Search results for: Generalized EM-Like Interactions

Authors: Mishak Rahul, Naveen Kumar, Bharath Kumar

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Conceived as a high-end engine to revolutionise sustainable agri-food production, the intelligent crop circle (ICC) aims to incorporate the Internet of Things (IoT), blockchain technology and artificial intelligence (AI) to bolster resource efficiency and prevent waste, increase the volume of production and bring about sustainable solutions with long-term ecosystem conservation as the guiding principle. The operating principle of the ICC relies on bringing together multidisciplinary bottom-up collaborations between producers, researchers and consumers. Key elements of the framework include IoT-based smart sensors for sensing soil moisture, temperature, humidity, nutrient and air quality, which provide short-interval and timely data; blockchain technology for data storage on a private chain, which maintains data integrity, traceability and transparency; and AI-based predictive analysis, which actively predicts resource utilisation, plant growth and environment. This data and AI insights are built into the ICC platform, which uses the resulting DSS (Decision Support System) outlined as help in decision making, delivered through an easy-touse mobile app or web-based interface. Farmers are assumed to use such a decision-making aid behind the power of the logic informed by the data pool. Building on existing data available in the farm management systems, the ICC platform is easily interoperable with other IoT devices. ICC facilitates connections and information sharing in real-time between users, including farmers, researchers and industrial partners, enabling them to cooperate in farming innovation and knowledge exchange. Moreover, ICC supports sustainable practice in agriculture by integrating gamification techniques to stimulate farm adopters, deploying VR technologies to model and visualise 3D farm environments and farm conditions, framing the field scenarios using VR headsets and Real-Time 3D engines, and leveraging edge technologies to facilitate secure and fast communication and collaboration between users involved. And through allowing blockchain-based marketplaces, ICC offers traceability from farm to fork – that is: from producer to consumer. It empowers informed decision-making through tailor-made recommendations generated by means of AI-driven analysis and technology democratisation, enabling small-scale and resource-limited farmers to get their voice heard. It connects with traditional knowledge, brings together multi-stakeholder interactions as well as establishes a participatory ecosystem to incentivise continuous growth and development towards more sustainable agro-ecological food systems. This integrated approach leverages the power of emerging technologies to provide sustainable solutions for a resilient food system, ensuring sustainable agriculture worldwide.

Keywords: blockchain, internet of things, artificial intelligence, decision support system, virtual reality, gamification, traceability, sustainable agriculture

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Authors: Igor B. Sivaev

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Design of molecular machines is an extraordinary growing and very important area of research that it was recognized by awarding Sauvage, Stoddart and Feringa the Nobel Prize in Chemistry in 2016 'for the design and synthesis of molecular machines'. Based on the type of motion being performed, molecular machines can be divided into two main types: molecular motors and molecular switches. Molecular switches are molecules or supramolecular complexes having bistability, i.e., the ability to exist in two or more stable forms, among which may be reversible transitions under external influence (heating, lighting, changing the medium acidity, the action of chemicals, exposure to magnetic or electric field). Molecular switches are the main structural element of any molecular electronics devices. Therefore, the design and the study of molecules and supramolecular systems capable of performing mechanical movement is an important and urgent problem of modern chemistry. There is growing interest in molecular switches and other devices of molecular electronics based on transition metal complexes; therefore choice of suitable stable organometallic unit is of great importance. An example of such unit is bis(dicarbollide) complexes of transition metals [3,3’-M(1,2-C₂B₉H₁₁)₂]ⁿ⁻. The control on the ligand rotation in such complexes can be reached by introducing substituents which could provide stabilization of certain rotamers due to specific interactions between the ligands, on the one hand, and which can participate as Lewis bases in complex formation with external metals resulting in a change in the rotation angle of the ligands, on the other hand. A series of isomeric methyl sulfide derivatives of cobalt bis(dicarbollide) complexes containing methyl sulfide substituents at boron atoms in different positions of the pentagonal face of the dicarbollide ligands [8,8’-(MeS)₂-3,3’-Co(1,2-C₂B₉H₁₀)₂]⁻, rac-[4,4’-(MeS)₂-3,3’-Co(1,2-C₂B₉H₁₀)₂]⁻ and meso-[4,7’-(MeS)₂-3,3’-Co(1,2-C₂B₉H₁₀)₂]⁻ were synthesized by the reaction of CoCl₂ with the corresponding methyl sulfide carborane derivatives [10-MeS-7,8-C₂B₉H₁₁)₂]⁻ and [10-MeS-7,8-C₂B₉H₁₁)₂]⁻. In the case of asymmetrically substituted cobalt bis(dicarbollide) complexes the corresponding rac- and meso-isomers were successfully separated by column chromatography as the tetrabutylammonium salts. The compounds obtained were studied by the methods of ¹H, ¹³C, and ¹¹B NMR spectroscopy, single crystal X-ray diffraction, cyclic voltammetry, controlled potential coulometry and quantum chemical calculations. It was found that in the solid state, the transoid- and gauche-conformations of the 8,8’- and 4,4’-isomers are stabilized by four intramolecular CH···S(Me)B hydrogen bonds each one (2.683-2.712 Å and 2.709-2.752 Å, respectively), whereas gauche-conformation of the 4,7’-isomer is stabilized by two intramolecular CH···S hydrogen bonds (2.699-2.711 Å). The existence of the intramolecular CH·S(Me)B hydrogen bonding in solutions was supported by the 1H NMR spectroscopy. These data are in a good agreement with results of the quantum chemical calculations. The corresponding iron and nickel complexes were synthesized as well. The reaction of the methyl sulfide derivatives of cobalt bis(dicarbollide) with various labile transition metal complexes results in rupture of intramolecular hydrogen bonds and complexation of the methyl sulfide groups with external metal. This results in stabilization of other rotational conformation of cobalt bis(dicarbollide) and can be used in design of molecular switches. This work was supported by the Russian Science Foundation (16-13-10331).

Keywords: molecular switches, NMR spectroscopy, single crystal X-ray diffraction, transition metal bis(dicarbollide) complexes, quantum chemical calculations

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Authors: Aura Alejandra Burbano Patino, Mariela Agotegaray, Veronica Lassalle, Fernanda Horst

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Water pollution is of worldwide concern due to its importance as an essential resource for life. Industrial and urbanistic growth are anthropogenic activities that have caused an increase of undesirable compounds in water. In the last decade, emerging pollutants have become of great interest since, at very low concentrations (µg/L and ng/L), they exhibit a hazardous effect on wildlife, aquatic ecosystems, and human organisms. One group of emerging pollutants that are a matter of study are pharmaceuticals. Their high consumption rate and their inappropriate disposal have led to their detection in wastewater treatment plant influent, effluent, surface water, and drinking water. In consequence, numerous technologies have been developed to efficiently treat these pollutants. Adsorption appears like an easy and cost-effective technology. One of the most used adsorbents of emerging pollutants removal is carbon-based materials such as hydrochars. This study aims to use a magnetic hydrochar nanocomposite to be employed as an adsorbent for diclofenac removal. Kinetics models and the adsorption efficiency in real water samples were analyzed. For this purpose, a magnetic hydrochar nanocomposite was synthesized through the hydrothermal carbonization (HTC) technique hybridized to co-precipitation to add the magnetic component into the hydrochar, based on iron oxide nanoparticles. The hydrochar was obtained from sunflower husk residue as the precursor. TEM, TGA, FTIR, Zeta potential as a function of pH, DLS, BET technique, and elemental analysis were employed to characterize the material in terms of composition and chemical structure. Adsorption kinetics were carried out in distilled water and real water at room temperature, pH of 5.5 for distilled water and natural pH for real water samples, 1:1 adsorbent: adsorbate dosage ratio, contact times from 10-120 minutes, and 50% dosage concentration of DCF. Results have demonstrated that magnetic hydrochar presents superparamagnetic properties with a saturation magnetization value of 55.28 emu/g. Besides, it is mesoporous with a surface area of 55.52 m²/g. It is composed of magnetite nanoparticles incorporated into the hydrochar matrix, as can be proven by TEM micrographs, FTIR spectra, and zeta potential. On the other hand, kinetic studies were carried out using DCF models, finding percent removal efficiencies up to 85.34% after 80 minutes of contact time. In addition, after 120 minutes of contact time, desorption of emerging pollutants from active sites took place, which indicated that the material got saturated after that t time. In real water samples, percent removal efficiencies decrease up to 57.39%, ascribable to a possible mechanism of competitive adsorption of organic or inorganic compounds, ions for active sites of the magnetic hydrochar. The main suggested adsorption mechanism between the magnetic hydrochar and diclofenac include hydrophobic and electrostatic interactions as well as hydrogen bonds. It can be concluded that the magnetic hydrochar nanocomposite could be valorized into a by-product which appears as an efficient adsorbent for DCF removal as a model emerging pollutant. These results are being complemented by modifying experimental variables such as pollutant’s initial concentration, adsorbent: adsorbate dosage ratio, and temperature. Currently, adsorption assays of other emerging pollutants are being been carried out.

Keywords: environmental remediation, emerging pollutants, hydrochar, magnetite nanoparticles

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Authors: Mohsun Rasim Alizada, Azar Inshalla Ahmdov

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Prompt photons are perhaps the most versatile tools for studying the dynamics of relativistic collisions of heavy ions. The study of photon radiation is of interest that in most hadron interactions, photons fly out as a background to other studied signals. The study of the birth of prompt photons in nucleon-nucleon collisions was previously carried out in experiments on Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC). Due to the large energy of colliding nucleons, in addition to prompt photons, many different elementary particles are born. However, the birth of additional elementary particles makes it difficult to determine the accuracy of the effective section of the birth of prompt photons. From this point of view, the experiments planned on the Nuclotron-based Ion Collider Facility (NICA) complex will have a great advantage, since the energy obtained for colliding heavy ions will reduce the number of additionally born elementary particles. Of particular importance is the study of the processes of birth of prompt photons to determine the gluon leaving hadrons since the photon carries information about a rigid subprocess. At present, paper production of prompt photon in Compton scattering of quark-gluon and annihilation of quark–antiquark processes is investigated. The matrix elements Compton scattering of quark-gluon and annihilation of quark-antiquark pair processes has been written. The Square of matrix elements of processes has been calculated in FeynCalc. The phase volume of subprocesses has been determined. Expression to calculate the differential cross-section of subprocesses has been obtained: Given the resulting expressions for the square of the matrix element in the differential section expression, we see that the differential section depends not only on the energy of colliding protons, but also on the mass of quarks, etc. Differential cross-section of subprocesses is estimated. It is shown that the differential cross-section of subprocesses decreases with the increasing energy of colliding protons. Asymmetry coefficient with polarization of colliding protons is determined. The calculation showed that the squares of the matrix element of the Compton scattering process without and taking into account the polarization of colliding protons are identical. The asymmetry coefficient of this subprocess is zero, which is consistent with the literary data. It is known that in any single polarization processes with a photon, squares of matrix elements without taking into account and taking into account the polarization of the original particle must coincide, that is, the terms in the square of the matrix element with the degree of polarization are equal to zero. The coincidence of the squares of the matrix elements indicates that the parity of the system is preserved. The asymmetry coefficient of annihilation of quark–antiquark pair process linearly decreases from positive unit to negative unit with increasing the production of the polarization degrees of colliding protons. Thus, it was obtained that the differential cross-section of the subprocesses decreases with the increasing energy of colliding protons. The value of the asymmetry coefficient is maximal when the polarization of colliding protons is opposite and minimal when they are directed equally. Taking into account the polarization of only the initial quarks and gluons in Compton scattering does not contribute to the differential section of the subprocess.

Keywords: annihilation of a quark-antiquark pair, coefficient of asymmetry, Compton scattering, effective cross-section

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Authors: Thiago E. Goto, Carla C. Lopes, Helena B. Nader, Anielle C. A. Silva, Noelio O. Dantas, José R. Siqueira Jr., Luciano Caseli

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Quantum dots (QD) are semiconductor nanocrystals that can be employed in biological research as a tool for fluorescence imagings, having the potential to expand in vivo and in vitro analysis as cancerous cell biomarkers. Particularly, cadmium selenide (CdSe) magic-sized quantum dots (MSQDs) exhibit stable luminescence that is feasible for biological applications, especially for imaging of tumor cells. For these facts, it is interesting to know the mechanisms of action of how such QDs mark biological cells. For that, simplified models are a suitable strategy. Among these models, Langmuir films of lipids formed at the air-water interface seem to be adequate since they can mimic half a membrane. They are monomolecular films formed at liquid-gas interfaces that can spontaneously form when organic solutions of amphiphilic compounds are spread on the liquid-gas interface. After solvent evaporation, the monomolecular film is formed, and a variety of techniques, including tensiometric, spectroscopic and optic can be applied. When the monolayer is formed by membrane lipids at the air-water interface, a model for half a membrane can be inferred where the aqueous subphase serve as a model for external or internal compartment of the cell. These films can be transferred to solid supports forming the so-called Langmuir-Blodgett (LB) films, and an ampler variety of techniques can be additionally used to characterize the film, allowing for the formation of devices and sensors. With these ideas in mind, the objective of this work was to investigate the specific interactions of CdSe MSQDs with tumorigenic and non-tumorigenic cells using Langmuir monolayers and LB films of lipids and specific cell extracts as membrane models for diagnosis of cancerous cells. Surface pressure-area isotherms and polarization modulation reflection-absorption spectroscopy (PM-IRRAS) showed an intrinsic interaction between the quantum dots, inserted in the aqueous subphase, and Langmuir monolayers, constructed either of selected lipids or of non-tumorigenic and tumorigenic cells extracts. The quantum dots expanded the monolayers and changed the PM-IRRAS spectra for the lipid monolayers. The mixed films were then compressed to high surface pressures and transferred from the floating monolayer to solid supports by using the LB technique. Images of the films were then obtained with atomic force microscopy (AFM) and confocal microscopy, which provided information about the morphology of the films. Similarities and differences between films with different composition representing cell membranes, with or without CdSe MSQDs, was analyzed. The results indicated that the interaction of quantum dots with the bioinspired films is modulated by the lipid composition. The properties of the normal cell monolayer were not significantly altered, whereas for the tumorigenic cell monolayer models, the films presented significant alteration. The images therefore exhibited a stronger effect of CdSe MSQDs on the models representing cancerous cells. As important implication of these findings, one may envisage for new bioinspired surfaces based on molecular recognition for biomedical applications.

Keywords: biomembrane, langmuir monolayers, quantum dots, surfaces

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Authors: Ann P. Daunic, Nancy Corbett

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Background and Significance: Researchers emphasize the role that motivation, self-esteem, and self-regulation play in children’s early adjustment to the school culture, including skills such as identifying their own feelings and understanding the feelings of others. As social-emotional growth, academic learning, and successful integration within culture and society are inextricably connected, the Social-Emotional Learning Foundations (SELF) curriculum was designed to integrate social-emotional learning (SEL) instruction within early literacy instruction (specifically, reading) for Kindergarten and first-grade students at risk for emotional and behavioral difficulties. Storybook reading is a typically occurring activity in the primary grades; thus SELF provides an intervention that is both theoretically and practically sound. Methodology: The researchers will report on findings from the first two years of a three-year study funded by the US Department of Education’s Institute of Education Sciences to evaluate the effects of the SELF curriculum versus “business as usual” (BAU). SELF promotes the development of self-regulation by incorporating instructional strategies that support children’s use of SEL related vocabulary, self-talk, and critical thinking. The curriculum consists of a carefully coordinated set of materials and pedagogy designed specifically for primary grade children at early risk for emotional and behavioral difficulties. SELF lessons (approximately 50 at each grade level) are organized around 17 SEL topics within five critical competencies. SELF combines whole-group (the first in each topic) and small-group lessons (the 2nd and 3rd in each topic) to maximize opportunities for teacher modeling and language interactions. The researchers hypothesize that SELF offers a feasible and substantial opportunity within the classroom setting to provide a small-group social-emotional learning intervention integrated with K-1 literacy-related instruction. Participating target students (N = 876) were identified by their teachers as potentially at risk for emotional or behavioral issues. These students were selected from 122 Kindergarten and 100 first grade classrooms across diverse school districts in a southern state in the US. To measure the effectiveness of the SELF intervention, the researchers asked teachers to complete assessments related to social-emotional learning and adjustment to the school culture. A social-emotional learning related vocabulary assessment was administered directly to target students receiving small-group instruction. Data were analyzed using a 3-level MANOVA model with full information maximum likelihood to estimate coefficients and test hypotheses. Major Findings: SELF had significant positive effects on vocabulary, knowledge, and skills associated with social-emotional competencies, as evidenced by results from the measures administered. Effect sizes ranged from 0.41 for group (SELF vs. BAU) differences in vocabulary development to 0.68 for group differences in SEL related knowledge. Conclusion: Findings from two years of data collection indicate that SELF improved outcomes related to social-emotional learning and adjustment to the school culture. This study thus supports the integration of SEL with literacy instruction as a feasible and effective strategy to improve outcomes for K-1 students at risk for emotional and behavioral difficulties.

Keywords: Socio-cultural context for learning, social-emotional learning, social skills, vocabulary development

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Authors: Prince S. Campbell, Janine B. Adams, Melusi Thwala, Opeoluwa Oyedele, Paula E. Melariri

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Schistosomiasis is an infectious water-borne disease that holds substantial medical and veterinary importance and is transmitted by Schistosoma flatworms. The transmission and spread of the disease are geographically and temporally confined to water bodies (rivers, lakes, lagoons, dams, etc.) inhabited by its obligate intermediate host snails and human water contact. Human infection with the parasite occurs via skin penetration subsequent to exposure to water infested with schistosome cercariae. Environmental factors play a crucial role in the spread of the disease, as the survival of intermediate host snails is dependent on favourable conditions. These factors include physical and chemical components of water, including pH, salinity, temperature, electrical conductivity, dissolved oxygen, turbidity, water hardness, total dissolved solids, and velocity, as well as biological factors such as predator-prey interactions, competition, food availability, and the presence and density of aquatic vegetation. This study evaluated the physicochemical properties of the water bodies, vegetation type, distribution, and habitat presence of the snail intermediate host. A quantitative cross-sectional research design approach was employed in this study. Eight sampling sites were selected based on their proximity to residential areas. Snails and water physicochemical properties were collected over different seasons for 9 months. A simple dip method was used for surface water samples and measurements were done using multiparameter meters. Snails captured using a 300 µm mesh scoop net and predominant plant species were gathered and transported to experts for identification. Vegetation composition and cover were visually estimated and recorded at each sampling point. Data was analysed using R software (version 4.3.1). A total of 844 freshwater snails were collected, with Physa genera accounting for 95.9% of the snails. Bulinus and Biomphalaria snails, which serve as intermediate hosts for the disease, accounted for (0.9%) and (0.6%) respectively. Indicator macrophytes such as Eicchornia crassipes, Stuckenia pectinate, Typha capensis, and floating macroalgae were found in several water bodies. A negative and weak correlation existed between the number of snails and physicochemical properties such as electrical conductivity (r=-0.240), dissolved oxygen (r=-0.185), hardness (r=-0.210), pH (r=-0.235), salinity (r=-0.242), temperature (r=-0.273), and total dissolved solids (r=-0.236). There was no correlation between the number of snails and turbidity (r=-0.070). Moreover, there was a negative and weak correlation between snails and vegetation coverage (r=-0.127). Findings indicated that snail abundance marginally declined with rising physicochemical concentrations, and the majority of snails were located in regions with less vegetation cover. The reduction in Bulinus and Biomphalaria snail populations may also be attributed to other factors, such as competition among the snails. Snails of the Physa genus were abundant due to their noteworthy resilience in difficult environments. These snails have the potential to function as biological control agents in areas where the disease is endemic, as they outcompete other snails, including schistosomiasis intermediate host snails.

Keywords: intermediate host snails, physicochemical properties, schistosomiasis, vegetation type

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Sudeshna Sengupta

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Women’s work lives weave a complex fabric of myriad work relations and complex structures. Lives, when seen from the lens of work, is a saga of conjugated oppression by intertwined structures that are vertically and horizontally interwoven in a very complex manner. Women interact with multiple institutions through their work. The interactions and interplay of institutions shape their organization of work. They intersperse productive work with reproductive work, unpaid economic activities with unpaid care work, and all kinds of activities with leisure and self-care. The proposed paper intends to understand how women working as home-based workers in the National Capital Region (NCR) of India are organizing their everyday work, and how the organization of work is influenced by the interplay of structures. Situating itself in a multidisciplinary theoretical framework, this paper brings out how the gendering of work is playing out in the political, economic and social domain and shaping the work-life within the family, and in the paid workspace. The paper will use a primary data source, which is qualitative in nature. It will comprise 15 qualitative interviews of women home-based workers from the National Capital Region. The research uses a life history approach. The sampling was purposive using snowballing as a method. The dataset is part of the primary data (qualitative) collected for the ongoing Ph.D. work in Gender Studies at Ambedkar University Delhi. The home-based workers interviewed were in “non-factory” wage relations based on piece rates with flexible working hours. Their workplaces were their own homes with no spatial divide between living spaces and workspaces. Home-based workers were recognized as a group in the domain of labor economics in the 1980s. When menial work was cheaper than machine work, the capital owners preferred to outsource work as home-based work to women. These production spaces are fragmented and the identity of gender is created within labor processes to favor material accumulation. Both the employers and employees acknowledged the material gain of the capital owner when work was subcontracted to women at home. Simultaneously the market reinforced women’s reproductive role by conforming to patriarchal ideology. The contractors played an important role in implementing localized control on workers and also in finding workers for fragmented, gendered production processes. Their presence helped the employers in bringing together multiple forms of oppression that ranged from creating a structure to flout laws by creating shadow employers. It created an intertwined social and economic structure as well as a workspace where the line between productive and reproductive work gets blurred. The state invisibilized itself either by keeping the sector out of the domain of laws or by not implementing its own laws regulating working conditions or social security. It allowed the local hierarchy to function and define localized working conditions. The productive reproductive continuum reveals a labor control that influenced both the productive and reproductive work of women.

Keywords: informal sector, paid work, women workers, labor processes

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Authors: Ishita Singh

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Social-dimension of Climate Change is no longer peripheral to Science, Technology and Innovation (STI). Indeed, STI is being mobilized to address small farmers’ vulnerability and adaptation to Climate Change. The experiences from the cold desert of Leh (Ladakh) in North-West Himalaya illustrate the potential of STI to address the challenges of Climate Change and the needs of small farmers through the use of Artificial Glacier Techniques. Small farmers have a unique technique of water harvesting to augment irrigation, called “Artificial Glaciers” - an intricate network of water channels and dams along the upper slope of a valley that are located closer to villages and at lower altitudes than natural glaciers. It starts to melt much earlier and supplements additional irrigation to small farmers’ improving their livelihoods. Therefore, the issue of vulnerability, adaptive capacity and adaptation strategy needs to be analyzed in a local context and the communities as well as regions where people live. Leh (Ladakh) in North-West Himalaya provides a Case Study for exploring the ways in which adaptation to Climate Change is taking place at a community scale using Artificial Glacier Technology. With the above backdrop, an attempt has been made to analyze the rural poor households' vulnerability and adaptation practices to Climate Change using this technology, thereby drawing lessons on vulnerability-livelihood interactions in the cold desert of Leh (Ladakh) in North-West Himalaya, India. The study is based on primary data and information collected from 675 households confined to 27 villages of Leh (Ladakh) in North-West Himalaya, India. It reveals that 61.18% of the population is driving livelihoods from agriculture and allied activities. With increased irrigation potential due to the use of Artificial Glaciers, food security has been assured to 77.56% of households and health vulnerability has been reduced in 31% of households. Seasonal migration as a livelihood diversification mechanism has declined in nearly two-thirds of households, thereby improving livelihood strategies. Use of tactical adaptations by small farmers in response to persistent droughts, such as selling livestock, expanding agriculture lands, and use of relief cash and foods, have declined to 20.44%, 24.74% and 63% of households. However, these measures are unsustainable on a long-term basis. The role of policymakers and societal stakeholders becomes important in this context. To address livelihood challenges, the role of technology is critical in a multidisciplinary approach involving multilateral collaboration among different stakeholders. The presence of social entrepreneurs and new actors on the adaptation scene is necessary to bring forth adaptation measures. Better linkage between Science and Technology policies, together with other policies, should be encouraged. Better health care, access to safe drinking water, better sanitary conditions, and improved standards of education and infrastructure are effective measures to enhance a community’s adaptive capacity. However, social transfers for supporting climate adaptive capacity require significant amounts of additional investment. Developing institutional mechanisms for specific adaptation interventions can be one of the most effective ways of implementing a plan to enhance adaptation and build resilience.

Keywords: climate change, adaptation, livelihood, stakeholders

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Authors: Thilina Ranaweera, Enes Makalic, John L. Hopper, Adrian Bickerstaffe

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Data are the primary asset of biomedical researchers, and the engine for both discovery and research translation. As the volume and complexity of research datasets increase, especially with new technologies such as large single nucleotide polymorphism (SNP) chips, so too does the requirement for software to manage, process and analyze the data. Researchers often need to execute complicated queries and conduct complex analyzes of large-scale datasets. Existing tools to analyze such data, and other types of high-dimensional data, unfortunately suffer from one or more major problems. They typically require a high level of computing expertise, are too simplistic (i.e., do not fit realistic models that allow for complex interactions), are limited by computing power, do not exploit the computing power of large-scale parallel architectures (e.g. supercomputers, GPU clusters etc.), or are limited in the types of analysis available, compounded by the fact that integrating new analysis methods is not straightforward. Solutions to these problems, such as those developed and implemented on parallel architectures, are currently available to only a relatively small portion of medical researchers with access and know-how. The past decade has seen a rapid expansion of data management systems for the medical domain. Much attention has been given to systems that manage phenotype datasets generated by medical studies. The introduction of heterogeneous genomic data for research subjects that reside in these systems has highlighted the need for substantial improvements in software architecture. To address this problem, we have developed SPARK, an enabling and translational system for medical research, leveraging existing high performance computing resources, and analysis techniques currently available or being developed. It builds these into The Ark, an open-source web-based system designed to manage medical data. SPARK provides a next-generation biomedical data management solution that is based upon a novel Micro-Service architecture and Big Data technologies. The system serves to demonstrate the applicability of Micro-Service architectures for the development of high performance computing applications. When applied to high-dimensional medical datasets such as genomic data, relational data management approaches with normalized data structures suffer from unfeasibly high execution times for basic operations such as insert (i.e. importing a GWAS dataset) and the queries that are typical of the genomics research domain. SPARK resolves these problems by incorporating non-relational NoSQL databases that have been driven by the emergence of Big Data. SPARK provides researchers across the world with user-friendly access to state-of-the-art data management and analysis tools while eliminating the need for high-level informatics and programming skills. The system will benefit health and medical research by eliminating the burden of large-scale data management, querying, cleaning, and analysis. SPARK represents a major advancement in genome research technologies, vastly reducing the burden of working with genomic datasets, and enabling cutting edge analysis approaches that have previously been out of reach for many medical researchers.

Keywords: biomedical research, genomics, information systems, software

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Authors: J. Ticona Chambi, E. A. De Almeida, C. A. Andrade Raymundo Gaiotto, A. M. Do Espírito Santo, L. Infantes, S. L. Cuffini

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The solubility of active pharmaceutical ingredients (APIs) is challenging for the pharmaceutical industry. The new multicomponent crystalline forms as cocrystal and solvates present an opportunity to improve the solubility of APIs. Commonly, the procedure to obtain multicomponent crystalline forms of a drug starts by screening the drug molecule with the different coformers/solvents. However, it is necessary to develop methods to obtain multicomponent forms in an efficient way and with the least possible environmental impact. The Hansen Solubility Parameters (HSPs) is considered a tool to obtain theoretical knowledge of the solubility of the target compound in the chosen solvent. H-Bond Propensity (HBP), Molecular Complementarity (MC), Coordination Values (CV) are tools used for statistical prediction of cocrystals developed by the Cambridge Crystallographic Data Center (CCDC). The HSPs and the CCDC tools are based on inter- and intra-molecular interactions. The curcumin (Cur), target molecule, is commonly used as an anti‐inflammatory. The demethoxycurcumin (Demcur) and bisdemethoxycurcumin (Bisdcur) are natural analogues of Cur from turmeric. Those target molecules have differences in their solubilities. In this way, the work aimed to analyze and compare different tools for multicomponent forms prediction (solvates) of Cur, Demcur and Biscur. The HSP values were calculated for Cur, Demcur, and Biscur using the chemical group contribution methods and the statistical optimization from experimental data. The HSPmol software was used. From the HSPs of the target molecules and fifty solvents (listed in the HSP books), the relative energy difference (RED) was determined. The probability of the target molecules would be interacting with the solvent molecule was determined using the CCDC tools. A dataset of fifty molecules of different organic solvents was ranked for each prediction method and by a consensus ranking of different combinations: HSP, CV, HBP and MC values. Based on the prediction, 15 solvents were selected as Dimethyl Sulfoxide (DMSO), Tetrahydrofuran (THF), Acetonitrile (ACN), 1,4-Dioxane (DOX) and others. In a starting analysis, the slow evaporation technique from 50°C at room temperature and 4°C was used to obtain solvates. The single crystals were collected by using a Bruker D8 Venture diffractometer, detector Photon100. The data processing and crystal structure determination were performed using APEX3 and Olex2-1.5 software. According to the results, the HSPs (theoretical and optimized) and the Hansen solubility sphere for Cur, Demcur and Biscur were obtained. With respect to prediction analyses, a way to evaluate the predicting method was through the ranking and the consensus ranking position of solvates already reported in the literature. It was observed that the combination of HSP-CV obtained the best results when compared to the other methods. Furthermore, as a result of solvent selected, six new solvates, Cur-DOX, Cur-DMSO, Bicur-DOX, Bircur-THF, Demcur-DOX, Demcur-ACN and a new Biscur hydrate, were obtained. Crystal structures were determined for Cur-DOX, Biscur-DOX, Demcur-DOX and Bicur-Water. Moreover, the unit-cell parameter information for Cur-DMSO, Biscur-THF and Demcur-ACN were obtained. The preliminary results showed that the prediction method is showing a promising strategy to evaluate the possibility of forming multicomponent. It is currently working on obtaining multicomponent single crystals.

Keywords: curcumin, HSPs, prediction, solvates, solubility

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Authors: Caspar Goeke, Holger Finger, Dorena Diekamp, Peter König

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Online-based research has recently gained increasing attention from various fields of research in the cognitive sciences. Technological advances in the form of online crowdsourcing (Amazon Mechanical Turk), open data repositories (Open Science Framework), and online analysis (Ipython notebook) offer rich possibilities to improve, validate, and speed up research. However, until today there is no cross-platform integration of these subsystems. Furthermore, implementation of online studies still suffers from the complex implementation (server infrastructure, database programming, security considerations etc.). Here we propose and test a new JavaScript framework that enables researchers to conduct any kind of behavioral research in the browser without the need to program a single line of code. In particular our framework offers the possibility to manipulate and combine the experimental stimuli via a graphical editor, directly in the browser. Moreover, we included an action-event system that can be used to handle user interactions, interactively change stimuli properties or store participants’ responses. Besides traditional recordings such as reaction time, mouse and keyboard presses, the tool offers webcam based eye and face-tracking. On top of these features our framework also takes care about the participant recruitment, via crowdsourcing platforms such as Amazon Mechanical Turk. Furthermore, the build in functionality of google translate will ensure automatic text translations of the experimental content. Thereby, thousands of participants from different cultures and nationalities can be recruited literally within hours. Finally, the recorded data can be visualized and cleaned online, and then exported into the desired formats (csv, xls, sav, mat) for statistical analysis. Alternatively, the data can also be analyzed online within our framework using the integrated Ipython notebook. The framework was designed such that studies can be used interchangeably between researchers. This will support not only the idea of open data repositories but also constitutes the possibility to share and reuse the experimental designs and analyses such that the validity of the paradigms will be improved. Particularly, sharing and integrating the experimental designs and analysis will lead to an increased consistency of experimental paradigms. To demonstrate the functionality of the framework we present the results of a pilot study in the field of spatial navigation that was conducted using the framework. Specifically, we recruited over 2000 subjects with various cultural backgrounds and consequently analyzed performance difference in dependence on the factors culture, gender and age. Overall, our results demonstrate a strong influence of cultural factors in spatial cognition. Such an influence has not yet been reported before and would not have been possible to show without the massive amount of data collected via our framework. In fact, these findings shed new lights on cultural differences in spatial navigation. As a consequence we conclude that our new framework constitutes a wide range of advantages for online research and a methodological innovation, by which new insights can be revealed on the basis of massive data collection.

Keywords: cultural differences, crowdsourcing, JavaScript framework, methodological innovation, online data collection, online study, spatial cognition

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Authors: Loyd R. Hook, Maryam Moharek

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With the envisioned future growth of low altitude urban aircraft operations for airborne delivery service and advanced air mobility, strategies to coordinate and deconflict aircraft flight paths must be prioritized. Autonomous coordination and planning of flight trajectories is the preferred approach to the future vision in order to increase safety, density, and efficiency over manual methods employed today. Difficulties arise because any conflict resolution must be constrained by all other aircraft, all airspace restrictions, and all ground-based obstacles in the vicinity. These considerations make pair-wise tactical deconfliction difficult at best and unlikely to find a suitable solution for the entire system of vehicles. In addition, more traditional methods which rely on long time scales and large protected zones will artificially limit vehicle density and drastically decrease efficiency. Instead, strategic planning, which is able to respond to highly dynamic conditions and still account for high density operations, will be required to coordinate multiple vehicles in the highly constrained low altitude urban environment. This paper develops and evaluates such a planning algorithm which can be implemented autonomously across multiple aircraft and situations. Data from this evaluation provide promising results with simulations showing up to 10 aircraft deconflicted through a relatively narrow low-altitude urban canyon without any vehicle to vehicle or obstacle conflict. The algorithm achieves this level of coordination beginning with the assumption that each vehicle is controlled to follow an independently constructed flight path, which is itself free of obstacle conflict and restricted airspace. Then, by preferencing speed change deconfliction maneuvers constrained by the vehicles flight envelope, vehicles can remain as close to the original planned path and prevent cascading vehicle to vehicle conflicts. Performing the search for a set of commands which can simultaneously ensure separation for each pair-wise aircraft interaction and optimize the total velocities of all the aircraft is further complicated by the fact that each aircraft's flight plan could contain multiple segments. This means that relative velocities will change when any aircraft achieves a waypoint and changes course. Additionally, the timing of when that aircraft will achieve a waypoint (or, more directly, the order upon which all of the aircraft will achieve their respective waypoints) will change with the commanded speed. Put all together, the continuous relative velocity of each vehicle pair and the discretized change in relative velocity at waypoints resembles a hybrid reachability problem - a form of control reachability. This paper proposes two methods for finding solutions to these multi-body problems. First, an analytical formulation of the continuous problem is developed with an exhaustive search of the combined state space. However, because of computational complexity, this technique is only computable for pairwise interactions. For more complicated scenarios, including the proposed 10 vehicle example, a discretized search space is used, and a depth-first search with early stopping is employed to find the first solution that solves the constraints.

Keywords: strategic planning, autonomous, aircraft, deconfliction

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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

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Authors: Nilki Upathissa, Torin Wirasinghe

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

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

Procedia PDF Downloads 94

Authors: Jérémy Philippe, Lucien Baldas, Batoul Attar, Jean-Charles Mare

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The proposed communication deals with the research and development of a rotary direct-drive servo valve for aerospace applications. A key challenge of the project is to downsize the electromagnetic torque motor by reducing the torque required to drive the rotary spool. It is intended to optimize the spool and the sleeve geometries by combining a Computational Fluid Dynamics (CFD) approach with commercial optimization software. The present communication addresses an important phase of the project, which consists firstly of gaining confidence in the simulation results. It is well known that the force needed to pilot a sliding spool valve comes from several physical effects: hydraulic forces, friction and inertia/mass of the moving assembly. Among them, the flow force is usually a major contributor to the steady-state (or Root Mean Square) driving torque. In recent decades, CFD has gradually become a standard simulation tool for studying fluid-structure interactions. However, in the particular case of high-pressure valve design, the authors have experienced that the calculated overall hydraulic force depends on the parameterization and options used to build and run the CFD model. To solve this issue, the authors have selected the standard case of the linear spool valve, which is addressed in detail in numerous scientific references (analytical models, experiments, CFD simulations). The first CFD simulations run by the authors have shown that the evolution of the equivalent discharge coefficient vs. Reynolds number at the metering orifice corresponds well to the values that can be predicted by the classical analytical models. Oppositely, the simulated flow force was found to be quite different from the value calculated analytically. This drove the authors to investigate minutely the influence of the studied domain and the setting of the CFD simulation. It was firstly shown that the flow recirculates in the inlet and outlet channels if their length is not sufficient regarding their hydraulic diameter. The dead volume on the uncontrolled orifice side also plays a significant role. These examples highlight the influence of the geometry of the fluid domain considered. The second action was to investigate the influence of the type of mesh, the turbulence models and near-wall approaches, and the numerical solver and discretization scheme order. Two approaches were used to determine the overall hydraulic force acting on the moving spool. First, the force was deduced from the momentum balance on a control domain delimited by the valve inlet and outlet and the spool walls. Second, the overall hydraulic force was calculated from the integral of pressure and shear forces acting at the boundaries of the fluid domain. This underlined the significant contribution of the viscous forces acting on the spool between the inlet and outlet orifices, which are generally not considered in the literature. This also emphasized the influence of the choices made for the implementation of CFD calculation and results analysis. With the step-by-step process adopted to increase confidence in the CFD simulations, the authors propose a set of best practices and recommendations for the efficient use of CFD to design high-pressure spool valves.

Keywords: computational fluid dynamics, hydraulic forces, servovalve, rotary servovalve

Procedia PDF Downloads 43

Authors: Lucille Alonso, Florent Renard

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The characterizations of urban heat island (UHI) and their interactions with climate change and urban climates are the main research and public health issue, due to the increasing urbanization of the population. These solutions require a better knowledge of the UHI and micro-climate in urban areas, by combining measurements and modelling. This study is part of this topic by evaluating microclimatic conditions in dense urban areas in the Lyon Metropolitan Area (France) using a combination of data traditionally used such as topography, but also from LiDAR (Light Detection And Ranging) data, Landsat 8 satellite observation and Sentinel and ground measurements by bike. These bicycle-dependent weather data collections are used to build the database of the variable to be modelled, the air temperature, over Lyon’s hyper-center. This study aims to model the air temperature, measured during 6 mobile campaigns in Lyon in clear weather, using multiple linear regressions based on 33 explanatory variables. They are of various categories such as meteorological parameters from remote sensing, topographic variables, vegetation indices, the presence of water, humidity, bare soil, buildings, radiation, urban morphology or proximity and density to various land uses (water surfaces, vegetation, bare soil, etc.). The acquisition sources are multiple and come from the Landsat 8 and Sentinel satellites, LiDAR points, and cartographic products downloaded from an open data platform in Greater Lyon. Regarding the presence of low, medium, and high vegetation, the presence of buildings and ground, several buffers close to these factors were tested (5, 10, 20, 25, 50, 100, 200 and 500m). The buffers with the best linear correlations with air temperature for ground are 5m around the measurement points, for low and medium vegetation, and for building 50m and for high vegetation is 100m. The explanatory model of the dependent variable is obtained by multiple linear regression of the remaining explanatory variables (Pearson correlation matrix with a |r| < 0.7 and VIF with < 5) by integrating a stepwise sorting algorithm. Moreover, holdout cross-validation is performed, due to its ability to detect over-fitting of multiple regression, although multiple regression provides internal validation and randomization (80% training, 20% testing). Multiple linear regression explained, on average, 72% of the variance for the study days, with an average RMSE of only 0.20°C. The impact on the model of surface temperature in the estimation of air temperature is the most important variable. Other variables are recurrent such as distance to subway stations, distance to water areas, NDVI, digital elevation model, sky view factor, average vegetation density, or building density. Changing urban morphology influences the city's thermal patterns. The thermal atmosphere in dense urban areas can only be analysed on a microscale to be able to consider the local impact of trees, streets, and buildings. There is currently no network of fixed weather stations sufficiently deployed in central Lyon and most major urban areas. Therefore, it is necessary to use mobile measurements, followed by modelling to characterize the city's multiple thermal environments.

Keywords: air temperature, LIDAR, multiple linear regression, surface temperature, urban heat island

Procedia PDF Downloads 137

Authors: David Bakker, Nikki Rickard

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Smartphone apps hold immense potential as mental health and wellbeing tools. Support can be made easily accessible and can be used in real-time while users are experiencing distress. Furthermore, data can be collected to enable machine learning and automated tailoring of support to users. While many apps have been developed for mental health purposes, few have adhered to evidence-based recommendations and even fewer have pursued experimental validation. This paper details the development and experimental evaluation of an app, MoodMission, that aims to provide support for low moods and anxiety, help prevent clinical depression and anxiety disorders, and serve as an adjunct to professional clinical supports. MoodMission was designed to deliver cognitive behavioural therapy for specifically reported problems in real-time, momentary interactions. Users report their low moods or anxious feelings to the app along with a subjective units of distress scale (SUDS) rating. MoodMission then provides a choice of 5-10 short, evidence-based mental health strategies called Missions. Users choose a Mission, complete it, and report their distress again. Automated tailoring, gamification, and in-built data collection for analysis of effectiveness was also included in the app’s design. The development process involved construction of an evidence-based behavioural plan, designing of the app, building and testing procedures, feedback-informed changes, and a public launch. A randomized controlled trial (RCT) was conducted comparing MoodMission to two other apps and a waitlist control condition. Participants completed measures of anxiety, depression, well-being, emotional self-awareness, coping self-efficacy and mental health literacy at the start of their app use and 30 days later. At the time of submission (November 2016) over 300 participants have participated in the RCT. Data analysis will begin in January 2017. At the time of this submission, MoodMission has over 4000 users. A repeated-measures ANOVA of 1390 completed Missions reveals that SUDS (0-10) ratings were significantly reduced between pre-Mission ratings (M=6.20, SD=2.39) and post-Mission ratings (M=4.93, SD=2.25), F(1,1389)=585.86, p < .001, np2=.30. This effect was consistent across both low moods and anxiety. Preliminary analyses of the data from the outcome measures surveys reveal improvements across mental health and wellbeing measures as a result of using the app over 30 days. This includes a significant increase in coping self-efficacy, F(1,22)=5.91, p=.024, np2=.21. Complete results from the RCT in which MoodMission was evaluated will be presented. Results will also be presented from the continuous outcome data being recorded by MoodMission. MoodMission was successfully developed and launched, and preliminary analysis suggest that it is an effective mental health and wellbeing tool. In addition to the clinical applications of MoodMission, the app holds promise as a research tool to conduct component analysis of psychological therapies and overcome restraints of laboratory based studies. The support provided by the app is discrete, tailored, evidence-based, and transcends barriers of stigma, geographic isolation, financial limitations, and low health literacy.

Keywords: anxiety, app, CBT, cognitive behavioural therapy, depression, eHealth, mission, mobile, mood, MoodMission

Procedia PDF Downloads 271

Authors: Adam J. Bowen

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Abstract— Should robots have rights or legal protections. Often debates concerning whether robots and AI should be afforded rights focus on conditions of personhood and the possibility of future advanced forms of AI satisfying particular intrinsic cognitive and moral attributes of rights-holding persons. Such discussions raise compelling questions about machine consciousness, autonomy, and value alignment with human interests. Although these are important theoretical concerns, especially from a future design perspective, they provide limited guidance for addressing the moral and legal standing of current and near-term AI that operate well below the cognitive and moral agency of human persons. Robots and AI are already being pressed into service in a wide range of roles, especially in healthcare and biomedical contexts. The design and large-scale implementation of robots in the context of core societal institutions like healthcare systems continues to rapidly develop. For example, we bring them into our homes, hospitals, and other care facilities to assist in care for the sick, disabled, elderly, children, or otherwise vulnerable persons. We enlist surgical robotic systems in precision tasks, albeit still human-in-the-loop technology controlled by surgeons. We also entrust them with social roles involving companionship and even assisting in intimate caregiving tasks (e.g., bathing, feeding, turning, medicine administration, monitoring, transporting). There have been advances to enable severely disabled persons to use robots to feed themselves or pilot robot avatars to work in service industries. As the applications for near-term AI increase and the roles of robots in restructuring our biomedical practices expand, we face pressing questions about the normative implications of human-robot interactions and collaborations in our collective worldmaking, as well as the moral and legal status of robots. This paper argues that robots operating in public and private spaces be afforded some protections as either moral patients or legal agents to establish prohibitions on robot abuse, misuse, and mistreatment. We already implement robots and embed them in our practices and institutions, which generates a host of human-to-machine and machine-to-machine relationships. As we interact with machines, whether in service contexts, medical assistance, or home health companions, these robots are first encountered in relationship to us and our respective roles in the encounter (e.g., surgeon, physical or occupational therapist, recipient of care, patient’s family, healthcare professional, stakeholder). This proposal aims to outline a framework for establishing limiting factors and determining the extent of moral or legal protections for robots. In doing so, it advocates for a relational approach that emphasizes the priority of mapping the complex contextually sensitive roles played and the relations in which humans and robots stand to guide policy determinations by relevant institutions and authorities. The relational approach must also be technically informed by the intended uses of the biomedical technologies in question, Design History Files, extensive risk assessments and hazard analyses, as well as use case social impact assessments.

Keywords: biomedical robots, robot ethics, robot laws, human-robot interaction

Procedia PDF Downloads 120

Authors: Iñaki Iturria, Pasquale Russo, Montserrat Nacher-Vázquez, Giuseppe Spano, Paloma López, Miguel Angel Pardo

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Introduction: It is known that some Lactic Acid Bacteria (LAB) present an interesting probiotic effect. Probiotic bacteria stimulate host resistance to microbial pathogens and thereby aid in immune response, and modulate the host's immune responses to antigens with a potential to down-regulate hypersensitivity reactions. Therefore, probiotic therapy is valuable against intestinal infections and may be beneficial in the treatment of Inflammatory Bowel Disease (IBD). Several in vitro tests are available to evaluate the probiotic potential of a LAB strain. However, an in vivo model is required to understand the interaction between the host immune system and the bacteria. During the last few years, zebrafish (Danio rerio) has gained interest as a promising vertebrate model in this field. This organism has been extensively used to study the interaction between the host and the microbiota, as well as the host immune response under several microbial infections. In this work, we report on the use of the zebrafish model to investigate in vivo the colonizing ability and the immunomodulatory effect of probiotic LAB. Methods: Lactobacillus strains belonging to different LAB species were fluorescently tagged and used to colonize germ-free zebrafish larvae gastrointestinal tract (GIT). Some of the strains had a well-documented probiotic effect (L. acidophilus LA5); while others presented an exopolysaccharide (EPS) producing phenotype, thus allowing evaluating the influence of EPS in the colonization and immunomodulatory effect. Bacteria colonization was monitored for 72 h by direct observation in real time using fluorescent microscopy. CFU count per larva was also evaluated at different times. The immunomodulatory effect was assessed analysing the differential expression of several innate immune system genes (MyD88, NF-κB, Tlr4, Il1β and Il10) by qRT- PCR. The anti-inflammatory effect was evaluated using a chemical enterocolitis zebrafish model. The protective effect against a pathogen was also studied. To that end, a challenge test was developed using a fluorescently tagged pathogen (Vibrio anguillarum-GFP+). The progression of the infection was monitored up to 3 days using a fluorescent stereomicroscope. Mortality rates and CFU counts were also registered. Results and conclusions: Larvae exposed to EPS-producing bacteria showed a higher fluorescence and CFU count than those colonized with no-EPS phenotype LAB. In the same way, qRT-PCR results revealed an immunomodulatory effect on the host after the administration of the strains with probiotic activity. A downregulation of proinflammatory cytoquines as well as other cellular mediators of inflammation was observed. The anti-inflammatory effect was found to be particularly marked following exposure to LA% strain, as well as EPS producing strains. Furthermore, the challenge test revealed a protective effect of probiotic administration. As a matter of fact, larvae fed with probiotics showed a decrease in the mortality rate ranging from 20 to 35%. Discussion: In this work, we developed a promising model, based on the use of gnotobiotic zebrafish coupled with a bacterial fluorescent tagging in order to evaluate the probiotic potential of different LAB strains. We have successfully used this system to monitor in real time the colonization and persistence of exogenous LAB within the gut of zebrafish larvae, to evaluate their immunomodulatory effect and for in vivo competition assays. This approach could bring further insights into the complex microbial-host interactions at intestinal level.

Keywords: gnotobiotic, immune system, lactic acid bacteria, probiotics, zebrafish

Procedia PDF Downloads 328

Authors: Rupesh S. Gundewar, Kanchan C. Khare

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Runoff contribution from urban areas is generally from manmade structures and few natural contributors. The manmade structures are buildings; roads and other paved areas whereas natural contributors are groundwater and overland flows etc. Runoff alleviation is done by manmade as well as natural storages. Manmade storages are storage tanks or other storage structures such as soakways or soak pits which are more common in western and European countries. Natural storages are catchment slope, infiltration, catchment length, channel rerouting, drainage density, depression storage etc. A literature survey on the manmade and natural storages/inflow has presented percentage contribution of each individually. Sanders et.al. in their research have reported that a vegetation canopy reduces runoff by 7% to 12%. Nassif et el in their research have reported that catchment slope has an impact of 16% on bare standard soil and 24% on grassed soil on rainfall runoff. Infiltration being a pervious/impervious ratio dependent parameter is catchment specific. But a literature survey has presented a range of 15% to 30% loss of rainfall runoff in various catchment study areas. Catchment length and channel rerouting too play a considerable role in reduction of rainfall runoff. Ground infiltration inflow adds to the runoff where the groundwater table is very shallow and soil saturates even in a lower intensity storm. An approximate percent contribution through this inflow and surface inflow contributes to about 2% of total runoff volume. Considering the various contributing factors in runoff it has been observed during a literature survey that integrated modelling approach needs to be considered. The traditional storm water network models are able to predict to a fair/acceptable degree of accuracy provided no interaction with receiving water (river, sea, canal etc), ground infiltration, treatment works etc. are assumed. When such interactions are significant then it becomes difficult to reproduce the actual flood extent using the traditional discrete modelling approach. As a result the correct flooding situation is very rarely addressed accurately. Since the development of spatially distributed hydrologic model the predictions have become more accurate at the cost of requiring more accurate spatial information.The integrated approach provides a greater understanding of performance of the entire catchment. It enables to identify the source of flow in the system, understand how it is conveyed and also its impact on the receiving body. It also confirms important pain points, hydraulic controls and the source of flooding which could not be easily understood with discrete modelling approach. This also enables the decision makers to identify solutions which can be spread throughout the catchment rather than being concentrated at single point where the problem exists. Thus it can be concluded from the literature survey that the representation of urban details can be a key differentiator to the successful understanding of flooding issue. The intent of this study is to accurately predict the runoff from impermeable areas from urban area in India. A representative area has been selected for which data was available and predictions have been made which are corroborated with the actual measured data.

Keywords: runoff, urbanization, impermeable response, flooding

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Authors: Salvatore Brischetto, Domenico Cesare

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Major improvements in future aircraft and spacecraft could be those dependent on an increasing use of conventional and unconventional multilayered structures embedding composite materials, functionally graded materials, piezoelectric or piezomagnetic materials, and soft foam or honeycomb cores. Layers made of such materials can be combined in different ways to obtain structures that are able to fulfill several structural requirements. The next generation of aircraft and spacecraft will be manufactured as multilayered structures under the action of a combination of two or more physical fields. In multifield problems for multilayered structures, several physical fields (thermal, hygroscopic, electric and magnetic ones) interact each other with different levels of influence and importance. An exact 3D shell model is here proposed for these types of analyses. This model is based on a coupled system including 3D equilibrium equations, 3D Fourier heat conduction equation, 3D Fick diffusion equation and electric and magnetic divergence equations. The set of partial differential equations of second order in z is written using a mixed curvilinear orthogonal reference system valid for spherical and cylindrical shell panels, cylinders and plates. The order of partial differential equations is reduced to the first one thanks to the redoubling of the number of variables. The solution in the thickness z direction is obtained by means of the exponential matrix method and the correct imposition of interlaminar continuity conditions in terms of displacements, transverse stresses, electric and magnetic potentials, temperature, moisture content and transverse normal multifield fluxes. The investigated structures have simply supported sides in order to obtain a closed form solution in the in-plane directions. Moreover, a layerwise approach is proposed which allows a 3D correct description of multilayered anisotropic structures subjected to field loads. Several results will be proposed in tabular and graphical formto evaluate displacements, stresses and strains when mechanical loads, temperature gradients, moisture content gradients, electric potentials and magnetic potentials are applied at the external surfaces of the structures in steady-state conditions. In the case of inclusions of piezoelectric and piezomagnetic layers in the multilayered structures, so called smart structures are obtained. In this case, a free vibration analysis in open and closed circuit configurations and a static analysis for sensor and actuator applications will be proposed. The proposed results will be useful to better understand the physical and structural behaviour of multilayered advanced composite structures in the case of multifield interactions. Moreover, these analytical results could be used as reference solutions for those scientists interested in the development of 3D and 2D numerical shell/plate models based, for example, on the finite element approach or on the differential quadrature methodology. The correct impositions of boundary geometrical and load conditions, interlaminar continuity conditions and the zigzag behaviour description due to transverse anisotropy will be also discussed and verified.

Keywords: composite structures, 3D shell model, stress analysis, multifield loads, exponential matrix method, layer wise approach

Procedia PDF Downloads 67

Authors: Dimitra Das, Anuradha Mitra, Kalyan Kumar Chattopadhyay

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Organic polymers, constructed from light elements like carbon, hydrogen, nitrogen, oxygen, sulphur, and boron atoms, are the emergent class of non-toxic, metal-free, environmental benign advanced materials. Covalent triazine-based polymers with a functional triazine group are significant class of organic materials due to their remarkable stability arising out of strong covalent bonds. They can conventionally form hydrogen bonds, favour π–π contacts, and they were recently revealed to be involved in interesting anion–π interactions. The present work mainly focuses upon the development of a single-crystalline, highly cross-linked triazine-based nitrogen-rich organic polymer with nanodendritic morphology and significant thermal stability. The polymer has been synthesized through hydrothermal treatment of melamine and ethylene glycol resulting in cross-polymerization via condensation-polymerization reaction. The crystal structure of the polymer has been evaluated by employing Rietveld whole profile fitting method. The polymer has been found to be composed of monoclinic melamine having space group P21/a. A detailed insight into the chemical structure of the as synthesized polymer has been elucidated by Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopic analysis. X-Ray Photoelectron Spectroscopic (XPS) analysis has also been carried out for further understanding of the different types of linkages required to create the backbone of the polymer. The unique rod-like morphology of the triazine based polymer has been revealed from the images obtained from Field Emission Scanning Electron Microscopy (FESEM) and Transmission Electron Microscopy (TEM). Interestingly, this polymer has been found to selectively detect mercury (Hg²⁺) ions at an extremely low concentration through fluorescent quenching with detection limit as low as 0.03 ppb. The high toxicity of mercury ions (Hg²⁺) arise from its strong affinity towards the sulphur atoms of biological building blocks. Even a trace quantity of this metal is dangerous for human health. Furthermore, owing to its small ionic radius and high solvation energy, Hg²⁺ ions remain encapsulated by water molecules making its detection a challenging task. There are some existing reports on fluorescent-based heavy metal ion sensors using covalent organic frameworks (COFs) but reports on mercury sensing using triazine based polymers are rather undeveloped. Thus, the importance of ultra-trace detection of Hg²⁺ ions with high level of selectivity and sensitivity has contemporary significance. A plausible sensing phenomenon by the polymer has been proposed to understand the applicability of the material as a potential sensor. The impressive sensitivity of the polymer sample towards Hg²⁺ is the very first report in the field of highly crystalline triazine based polymers (without the introduction of any sulphur groups or functionalization) towards mercury ion detection through photoluminescence quenching technique. This crystalline metal-free organic polymer being cheap, non-toxic and scalable has current relevance and could be a promising candidate for Hg²⁺ ion sensing at commercial level.

Keywords: fluorescence quenching , mercury ion sensing, single-crystalline, triazine-based polymer

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Authors: Swati Bhomia, C. M. Kishtawal, Neeru Jaiswal

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Traditionally, monsoon forecasts have encountered many difficulties that stem from numerous issues such as lack of adequate upper air observations, mesoscale nature of convection, proper resolution, radiative interactions, planetary boundary layer physics, mesoscale air-sea fluxes, representation of orography, etc. Uncertainties in any of these areas lead to large systematic errors. Global circulation models (GCMs), which are developed independently at different institutes, each of which carries somewhat different representation of the above processes, can be combined to reduce the collective local biases in space, time, and for different variables from different models. This is the basic concept behind the multi-model superensemble and comprises of a training and a forecast phase. The training phase learns from the recent past performances of models and is used to determine statistical weights from a least square minimization via a simple multiple regression. These weights are then used in the forecast phase. The superensemble forecasts carry the highest skill compared to simple ensemble mean, bias corrected ensemble mean and the best model out of the participating member models. This approach is a powerful post-processing method for the estimation of weather forecast parameters reducing the direct model output errors. Although it can be applied successfully to the continuous parameters like temperature, humidity, wind speed, mean sea level pressure etc., in this paper, this approach is applied to rainfall, a parameter quite difficult to handle with standard post-processing methods, due to its high temporal and spatial variability. The present study aims at the development of advanced superensemble schemes comprising of 1-5 day daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.) obtained from THORPEX Interactive Grand Global Ensemble (TIGGE), which is one of the most complete data set available. The novel approaches include the dynamical model selection approach in which the selection of the superior models from the participating member models at each grid and for each forecast step in the training period is carried out. Multi-model superensemble based on the training using similar conditions is also discussed in the present study, which is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional multi-model ensemble (MME) approaches. Further, a variety of methods that incorporate a 'neighborhood' around each grid point which is available in literature to allow for spatial error or uncertainty, have also been experimented with the above mentioned approaches. The comparison of these schemes with respect to the observations verifies that the newly developed approaches provide more unified and skillful prediction of the summer monsoon (viz. June to September) rainfall compared to the conventional multi-model approach and the member models.

Keywords: multi-model superensemble, dynamical model selection, similarity criteria, neighborhood technique, rainfall prediction

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Authors: Zhenmin Tao, Jasper De Smet, Koen Laurijssen, Jeroen Stuyts, Sonja Sioncke

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Today, the quality of knowledge management (KM)become one of the underpinning factors in the success of an organization, as it determines the effectiveness of capitalizing the organization’s knowledge. Overall, KMin an organization consists of five aspects: (knowledge) creation, validation, presentation, distribution, and application. Among others, KM in research institutes is considered as the cornerstone as their activities cover all five aspects. Furthermore, KM in a research institute facilitates the steering committee to envision the future roadmap, identify knowledge gaps, and make decisions on future research directions. Likewise, KMis even more challenging in industrial research institutes. From a technical perspective, technology advancement in the past decades calls for combinations of breadth and depth in expertise that poses challenges in talent acquisition and, therefore, knowledge creation. From a regulatory perspective, the strict intellectual property protection from industry collaborators and/or the contractual agreements made by possible funding authoritiesform extra barriers to knowledge validation, presentation, and distribution. From a management perspective, seamless KM activities are only guaranteed by inter-disciplinary talents that combine technical background knowledge, management skills, and leadership, let alone international vision. From a financial perspective, the long feedback period of new knowledge, together with the massive upfront investment costs and low reusability of the fixed assets, lead to low RORC (return on research capital) that jeopardize KM practice. In this study, we aim to address the challenges, practices, and opportunitiesof KM in Flanders Make – a leading European research institute specialized in the manufacturing industry. In particular, the analyses encompass an internal KM project which involves functionalities ranging from management to technical domain experts. This wide range of functionalities provides comprehensive empirical evidence on the challenges and practices w.r.t.the abovementioned KMaspects. Then, we ground our analysis onto the critical dimensions ofKM–individuals, socio‐organizational processes, and technology. The analyses have three steps: First, we lay the foundation and define the environment of this study by briefing the KM roles played by different functionalities in Flanders Make. Second, we zoom in to the CoreLab MotionS where the KM project is located. In this step, given the technical domains covered by MotionS products, the challenges in KM will be addressed w.r.t. the five KM aspects and three critical dimensions. Third, by detailing the objectives, practices, results, and limitations of the MotionSKMproject, we justify the practices and opportunities derived in the execution ofKMw.r.t. the challenges addressed in the second step. The results of this study are twofold: First, a KM framework that consolidates past knowledge is developed. A library based on this framework can, therefore1) overlook past research output, 2) accelerate ongoing research activities, and 3) envision future research projects. Second, the challenges inKM on both individual (actions) level and socio-organizational level (e.g., interactions between individuals)are identified. By doing so, suggestions and guidelines will be provided in KM in the context of industrial research institute. To this end, the results in this study are reflected towards the findings in existing literature.

Keywords: technical knowledge management framework, industrial research institutes, individual knowledge management, socio-organizational knowledge management.

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Authors: Elvin P. Chizenga, Heidi Abrahamse

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Cancer cell resistance to therapy is the main cause of treatment failures and the poor prognosis of cancer convalescence. The progression of cervical cancer to other parts of the genitourinary system and the reported recurrence rates are overwhelming. Current treatments, including surgery, chemo and radiation have been inefficient in eradicating the tumor cells. These treatments are also associated with poor prognosis and reduced quality of life, including fertility loss. This has inspired the need for the development of new treatment modalities to eradicate cervical cancer successfully. Photodynamic Therapy (PDT) is a modern treatment modality that induces cell death by photochemical interactions of light and a photosensitizer, which in the presence of molecular oxygen, yields a set of chemical reactions that generate Reactive Oxygen Species (ROS) and other free radical species causing cell damage. Enhancing PDT using modified drug delivery can increase the concentration of the photosensitizer in the tumor cells, and this has the potential to maximize its therapeutic efficacy. In cervical cancer, all infected cells constitutively express genes of the E6 and E7 HPV viral oncoproteins, resulting in high concentrations of E6 and E7 in the cytoplasm. This provides an opportunity for active targeting of cervical cancer cells using immune-mediated drug delivery to maximize therapeutic efficacy. The use of nanoparticles in PDT has also proven effective in enhancing therapeutic efficacy. Gold nanoparticles (AuNps) in particular, are explored for their use in biomedicine due to their biocompatibility, low toxicity, and enhancement of drug uptake by tumor cells. In this present study, a biomolecule comprising of AuNPs, anti-E6 monoclonal antibodies, and Aluminium Phthalocyanine photosensitizer was synthesized for use in targeted PDT of cervical cancer. The AuNp-Anti-E6-Sulfonated Aluminium Phthalocyanine mix (AlPcSmix) photosensitizing biomolecule was synthesized by coupling AuNps and anti-E6 monoclonal antibodies to the AlPcSmix via Polyethylene Glycol (PEG) chemical links. The final product was characterized using Transmission Electron Microscope (TEM), Zeta Potential, Uv-Vis Spectrophotometry, Fourier Transform Infrared Spectroscopy (FTIR), and X-ray diffraction (XRD), to confirm its chemical structure and functionality. To observe its therapeutic role in treating cervical cancer, cervical cancer cells, HeLa cells were seeded in 3.4 cm² diameter culture dishes at a concentration of 5x10⁵ cells/ml, in vitro. The cells were treated with varying concentrations of the photosensitizing biomolecule and irradiated using a 673.2 nm wavelength of laser light. Post irradiation cellular responses were performed to observe changes in morphology, viability, proliferation, cytotoxicity, and cell death pathways induced. Dose-Dependent response of the cells to treatment was demonstrated as significant morphologic changes, increased cytotoxicity, and decreased cell viability and proliferation This study presented a synthetic biomolecule for targeted PDT of cervical cancer. The study suggested that PDT using this AuNp- Anti-E6- AlPcSmix photosensitizing biomolecule is a very effective treatment method for the eradication of cervical cancer cells, in vitro. Further studies in vivo need to be conducted to support the use of this biomolecule in treating cervical cancer in clinical settings.

Keywords: anti-E6 monoclonal antibody, cervical cancer, gold nanoparticles, photodynamic therapy

Procedia PDF Downloads 125

Authors: Avdhesh K. Sharma

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Key, in the development of energy-efficient micro-scale heat exchanger devices, is to select large heat transfer surface to volume ratio without much expanse on re-circulated pumps. The increased interest in short heat exchanger (SHE) is due to accessibility of advanced technologies for manufacturing of micro-tubes in range of 1 micron m - 1 mm. Such SHE using micro-tubes are highly effective for high flux heat transfer technologies. Nanofluids, are used to enhance the thermal conductivity of re-circulated coolant and thus enhances heat transfer rate further. Higher viscosity associated with nanofluid expands more pumping power. Thus, there is a trade-off between heat transfer rate and pressure drop with geometry of micro-tubes. Herein, a novel design of short counter flow heat exchanger (SCFHE) using non-circular micro-tubes flooded with CuO-water nanofluid is conceptualized by varying the ratio of surface area to cross-sectional area of micro-tubes. A framework for comparative analysis of SCFHE using micro-tubes non-circular shape flooded by CuO-water nanofluid is presented. In SCFHE concept, micro-tubes having various geometrical shapes (viz., triangular, rectangular and trapezoidal) has been arranged row-wise to facilitate two aspects: (1) allowing easy flow distribution for cold and hot stream, and (2) maximizing the thermal interactions with neighboring channels. Adequate distribution of rows for cold and hot flow streams enables above two aspects. For comparative analysis, a specific volume or cross-section area is assigned to each elemental cell (which includes flow area and area corresponds to half wall thickness). A specific volume or cross-section area is assumed to be constant for each elemental cell (which includes flow area and half wall thickness area) and variation in surface area is allowed by selecting different geometry of micro-tubes in SCFHE. Effective thermal conductivity model for CuO-water nanofluid has been adopted, while the viscosity values for water based nanofluids are obtained empirically. Correlations for Nusselt number (Nu) and Poiseuille number (Po) for micro-tubes have been derived or adopted. Entrance effect is accounted for. Thermal and hydrodynamic performances of SCFHE are defined in terms of effectiveness and pressure drop or pumping power, respectively. For defining the overall performance index of SCFHE, two links are employed. First one relates heat transfer between the fluid streams q and pumping power PP as (=qj/PPj); while another link relates effectiveness eff and pressure drop dP as (=effj/dPj). For analysis, the inlet temperatures of hot and cold streams are varied in usual range of 20dC-65dC. Fully turbulent regime is seldom encountered in micro-tubes and transition of flow regime occurs much early (i.e., ~Re=1000). Thus, Re is fixed at 900, however, the uncertainty in Re due to addition of nanoparticles in base fluid is quantified by averaging of Re. Moreover, for minimizing error, volumetric concentration is limited to range 0% to ≤4% only. Such framework may be helpful in utilizing maximum peripheral surface area of SCFHE without any serious severity on pumping power and towards developing advanced short heat exchangers.

Keywords: CuO-water nanofluid, non-circular micro-tubes, performance index, short counter flow heat exchanger

Procedia PDF Downloads 211

Authors: Anil Kumar, Rajnish Kaur, Mateusz Czyzycki, Alessandro Migilori, Andreas Germanos Karydas, Sanjiv Puri

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The radiative decay of Li(i=1-3) sub-shell vacancies produced through photoionization results in production of the characteristic emission spectrum comprising several X-ray lines, whereas non-radiative vacancy decay results in Auger electron spectrum. Accurate reliable data on the Li(i=1-3) sub-shell X-ray production (XRP) cross sections is of considerable importance for investigation of atomic inner-shell ionization processes as well as for quantitative elemental analysis of different types of samples employing the energy dispersive X-ray fluorescence (EDXRF) analysis technique. At incident photon energies in vicinity of the absorption edge energies of an element, the many body effects including the electron correlation, core relaxation, inter-channel coupling and post-collision interactions become significant in the photoionization of atomic inner-shells. Further, in case of compounds, the characteristic emission spectrum of the specific element is expected to get influenced by the chemical environment (coordination number, oxidation state, nature of ligand/functional groups attached to central atom, etc.). These chemical effects on L X-ray fluorescence parameters have been investigated by performing the measurements at incident photon energies much higher than the Li(i=1-3) sub-shell absorption edge energies using EDXRF spectrometers. In the present work, the cross sections for production of the Lk(k= γ2,3, γ4) X-rays have been measured for some compounds of 66Dy, namely, Dy2O3, Dy2(CO3)3, Dy2(SO4)3.8H2O, DyI2 and Dy metal by tuning the incident photon energies few eV above the L1 absorption-edge energy in order to investigate the influence of chemical effects on these cross sections in presence of the many body effects which become significant at photon energies close to the absorption-edge energies. The present measurements have been performed under vacuum at the IAEA end-station of the X-ray fluorescence beam line (10.1L) of ELETTRA synchrotron radiation facility (Trieste, Italy) using self-supporting pressed pellet targets (1.3 cm diameter, nominal thicknesses ~ 176 mg/cm2) of 66Dy compounds (procured from Sigma Aldrich) and a metallic foil of 66Dy (nominal thickness ~ 3.9 mg/cm2, procured from Good Fellow, UK). The present measured cross sections have been compared with theoretical values calculated using the Dirac-Hartree-Slater(DHS) model based fluorescence and Coster-Kronig yields, Dirac-Fock(DF) model based X-ray emission rates and two sets of L1 sub-shell photoionization cross sections based on the non-relativistic Hartree-Fock-Slater(HFS) model and those deduced from the self-consistent Dirac-Hartree-Fock(DHF) model based total photoionization cross sections. The present measured XRP cross sections for 66Dy as well as for its compounds for the L2,3 and L4 X-rays, are found to be higher by ~14-36% than the two calculated set values. It is worth to be mentioned that L2,3 and L4 X-ray lines are originated by filling up of the L1 sub-shell vacancies by the outer sub-shell (N2,3 and O2,3) electrons which are much more sensitive to the chemical environment around the central atom. The present observed differences between measured and theoretical values are expected due to combined influence of the many-body effects and the chemical effects.

Keywords: chemical effects, L X-ray production cross sections, Many body effects, Synchrotron radiation

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Authors: Rawan Hakawati, Beatrice Smyth, David Rooney, Geoffrey McCullough

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Targets have been set in the EU to increase the share of renewable energy consumption to 20% by 2020, but developments have not occurred evenly across the member states. Northern Ireland is almost 90% dependent on imported fossil fuels. With such high energy dependency, Northern Ireland is particularly susceptible to the security of supply issues. Linked to fossil fuels are greenhouse gas emissions, and the EU plans to reduce emissions by 20% by 2020. The use of indigenously produced biomass could reduce both greenhouse gas emissions and external energy dependence. With a wide range of both crop and waste feedstock potentially available in Northern Ireland, anaerobic digestion has been put forward as a possible solution for renewable energy production, waste management, and greenhouse gas reduction. Not all feedstock, however, is the same, and an understanding of feedstock suitability is important for both plant operators and policy makers. The aim of this paper is to investigate biomass suitability for anaerobic digestion in Northern Ireland. It is also important that decisions are based on solid scientific evidence. For this reason, the methodology used is multi-criteria decision matrix analysis which takes multiple criteria into account simultaneously and ranks alternatives accordingly. The model uses the weighted sum method (which follows the Entropy Method to measure uncertainty using probability theory) to decide on weights. The Topsis method is utilized to carry out the mathematical analysis to provide the final scores. Feedstock that is currently available in Northern Ireland was classified into two categories: wastes (manure, sewage sludge and food waste) and energy crops, specifically grass silage. To select the most suitable feedstock, methane yield, feedstock availability, feedstock production cost, biogas production, calorific value, produced kilowatt-hours, dry matter content, and carbon to nitrogen ratio were assessed. The highest weight (0.249) corresponded to production cost reflecting a variation of £41 gate fee to 22£/tonne cost. The weights calculated found that grass silage was the most suitable feedstock. A sensitivity analysis was then conducted to investigate the impact of weights. The analysis used the Pugh Matrix Method which relies upon The Analytical Hierarchy Process and pairwise comparisons to determine a weighting for each criterion. The results showed that the highest weight (0.193) corresponded to biogas production indicating that grass silage and manure are the most suitable feedstock. Introducing co-digestion of two or more substrates can boost the biogas yield due to a synergistic effect induced by the feedstock to favor positive biological interactions. A further benefit of co-digesting manure is that the anaerobic digestion process also acts as a waste management strategy. From the research, it was concluded that energy from agricultural biomass is highly advantageous in Northern Ireland because it would increase the country's production of renewable energy, manage waste production, and would limit the production of greenhouse gases (current contribution from agriculture sector is 26%). Decision-making methods based on scientific evidence aid policy makers in classifying multiple criteria in a logical mathematical manner in order to reach a resolution.

Keywords: anaerobic digestion, biomass as feedstock, decision matrix, renewable energy

Procedia PDF Downloads 462