industrial ones. The only task successfully realized by these appliances and utilized on a wider scale is the heating of liquids. For this reason, their usability was limited to the function of heating installations. Design of the presented cavitation generator allows achieving satisfactory energy efficiency and enables its use under industrial conditions in depolymerization processes of biomass with various characteristics. Investigations conducted on the laboratory and industrial scale confirmed the effectiveness of applying cavitation in the process of biomass destruction. The use of the cavitation generator in laboratory studies for disintegration of sewage sludge allowed increasing biogas production by ca. 30% and shortening the treatment process by ca. 20 - 25%. The shortening of the technological process and increase of wastewater treatment plant effectiveness may delay investments aimed at increasing system output. The use of a mechanical cavitator and application of repeated cavitation process (4-6 times) enables significant acceleration of the biogassing process. In addition, mechanical cavitation accelerates increases in COD and VFA levels.

Keywords: hydrodynamic cavitation, pretreatment, biomass, methane fermentation, Virginia fanpetals

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Authors: Beste Ozcan, Valerio Sperati, Laura Romano, Tania Moretta, Simone Scaffaro, Noemi Faedda, Federica Giovannone, Carla Sogos, Vincenzo Guidetti, Gianluca Baldassarre

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+me is an experimental interactive toy with the appearance of a soft, pillow-like, panda. Shape and consistency are designed to arise emotional attachment in young children: a child can wear it around his/her neck and treat it as a companion (i.e. a transitional object). When caressed on paws or head, the panda emits appealing, interesting outputs like colored lights or amusing sounds, thanks to embedded electronics. Such sensory patterns can be modified through a wirelessly connected tablet: by this, an adult caregiver can adapt +me responses to a child's reactions or requests, for example, changing the light hue or the type of sound. The toy control is therefore shared, as it depends on both the child (who handles the panda) and the adult (who manages the tablet and mediates the sensory input-output contingencies). These features make +me a potential tool for therapy with children with Neurodevelopmental Disorders (ND), characterized by impairments in the social area, like Autism Spectrum Disorders (ASD) and Language Disorders (LD): as a proposal, the toy could be used together with a therapist, in rehabilitative play activities aimed at encouraging simple social interactions and reinforcing basic relational and communication skills. +me was tested in two pilot experiments, the first one involving 15 Typically Developed (TD) children aged in 8-34 months, the second one involving 7 children with ASD, and 7 with LD, aged in 30-48 months. In both studies a researcher/caregiver, during a one-to-one, ten-minute activity plays with the panda and encourages the child to do the same. The purpose of both studies was to ascertain the general acceptability of the device as an interesting toy that is an object able to capture the child's attention and to maintain a high motivation to interact with it and with the adult. Behavioral indexes for estimating the interplay between the child, +me and caregiver were rated from the video recording of the experimental sessions. Preliminary results show how -on average- participants from 3 groups exhibit a good engagement: they touch, caress, explore the panda and show enjoyment when they manage to trigger luminous and sound responses. During the experiments, children tend to imitate the caregiver's actions on +me, often looking (and smiling) at him/her. Interesting behavioral differences between TD, ASD, and LD groups are scored: for example, ASD participants produce a fewer number of smiles both to panda and to a caregiver with respect to TD group, while LD scores stand between ASD and TD subjects. These preliminary observations suggest that the interactive toy +me is able to raise and maintain the interest of toddlers and therefore it can be reasonably used as a supporting tool during therapy, to stimulate pivotal social skills as imitation, turn-taking, eye contact, and social smiles. Interestingly, the young age of participants, along with the behavioral differences between groups, seem to suggest a further potential use of the device: a tool for early differential diagnosis (the average age of a child

Keywords: autism spectrum disorders, interactive toy, social interaction, therapy, transitional wearable companion

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Authors: Shilpa Murthy, Hazlina Binti Abu Bakar, Juliet Mathew, Chandrashekhar Thummala Hlly Sreerama Reddy, Pathiyil Ravi Shankar

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Simulation is defined as a technique that replaces or expands real experiences with guided experiences that interactively imitate real-world processes or systems. Simulation enables learners to train in a safe and non-threatening environment. For decades, simulation has been considered an integral part of clinical teaching and learning strategy in medical education. The several types of simulation used in medical education and the clinical environment can be applied to several models, including full-body mannequins, task trainers, standardized simulated patients, virtual or computer-generated simulation, or Hybrid simulation that can be used to facilitate learning. Simulation allows healthcare practitioners to acquire skills and experience while taking care of patient safety. The recent COVID pandemic has also led to an increase in simulation use, as there were limitations on medical student placements in hospitals and clinics. The learning is tailored according to the educational needs of students to make the learning experience more valuable. Simulation in the pre-clinical years has challenges with resource constraints, effective curricular integration, student engagement and motivation, and evidence of educational impact, to mention a few. As instructors, we may have more reliance on the use of simulation for pre-clinical students while the students’ confidence levels and perceived competence are to be evaluated. Our research question was whether the implementation of simulation-based learning positively influences preclinical medical students' confidence levels and perceived competence. This study was done to align the teaching activities with the student’s learning experience to introduce more low-fidelity simulation-based teaching sessions for pre-clinical years and to obtain students’ input into the curriculum development as part of inclusivity. The study was carried out at International Medical University, involving pre-clinical year (Medical) students who were started with low-fidelity simulation-based medical education from their first semester and were gradually introduced to medium fidelity, too. The Student Satisfaction and Self-Confidence in Learning Scale questionnaire from the National League of Nursing was employed to collect the responses. The internal consistency reliability for the survey items was tested with Cronbach’s alpha using an Excel file. IBM SPSS for Windows version 28.0 was used to analyze the data. Spearman’s rank correlation was used to analyze the correlation between students’ satisfaction and self-confidence in learning. The significance level was set at p value less than 0.05. The results from this study have prompted the researchers to undertake a larger-scale evaluation, which is currently underway. The current results show that 70% of students agreed that the teaching methods used in the simulation were helpful and effective. The sessions are dependent on the learning materials that are provided and how the facilitators engage the students and make the session more enjoyable. The feedback provided inputs on the following areas to focus on while designing simulations for pre-clinical students. There are quality learning materials, an interactive environment, motivating content, skills and knowledge of the facilitator, and effective feedback.

Keywords: low-fidelity simulation, pre-clinical simulation, students satisfaction, self-confidence

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Authors: Kaustav Mukherjee

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In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

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Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Lazzat M. Zhamaliyeva, Nurgul A. Abenova, Gauhar S. Dilmagambetova, Ziyash Zh. Tanbetova, Moldir B. Ahmetzhanova, Tatyana P. Ostretcova, Aliya A. Yegemberdiyeva

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Introduction: There are many reasons for the weak training of family doctors in Kazakhstan: the unified national educational program is not focused on competencies, the role of a general practitioner (GP) is not clear, poor funding for the health care and education system, outdated teaching and assessment methods, inefficient management. We highlight two issues in particular. Firstly, academic teachers of family medicine (FM) in Kazakhstan do not practice as family doctors; most of them are narrow specialists (pediatricians, therapists, surgeons, etc.); they usually hold one-time consultations; clinical mentors from practical healthcare (non-academic teachers) do not have the teaching competences, and the vast majority of them are also narrow specialists. Secondly, clinical sites (polyclinics) are unprepared for general practice and do not follow the principles of family medicine; residents do not like to be in primary health care (PHC) settings due to the chaos that is happening there, as well as due to the lack of the necessary equipment for mastering and consolidating practical skills. Aim: We present the concept of the family physicians’ training office (FPTO), which is being created as a friendly learning environment for young general practitioners and for the involvement of academic teachers of family medicine in the practical work and innovative development of PHC. Methodology: In developing the conceptual framework and identifying practical activities, we drew on literature and expert input, and interviews. Results: The goal of the FPTO is to create a favorable educational and clinical environment for the development of the FM residents’ competencies, in which the residents with academic teachers and clinical mentors could understand and accept the principles of family medicine, improve clinical knowledge and skills, and gain experience in improving the quality of their practice in scientific basis. Three main areas of office activity are providing primary care to the patients, improving educational services for FM residents and other medical workers, and promoting research in PHC and innovations. The office arranges for residents to see outpatients at least 50% of the time, and teachers of FM departments at least 1/4 of their working time conduct general medical appointments next to residents. Taking into account the educational and scientific workload, the number of attached population for one GP does not exceed 500 persons. The equipment of the office allows FPTO workers to perform invasive and other manipulations without being sent to other clinics. In the office, training for residents is focused on their needs and aimed at achieving the required level of competence. International methodologies and assessment tools are adapted to local conditions and evaluated for their effectiveness and acceptability. Residents and their faculty actively conduct research in the field of family medicine. Conclusions: We propose to change the learning environment in order to create teams of like-minded people, to unite residents and teachers even more for the development of family medicine. The offices will also invest resources in developing and maintaining young doctors' interest in family medicine.

Keywords: educational environment, family medicine residents, family physicians’ training office, primary care research

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Authors: Hsien-Te Lin

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The purpose of this paper is to introduce the BCFES (building carbon footprint evaluation system), which is a LCA (life cycle assessment) tool developed by the Low Carbon Building Alliance (LCBA) in Taiwan. A qualified BCFES for the building industry should fulfill the function of evaluating carbon footprint throughout all stages in the life cycle of building projects, including the production, transportation and manufacturing of materials, construction, daily energy usage, renovation and demolition. However, many existing BCFESs are too complicated and not very designer-friendly, creating obstacles in the implementation of carbon reduction policies. One of the greatest obstacle is the misapplication of the carbon footprint inventory standards of PAS2050 or ISO14067, which are designed for mass-produced goods rather than building projects. When these product-oriented rules are applied to building projects, one must compute a tremendous amount of data for raw materials and the transportation of construction equipment throughout the construction period based on purchasing lists and construction logs. This verification method is very cumbersome by nature and unhelpful to the promotion of low carbon design. With a view to provide an engineer-oriented BCFE with pre-diagnosis functions, a component input/output (I/O) database system and a scenario simulation method for building energy are proposed herein. Most existing BCFESs base their calculations on a product-oriented carbon database for raw materials like cement, steel, glass, and wood. However, data on raw materials is meaningless for the purpose of encouraging carbon reduction design without a feedback mechanism, because an engineering project is not designed based on raw materials but rather on building components, such as flooring, walls, roofs, ceilings, roads or cabinets. The LCBA Database has been composited from existing carbon footprint databases for raw materials and architectural graphic standards. Project designers can now use the LCBA Database to conduct low carbon design in a much more simple and efficient way. Daily energy usage throughout a building's life cycle, including air conditioning, lighting, and electric equipment, is very difficult for the building designer to predict. A good BCFES should provide a simplified and designer-friendly method to overcome this obstacle in predicting energy consumption. In this paper, the author has developed a simplified tool, the dynamic Energy Use Intensity (EUI) method, to accurately predict energy usage with simple multiplications and additions using EUI data and the designed efficiency levels for the building envelope, AC, lighting and electrical equipment. Remarkably simple to use, it can help designers pre-diagnose hotspots in building carbon footprint and further enhance low carbon designs. The BCFES-LCBA offers the advantages of an engineer-friendly component I/O database, simplified energy prediction methods, pre-diagnosis of carbon hotspots and sensitivity to good low carbon designs, making it an increasingly popular carbon management tool in Taiwan. To date, about thirty projects have been awarded BCFES-LCBA certification and the assessment has become mandatory in some cities.

Keywords: building carbon footprint, life cycle assessment, energy use intensity, building energy

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Authors: Bai-Chen Xie, Xian-Peng Chen

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China has become the world's largest energy producer and consumer, and its development of renewable energy is of great significance to global energy governance and the fight against climate change. The rapid growth of solar power in China could help achieve its ambitious carbon peak and carbon neutrality targets early. However, the non-technical costs of solar power in China are much higher than at international levels, meaning that inefficiencies are rooted in poor management and improper policy design and that efficiency distortions have become a serious challenge to the sustainable development of the renewable energy industry. Unlike fossil energy generation technologies, the output of solar power is closely related to the volatile solar resource, and the spatial unevenness of solar resource distribution leads to potential efficiency spatial distribution differences. It is necessary to develop an efficiency evaluation method that considers the volatility of solar resources and explores the mechanism of the influence of natural geography and social environment on the spatially varying characteristics of efficiency distribution to uncover the root causes of managing inefficiencies. The study sets solar resources as stochastic inputs, introduces a chance-constrained data envelopment analysis model combined with the directional distance function, and measures the solar resource utilization efficiency of 222 solar power plants in representative photovoltaic bases in northwestern China. By the meta-frontier analysis, we measured the characteristics of different power plant clusters and compared the differences among groups, discussed the mechanism of environmental factors influencing inefficiencies, and performed statistical tests through the system generalized method of moments. Rational localization of power plants is a systematic project that requires careful consideration of the full utilization of solar resources, low transmission costs, and power consumption guarantee. Suitable temperature, precipitation, and wind speed can improve the working performance of photovoltaic modules, reasonable terrain inclination can reduce land cost, and the proximity to cities strongly guarantees the consumption of electricity. The density of electricity demand and high-tech industries is more important than resource abundance because they trigger the clustering of power plants to result in a good demonstration and competitive effect. To ensure renewable energy consumption, increased support for rural grids and encouraging direct trading between generators and neighboring users will provide solutions. The study will provide proposals for improving the full life-cycle operational activities of solar power plants in China to reduce high non-technical costs and improve competitiveness against fossil energy sources.

Keywords: solar power plants, environmental factors, data envelopment analysis, efficiency evaluation

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Authors: Imen Soltani, Takoua Soltani, Taoufik Aguili

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Metamaterials crossover classic physical boundaries and gives rise to new phenomena and applications in the domain of beam steering and shaping. Where electromagnetic near and far field manipulations were achieved in an accurate manner. In this sense, 3D imaging is one of the beneficiaries and in particular Denis Gabor’s invention: holography. But, the major difficulty here is the lack of a suitable recording medium. So some enhancements were essential, where the 2D version of bulk metamaterials have been introduced the so-called metasurface. This new class of interfaces simplifies the problem of recording medium with the capability of tuning the phase, amplitude, and polarization at a given frequency. In order to achieve an intelligible wavefront control, the electromagnetic properties of the metasurface should be optimized by means of solving Maxwell’s equations. In this context, integral methods are emerging as an important method to study electromagnetic from microwave to optical frequencies. The method of moment presents an accurate solution to reduce the problem of dimensions by writing its boundary conditions in the form of integral equations. But solving this kind of equations tends to be more complicated and time-consuming as the structural complexity increases. Here, the use of equivalent circuit’s method exhibits the most scalable experience to develop an integral method formulation. In fact, for allaying the resolution of Maxwell’s equations, the method of Generalised Equivalent Circuit was proposed to convey the resolution from the domain of integral equations to the domain of equivalent circuits. In point of fact, this technique consists in creating an electric image of the studied structure using discontinuity plan paradigm and taken into account its environment. So that, the electromagnetic state of the discontinuity plan is described by generalised test functions which are modelled by virtual sources not storing energy. The environmental effects are included by the use of an impedance or admittance operator. Here, we propose a tunable metasurface composed of graphene-based elements which combine the advantages of reflectarrays concept and graphene as a pillar constituent element at Terahertz frequencies. The metasurface’s building block consists of a thin gold film, a dielectric spacer SiO₂ and graphene patch antenna. Our electromagnetic analysis is based on the method of moment combined with generalised equivalent circuit (MoM-GEC). We begin by restricting our attention to study the effects of varying graphene’s chemical potential on the unit cell input impedance. So, it was found that the variation of complex conductivity of graphene allows controlling the phase and amplitude of the reflection coefficient at each element of the array. From the results obtained here, we were able to determine that the phase modulation is realized by adjusting graphene’s complex conductivity. This modulation is a viable solution compared to tunning the phase by varying the antenna length because it offers a full 2π reflection phase control.

Keywords: graphene, method of moment combined with generalised equivalent circuit, reconfigurable metasurface, reflectarray, terahertz domain

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Authors: Ágnes Győri, Éva Perpék, Zsófia Bauer, Zsuzsanna Elek

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The aim of the research (funded by Hungarian national grant, NFKI- FK 138315) is to examine the professional mobility, mental health and work environment of social workers with a complex approach. Previous international and Hungarian research has pointed out that those working in the helping professions are strongly exposed to the risk of emotional-mental-physical exhaustion due to stress. Mental and physical strain, as well as lack of coping (can) cause health problems, but its role in career change and high labor turnover has also been proven. Even though satisfaction with working conditions of those employed in the human service sector in the context of the stress burden has been researched extensively, there is a lack of large-sample international and Hungarian domestic studies exploring the effects of profession-specific conditions. Nor has it been examined how the specific features of the social profession and mental health affect the career mobility of the professionals concerned. In our research, these factors and their correlations are analyzed by means of mixed methodology, utilizing the benefits of netnographic big data analysis and a sector-specific quantitative survey. The netnographic analysis of open web content generated inside and outside the social profession offers a holistic overview of the influencing factors related to mental health and the work environment of social workers. On the one hand, the topics and topoi emerging in the external discourse concerning the sector are examined, and on the other hand, focus on mentions and streams of comments regarding the profession, burnout, stress, coping, as well as labor turnover and career changes among social professionals. The analysis focuses on new trends and changes in discourse that have emerged during and after the pandemic. In addition to the online conversation analysis, a survey of social professionals with a specific focus has been conducted. The questionnaire is based on input from the first two research phases. The applied approach underlines that the mobility paths of social professionals can only be understood if, apart from the general working conditions, the specific features of social work and the effects of certain aspects of mental health (emotional-mental-physical strain, resilience) are taken into account as well. In this paper, the preliminary results from this innovative methodological mix are presented, with the aim of highlighting new opportunities and dimensions in the research on social work. A gap in existing research is aimed to be filled both on a methodological and empirical level, and the Hungarian domestic findings can create a feasible and relevant framework for a further international investigation and cross-cultural comparative analysis. Said results can contribute to the foundation of organizational and policy-level interventions, targeted programs whereby the risk of burnout and the rate of career abandonment can be reduced. Exploring different aspects of resilience and mapping personality strengths can be a starting point for stress-management, motivation-building, and personality-development training for social professionals.

Keywords: burnout, mixed methods, netnography, professional mobility, social work

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Authors: Pedro A. V. Ramos, Duarte M. S. Albuquerque, José C. F. Pereira

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Global warming mitigation is one of the main challenges of this century, having the net balance of greenhouse gas (GHG) emissions to be null or negative in 2050. Industry electrification is one of the main paths to achieving carbon neutrality within the goals of the Paris Agreement. Microwave heating is becoming a popular industrial heating mechanism due to the absence of direct GHG emissions, but also the rapid, volumetric, and efficient heating. In the present study, a mathematical model is used to simulate the production using microwave heating of two ceramic pigments, at high temperatures (above 1200 Celsius degrees). The two pigments studied were the yellow (Pr, Zr)SiO₂ and the brown (Ti, Sb, Cr)O₂. The chemical conversion of reactants into products was included in the model by using the kinetic triplet obtained with the model-fitting method and experimental data present in the Literature. The coupling between the electromagnetic, thermal, and chemical interfaces was also included. The simulations were computed in COMSOL Multiphysics. The geometry includes a moving plunger to allow for the cavity impedance matching and thus maximize the electromagnetic efficiency. To accomplish this goal, a MATLAB controller was developed to automatically search the position of the moving plunger that guarantees the maximum efficiency. The power is automatically and permanently adjusted during the transient simulation to impose stationary regime and total conversion, the two requisites of every converged solution. Both 2D and 3D geometries were used and a parametric study regarding the axial bed velocity and the heat transfer coefficient at the boundaries was performed. Moreover, a Verification and Validation study was carried out by comparing the conversion profiles obtained numerically with the experimental data available in the Literature; the numerical uncertainty was also estimated to attest to the result's reliability. The results show that the model-fitting method employed in this work is a suitable tool to predict the chemical conversion of reactants into the pigment, showing excellent agreement between the numerical results and the experimental data. Moreover, it was demonstrated that higher velocities lead to higher thermal efficiencies and thus lower energy consumption during the process. This work concludes that the electromagnetic heating of materials having high loss tangent and low thermal conductivity, like ceramic materials, maybe a challenge due to the presence of hot spots, which may jeopardize the product quality or even the experimental apparatus. The MATLAB controller increased the electromagnetic efficiency by 25% and global efficiency of 54% was obtained for the titanate brown pigment. This work shows that electromagnetic heating will be a key technology in the decarbonization of the ceramic sector as reductions up to 98% in the specific GHG emissions were obtained when compared to the conventional process. Furthermore, numerical simulations appear as a suitable technique to be used in the design and optimization of microwave applicators, showing high agreement with experimental data.

Keywords: automatic impedance matching, ceramic pigments, efficiency maximization, high-temperature microwave heating, input power control, numerical simulation

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Authors: Qianxi Lv, Junying Liang

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Among the various modes of interpreting, simultaneous interpreting (SI) is regarded as a ‘complex’ and ‘extreme condition’ of cognitive tasks while consecutive interpreters (CI) do not have to share processing capacity between tasks. Given that SI exerts great cognitive demand, it makes sense to posit that the output of SI may be more compromised than that of CI in the linguistic features. The bulk of the research has stressed the varying cognitive demand and processes involved in different modes of interpreting; however, related empirical research is sparse. In keeping with our interest in investigating the quantitative linguistic factors discriminating between SI and CI, the current study seeks to examine the potential lexical simplification, syntactic complexity and sequential organization mechanism with a self-made inter-model corpus of transcribed simultaneous and consecutive interpretation, translated speech and original speech texts with a total running word of 321960. The lexical features are extracted in terms of the lexical density, list head coverage, hapax legomena, and type-token ratio, as well as core vocabulary percentage. Dependency distance, an index for syntactic complexity and reflective of processing demand is employed. Frequency motif is a non-grammatically-bound sequential unit and is also used to visualize the local function distribution of interpreting the output. While SI is generally regarded as multitasking with high cognitive load, our findings evidently show that CI may impose heavier or taxing cognitive resource differently and hence yields more lexically and syntactically simplified output. In addition, the sequential features manifest that SI and CI organize the sequences from the source text in different ways into the output, to minimize the cognitive load respectively. We reasoned the results in the framework that cognitive demand is exerted both on maintaining and coordinating component of Working Memory. On the one hand, the information maintained in CI is inherently larger in volume compared to SI. On the other hand, time constraints directly influence the sentence reformulation process. The temporal pressure from the input in SI makes the interpreters only keep a small chunk of information in the focus of attention. Thus, SI interpreters usually produce the output by largely retaining the source structure so as to relieve the information from the working memory immediately after formulated in the target language. Conversely, CI interpreters receive at least a few sentences before reformulation, when they are more self-paced. CI interpreters may thus tend to retain and generate the information in a way to lessen the demand. In other words, interpreters cope with the high demand in the reformulation phase of CI by generating output with densely distributed function words, more content words of higher frequency values and fewer variations, simpler structures and more frequently used language sequences. We consequently propose a revised effort model based on the result for a better illustration of cognitive demand during both interpreting types.

Keywords: cognitive demand, corpus-based, dependency distance, frequency motif, interpreting types, lexical simplification, sequential units distribution, syntactic complexity

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: Frances Turner

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Gloves are significant physical objects, being one of the oldest forms of dress. Glove culture is part of every facet of life; its extraordinary history encompasses practicality, and symbolism reflecting a wide range of social practices. The survival of not only the gloves but associated articles enables the possibility to analyse real lives, however so far this area has been largely neglected. Limited information is available to students, researchers, or those involved with the design and making of gloves. There are several museums and independent collectors in England that hold collections of gloves (some from as early as 16th century), machinery, tools, designs and patterns, marketing materials and significant archives which demonstrate the rich heritage of English glove design and manufacturing, being of national significance and worthy of international interest. Through a research glove network which now exists thanks to research grant funding, there is potential for the holders of glove collections to make connections and explore links between these resources to promote a stronger understanding of the significance, breadth and heritage of the English glove industry. The network takes an interdisciplinary approach to bring together interested parties from academia, museums and manufacturing, with expert knowledge of the production, collections, conservation and display of English leather gloves. Academics from diverse arts and humanities disciplines benefit from the opportunities to share research and discuss ideas with network members from non-academic contexts including museums and heritage organisations, industry, and contemporary designers. The fragmented collections when considered in entirety provide an overview of English glove making since earliest times and those who wore them. This paper makes connections and explores links between these resources to promote a stronger understanding of the significance, breadth and heritage of the English Glove industry. The following areas are explored: current content and status of the individual museum collections, potential links, sharing of information histories, social and cultural and relationship to history of fashion design, manufacturing and materials, approaches to maintenance and conservation, access to the collections and strategies for future understanding of their national significance. The facilitation of knowledge exchange and exploration of the collections through the network informs organisations’ future strategies for the maintenance, access and conservation of their collections. By involving industry in the network, it is possible to ensure a contemporary perspective on glove-making in addition to the input from heritage partners. The slow fashion movement and awareness of artisan craft and how these can be preserved and adopted for glove and accessory design is addressed. Artisan leather glove making was a skilled and significant industry in England that has now declined to the point where there is little production remaining utilising the specialist skills that have hardly changed since earliest times. This heritage will be identified and preserved for future generations of the rich cultural history of gloves may be lost.

Keywords: artisan glove-making skills, English leather gloves, glove culture, the glove network

Procedia PDF Downloads 89

Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Sinead Impey, Gaye Stephens, Declan O’Sullivan

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Mitigating organisational knowledge loss presents challenges for knowledge managers. Expert knowledge is embodied in people and captured in ‘routines, processes, practices and norms’ as well as in the paper system. These knowledge stores have limitations in so far as they make knowledge diffusion beyond geography or over time difficult. However, technology could present a potential solution by facilitating the capture and management of expert knowledge in a codified and sharable format. Before it can be digitised, however, the knowledge of healthcare experts must be captured. Methods: As a first step in a larger project on this topic, a scoping review was conducted to identify how expert healthcare knowledge is captured digitally. The aim of the review was to identify current healthcare knowledge capture practices, identify gaps in the literature, and justify future research. The review followed a scoping review framework. From an initial 3,430 papers retrieved, 22 were deemed relevant and included in the review. Findings: Two broad approaches –direct and indirect- with themes and subthemes emerged. ‘Direct’ describes a process whereby knowledge is taken directly from subject experts. The themes identified were: ‘Researcher mediated capture’ and ‘Digital mediated capture’. The latter was further distilled into two sub-themes: ‘Captured in specified purpose platforms (SPP)’ and ‘Captured in a virtual community of practice (vCoP)’. ‘Indirect’ processes rely on extracting new knowledge using artificial intelligence techniques from previously captured data. Using this approach, the theme ‘Generated using artificial intelligence methods’ was identified. Although presented as distinct themes, some papers retrieved discuss combining more than one approach to capture knowledge. While no approach emerged as superior, two points arose from the literature. Firstly, human input was evident across themes, even with indirect approaches. Secondly, a range of challenges common among approaches was highlighted. These were (i) ‘Capturing an expert’s knowledge’- Difficulties surrounding capturing an expert’s knowledge related to identifying the ‘expert’ say from the very experienced and how to capture their tacit or difficult to articulate knowledge. (ii) ‘Confirming quality of knowledge’- Once captured, challenges noted surrounded how to validate knowledge captured and, therefore, quality. (iii) ‘Continual knowledge capture’- Once knowledge is captured, validated, and used in a system; however, the process is not complete. Healthcare is a knowledge-rich environment with new evidence emerging frequently. As such, knowledge needs to be reviewed, updated, or removed (redundancy) as appropriate. Although some methods were proposed to address this, such as plausible reasoning or case-based reasoning, conclusions could not be drawn from the papers retrieved. It was, therefore, highlighted as an area for future research. Conclusion: The results described two broad approaches – direct and indirect. Three themes were identified: ‘Researcher mediated capture (Direct)’; ‘Digital mediated capture (Direct)’ and ‘Generated using artificial intelligence methods (Indirect)’. While no single approach was deemed superior, common challenges noted among approaches were: ‘capturing an expert’s knowledge’, ‘confirming quality of knowledge’, and ‘continual knowledge capture’. However, continual knowledge capture was not fully explored in the papers retrieved and was highlighted as an important area for future research. Acknowledgments: This research is partially funded by the ADAPT Centre under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

Keywords: expert knowledge, healthcare, knowledge capture and knowledge management

Procedia PDF Downloads 112

Authors: D. Heinemann, S. Schramm, S. Knabner, D. Baumgarten

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Purpose: Applying solder paste to printed circuit boards (PCB) with stencils has been the method of choice over the past years. A new method uses a jet printer to deposit tiny droplets of solder paste through an ejector mechanism onto the board. This allows for more flexible PCB layouts with smaller components. Due to the viscosity of the solder paste, air blisters can be trapped in the cartridge. This can lead to missing solder joints or deviations in the applied solder volume. Therefore, a built-in and real-time inspection of the printing process is needed to minimize uncertainties and increase the efficiency of the process by immediate correction. The objective of the current study is the design of an optimal imaging system and the development of an automatic algorithm for the detection of applied solder joints from optical from the captured images. Methods: In a first approach, a camera module connected to a microcomputer and LED strips are employed to capture images of the printed circuit board under four different illuminations (white, red, green and blue). Subsequently, an improved system including a ring light, an objective lens, and a monochromatic camera was set up to acquire higher quality images. The obtained images can be divided into three main components: the PCB itself (i.e., the background), the reflections induced by unsoldered positions or screw holes and the solder joints. Non-uniform illumination is corrected by estimating the background using a morphological opening and subtraction from the input image. Image sharpening is applied in order to prevent error pixels in the subsequent segmentation. The intensity thresholds which divide the main components are obtained from the multimodal histogram using three probability density functions. Determining the intersections delivers proper thresholds for the segmentation. Remaining edge gradients produces small error areas which are removed by another morphological opening. For quantitative analysis of the segmentation results, the dice coefficient is used. Results: The obtained PCB images show a significant gradient in all RGB channels, resulting from ambient light. Using different lightings and color channels 12 images of a single PCB are available. A visual inspection and the investigation of 27 specific points show the best differentiation between those points using a red lighting and a green color channel. Estimating two thresholds from analyzing the multimodal histogram of the corrected images and using them for segmentation precisely extracts the solder joints. The comparison of the results to manually segmented images yield high sensitivity and specificity values. Analyzing the overall result delivers a Dice coefficient of 0.89 which varies for single object segmentations between 0.96 for a good segmented solder joints and 0.25 for single negative outliers. Conclusion: Our results demonstrate that the presented optical imaging system and the developed algorithm can robustly detect solder joints on printed circuit boards. Future work will comprise a modified lighting system which allows for more precise segmentation results using structure analysis.

Keywords: printed circuit board jet-printing, inspection, segmentation, solder paste detection

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Authors: Diana Reckien

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Vulnerability assessments are increasingly used to support policy-making in complex environments, like urban areas. Usually, vulnerability studies include the construction of aggregate (sub-) indices and the subsequent mapping of indices across an area of interest. Vulnerability studies show a couple of advantages: they are great communication tools, can inform a wider general debate about environmental issues, and can help allocating and efficiently targeting scarce resources for adaptation policy and planning. However, they also have a number of challenges: Vulnerability assessments are constructed on the basis of a wide range of methodologies and there is no single framework or methodology that has proven to serve best in certain environments, indicators vary highly according to the spatial scale used, different variables and metrics produce different results, and aggregate or composite vulnerability indicators that are mapped easily distort or bias the picture of vulnerability as they hide the underlying causes of vulnerability and level out conflicting reasons of vulnerability in space. So, there is urgent need to further develop the methodology of vulnerability studies towards a common framework, which is one reason of the paper. We introduce a social vulnerability approach, which is compared with other approaches of bio-physical or sectoral vulnerability studies relatively developed in terms of a common methodology for index construction, guidelines for mapping, assessment of sensitivity, and verification of variables. Two approaches are commonly pursued in the literature. The first one is an additive approach, in which all potentially influential variables are weighted according to their importance for the vulnerability aspect, and then added to form a composite vulnerability index per unit area. The second approach includes variable reduction, mostly Principal Component Analysis (PCA) that reduces the number of variables that are interrelated into a smaller number of less correlating components, which are also added to form a composite index. We test these two approaches of constructing indices on the area of New York City as well as two different metrics of variables used as input and compare the outcome for the 5 boroughs of NY. Our analysis yields that the mapping exercise yields particularly different results in the outer regions and parts of the boroughs, such as Outer Queens and Staten Island. However, some of these parts, particularly the coastal areas receive the highest attention in the current adaptation policy. We imply from this that the current adaptation policy and practice in NY might need to be discussed, as these outer urban areas show relatively low social vulnerability as compared with the more central parts, i.e. the high dense areas of Manhattan, Central Brooklyn, Central Queens and the Southern Bronx. The inner urban parts receive lesser adaptation attention, but bear a higher risk of damage in case of hazards in those areas. This is conceivable, e.g., during large heatwaves, which would more affect more the inner and poorer parts of the city as compared with the outer urban areas. In light of the recent planning practice of NY one needs to question and discuss who in NY makes adaptation policy for whom, but the presented analyses points towards an under representation of the needs of the socially vulnerable population, such as the poor, the elderly, and ethnic minorities, in the current adaptation practice in New York City.

Keywords: vulnerability mapping, social vulnerability, additive approach, Principal Component Analysis (PCA), New York City, United States, adaptation, social sensitivity

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Authors: Tim Bolle, Franziska Kreimendahl, Thomas Gries, Stefan Jockenhoevel

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The therapeutic treatment of extensive, deep wounds is limited. Autologous split-skin grafts are used as a so-called ‘gold standard’. Most common deficits are the defects at the donor site, the risk of scarring as well as the limited availability and quality of the autologous grafts. The aim of this project is a tissue engineered dermal-epidermal skin replacement to overcome the limitations of the gold standard. A key requirement for the development of such a three-dimensional implant is the formation of a functional capillary-like network inside the implant to ensure a sufficient nutrient and gas supply. Tailored three-dimensional warp knitted spacer fabrics are used to reinforce the mechanically week fibrin gel-based scaffold and further to create a directed in vitro pre-vascularization along the parallel-oriented pile yarns within a co-culture. In this study various three-dimensional warp knitted spacer fabrics were developed in a factorial design to analyze the influence of the machine parameters such as the stitch density and the pattern of the fabric on the scaffold performance and further to determine suitable parameters for a successful fibrin gel-incorporation and a physiological performance of the scaffold. The fabrics were manufactured on a Karl Mayer double-bar raschel machine DR 16 EEC/EAC. A fine machine gauge of E30 was used to ensure a high pile yarn density for sufficient nutrient, gas and waste exchange. In order to ensure a high mechanical stability of the graft, the fabrics were made of biocompatible PVDF yarns. Key parameters such as the pore size, porosity and stress/strain behavior were investigated under standardized, controlled climate conditions. The influence of the input parameters on the mechanical and morphological properties as well as the ability of fibrin gel incorporation into the spacer fabric was analyzed. Subsequently, the pile yarns of the spacer fabrics were colonized with Human Umbilical Vein Endothelial Cells (HUVEC) to analyze the ability of the fabric to further function as a guiding structure for a directed vascularization. The cells were stained with DAPI and investigated using fluorescence microscopy. The analysis revealed that the stitch density and the binding pattern have a strong influence on both the mechanical and morphological properties of the fabric. As expected, the incorporation of the fibrin gel was significantly improved with higher pore sizes and porosities, whereas the mechanical strength decreases. Furthermore, the colonization trials revealed a high cell distribution and density on the pile yarns of the spacer fabrics. For a tailored reinforcing structure, the minimum porosity and pore size needs to be evaluated which still ensures a complete incorporation of the reinforcing structure into the fibrin gel matrix. That will enable a mechanically stable dermal graft with a dense vascular network for a sufficient nutrient and oxygen supply of the cells. The results are promising for subsequent research in the field of reinforcing mechanically weak biological scaffolds and develop functional three-dimensional scaffolds with an oriented pre-vascularization.

Keywords: fibrin-gel, skin replacement, spacer fabric, pre-vascularization

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Authors: N. K. Ambujam, V. Sudha

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Reservoirs are being contaminated all around the world with point source and Non-Point Source (NPS) pollution. The most common NPS pollutants are sediments and nutrients. Krishnagiri Reservoir (KR) has been chosen for the present case study, which is located in the tropical semi-arid climatic zone of Tamil Nadu, South India. It is the main source of surface water in Krishnagiri district to meet the freshwater demands. The reservoir has lost about 40% of its water holding capacity due to sedimentation over the period of 50 years. Hence, from the research and management perspective, there is a need for a sound knowledge on the spatial and seasonal variations of KR water quality. The present study encompasses the specific objectives as (i) to investigate the longitudinal heterogeneity and seasonal variations of physicochemical parameters, nutrients and biological characteristics of KR water and (ii) to examine the extent of degradation of water quality in KR. 15 sampling points were identified by uniform stratified method and a systematic monthly sampling strategy was selected due to high dynamic nature in its hydrological characteristics. The physicochemical parameters, major ions, nutrients and Chlorophyll a (Chl a) were analysed. Trophic status of KR was classified by using Carlson's Trophic State Index (TSI). All statistical analyses were performed by using Statistical Package for Social Sciences programme, version-16.0. Spatial maps were prepared for Chl a using Arc GIS. Observations in KR pointed out that electrical conductivity and major ions are highly variable factors as it receives inflow from the catchment with different land use activities. The study of major ions in KR exhibited different trends in their values and it could be concluded that as the monsoon progresses the major ions in the water decreases or water quality stabilizes. The inflow point of KR showed comparatively higher concentration of nutrients including nitrate, soluble reactive phosphorus (SRP), total phosphors (TP), total suspended phosphorus (TSP) and total dissolved phosphorus (TDP) during monsoon seasons. This evidently showed the input of significant amount of nutrients from the catchment side through agricultural runoff. High concentration of TDP and TSP at the lacustrine zone of the reservoir during summer season evidently revealed that there was a significant release of phosphorus from the bottom sediments. Carlson’s TSI of KR ranged between 81 and 92 during northeast monsoon and summer seasons. High and permanent Cyanobacterial bloom in KR could be mainly due to the internal loading of phosphorus from the bottom sediments. According to Carlson’s TSI classification Krishnagiri reservoir was ranked in the hyper-eutrophic category. This study provides necessary basic data on the spatio-temporal variations of water quality in KR and also proves the impact of point and NPS pollution from the catchment area. High TSI warrants a greater threat for the recovery of internal P loading and hyper-eutrophic condition of KR. Several expensive internal measures for the reduction of internal loading of P were introduced by many scientists. However, the outcome of the present research suggests for the innovative algae harvesting technique for the removal of sediment nutrients.

Keywords: NPS pollution, nutrients, hyper-eutrophication, krishnagiri reservoir

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Authors: Stephen Tullis, M. David DiDonato, Hong Sung Park

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Rowing boats (shells) are often steered by a small rudder operated by one of the backward-facing rowers; the attention required of that athlete then slightly decreases the power that that athlete can provide. Reducing the steering distraction would then increase the overall boat speed. Races are straight 2000 m courses with each boat in a 13.5 m wide lane marked by small (~15 cm) widely-spaced (~10 m) buoys, and the boat trajectory is affected by both cross-currents and winds. An optical buoy recognition and tracking system has been developed that provides the boat’s location and orientation with respect to the lane edges. This information is provided to the steering athlete as either: a simple overlay on a video display, or fed to a simplified autopilot system giving steering directions to the athlete or directly controlling the rudder. The system is then effectively a “lane-assist” device but with small, widely-spaced lane markers viewed from a very shallow angle due to constraints on camera height. The image is captured with a lightweight 1080p webcam, and most of the image analysis is done in OpenCV. The colour RGB-image is converted to a grayscale using the difference of the red and blue channels, which provides good contrast between the red/yellow buoys and the water, sky, land background and white reflections and noise. Buoy detection is done with thresholding within a tight mask applied to the image. Robust linear regression using Tukey’s biweight estimator of the previously detected buoy locations is used to develop the mask; this avoids the false detection of noise such as waves (reflections) and, in particular, buoys in other lanes. The robust regression also provides the current lane edges in the camera frame that are used to calculate the displacement of the boat from the lane centre (lane location), and its yaw angle. The interception of the detected lane edges provides a lane vanishing point, and yaw angle can be calculated simply based on the displacement of this vanishing point from the camera axis and the image plane distance. Lane location is simply based on the lateral displacement of the vanishing point from any horizontal cut through the lane edges. The boat lane position and yaw are currently fed what is essentially a stripped down marine auto-pilot system. Currently, only the lane location is used in a PID controller of a rudder actuator with integrator anti-windup to deal with saturation of the rudder angle. Low Kp and Kd values decrease unnecessarily fast return to lane centrelines and response to noise, and limiters can be used to avoid lane departure and disqualification. Yaw is not used as a control input, as cross-winds and currents can cause a straight course with considerable yaw or crab angle. Mapping of the controller with rudder angle “overall effectiveness” has not been finalized - very large rudder angles stall and have decreased turning moments, but at less extreme angles the increased rudder drag slows the boat and upsets boat balance. The full system has many features similar to automotive lane-assist systems, but with the added constraints of the lane markers, camera positioning, control response and noise increasing the challenge.

Keywords: auto-pilot, lane-assist, marine, optical, rowing

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

Procedia PDF Downloads 133

Authors: Ousmane Sarr

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The Senegalese coasts have high productivity of fishery resources due to the frequency of intense up-welling system that occurs along its coast, caused by the maritime trade winds making its waters nutrients rich. Fishing plays a primordial role in Senegal's socioeconomic plans and food security. However, a global diagnosis of the Senegalese maritime fishing sector has highlighted the challenges this sector encounters. Among these concerns, some significant stocks, a priority target for artisanal fishing, need further assessment. If no efforts are made in this direction, most stock will be overexploited or even in decline. It is in this context that this research was initiated. This investigation aimed to apply a multi-modal approach (LBB, Catch-only-based CMSY model and its most recent version (CMSY++); JABBA, and JABBA-Select) to assess the stock of Atlantic bonito, Sarda sarda (Bloch, 1793) in the Senegalese Exclusive Economic Zone (SEEZ). Available catch, effort, and size data from Atlantic bonito over 15 years (2004-2018) were used to calculate the nominal and standardized CPUE, size-frequency distribution, and length at retentions (50 % and 95 % selectivity) of the species. These relevant results were employed as input parameters for stock assessment models mentioned above to define the stock status of this species in this region of the Atlantic Ocean. The LBB model indicated an Atlantic bonito healthy stock status with B/BMSY values ranging from 1.3 to 1.6 and B/B0 values varying from 0.47 to 0.61 of the main scenarios performed (BON_AFG_CL, BON_GN_Length, and BON_PS_Length). The results estimated by LBB are consistent with those obtained by CMSY. The CMSY model results demonstrate that the SEEZ Atlantic bonito stock is in a sound condition in the final year of the main scenarios analyzed (BON, BON-bt, BON-GN-bt, and BON-PS-bt) with sustainable relative stock biomass (B2018/BMSY = 1.13 to 1.3) and fishing pressure levels (F2018/FMSY= 0.52 to 1.43). The B/BMSY and F/FMSY results for the JABBA model ranged between 2.01 to 2.14 and 0.47 to 0.33, respectively. In contrast, The estimated B/BMSY and F/FMSY for JABBA-Select ranged from 1.91 to 1.92 and 0.52 to 0.54. The Kobe plots results of the base case scenarios ranged from 75% to 89% probability in the green area, indicating sustainable fishing pressure and an Atlantic bonito healthy stock size capable of producing high yields close to the MSY. Based on the stock assessment results, this study highlighted scientific advice for temporary management measures. This study suggests an improvement of the selectivity parameters of longlines and purse seines and a temporary prohibition of the use of sleeping nets in the fishery for the Atlantic bonito stock in the SEEZ based on the results of the length-base models. Although these actions are temporary, they can be essential to reduce or avoid intense pressure on the Atlantic bonito stock in the SEEZ. However, it is necessary to establish harvest control rules to provide coherent and solid scientific information that leads to appropriate decision-making for rational and sustainable exploitation of Atlantic bonito in the SEEZ and the Eastern Atlantic Ocean.

Keywords: multi-model approach, stock assessment, atlantic bonito, SEEZ

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Authors: Md. Nuru Miah, A. F. M. Akhter Uddin

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Medicinal plants are grown naturally in a tropical environment in Bangladesh and used as drug and therapeutic agents in the health care system. According to Bangladesh Agricultural Research Institute (BARI), there are 722 species of medicinal plants in the country. Of them, 255 plants are utilized by the manufacturers of Ayurvedic and Unani medicines. Medicinal plants like Aloevera, Ashwagonda, shotomul,Tulsi, Vuikumra, Misridana are extensively cultivated in some selected areas as well; where Aloevera scored the highest position in production. In the early 1980, Ayurvedic and Unani companies procured 80 percent of medicinal plants from natural forests, and the rest 20 percent was imported. Now the scenario has changed; 80 percent is imported, and the rest 20 percent is collected from local products(Source: Astudy on sectorbased need assessment of Business promotion council-Herbal products and medicinal plants, page-4). Uttara Development Program Society, a leading Non- Government development organization in Bangladesh, has been implementing a value chain development project under promoting Agricultural commercialization and Enterprises of Pally Karma Sahayak Foundation (PKSF) funded by the International Fund for Agricultural Development (IFAD) in Natore Sadar Upazila from April 2017 to sustainably develop the technological interventions for products and market development. The ultimate goal of the project is to increase income, generate employment and develop this sector as a sustainable business enterprise. Altogether 10,000 farmers (Nursery owners, growers, input supplier, processors, whole sellers, and retailers) are engaged in different activities of the project. The entrepreneurs engaged in medicinal plant cultivation did not know and follow environmental and good agricultural practices. They used to adopt traditional methodology in production and processing. Locally the farmers didn’t have any positive initiative to expand their business as well as developvalue added products. A lot of diversified products could be possible to develop and marketed with the introduction of post-harvest processing technology and market linkage with the local and global buyer. Training is imparted to the nursery owners and herbal growers on production technologies, sowing method, use of organic fertilizers/compost/pesticides, harvesting procedures, and storage facilities. Different types of herbal tea like Rosella, Moringa, Tulshi, and Basak are being produced and packed locally with a good scope of its marketing in different cities of the country. The project has been able to achieve a significant impact in the development of production technologies, but still, there is room for further improvement in processing, packaging, and marketing level. The core intervention of the current project to develop some entrepreneurs for branding, packaging, promotion, and marketing while considering environment friendly practices. The present strategies will strengthen the knowledge and skills of the entrepreneurs for the production and marketing of their products, maintaining worldwide accepted compliance system for easy access to the global market.

Keywords: aloe vera, herbs and shrubs, market, interventions

Procedia PDF Downloads 57

Authors: Ricardo Alberto Chiong Zevallos, Eduardo Moraes Rego Reis

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Pancreatic adenocarcinoma (PDAC) patients have a less than 10% 5-year survival rate. PDAC has no defined diagnostic and prognostic biomarkers. Gemcitabine is the first-line drug in PDAC and several other cancers. Long non-coding RNAs (lncRNAs) contribute to the tumorigenesis and are potential biomarkers for PDAC. Although lncRNAs aren’t translated into proteins, they have important functions. LncRNAs can decoy or recruit proteins from the epigenetic machinery, act as microRNA sponges, participate in protein translocation through different cellular compartments, and even promote chemoresistance. The chromatin remodeling enzyme EZH2 is a histone methyltransferase that catalyzes the methylation of histone 3 at lysine 27, silencing local expression. EZH2 is ambivalent, it can also activate gene expression independently of its histone methyltransferase activity. EZH2 is overexpressed in several cancers and interacts with lncRNAs, being recruited to a specific locus. EZH2 can be recruited to activate an oncogene or silence a tumor suppressor. The lncRNAs misregulation in cancer can result in the differential recruitment of EZH2 and in a distinct epigenetic landscape, promoting chemoresistance. The relevance of the EZH2-lncRNAs interaction to chemoresistant PDAC was assessed by Real Time quantitative PCR (RT-qPCR) and RNA Immunoprecipitation (RIP) experiments with naïve and gemcitabine-resistant PDAC cells. The expression of several lncRNAs and EZH2 gene targets was evaluated contrasting naïve and resistant cells. Selection of candidate genes was made by bioinformatic analysis and literature curation. Indeed, the resistant cell line showed higher expression of chemoresistant-associated lncRNAs and protein coding genes. RIP detected lncRNAs interacting with EZH2 with varying intensity levels in the cell lines. During RIP, the nuclear fraction of the cells was incubated with an antibody for EZH2 and with magnetic beads. The RNA precipitated with the beads-antibody-EZH2 complex was isolated and reverse transcribed. The presence of candidate lncRNAs was detected by RT-qPCR, and the enrichment was calculated relative to INPUT (total lysate control sample collected before RIP). The enrichment levels varied across the several lncRNAs and cell lines. The EZH2-lncRNA interaction might be responsible for the regulation of chemoresistance-associated genes in multiple cancers. The relevance of the lncRNA-EZH2 interaction to PDAC was assessed by siRNA knockdown of a lncRNA, followed by the analysis of the EZH2 target expression by RT-qPCR. The chromatin immunoprecipitation (ChIP) of EZH2 and H3K27me3 followed by RT-qPCR with primers for EZH2 targets also assess the specificity of the EZH2 recruitment by the lncRNA. This is the first report of the interaction of EZH2 and lncRNAs HOTTIP and PVT1 in chemoresistant PDAC. HOTTIP and PVT1 were described as promoting chemoresistance in several cancers, but the role of EZH2 is not clarified. For the first time, the lncRNA LINC01133 was detected in a chemoresistant cancer. The interaction of EZH2 with LINC02577, LINC00920, LINC00941, and LINC01559 have never been reported in any context. The novel lncRNAs-EZH2 interactions regulate chemoresistant-associated genes in PDAC and might be relevant to other cancers. Therapies targeting EZH2 alone weren’t successful, and a combinatorial approach also targeting the lncRNAs interacting with it might be key to overcome chemoresistance in several cancers.

Keywords: epigenetics, chemoresistance, long non-coding RNAs, pancreatic cancer, histone modification

Procedia PDF Downloads 61

Authors: Irina Mizeva, Elena Potapova, Viktor Dremin, Mikhail Mezentsev, Valeri Shupletsov

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The local tissue perfusion is regulated by the microvascular tone which is under the control of a number of physiological mechanisms. Laser Doppler flowmetry (LDF) together with wavelet analyses is the most commonly used technique to study the regulatory mechanisms of cutaneous microcirculation. External factors such as temperature, local pressure of the probe on the skin, etc. influence on the blood flow characteristics and are used as physiological tests to evaluate microvascular regulatory mechanisms. Local probe pressure influences on the microcirculation parameters measured by optical methods: diffuse reflectance spectroscopy, fluorescence spectroscopy, and LDF. Therefore, further study of probe pressure effects can be useful to improve the reliability of optical measurement. During pressure tests variation of the mean perfusion measured by means of LDF usually is estimated. An additional information concerning the physiological mechanisms of the vascular tone regulation system in response to local pressure can be obtained using spectral analyses of LDF samples. The aim of the present work was to develop protocol and algorithm of data processing appropriate for study physiological response to the local pressure test. Involving 6 subjects (20±2 years) and providing 5 measurements for every subject we estimated intersubject and-inter group variability of response of both averaged and oscillating parts of the LDF sample on external surface pressure. The final purpose of the work was to find special features which further can be used in wider clinic studies. The cutaneous perfusion measurements were carried out by LAKK-02 (SPE LAZMA Ltd., Russia), the skin loading was provided by the originally designed device which allows one to distribute the pressure around the LDF probe. The probe was installed on the dorsal part of the distal finger of the index figure. We collected measurements continuously for one hour and varied loading from 0 to 180mmHg stepwise with a step duration of 10 minutes. Further, we post-processed the samples using the wavelet transform and traced the energy of oscillations in five frequency bands over time. Weak loading leads to pressure-induced vasodilation, so one should take into account that the perfusion measured under pressure conditions will be overestimated. On the other hand, we revealed a decrease in endothelial associated fluctuations. Further loading (88 mmHg) induces amplification of pulsations in all frequency bands. We assume that such loading leads to a higher number of closed capillaries, higher input of arterioles in the LDF signal and as a consequence more vivid oscillations which mainly are formed in arterioles. External pressure higher than 144 mmHg leads to the decrease of oscillating components, after removing the loading very rapid restore of the tissue perfusion takes place. In this work, we have demonstrated that local skin loading influence on the microcirculation parameters measured by optic technique; this should be taken into account while developing portable electronic devices. The proposed protocol of local loading allows one to evaluate PIV as far as to trace dynamic of blood flow oscillations. This study was supported by the Russian Science Foundation under project N 18-15-00201.

Keywords: blood microcirculation, laser Doppler flowmetry, pressure-induced vasodilation, wavelet analyses blood

Procedia PDF Downloads 120

Authors: Nilki Upathissa, Torin Wirasinghe

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

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

Procedia PDF Downloads 56