Search results for: deep eutectic solvents

Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Danuta Barnat-Hunek

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The studies are devoted to assessing the effectiveness of hydrophobic and air entraining admixtures based on organ silicon compounds. Mortars with lightweight aggregate–perlite were the subjects of the investigation. The following laboratory tests were performed: density, open porosity, total porosity, absorptivity, capability to diffuse water vapour, compressive strength, flexural strength, frost resistance, sodium sulphate corrosion resistance and the thermal conductivity coefficient. The composition of the two mixtures of mortars was prepared: mortars without a hydrophobic admixture and mortars with cementitious waterproofing material. Surface hydrophobisation was produced on the mortars without a hydrophobic admixture using a methyl silicone resin, a water-based emulsion of methyl silicone resin in potassium hydroxide and alkyl-alkoxy-silane in organic solvents. The results of the effectiveness of hydrophobisation of mortars are the following: The highest absorption after 14 days of testing was shown by mortar without an agent (57.5%), while the lowest absorption was demonstrated by the mortar with methyl silicone resin (52.7%). After 14 days in water the hydrophobisation treatment of the samples proved to be ineffective. The hydrophobised mortars are characterized by an insignificant mass change due to freezing and thawing processes in the case of the methyl silicone resin – 1%, samples without hydrophobisation –5%. This agent efficiently protected the mortars against frost corrosion. The standard samples showed very good resistance to the pressure of sodium sulphate crystallization. Organosilicon compounds have a negative influence on the chemical resistance (weight loss about 7%). The mass loss of non-hydrophobic mortar was 2 times lower than mortar with the hydrophobic admixture. Hydrophobic and aeration admixtures significantly affect the thermal conductivity and the difference is mainly due to the difference in porosity of the compared materials. Hydrophobisation of the mortar mass slightly decreased the porosity of the mortar, and thus in an increase of 20% of its compressive strength. The admixture adversely affected the ability of the hydrophobic mortar – it achieved the opposite effect. As a result of hydrophobising the mass, the mortar samples decreased in density and had improved wettability. Poor protection of the mortar surface is probably due to the short time of saturating the sample in the preparation. The mortars were characterized by high porosity (65%) and water absorption (57.5%), so in order to achieve better efficiency, extending the time of hydrophobisation would be advisable. The highest efficiency was obtained for the surface hydrophobised with the methyl silicone resin.

Keywords: hydrophobisation, mortars, salt crystallization, frost resistance

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Authors: ELdessouky Sheta, Sayed Elzomor, Haithem Farghali, Kawkab A. Ahmed, Naglaa A. Abd Elkader

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The present investigation has been dealt with Spirocerca (S.) lupi infested mongrel dogs. This parasitic disease is highly infective to human beings and carnivores. The diagnosis march has been comprised the lateral contrast thoracic radiographs, fecal examination, blood profile, endoscopic examination and histopathological sections of deep seated pinch biopsies. These infested dogs have been put under an adopted treatment with Ivermectin injection combined with oral prednisolone. The obtained results reveal an absence of the pessimistic recognitions particularly after 3 weeks from the onset of treatment. Endoscopically the presented esophageal nodules are marked out in the distal third of infested dogs' esophagus as masses assigned into the esophageal lumen and fundus of stomach. The endoscopic outlook of Spirocerca lupi lesions has been considered an integral procedure of the diagnostic march and for evaluation of treatment follow up. The diagnostic procedures and the recommended treatment are the vet's guidance to care for Spirocerca lupi in dogs, hoping in future to prevent this disease from being spread among human beings and other carnivores.

Keywords: endoscopy, esophagus, stomach spirocercosis, dogs

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Authors: Siyao Zhu, Yifang Xu

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After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. The hands-free requirement from the first responders excludes the use of tedious manual control and operation. In unknown, unstructured, and obstructed environments, natural-language-based supervision is not amenable for first responders to formulate, and is difficult for robots to understand. Brain-computer interface is a promising option to overcome the limitations. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: consensus assessment, electroencephalogram, emergency response, human-robot collaboration, intention recognition, search and rescue

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Authors: Ebasa Girma, Nathnael Minyelshowa, Lebsework Negash

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The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and models them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition, and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests show that the architecture offers a number of advantages that include fault-tolerance, robustness, developer, and user-friendliness.

Keywords: deep learning, multi-UAVs, precision agriculture, UAVs architecture

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Authors: Mariam Dianat Sabet Gilani, Lars M. Blank, Birgitta E. Ebert

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Plant-derived lupane-type, pentacyclic triterpenoids are biologically active compounds that are highly interesting for applications in medical, pharmaceutical, and cosmetic industries. Due to the low abundance of these valuable compounds in their natural sources, and the environmentally harmful downstream process, alternative production methods, such as microbial cell factories, are investigated. Engineered Saccharomyces cerevisiae strains, harboring the heterologous genes for betulinic acid synthesis, can produce up to 2 g L-1 triterpenoids, showing high potential for large-scale production of triterpenoids. One limitation of the microbial synthesis is the intracellular product accumulation. It not only makes cell disruption a necessary step in the downstream processing but also limits productivity and product yield per cell. To overcome these restrictions, the aim of this study is to develop an in-situ extraction method, which extracts triterpenoids into a second organic phase. Such a continuous or sequential product removal from the biomass keeps the cells in an active state and enables extended production time or biomass recycling. After screening of twelve different solvents, selected based on product solubility, biocompatibility, as well as environmental and health impact, isopropyl myristate (IPM) was chosen as a suitable solvent for in-situ product removal from S. cerevisiae. Impedance-based single-cell analysis and off-gas measurement of carbon dioxide emission showed that cell viability and physiology were not affected by the presence of IPM. Initial experiments demonstrated that after the addition of 20 vol % IPM to cultures in the stationary phase, 40 % of the total produced triterpenoids were extracted from the cells into the organic phase. In future experiments, the application of IPM in a repeated batch process will be tested, where IPM is added at the end of each batch run to remove triterpenoids from the cells, allowing the same biocatalysts to be used in several sequential batch steps. Due to its high biocompatibility, the amount of IPM added to the culture can also be increased to more than 20 vol % to extract more than 40 % triterpenoids in the organic phase, allowing the cells to produce more triterpenoids. This highlights the potential for the development of a continuous large-scale process, which allows biocatalysts to produce intracellular products continuously without the necessity of cell disruption and without limitation of the cell capacity.

Keywords: betulinic acid, biocompatible solvent, in-situ extraction, isopropyl myristate, process development, secondary metabolites, triterpenoids, yeast

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Authors: Prashant Dhiman, Viranshu Kumar, Pradeep Joshi

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Lot of research is going on to study the effect of cryogenic treatment on materials. Cryogenic treatment is a heat treatment process which is used widely to enhance the mechanical and metallurgical properties of various materials whether the material is ferrous or non ferrous. In almost all ferrous metals, it is found that retained austenite is converted into martensite. Generally deep cryogenic treatment is done using liquid nitrogen having temperature of -195 ℃. The austenite is unstable at this stage and converts into martensite. In non ferrous materials there presents a microcavity and under the action of stress it becomes crack. When this crack propagates, fracture takes place. As the metal contract under low temperature, by doing cryogenic treatment these microcavities will be filled hence increases the soundness of the material. Properties which are enhanced by cryogenic treatment of both ferrous and non ferrous materials are hardness, tensile strength, wear rate, electrical and thermal conductivity, and others. Also there is decrease in residual stress. A large number of manufacturing process (EDM, CNC etc.) are using cryogenic treatment on different tools or workpiece to reduce their wear. In this Review paper the use of cryogenic heat treatment in different manufacturing has been shown along with their advantages.

Keywords: cyrogenic treatment, EDM (Electrical Discharge Machining), CNC (Computer Numeric Control), Mechanical and Metallurgical Properties

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Authors: Emi Yuda, Yutaka Yoshida, Junichiro Hayano

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Distinct circadian rhythm of activity, i.e., high activity during the day and deep rest at night are a typical feature of a healthy lifestyle. Exposure to the skylight is thought to be an important factor to increase arousal level and maintain normal circadian rhythm. To examine whether sunshine hours influence the day-night contract of activity, we analyzed the relationship between 24-hour heart rate (HR) and weather data of the recording day. We analyzed data in 36,500 males and 49,854 females of Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR) database in Japan. Median (IQR) sunshine duration was 5.3 (2.8-7.9) hr. While sunshine hours had only modest effects of increasing 24-hour average HR in either gender (P=0.0282 and 0.0248 for male and female) and no significant effects on nighttime HR in either gender, it increased daytime HR (P = 0.0007 and 0.0015) and day-night HF difference in both genders (P < 0.0001 for both) even after adjusting for the effects of average temperature, atmospheric pressure, and humidity. Our observations support for the hypothesis that longer sunshine hours enhance circadian rhythm of activity.

Keywords: big data, circadian rhythm, heart rate, sunshine

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Authors: Randy Price, Harbir Antil, Rainald Löhner, Fumiya Togashi

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Chemically reacting flows are common in engineering applications such as hypersonic flow, combustion, explosions, manufacturing process, and environmental assessments. The number of reactions in combustion simulations can exceed 100, making a large number of flow and combustion problems beyond the capabilities of current supercomputers. Motivated by this, deep neural networks (DNNs) will be introduced with the goal of eventually replacing the existing chemistry software packages with DNNs. The DNNs used in this paper are motivated by the Residual Neural Network (ResNet) architecture. In the continuum limit, ResNets become an optimization problem constrained by an ODE. Such a feature allows the use of ODE control techniques to enhance the DNNs. In this work, DNNs are constructed, which update the species un at the nᵗʰ timestep to uⁿ⁺¹ at the n+1ᵗʰ timestep. Parallel DNNs are trained for each species, taking in uⁿ as input and outputting one component of uⁿ⁺¹. These DNNs are applied to multiple species and reactions common in chemically reacting flows such as H₂-O₂ reactions. Experimental results show that the DNNs are able to accurately replicate the dynamics in various situations and in the presence of errors.

Keywords: chemical reacting flows, computational fluid dynamics, ODEs, residual neural networks, ResNets

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Authors: Farhad Ghorbandordinejad, Shamsoddin Aref

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Wikis and blogs, defined as educational tools in line with the objectives of collaborative writing, are regarded as innovative ways of writing addressing the problems of conventional types of writing. Although writing in wikis and blogs step in different contexts, they are both aiming at betterment of collaborative writing procedures. It is believed that due to certain reasons bringing in wikis and blogs to learners' life can lead to better performance of writing. This study aimed at dipping into pedagogical aspects of wikis and blogs in the hope of eliminating prior traditional mistakes and bringing students together in a more constructive L2 context. To this end, three groups of intermediate students were experimented in three settings of wiki-group, blog-group and conventional (control) group. Despite conventional group learners, participants in both experimental groups experienced L2 writing in a new telecollaborative context. An achievement test was administered after the treatment to check learners’ degree of improvement in EFL writing. The results of this study provide a deep insight towards the effectiveness of writing in the contexts of wikis and blogs compared with conventional writing procedures. The overall conclusion drawn from the distinction of conventional writing, on one hand, and wikis and blogs, on the other hand, indicates that the latter channels of writing are more constructive for learners’ writing improvements.

Keywords: collaborative writing, wikis, blogs, writing achievement

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Authors: Barun Chakrabarti, Evangelos Kalamaras, Vladimir Yufit, Xinhua Liu, Billy Wu, Nigel Brandon, C. T. John Low

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In this work, we perform electrophoretic deposition of activated carbon on a number of substrates to prepare symmetrical coin cells for supercapacitor applications. From several recipes that involve the evaluation of a few solvents such as isopropyl alcohol, N-Methyl-2-pyrrolidone (NMP), or acetone to binders such as polyvinylidene fluoride (PVDF) and charging agents such as magnesium chloride, we display a working means for achieving supercapacitors that can achieve 100 F/g in a consistent manner. We then adapt this EPD method to deposit reduced graphene oxide on SGL 10AA carbon paper to achieve cathodic materials for testing in a hydrogen/vanadium flow battery. In addition, a self-supported hierarchical carbon nano-fibre is prepared by means of electrospray deposition of an iron phthalocyanine solution onto a temporary substrate followed by carbonisation to remove heteroatoms. This process also induces a degree of nitrogen doping on the carbon nano-fibres (CNFs), which allows its catalytic performance to improve significantly as detailed in other publications. The CNFs are then used as catalysts by attaching them to graphite felt electrodes facing the membrane inside an all-vanadium flow battery (Scribner cell using serpentine flow distribution channels) and efficiencies as high as 60% is noted at high current densities of 150 mA/cm². About 20 charge and discharge cycling show that the CNF catalysts consistently perform better than pristine graphite felt electrodes. Following this, we also test the CNF as an electro-catalyst in the hydrogen/vanadium flow battery (cathodic side as mentioned briefly in the first paragraph) facing the membrane, based upon past studies from our group. Once again, we note consistently good efficiencies of 85% and above for CNF modified graphite felt electrodes in comparison to 60% for pristine felts at low current density of 50 mA/cm² (this reports 20 charge and discharge cycles of the battery). From this preliminary investigation, we conclude that the CNFs may be used as catalysts for other systems such as vanadium/manganese, manganese/manganese and manganese/hydrogen flow batteries in the future. We are generating data for such systems at present, and further publications are expected.

Keywords: electrospinning, carbon nano-fibres, all-vanadium redox flow battery, hydrogen-vanadium fuel cell, electrocatalysis

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Zi Zhu

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The last decade has witnessed increasing urban environmental uprisings in China, as thousands of citizens swarmed into streets to express their deep concerns about the environmental threat and public health through various collective actions. The prevalent western approaches to collective actions, which usually treat urban riots and social movements as distinct phenomenon, have plagued an adequate analysis of the urban environmental uprisings in China. The increasing urban environmental contention can neither be categorized into riots nor social movements, as they carry the features of both: at first sight, they are spontaneous, disorganized and disruptive with an absence of observable mobilization process; however, unlike riots in the west, these collective actions conveyed explicit demand in a mostly non-destructive way rather than a pure expression of frustration. This article proposes a different approach to urban environmental uprisings in China which concerns the diminishing boundaries between riots and social movements and points to the underlying structural causes to the unique forms of urban environmental contention. Taking the urban anti-PX protests as examples, this article analyzes the societal and political structural environment faced by the Chinese environmental protesters and its influence on the origin and development of their contention.

Keywords: urban environmental uprisings, China, anti-PX protests, opportunity structure

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Authors: Osamede Asowata

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The current resurgence of interest in the use of renewable energy is driven by the need to reduce the high environmental impact of fossil-based energy. The aim of this paper is to evaluate the effect of a stationary PV panel on the charging rate of deep-cycle valve regulated lead-acid (DCVRLA) batteries. Stationary PV panels are set to a fixed tilt and orientation angle, which plays a major role in dictating the output power of a PV panel and subsequently on the charging time of a DCVRLA battery. In a basic PV system, an energy storage device that stores the power from the PV panel is necessary due to the fluctuating nature of the PV voltage caused by climatic conditions. The charging and discharging times of a DCVRLA battery were determined for a twelve month period from January through December 2012. Preliminary results, which include regression analysis (R2), conversion-time per week and work-time per day, indicate that a 36 degrees tilt angle produces a good charging rate for a latitude of 26 degrees south throughout the year.

Keywords: tilt and orientation angles, solar chargers, PV panels, storage devices, direct solar radiation.

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Authors: Aleksandras Velykis, Antanas Satkus

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Clay loam and clay soils are typical for northern Lithuania. These soils are susceptible to physical degradation in the case of intensive use of heavy machinery for field operations. However, clayey soils having poor physical properties by origin require more intensive tillage to maintain proper physical condition for grown crops. Therefore not only choice of suitable tillage system is very important for these soils in the region, but also additional search of other measures is essential for good soil physical state maintenance. Research objective: To evaluate the long-term effects of different intensity tillage as well as its combinations with supplementary agronomic practices on improvement of soil physical conditions and environmental sustainability. The experiment examined the influence of deep and shallow ploughing, ploughless tillage, combinations of ploughless tillage with incorporation of lime sludge and cover crop for green manure and application of the same cover crop for mulch without autumn tillage under spring and winter crop growing conditions on clay loam (27% clay, 50% silt, 23% sand) Endocalcaric Endogleyic Cambisol. Methods: The indicators characterizing the impact of investigated measures were determined using the following methods and devices: Soil dry bulk density – by Eijkelkamp cylinder (100 cm3), soil water content – by weighing, soil structure – by Retsch sieve shaker, aggregate stability – by Eijkelkamp wet sieving apparatus, soil mineral nitrogen – in 1 N KCL extract using colorimetric method. Results: Clay loam soil physical state (dry bulk density, structure, aggregate stability, water content) depends on tillage system and its combination with additional practices used. Application of cover crop winter mulch without tillage in autumn, ploughless tillage and shallow ploughing causes the compaction of bottom (15-25 cm) topsoil layer. However, due to ploughless tillage the soil dry bulk density in subsoil (25-35 cm) layer is less compared to deep ploughing. Soil structure in the upper (0-15 cm) topsoil layer and in the seedbed (0-5 cm), prepared for spring crops is usually worse when applying the ploughless tillage or cover crop mulch without autumn tillage. Application of lime sludge under ploughless tillage conditions helped to avoid the compaction and structure worsening in upper topsoil layer, as well as increase aggregate stability. Application of reduced tillage increased soil water content at upper topsoil layer directly after spring crop sowing. However, due to reduced tillage the water content in all topsoil markedly decreased when droughty periods lasted for a long time. Combination of reduced tillage with cover crop for green manure and winter mulch is significant for preserving the environment. Such application of cover crops reduces the leaching of mineral nitrogen into the deeper soil layers and environmental pollution. This work was supported by the National Science Program ‘The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems’ [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: clay loam, endocalcaric endogleyic cambisol, mineral nitrogen, physical state

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Authors: Reihaneh Mahdavishahri

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This paper provides a brief introduction to emotionally focused therapy (EFT) and its culturally sensitive and informed application when working with immigrant couples. EFT's grounding in humanistic psychology prioritizes a non-pathologizing and empathic understanding of individuals' experiences, creating a safe space for couples to explore and create new experiences without imposing judgment or prescribing the couple "the right way of interacting" with one another. EFT's emphasis on attachment, bonding, emotions, and corrective emotional experiences makes it a fitting approach to work with multicultural couples, allowing for the corrective emotional experience to be shaped and informed by the couples' unique cultural background. This paper highlights the challenges faced by immigrant couples and explores how immigration adds a complex layer to each partner’s sense of self, their attachment bond, and their sense of safety and security within their relationships. Navigating a new culture, creating a shared sense of purpose, and re-establishing emotional bonds can be daunting for immigrant couples, often leading to a deep sense of disconnection and vulnerability. Reestablishing and fostering secure attachment between the partners in the safety of the therapeutic space can be a protective factor for these couples.

Keywords: attachment, culturally informed care, emotionally focused therapy, immigration

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Navajyoti Panda, R. S. Pawar

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In this study, process parameters like punch angle, die opening, grain direction, and pre-bend condition of the strip for deep draw of high strength low alloy steel HSLA 420 are investigated. The finite element method (FEM) in association with the Taguchi and the analysis of variance (ANOVA) techniques are carried out to investigate the degree of importance of process parameters in V-bending process for HSLA 420&ST12 grade material. From results, it is observed that punch angle had a major influence on the spring-back. Die opening also showed very significant role on spring back. On the other hand, it is revealed that grain direction had the least impact on spring back; however, if strip from flat sheet is taken, then it is less prone to spring back as compared to the strip from sheet metal coil. HyperForm software is used for FEM simulation and experiments are designed using Taguchi method. Percentage contribution of the parameters is obtained through the ANOVA techniques.

Keywords: bending, spring-back, v-bending, FEM, Taguchi, HSLA 420 and St12 materials, HyperForm, profile projector

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Authors: Karthick Prasad Gunasekaran, B. Chase Babrich, Saurabh Shirodkar, Hee Hwang

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Construction preparation is crucial for the success of a construction project. By involving project participants early in the construction phase, project managers can plan ahead and resolve issues early, resulting in project success and satisfaction. This study uses quantitative data from construction management projects to determine the relationship between the pre-construction phase, construction schedule, and customer satisfaction. This study examined a total of 65 construction projects and 93 clients per job to (a) identify the relationship between the pre-construction phase and program reduction and (b) the pre-construction phase and customer retention. Based on a quantitative analysis, this study found a negative correlation between pre-construction status and project schedule in 65 construction projects. This finding means that the more preparatory work done on a particular project, the shorter the total construction time. The Net Promoter Score of 93 clients from 65 projects was then used to determine the relationship between construction preparation and client satisfaction. The pre-construction status and the projects were further analyzed, and a positive correlation between them was found. This shows that customers are happier with projects with a higher ready-to-build ratio than projects with less ready-to-build.

Keywords: NLP, BERT, LLM, deep learning, classification

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Authors: S. J. Yune, K. H. Park

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The purpose of this study was to understand the experiences of medical students in volunteer activities and to draw implications for medical education. For this purpose, the questionnaire and the reflection essay on the volunteer experience of 54 students in the first year and 57 students in the second year were analyzed and analyzed. As a result, the participation of the students in the volunteer activities was the highest in the first semester and once a month in the second grade. Activities were mostly through volunteer organizations. The essence of the volunteering activities experience revealed through reflection essays was 'I want to avoid with fear' and 'I feel far away' in the recognition before volunteering activities. In terms of knowledge after participating in volunteer activities, 'breaking eggs and getting to know the world' and 'intellectual growth through social experience' appeared. In terms of attitude, it revealed 'deep reflection on me and others', 'understanding of service life'. And in terms of behavior, 'Begin preparing for a life of service' appeared. The results of this study revealed that volunteering activities provide students with opportunities for growth and development. In order to obtain more meaningful results, consciousness education related to social service should be done in advance.

Keywords: volunteering activity, pre-medical school student, reflection essay, qualitative analysis

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Authors: T. Sadunishvili, L. Kutateladze, T. Urushadze, R. Khvedelidze, N. Zakariashvili, M. Jobava, G. Kvesitadze

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Multiple repeated soil-climatic zones in Georgia determines the diversity of microorganisms. Hundreds of microscopic fungi of different genera have been isolated from different ecological niches, including some extreme environments. Biosynthetic ability of microscopic fungi has been studied. Trichoderma ressei, representative of the Ascomycetes secrete cellulolytic and xylanolytic enzymes that act in synergy to hydrolyze polysaccharide polymers to glucose, xylose and arabinose, which can be fermented to biofuels. The other mesophilic strains producing cellulases are Allesheria terrestris, Chaetomium thermophile, Fusarium oxysporium, Piptoporus betulinus, Penicillium echinulatum, P. purpurogenum, Aspergillus niger, A. wentii, A. versicolor, A. fumigatus etc. In the majority of the cases the cellulases produced by strains of genus Aspergillus usually have high β-glucosidase activity and average endoglucanases levels (with some exceptions), whereas strains representing Trichoderma have high endo enzyme and low β-glucosidase, and hence has limited efficiency in cellulose hydrolysis. Six producers of stable cellulases and xylanases from mesophilic and thermophilic fungi have been selected. By optimization of submerged cultivation conditions, high activities of cellulases and xylanases were obtained. For enzymes purification, their sedimentation by organic solvents such as ethyl alcohol, acetone, isopropanol and by ammonium sulphate in different ratios have been carried out. Best results were obtained with precipitation by ethyl alcohol (1:3.5) and ammonium sulphate. The yields of enzyme according to cellulase activities were 80-85% in both cases. Cellulase activity of enzyme preparation obtained from the strain Trichoderma viride X 33 is 126 U/g, from the strain Penicillium canescence D 85–185U/g and from the strain Sporotrichum pulverulentum T 5-0 110 U/g. Cellulase activity of enzyme preparation obtained from the strain Aspergillus sp. Av10 is 120 U/g, xylanase activity of enzyme preparation obtained from the strain Aspergillus niger A 7-5–1155U/g and from the strain Aspergillus niger Aj 38-1250 U/g. Optimum pH and temperature of operation and thermostability, of the enzyme preparations, were established. The efficiency of hydrolyses of different agricultural residues by the microscopic fungi cellulases has been studied. The glucose yield from the residues as a result of enzymatic hydrolysis is highly determined by the ratio of enzyme to substrate, pH, temperature, and duration of the process. Hydrolysis efficiency was significantly increased as a result of different pretreatment of the residues by different methods. Acknowledgement: The Study was supported by the ISTC project G-2117, funded by Korea.

Keywords: cellulase, xylanase, microscopic fungi, enzymatic hydrolysis

Procedia PDF Downloads 382

Authors: Raju Singh, Shikha Singh

Abstract:

This article is note for managing Autistic Child during the Corona time line. It becomes very critical for the primary caregiver as corona pandemic poses new challenges and completely variety of threats to line of treatment, growth, socialization, mental health for children with autism spectrum disorder (ASD), and, so for the family of the children. It is a highly distressful situation, where the line of treatment has shrunken, physical contact has reduced and therapies footprints reduced in several parts of the world. As children with ASD already face socialization challenges, isolation rules imposed by individuals (or social groups), government agencies have only made the situation worse for the children with ASD and their family. This note will try to touch the basics on understanding the ASD and related development disorders, challenges, impact, and suggest approaches to deal with such situation. This document also covers data analysis, deep dive into the increasing impact of ASD on children. This document can also act as a baseline for many researchers, psychiatrists, psychologists, therapists to view the problem statement and measure its impact.

Keywords: autism spectrum disorder, mental health, applied behavior therapy, occupational therapy, social anxiety

Procedia PDF Downloads 136

Authors: Kobauri Sophio, Kantaria Tengiz, Tugushi David, Puiggali Jordi, Katsarava Ramaz

Abstract:

Biodegradable biomaterials (BB) are of high interest for numerous applications in modern medicine as resorbable surgical materials and drug delivery systems. This kind of materials can be cleared from the body after the fulfillment of their function that excludes a surgical intervention for their removal. One of the most promising BBare amino acids based biodegradable poly(ester amide)s (PEAs) which are composed of naturally occurring (α-amino acids) and non-toxic building blocks such as fatty diols and dicarboxylic acids. Key bis-nucleophilic monomers for synthesizing the PEAs are diamine-diesters-di-p-toluenesulfonic acid salts of bis-(α-amino acid)-alkylenediesters (TAADs) which form the PEAs after step-growth polymerization (polycondensation) with bis-electrophilic counter-partners - activated diesters of dicarboxylic acids. The PEAs combine all advantages of the 'parent polymers' – polyesters (PEs) and polyamides (PAs): Ability of biodegradation (PEs), a high affinity with tissues and a wide range of desired mechanical properties (PAs). The scopes of applications of thePEAs can substantially be expanded by their functionalization, e.g. through the incorporation of hydrophobic fragments into the polymeric backbones. Hydrophobically modified PEAs can form non-covalent adducts with various compounds that make them attractive as drug carriers. For hydrophobic modification of the PEAs, we selected so-called 'Azlactone Method' based on the application of p-phenylene-bis-oxazolinons (bis-azlactones, BALs) as active bis-electrophilic monomers in step-growth polymerization with TAADs. Interaction of BALs with TAADs resulted in the PEAs with low MWs (Mw2,800-19,600 Da) and poor material properties. The high-molecular-weight PEAs (Mw up to 100,000) with desirable material properties were synthesized after replacement of a part of BALs with activated diester - di-p-nitrophenylsebacate, or a part of TAAD with alkylenediamine – 1,6-hexamethylenediamine. The new hydrophobically modified PEAs were characterized by FTIR, NMR, GPC, and DSC. It was shown that after the hydrophobic modification the PEAs retain the biodegradability (in vitro study catalyzed by α-chymptrypsin and lipase), and are of interest for constructing resorbable surgical and pharmaceutical devices including drug delivering containers such as microspheres. The new PEAs are insoluble in hydrophobic organic solvents such as chloroform or dichloromethane (swell only) that allowed elaborating a new technology of fabricating microspheres.

Keywords: amino acids, biodegradable polymers, bis-azlactones, microspheres

Procedia PDF Downloads 167

Authors: Tamas Madl

Abstract:

Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

Procedia PDF Downloads 225

Authors: Aysun Sezer

Abstract:

Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

Procedia PDF Downloads 56

Authors: Hamid R. Chabok, Demetrios N. Christodoulides, Mercedeh Khajavikhan

Abstract:

In recent years, acousto-optic (AO) lenses with tunable foci have emerged as a powerful tool for optical beam shaping, imaging, and particle manipulation. In most current AO lenses, the incident light that propagates orthogonally to a standing ultrasonic wave converts to a Bessel-like beam pattern due to the Raman-Nath effect, thus forming annular fringes that result in compromised focus response. Here, we report a new class of AO dynamic lensing based on generating a 3D-variable refractive index profile via a z-axis-scan ultrasound transducer. By utilizing the co- /counter propagation of light and acoustic waves that interact over a longer distance, the laser beam can be strongly focused in a fully controllable manner. Using this approach, we demonstrate AO lenses with instantaneous extended depth of field (DoF) and laterally localized dynamic focusing. This new light-sound interaction scheme may pave the way towards applications that require remote focusing, 3D micromanipulation, and deep tissue therapy/imaging.

Keywords: acousto-optic, optical beam shaping, dynamic lensing, ultrasound

Procedia PDF Downloads 85

Authors: Cheng Li, Jie Shen, Yutian Tang

Abstract:

Embracing the upcoming era of urbanization with the challenges of limitation of resources, disappearing cultural identities and conflicts among different groups of stakeholders, new integrated approaches are offered in our urban practice to help decision-makers and stakeholders frame and develop well-conceived, practical strategies for urban developing trajectories to approach urban-level sustainability in multiple social, cultural, ecological dimensions. Through bottom-up participation, we take advantage of tourism and leisure industries as driving forces for urbanization in China to promote integrated sustainable systems, with the hope of approaching both historical and ecological aspects of urban sustainability; and also thanks to top-down participation, we have codes, standards and rules established by the governments to strengthen the implementation of ecological urban sustainability. The results are monitored and evaluated experimentally and multidimensionally and the sustainable systems we constructed with local stakeholder groups turned out to be effective. The presentation of our selected projects would indicate our different focuses on urban sustainability.

Keywords: urban sustainability, integrated urban strategy, tourism and leisure industries, history, modern technology

Procedia PDF Downloads 371

Authors: Belayneh Matebie, Michael Melese

Abstract:

The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

Procedia PDF Downloads 35