Search results for: Computational Fluid Dynamics (CFD

Authors: Felipe M. de Freitas, Ícaro V. Soares, Lucas L. L. Fortes, Sandro T. M. Gonçalves, Úrsula D. C. Resende

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There is a dispersed energy in Radio Frequencies (RF) that can be reused to power electronics circuits such as: sensors, actuators, identification devices, among other systems, without wire connections or a battery supply requirement. In this context, there are different types of energy harvesting systems, including rectennas, coil systems, graphene and new materials. A secondary step of an energy harvesting system is the rectification of the collected signal which may be carried out, for example, by the combination of one or more Schottky diodes connected in series or shunt. In the case of a rectenna-based system, for instance, the diode used must be able to receive low power signals at ultra-high frequencies. Therefore, it is required low values of series resistance, junction capacitance and potential barrier voltage. Due to this low-power condition, voltage multiplier configurations are used such as voltage doublers or modified bridge converters. Lowpass filter (LPF) at the input, DC output filter, and a resistive load are also commonly used in the rectifier design. The electronic circuits projects are commonly analyzed through simulation in SPICE (Simulation Program with Integrated Circuit Emphasis) environment. Despite the remarkable potential of SPICE-based simulators for complex circuit modeling and analysis of quasi-static electromagnetic fields interaction, i.e., at low frequency, these simulators are limited and they cannot model properly applications of microwave hybrid circuits in which there are both, lumped elements as well as distributed elements. This work proposes, therefore, the electromagnetic modelling of electronic components in order to create models that satisfy the needs for simulations of circuits in ultra-high frequencies, with application in rectifiers coupled to antennas, as in energy harvesting systems, that is, in rectennas. For this purpose, the numerical method FDTD (Finite-Difference Time-Domain) is applied and SPICE computational tools are used for comparison. In the present work, initially the Ampere-Maxwell equation is applied to the equations of current density and electric field within the FDTD method and its circuital relation with the voltage drop in the modeled component for the case of lumped parameter using the FDTD (Lumped-Element Finite-Difference Time-Domain) proposed in for the passive components and the one proposed in for the diode. Next, a rectifier is built with the essential requirements for operating rectenna energy harvesting systems and the FDTD results are compared with experimental measurements.

Keywords: energy harvesting system, LE-FDTD, rectenna, rectifier, wireless power systems

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Authors: Nikhilesh Singh, Shishir Gaur, Anitha K. Sharma

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Climate change and anthropogenic stress have severely affected ecosystems all over the globe. Indian rivers are under immense pressure, facing challenges like pollution, encroachment, extreme fluctuation in the flow regime, local ignorance and lack of coordination between stakeholders. To counter all these issues a holistic river rejuvenation plan is needed that tests, innovates and implements sustainable solutions in the river space for sustainable river management. Smart Laboratory for Clean Rivers (SLCR) an Indo-Danish collaboration project, provides a living lab setup that brings all the stakeholders (government agencies, academic and industrial partners and locals) together to engage, learn, co-creating and experiment for a clean and sustainable river that last for ages. Just like every mega project requires piloting, SLCR has opted for a small catchment of the Varuna River, located in the Middle Ganga Basin in India. Considering the integrated approach of river rejuvenation, SLCR embraces various techniques and upgrades for rejuvenation. Likely, maintaining flow in the channel in the lean period, Managed Aquifer Recharge (MAR) is a proven technology. In SLCR, Floa-TEM high-resolution lithological data is used in MAR models to have better decision-making for MAR structures nearby of the river to enhance the river aquifer exchanges. Furthermore, the concerns of quality in the river are a big issue. A city like Varanasi which is located in the last stretch of the river, generates almost 260 MLD of domestic waste in the catchment. The existing STP system is working at full efficiency. Instead of installing a new STP for the future, SLCR is upgrading those STPs with an IoT-based system that optimizes according to the nutrient load and energy consumption. SLCR also advocate nature-based solutions like a reed bed for the drains having less flow. In search of micropollutants, SLCR uses fingerprint analysis involves employing advanced techniques like chromatography and mass spectrometry to create unique chemical profiles. However, rejuvenation attempts cannot be possible without involving the entire catchment. A holistic water management plan that includes storm management, water harvesting structure to efficiently manage the flow of water in the catchment and installation of several buffer zones to restrict pollutants entering into the river. Similarly, carbon (emission and sequestration) is also an important parameter for the catchment. By adopting eco-friendly practices, a ripple effect positively influences the catchment's water dynamics and aids in the revival of river systems. SLCR has adopted 4 villages to make them carbon-neutral and water-positive. Moreover, for the 24×7 monitoring of the river and the catchment, robust IoT devices are going to be installed to observe, river and groundwater quality, groundwater level, river discharge and carbon emission in the catchment and ultimately provide fuel for the data analytics. In its completion, SLCR will provide a river restoration manual, which will strategise the detailed plan and way of implementation for stakeholders. Lastly, the entire process is planned in such a way that will be managed by local administrations and stakeholders equipped with capacity-building activity. This holistic approach makes SLCR unique in the field of river rejuvenation.

Keywords: sustainable management, holistic approach, living lab, integrated river management

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Authors: Sif J. Friis, Mette Poulsen, Torben Strom Hansen, Peter Herskind, Jens V. Nygaard

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Gastric biomechanics governs a large range of scientific and engineering fields, from gastric health issues to interaction mechanisms between external devices and the tissue. Determination of mechanical properties of the stomach is, thus, crucial, both for understanding gastric pathologies as well as for the development of medical concepts and device designs. Although the field of gastric biomechanics is emerging, advances within medical devices interacting with the gastric tissue could greatly benefit from an increased understanding of tissue anisotropy and heterogeneity. Thus, in this study, uniaxial tensile tests of gastric tissue were executed in order to study biomechanical properties within the same individual as well as across individuals. With biomechanical tests in the strain domain, tissue from the antrum region of six porcine stomachs was tested using eight samples from each stomach (n = 48). The samples were cut so that they followed dominant fiber orientations. Accordingly, from each stomach, four samples were longitudinally oriented, and four samples were circumferentially oriented. A step-wise stress relaxation test with five incremental steps up to 25 % strain with 200 s rest periods for each step was performed, followed by a 25 % strain ramp test with three different strain rates. Theoretical analysis of the data provided stress-strain/time curves as well as 20 material parameters (e.g., stiffness coefficients, dissipative energy densities, and relaxation time coefficients) used for statistical comparisons between samples from the same stomach as well as in between stomachs. Results showed that, for the 20 material parameters, heterogeneity across individuals, when extracting samples from the same area, was in the same order of variation as the samples within the same stomach. For samples from the same stomach, the mean deviation percentage for all 20 parameters was 21 % and 18 % for longitudinal and circumferential orientations, compared to 25 % and 19 %, respectively, for samples across individuals. This observation was also supported by a nonparametric one-way ANOVA analysis, where results showed that the 20 material parameters from each of the six stomachs came from the same distribution with a level of statistical significance of P > 0.05. Direction-dependency was also examined, and it was found that the maximum stress for longitudinal samples was significantly higher than for circumferential samples. However, there were no significant differences in the 20 material parameters, with the exception of the equilibrium stiffness coefficient (P = 0.0039) and two other stiffness coefficients found from the relaxation tests (P = 0.0065, 0.0374). Nor did the stomach tissue show any significant differences between the three strain-rates used in the ramp test. Heterogeneity within the same region has not been examined earlier, yet, the importance of the sampling area has been demonstrated in this study. All material parameters found are essential to understand the passive mechanics of the stomach and may be used for mathematical and computational modeling. Additionally, an extension of the protocol used may be relevant for compiling a comparative study between the human stomach and the pig stomach.

Keywords: antrum region, gastric biomechanics, loading-unloading, stress relaxation, uniaxial tensile testing

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Authors: Yuri Laevsky, Tatyana Nosova

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The phenomenon of filtration gas combustion (FGC) had been discovered experimentally at the beginning of 80’s of the previous century. It has a number of important applications in such areas as chemical technologies, fire-explosion safety, energy-saving technologies, oil production. From the physical point of view, FGC may be defined as the propagation of region of gaseous exothermic reaction in chemically inert porous medium, as the gaseous reactants seep into the region of chemical transformation. The movement of the combustion front has different modes, and this investigation is focused on the low-velocity regime. The main characteristic of the process is the velocity of the combustion front propagation. Computation of this characteristic encounters substantial difficulties because of the strong heterogeneity of the process. The mathematical model of FGC is formed by the energy conservation laws for the temperature of the porous medium and the temperature of gas and the mass conservation law for the relative concentration of the reacting component of the gas mixture. In this case the homogenization of the model is performed with the use of the two-temperature approach when at each point of the continuous medium we specify the solid and gas phases with a Newtonian heat exchange between them. The construction of a computational scheme is based on the principles of mixed finite element method with the usage of a regular mesh. The approximation in time is performed by an explicit–implicit difference scheme. Special attention was given to determination of the combustion front propagation velocity. Straight computation of the velocity as grid derivative leads to extremely unstable algorithm. It is worth to note that the term ‘front propagation velocity’ makes sense for settled motion when some analytical formulae linking velocity and equilibrium temperature are correct. The numerical implementation of one of such formulae leading to the stable computation of instantaneous front velocity has been proposed. The algorithm obtained has been applied in subsequent numerical investigation of the FGC process. This way the dependence of the main characteristics of the process on various physical parameters has been studied. In particular, the influence of the combustible gas mixture consumption on the front propagation velocity has been investigated. It also has been reaffirmed numerically that there is an interval of critical values of the interfacial heat transfer coefficient at which a sort of a breakdown occurs from a slow combustion front propagation to a rapid one. Approximate boundaries of such an interval have been calculated for some specific parameters. All the results obtained are in full agreement with both experimental and theoretical data, confirming the adequacy of the model and the algorithm constructed. The presence of stable techniques to calculate the instantaneous velocity of the combustion wave allows considering the semi-Lagrangian approach to the solution of the problem.

Keywords: filtration gas combustion, low-velocity regime, mixed finite element method, numerical simulation

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Authors: Maayan Nakash

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The modern workplace is undergoing a revolution, with Generation Z (Gen-Z) at the forefront of this transformative shift. However, empirical investigations specifically targeting the workplace preferences of this generation remain limited. Through direct examination of their tendencies via a survey approach, this study offers vital insights for aligning organizational policies and practices. The results presented in this paper are part of a comprehensive study that explored Gen Z's viewpoints on various employment market aspects, likely to decisively influence the design of future work environments. Data were collected via an online survey distributed among a cohort of 461 individuals from Gen-Z, born between the mid-1990s and 2010, consisting of 241 males (52.28%) and 220 females (47.72%). Responses were gauged using Likert scale statements that probed preferences for teamwork versus individual work, virtual versus personal interactions, and open versus private workspaces. Descriptive statistics and analytical analyses were conducted to pinpoint key patterns. We discovered that a high proportion of respondents (81.99%, n=378) exhibited a preference for teamwork over individual work. Correspondingly, the data indicate strong support for the recognition of team-based tasks as a tool contributing to personal and professional development. In terms of communication, the majority of respondents (61.38%) either disagreed (n=154) or slightly agreed (n=129) with the exclusive reliance on virtual interactions with their organizational peers. This finding underscores that despite technological progress, digital natives place significant value on physical interaction and non-mediated communication. Moreover, we understand that they also value a quiet and private work environment, clearly preferring it over open and shared workspaces. Considering that Gen-Z does not necessarily experience high levels of stress within social frameworks in the workplace, this can be attributed to a desire for a space that allows for focused engagement with work tasks. A One-Sample Chi-Square Test was performed on the observed distribution of respondents' reactions to each examined statement. The results showed statistically significant deviations from a uniform distribution (p<.001), indicating that the response patterns did not occur by chance and that there were meaningful tendencies in the participants' responses. The findings expand the theoretical knowledge base on human resources in the dynamics of a multi-generational workforce, illuminating the values, approaches, and expectations of Gen-Z. Practically, the results may lead organizations to equip themselves with tools to create policies tailored to Gen-Z in the context of workspaces and social needs, which could potentially foster a fertile environment and aid in attracting and retaining young talent. Future studies might include investigating potential mitigating factors, such as cultural influences or individual personality traits, which could further clarify the nuances in Gen-Z's work style preferences. Longitudinal studies tracking changes in these preferences as the generation matures may also yield valuable insights. Ultimately, as the landscape of the workforce continues to evolve, ongoing investigations into the unique characteristics and aspirations of emerging generations remain essential for nurturing harmonious, productive, and future-ready organizational environments.

Keywords: workplace, future of work, generation Z, digital natives, human resources management

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Authors: Muhammad Qasim, Dolors Puigjaner, Josep Maria López, Joan Herrero, Carme Olivé, Gerard Fortuny

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Computational modeling of the human pelvis using the finite element (FE) method has become extremely important to understand the mechanics of prostate motion and deformation when transrectal ultrasound (TRUS) guided biopsy is performed. The number of reliable and validated hyperelastic constitutive FE models of the male pelvis region is limited, and given models did not precisely describe the anatomical behavior of pelvis organs, mainly of the prostate and its neoplasms location. The motion and deformation of the prostate during TRUS-guided biopsy makes it difficult to know the location of potential lesions in advance. When using this procedure, practitioners can only provide roughly estimations for the lesions locations. Consequently, multiple biopsy samples are required to target one single lesion. In this study, the whole pelvis model (comprised of the rectum, bladder, pelvic muscles, prostate transitional zone (TZ), and peripheral zone (PZ)) is used for the simulation results. An isotropic hyperelastic approach (Signorini model) was used for all the soft tissues except the vesical muscles. The vesical muscles are assumed to have a linear elastic behavior due to the lack of experimental data to determine the constants involved in hyperelastic models. The tissues and organ geometry is taken from the existing literature for 3D meshes. Then the biomechanical parameters were obtained under different testing techniques described in the literature. The acquired parametric values for uniaxial stress/strain data are used in the Signorini model to see the anatomical behavior of the pelvis model. The five mesh nodes in terms of small prostate lesions are selected prior to biopsy and each lesion’s final position is targeted when TRUS probe force of 30 N is applied at the inside rectum wall. Code_Aster open-source software is used for numerical simulations. Moreover, the overall effects of pelvis organ deformation were demonstrated when TRUS–guided biopsy is induced. The deformation of the prostate and neoplasms displacement showed that the appropriate material properties to organs altered the resulting lesion's migration parametrically. As a result, the distance traveled by these lesions ranged between 3.77 and 9.42 mm. The lesion displacement and organ deformation are compared and analyzed with our previous study in which we used linear elastic properties for all pelvic organs. Furthermore, the visual comparison of axial and sagittal slices are also compared, which is taken for Magnetic Resource Imaging (MRI) and TRUS images with our preliminary study.

Keywords: code-aster, magnetic resonance imaging, neoplasms, transrectal ultrasound, TRUS-guided biopsy

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Authors: Michael Mederle, Heinz Bernhardt

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The Management of machine fleets or autonomous vehicle control will considerably increase efficiency in future agricultural production. Especially entire process chains, e.g. harvesting complexes with several interacting combine harvesters, grain carts, and removal trucks, provide lots of optimization potential. Organization and pre-planning ensure to get these efficiency reserves accessible. One way to achieve this is to optimize infield path planning. Particularly autonomous machinery requires precise specifications about infield logistics to be navigated effectively and process optimized in the fields individually or in machine complexes. In the past, a lot of theoretical optimization has been done regarding infield logistics, mainly based on field geometry. However, there are reasons why farmers often do not apply the infield strategy suggested by mathematical route planning tools. To make the computational optimization more useful for farmers this study focuses on these influencing factors by expert interviews. As a result practice-oriented navigation not only to the field but also within the field will be possible. The survey study is intended to cover the entire range of German agriculture. Rural mixed farms with simple technology equipment are considered as well as large agricultural cooperatives which farm thousands of hectares using track guidance and various other electronic assistance systems. First results show that farm managers using guidance systems increasingly attune their infield-logistics on direction giving obstacles such as power lines. In consequence, they can avoid inefficient boom flippings while doing plant protection with the sprayer. Livestock farmers rather focus on the application of organic manure with its specific requirements concerning road conditions, landscape terrain or field access points. Cultivation of sugar beets makes great demands on infield patterns because of its particularities such as the row crop system or high logistics demands. Furthermore, several machines working in the same field simultaneously influence each other, regardless whether or not they are of the equal type. Specific infield strategies always are based on interactions of several different influences and decision criteria. Single working steps like tillage, seeding, plant protection or harvest mostly cannot be considered each individually. The entire production process has to be taken into consideration to detect the right infield logistics. One long-term objective of this examination is to integrate the obtained influences on infield strategies as decision criteria into an infield navigation tool. In this way, path planning will become more practical for farmers which is a basic requirement for automatic vehicle control and increasing process efficiency.

Keywords: autonomous vehicle control, infield logistics, path planning, process optimizing

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Authors: Danny Barash

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Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

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Authors: Mostafa Shahriari, Sergio Rojas, David Pardo, Angel Rodriguez- Rozas, Shaaban A. Bakr, Victor M. Calo, Ignacio Muga

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Logging-While Drilling (LWD) is a technique to record down-hole logging measurements while drilling the well. Nowadays, LWD devices (e.g., nuclear, sonic, resistivity) are mostly used commercially for geo-steering applications. Modern borehole resistivity tools are able to measure all components of the magnetic field by incorporating tilted coils. The depth of investigation of LWD tools is limited compared to the thickness of the geological layers. Thus, it is a common practice to approximate the Earth’s subsurface with a sequence of 1D models. For a 1D model, we can reduce the dimensionality of the problem using a Hankel transform. We can solve the resulting system of ordinary differential equations (ODEs) either (a) analytically, which results in a so-called semi-analytic method after performing a numerical inverse Hankel transform, or (b) numerically. Semi-analytic methods are used by the industry due to their high performance. However, they have major limitations, namely: -The analytical solution of the aforementioned system of ODEs exists only for piecewise constant resistivity distributions. For arbitrary resistivity distributions, the solution of the system of ODEs is unknown by today’s knowledge. -In geo-steering, we need to solve inverse problems with respect to the inversion variables (e.g., the constant resistivity value of each layer and bed boundary positions) using a gradient-based inversion method. Thus, we need to compute the corresponding derivatives. However, the analytical derivatives of cross-bedded formation and the analytical derivatives with respect to the bed boundary positions have not been published to the best of our knowledge. The main contribution of this work is to overcome the aforementioned limitations of semi-analytic methods by solving each 1D model (associated with each Hankel mode) using an efficient multi-scale finite element method. The main idea is to divide our computations into two parts: (a) offline computations, which are independent of the tool positions and we precompute only once and use them for all logging positions, and (b) online computations, which depend upon the logging position. With the above method, (a) we can consider arbitrary resistivity distributions along the 1D model, and (b) we can easily and rapidly compute the derivatives with respect to any inversion variable at a negligible additional cost by using an adjoint state formulation. Although the proposed method is slower than semi-analytic methods, its computational efficiency is still high. In the presentation, we shall derive the mathematical variational formulation, describe the proposed multi-scale finite element method, and verify the accuracy and efficiency of our method by performing a wide range of numerical experiments and comparing the numerical solutions to semi-analytic ones when the latest are available.

Keywords: logging-While-Drilling, resistivity measurements, multi-scale finite elements, Hankel transform

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Authors: Simona Maria Ignat

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Bullying online has been an increasing studied topic during the last years. Different approaches, psychological, linguistic, or computational, have been applied. To our best knowledge, a definition and a set of characteristics of phenomenon agreed internationally as a common framework are still waiting for answers. Thus, the objectives of this paper are the identification of bullying utterances on Twitter and their algorithms. This research paper is focused on the identification of words or groups of words, categorized as “utterances”, with bullying effect, from Twitter platform, extracted on a set of legislative criteria. This set is the result of analysis followed by synthesis of law documents on bullying(online) from United States of America, European Union, and Ireland. The outcome is a linguistic corpus with approximatively 10,000 entries. The methods applied to the first objective have been the following. The discourse analysis has been applied in identification of keywords with bullying effect in texts from Google search engine, Images link. Transcription and anonymization have been applied on texts grouped in CL1 (Corpus linguistics 1). The keywords search method and the legislative criteria have been used for identifying bullying utterances from Twitter. The texts with at least 30 representations on Twitter have been grouped. They form the second corpus linguistics, Bullying utterances from Twitter (CL2). The entries have been identified by using the legislative criteria on the the BoW method principle. The BoW is a method of extracting words or group of words with same meaning in any context. The methods applied for reaching the second objective is the conversion of parts of speech to alphabetical and numerical symbols and writing the bullying utterances as algorithms. The converted form of parts of speech has been chosen on the criterion of relevance within bullying message. The inductive reasoning approach has been applied in sampling and identifying the algorithms. The results are groups with interchangeable elements. The outcomes convey two aspects of bullying: the form and the content or meaning. The form conveys the intentional intimidation against somebody, expressed at the level of texts by grammatical and lexical marks. This outcome has applicability in the forensic linguistics for establishing the intentionality of an action. Another outcome of form is a complex of graphemic variations essential in detecting harmful texts online. This research enriches the lexicon already known on the topic. The second aspect, the content, revealed the topics like threat, harassment, assault, or suicide. They are subcategories of a broader harmful content which is a constant concern for task forces and legislators at national and international levels. These topic – outcomes of the dataset are a valuable source of detection. The analysis of content revealed algorithms and lexicons which could be applied to other harmful contents. A third outcome of content are the conveyances of Stylistics, which is a rich source of discourse analysis of social media platforms. In conclusion, this corpus linguistics is structured on legislative criteria and could be used in various fields.

Keywords: corpus linguistics, cyberbullying, legislation, natural language processing, twitter

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Milu T. Cherian, Sergio C. Chai, Morgan A. Casal, Taosheng Chen

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This study aims to use CINPA1, a recently discovered small-molecule inhibitor of the xenobiotic receptor CAR (constitutive androstane receptor) for understanding the binding modes of CAR and to guide CAR-mediated gene expression profiling studies in human primary hepatocytes. CAR and PXR are xenobiotic sensors that respond to drugs and endobiotics by modulating the expression of metabolic genes that enhance detoxification and elimination. Elevated levels of drug metabolizing enzymes and efflux transporters resulting from CAR activation promote the elimination of chemotherapeutic agents leading to reduced therapeutic effectiveness. Multidrug resistance in tumors after chemotherapy could be associated with errant CAR activity, as shown in the case of neuroblastoma. CAR inhibitors used in combination with existing chemotherapeutics could be utilized to attenuate multidrug resistance and resensitize chemo-resistant cancer cells. CAR and PXR have many overlapping modulating ligands as well as many overlapping target genes which confounded attempts to understand and regulate receptor-specific activity. Through a directed screening approach we previously identified a new CAR inhibitor, CINPA1, which is novel in its ability to inhibit CAR function without activating PXR. The cellular mechanisms by which CINPA1 inhibits CAR function were also extensively examined along with its pharmacokinetic properties. CINPA1 binding was shown to change CAR-coregulator interactions as well as modify CAR recruitment at DNA response elements of regulated genes. CINPA1 was shown to be broken down in the liver to form two, mostly inactive, metabolites. The structure-activity differences of CINPA1 and its metabolites were used to guide computational modeling using the CAR-LBD structure. To rationalize how ligand binding may lead to different CAR pharmacology, an analysis of the docked poses of human CAR bound to CITCO (a CAR activator) vs. CINPA1 or the metabolites was conducted. From our modeling, strong hydrogen bonding of CINPA1 with N165 and H203 in the CAR-LBD was predicted. These residues were validated to be important for CINPA1 binding using single amino-acid CAR mutants in a CAR-mediated functional reporter assay. Also predicted were residues making key hydrophobic interactions with CINPA1 but not the inactive metabolites. Some of these hydrophobic amino acids were also identified and additionally, the differential coregulator interactions of these mutants were determined in mammalian two-hybrid systems. CINPA1 represents an excellent starting point for future optimization into highly relevant probe molecules to study the function of the CAR receptor in normal- and pathophysiology, and possible development of therapeutics (for e.g. use for resensitizing chemoresistant neuroblastoma cells).

Keywords: antagonist, chemoresistance, constitutive androstane receptor (CAR), multi-drug resistance, structure activity relationship (SAR), xenobiotic resistance

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Authors: Pawel Magryta, Mateusz Paszko

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In today's world, a big problem of individual mobility, especially in large urban areas, occurs. Commonly used grand way of transport such as buses, trains or cars do not fulfill their tasks, i.e. they are not able to meet the increasing mobility needs of the growing urban population. Additional to that, the limitations of civil infrastructure construction in the cities exist. Nowadays the most common idea is to transfer the part of urban transport on the level of air transport. However to do this, there is a need to develop an individual flying transport vehicle. The biggest problem occurring in this concept is the type of the propulsion system from which the vehicle will obtain a lifting force. Standard propeller drives appear to be too noisy. One of the ideas is to provide the required take-off and flight power by the machine using the innovative ejector system. This kind of the system will be designed through a suitable choice of the three-dimensional geometric structure with special shape of nozzle in order to generate overpressure. The authors idea is to make a device that would allow to cumulate the overpressure using the a five-sided geometrical structure that will be limited on the one side by the blowing flow of air jet. In order to test this hypothesis a computer simulation study of aerodynamic drag of such 3D structures have been made. Based on the results of these studies, the tests on real model were also performed. The final stage of work was a comparative analysis of the results of simulation and real tests. The CFD simulation studies of air flow was conducted using the Star CD - Star Pro 3.2 software. The design of virtual model was made using the Catia v5 software. Apart from the objective to obtain advanced aviation propulsion system, all of the tests and modifications of 3D structures were also aimed at achieving high efficiency of this device while maintaining the ability to generate high value of overpressures. This was possible only in case of a large mass flow rate of air. All these aspects have been possible to verify using CFD methods for observing the flow of the working medium in the tested model. During the simulation tests, the distribution and size of pressure and velocity vectors were analyzed. Simulations were made with different boundary conditions (supply air pressure), but with a fixed external conditions (ambient temp., ambient pressure, etc.). The maximum value of obtained overpressure is 2 kPa. This value is too low to exploit the power of this device for the individual transport vehicle. Both the simulation model and real object shows a linear dependence of the overpressure values obtained from the different geometrical parameters of three-dimensional structures. Application of computational software greatly simplifies and streamlines the design and simulation capabilities. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: aviation propulsion, CFD, 3d structure, aerodynamic drag

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Authors: Roberto de Lieto Vollaro, Emanuele de Lieto Vollaro, Gianluca Coltrinari

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The building sector is responsible, in many industrialized countries, for about 40% of the total energy requirements, so it seems necessary to devote some efforts in this area in order to achieve a significant reduction of energy consumption and of greenhouse gases emissions. The paper presents a study aiming at providing a design methodology able to identify the best configuration of the system building/plant, from a technical, economic and environmentally point of view. Normally, the classical approach involves a building's energy loads analysis under steady state conditions, and subsequent selection of measures aimed at improving the energy performance, based on previous experience made by architects and engineers in the design team. Instead, the proposed approach uses a sequence of two well known scientifically validated calculation methods (TRNSYS and RETScreen), that allow quite a detailed feasibility analysis. To assess the validity of the calculation model, an existing, historical building in Central Italy, that will be the object of restoration and preservative redevelopment, was selected as a case-study. The building is made of a basement and three floors, with a total floor area of about 3,000 square meters. The first step has been the determination of the heating and cooling energy loads of the building in a dynamic regime by means of TRNSYS, which allows to simulate the real energy needs of the building in function of its use. Traditional methodologies, based as they are on steady-state conditions, cannot faithfully reproduce the effects of varying climatic conditions and of inertial properties of the structure. With TRNSYS it is possible to obtain quite accurate and reliable results, that allow to identify effective combinations building-HVAC system. The second step has consisted of using output data obtained with TRNSYS as input to the calculation model RETScreen, which enables to compare different system configurations from the energy, environmental and financial point of view, with an analysis of investment, and operation and maintenance costs, so allowing to determine the economic benefit of possible interventions. The classical methodology often leads to the choice of conventional plant systems, while RETScreen provides a financial-economic assessment for innovative energy systems and low environmental impact. Computational analysis can help in the design phase, particularly in the case of complex structures with centralized plant systems, by comparing the data returned by the calculation model RETScreen for different design options. For example, the analysis performed on the building, taken as a case study, found that the most suitable plant solution, taking into account technical, economic and environmental aspects, is the one based on a CCHP system (Combined Cooling, Heating, and Power) using an internal combustion engine.

Keywords: energy, system, building, cooling, electrical

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Authors: Shatarupa Thakurta Roy

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Along the limelight of mainstream academic practices in Indian art, exist a significant lot of habitual art practices that are mutually susceptible in their contemporary forms. Narrative folk paintings of regional India has successfully dispersed to its audience social messages through pulsating pictures and orations. The paper consists of images from narrative scroll paintings on ‘Ramayana’ theme from various neighboring states as well as districts in India, describing their subtle differences in style of execution, method, and use of material. Despite sharing commonness in the choice of subject matter, habitual and ceremonial Indian folk art in its formative phase thrived within isolated locations to yield in remarkable variety in the art styles. The differences in style took place district wise, cast wise and even gender wise. An open flow is only evident in the contemporary expressions as a result of substantial changes in social structures, mode of communicative devices, cross-cultural exposures and multimedia interactivities. To decipher the complex nature of popular cultural taste of contemporary India it is important to categorically identify its root in vernacular symbolism. The realization of modernity through European primitivism was rather elevated as a perplexed identity in Indian cultural margin in the light of nationalist and postcolonial ideology. To trace the guiding factor that has still managed to obtain ‘Indianness’ in today’s Indian art, researchers need evidences from the past that are yet to be listed in most instances. They are commonly created on ephemeral foundations. The artworks are also found in endangered state and hence, not counted much friendly for frequent handling. The museums are in dearth of proper technological guidelines to preserve them. Even though restoration activities are emerging in the country, the existing withered and damaged artworks are in threat to perish. An immediacy of digital achieving is therefore envisioned as an alternative to save this cultural legacy. The method of this study is, two folded. It primarily justifies the richness of the evidences by conducting categorical aesthetic analysis. The study is supported by comments on the stylistic variants, thematic aspects, and iconographic identities alongside its anthropological and anthropomorphic significance. Further, it explores the possible ways of cultural preservation to ensure cultural sustainability that includes technological intervention in the form of digital transformation as an altered paradigm for better accessibility to the available recourses. The study duly emphasizes on visual description in order to culturally interpret and judge the rare visual evidences following Feldman’s four-stepped method of formal analysis combined with thematic explanation. A habitual design that emerges and thrives within complex social circumstances may experience change placing its principle philosophy at risk by shuffling and altering with time. A tradition that respires in the modern setup struggles to maintain timeless values that operate its creative flow. Thus, the paper hypothesizes the survival and further growth of this practice within the dynamics of time and concludes in realization of the urgency to transform the implicitness of its knowledge into explicit records.

Keywords: aesthetic, identity, implicitness, paradigm

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Authors: Navid Kaboudi, Ali Shayanfar

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Introduction: Feeding infants with safe milk from the beginning of their life is an important issue. Drugs which are used by mothers can affect the composition of milk in a way that is not only unsuitable, but also toxic for infants. Consuming permeable drugs during that sensitive period by mother could lead to serious side effects to the infant. Due to the ethical restrictions of drug testing on humans, especially women, during their lactation period, computational approaches based on structural parameters could be useful. The aim of this study is to develop mechanistic models to predict the M/P ratio of drugs during breastfeeding period based on their structural descriptors. Methods: Two hundred and nine different chemicals with their M/P ratio were used in this study. All drugs were categorized into two groups based on their M/P value as Malone classification: 1: Drugs with M/P>1, which are considered as high risk 2: Drugs with M/P>1, which are considered as low risk Thirty eight chemical descriptors were calculated by ACD/labs 6.00 and Data warrior software in order to assess the penetration during breastfeeding period. Later on, four specific models based on the number of hydrogen bond acceptors, polar surface area, total surface area, and number of acidic oxygen were established for the prediction. The mentioned descriptors can predict the penetration with an acceptable accuracy. For the remaining compounds (N= 147, 158, 160, and 174 for models 1 to 4, respectively) of each model binary regression with SPSS 21 was done in order to give us a model to predict the penetration ratio of compounds. Only structural descriptors with p-value<0.1 remained in the final model. Results and discussion: Four different models based on the number of hydrogen bond acceptors, polar surface area, and total surface area were obtained in order to predict the penetration of drugs into human milk during breastfeeding period About 3-4% of milk consists of lipids, and the amount of lipid after parturition increases. Lipid soluble drugs diffuse alongside with fats from plasma to mammary glands. lipophilicity plays a vital role in predicting the penetration class of drugs during lactation period. It was shown in the logistic regression models that compounds with number of hydrogen bond acceptors, PSA and TSA above 5, 90 and 25 respectively, are less permeable to milk because they are less soluble in the amount of fats in milk. The pH of milk is acidic and due to that, basic compounds tend to be concentrated in milk than plasma while acidic compounds may consist lower concentrations in milk than plasma. Conclusion: In this study, we developed four regression-based models to predict the penetration class of drugs during the lactation period. The obtained models can lead to a higher speed in drug development process, saving energy, and costs. Milk/plasma ratio assessment of drugs requires multiple steps of animal testing, which has its own ethical issues. QSAR modeling could help scientist to reduce the amount of animal testing, and our models are also eligible to do that.

Keywords: logistic regression, breastfeeding, descriptors, penetration

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Keping Zuo, Jia Yin Chia, Gideon Praveen Kumar Vijayakumar, Foad Kabinejadian, Fangsen Cui, Pei Ho, Hwa Liang Leo

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Carotid artery is the major vessel supplying blood to the brain. Carotid artery stenosis is one of the three major causes of stroke and the stroke is the fourth leading cause of death and the first leading cause of disability in most developed countries. Although there is an increasing interest in carotid artery stenting for treatment of cervical carotid artery bifurcation therosclerotic disease, currently available bare metal stents cannot provide an adequate protection against the detachment of the plaque fragments over diseased carotid artery, which could result in the formation of micro-emboli and subsequent stroke. Our research group has recently developed a novel preferential covered-stent for carotid artery aims to prevent friable fragments of atherosclerotic plaques from flowing into the cerebral circulation, and yet retaining the ability to preserve the flow of the external carotid artery. The preliminary animal studies have demonstrated the potential of this novel covered-stent design for the treatment of carotid therosclerotic stenosis. The purpose of this study is to evaluate the biomechanical property of PU membrane of different concentration configurations in order to refine the stent coating technique and enhance the clinical performance of our novel carotid covered stent. Results from this study also provide necessary material property information crucial for accurate simulation analysis for our stents. Method: Medical grade Polyurethane (ChronoFlex AR) was used to prepare PU membrane specimens. Different PU membrane configurations were subjected to uniaxial test: 22%, 16%, and 11% PU solution were made by mixing the original solution with proper amount of the Dimethylacetamide (DMAC). The specimens were then immersed in physiological saline solution for 24 hours before test. All specimens were moistened with saline solution before mounting and subsequent uniaxial testing. The specimens were preconditioned by loading the PU membrane sample to a peak stress of 5.5 Mpa for 10 consecutive cycles at a rate of 50 mm/min. The specimens were then stretched to failure at the same loading rate. Result: The results showed that the stress-strain response curves of all PU membrane samples exhibited nonlinear characteristic. For the ultimate failure stress, 22% PU membrane was significantly higher than 16% (p<0.05). In general, our preliminary results showed that lower concentration PU membrane is stiffer than the higher concentration one. From the perspective of mechanical properties, 22% PU membrane is a better choice for the covered stent. Interestingly, the hyperelastic Ogden model is able to accurately capture the nonlinear, isotropic stress-strain behavior of PU membrane with R2 of 0.9977 ± 0.00172. This result will be useful for future biomechanical analysis of our stent designs and will play an important role for computational modeling of our covered stent fatigue study.

Keywords: carotid artery, covered stent, nonlinear, hyperelastic, stress, strain

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Authors: Daniel Martin Fellows, Sean P. Walton, Jennifer Thompson, Oubay Hassan, Ella Quigley, Kevin Tinkham

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Fire performance is a crucial aspect to consider when designing cladding products, and testing this performance is extremely expensive. Appropriate use of numerical simulation of fire performance has the potential to reduce the total number of fire tests required when designing a product by eliminating poor-performing design ideas early in the design phase. Due to the complexity of fire and the large spectrum of failures it can cause, multi-disciplinary models are needed to capture the complex fire behavior and its structural effects on its surroundings. Working alongside Tata Steel U.K., the authors have focused on completing a coupled CFD-FEA simulation model suited to test Polyisocyanurate (PIR) based sandwich panel products to gain confidence before costly experimental standards testing. The sandwich panels are part of a thermally insulating façade system primarily for large non-domestic buildings. The work presented in this paper compares two coupling methodologies of a replicated physical experimental standards test LPS 1181-1, carried out by Tata Steel U.K. The two coupling methodologies that are considered within this research are; one-way and two-way. A one-way coupled analysis consists of importing thermal data from the CFD solver into the FEA solver. A two-way coupling analysis consists of continuously importing the updated changes in thermal data, due to the fire's behavior, to the FEA solver throughout the simulation. Likewise, the mechanical changes will also be updated back to the CFD solver to include geometric changes within the solution. For CFD calculations, a solver called Fire Dynamic Simulator (FDS) has been chosen due to its adapted numerical scheme to focus solely on fire problems. Validation of FDS applicability has been achieved in past benchmark cases. In addition, an FEA solver called ABAQUS has been chosen to model the structural response to the fire due to its crushable foam plasticity model, which can accurately model the compressibility of PIR foam. An open-source code called FDS-2-ABAQUS is used to couple the two solvers together, using several python modules to complete the process, including failure checks. The coupling methodologies and experimental data acquired from Tata Steel U.K are compared using several variables. The comparison data includes; gas temperatures, surface temperatures, and mechanical deformation of the panels. Conclusions are drawn, noting improvements to be made on the current coupling open-source code FDS-2-ABAQUS to make it more applicable to Tata Steel U.K sandwich panel products. Future directions for reducing the computational cost of the simulation are also considered.

Keywords: fire engineering, numerical coupling, sandwich panels, thermo fluids

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Authors: Marthinus Conradie

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

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

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Authors: Kaviyarasan D., Satish Kumar S. R.

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India being vast in area and population with great scope of international business, roadways and railways network connection within the country is expected to have a big growth. There are numerous rail-cum-road bridges constructed across many major rivers in India and few are getting very old. So there is more possibility of repairing or coming up with such new bridges in India. Analysis and design of such bridges are practiced through conventional procedure and end up with heavy and uneconomical sections. Such heavy class steel bridges when subjected to high seismic shaking has more chance to fail by stability because the members are too much rigid and stocky rather than being flexible to dissipate the energy. This work is the collective study of the researches done in the truss bridge and steel concrete composite truss bridges presenting the method of analysis, tools for numerical and analytical modeling which evaluates its seismic behaviour and collapse mechanisms. To ascertain the inelastic and nonlinear behaviour of the structure, generally at research level static pushover analysis is adopted. Though the static pushover analysis is now extensively used for the framed steel and concrete buildings to study its lateral action behaviour, those findings by pushover analysis done for the buildings cannot directly be used for the bridges as such, because the bridges have completely a different performance requirement, behaviour and typology as compared to that of the buildings. Long span steel bridges are mostly the truss bridges. Truss bridges being formed by many members and connections, the failure of the system does not happen suddenly with single event or failure of one member. Failure usually initiates from one member and progresses gradually to the next member and so on when subjected to further loading. This kind of progressive collapse of the truss bridge structure is dependent on many factors, in which the live load distribution and span to length ratio are most significant. The ultimate collapse is anyhow by the buckling of the compression members only. For regular bridges, single step pushover analysis gives results closer to that of the non-linear dynamic analysis. But for a complicated bridge like heavy class steel bridge or the skewed bridges or complicated dynamic behaviour bridges, nonlinear analysis capturing the progressive yielding and collapse pattern is mandatory. With the knowledge of the postelastic behaviour of the bridge and advancements in the computational facility, the current level of analysis and design of bridges has moved to state of ascertaining the performance levels of the bridges based on the damage caused by seismic shaking. This is because the buildings performance levels deals much with the life safety and collapse prevention levels, whereas the bridges mostly deal with the extent damages and how quick it can be repaired with or without disturbing the traffic after a strong earthquake event. The paper would compile the wide spectrum of modeling to analysis of the steel concrete composite truss bridges in general.

Keywords: bridge engineering, performance based design of steel truss bridge, seismic design of composite bridge, steel-concrete composite bridge

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Authors: Roni Ramon-Gonen, Amir Dori, Shahar Shelly

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Background and objectives: Healthcare professionals produce abundant textual information in their daily clinical practice. The extraction of insights from all the gathered information, mainly unstructured and lacking in normalization, is one of the major challenges in computational medicine. In this respect, text mining assembles different techniques to derive valuable insights from unstructured textual data, so it has led to being especially relevant in Medicine. Neurological patient’s history allows the clinician to define the patient’s symptoms and along with the result of the nerve conduction study (NCS) and electromyography (EMG) test, assists in formulating a differential diagnosis. Past medical history (PMH) helps to direct the latter. In this study, we aimed to identify relevant PMH, understand which PMHs are common among patients in the referral cohort and documented by the medical staff, and examine the differences by sex and age in a large cohort based on textual format notes. Methods: We retrospectively identified all patients with abnormal NCS between May 2016 to February 2022. Age, gender, and all NCS attributes reports were recorded, including the summary text. All patients’ histories were extracted from the text report by a query. Basic text cleansing and data preparation were performed, as well as lemmatization. Very popular words (like ‘left’ and ‘right’) were deleted. Several words were replaced with their abbreviations. A bag of words approach was used to perform the analyses. Different visualizations which are common in text analysis, were created to easily grasp the results. Results: We identified 5282 unique patients. Three thousand and five (57%) patients had documented PMH. Of which 60.4% (n=1817) were males. The total median age was 62 years (range 0.12 – 97.2 years), and the majority of patients (83%) presented after the age of forty years. The top two documented medical histories were diabetes mellitus (DM) and surgery. DM was observed in 16.3% of the patients, and surgery at 15.4%. Other frequent patient histories (among the top 20) were fracture, cancer (ca), motor vehicle accident (MVA), leg, lumbar, discopathy, back and carpal tunnel release (CTR). When separating the data by sex, we can see that DM and MVA are more frequent among males, while cancer and CTR are less frequent. On the other hand, the top medical history in females was surgery and, after that, DM. Other frequent histories among females are breast cancer, fractures, and CTR. In the younger population (ages 18 to 26), the frequent PMH were surgery, fractures, trauma, and MVA. Discussion: By applying text mining approaches to unstructured data, we were able to better understand which medical histories are more relevant in these circumstances and, in addition, gain additional insights regarding sex and age differences. These insights might help to collect epidemiological demographical data as well as raise new hypotheses. One limitation of this work is that each clinician might use different words or abbreviations to describe the same condition, and therefore using a coding system can be beneficial.

Keywords: abnormal studies, healthcare analytics, medical history, nerve conduction studies, text mining, textual analysis

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Authors: B.L. España-Sánchez, E. Luna-Hernández, R.A. Mauricio-Sánchez, M.E. Cruz-Soto, F. Padilla-Vaca, R. Muñoz, L. Granados-López, L.R. Ovalle-Flores, J.L. Menchaca-Arredondo, G. Luna-Bárcenas

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Treatment of burn injured has been considered an important clinical problem due to the fluid control and the presence of microorganisms during the healing process. Conventional treatment includes antiseptic techniques, topical medication and surgical removal of damaged skin, to avoid bacterial growth. In order to accelerate this process, different alternatives for tissue regeneration have been explored, including artificial skin, polymers, hydrogels and hybrid materials. Some requirements consider a nonreactive organic polymer with high biocompatibility and skin adherence, avoiding bacterial infections. Chitin-derivative biopolymer such as chitosan (CS) has been used in skin regeneration following third-degree burns. The biological interest of CS is associated with the improvement of tissue cell stimulation, biocompatibility and antibacterial properties. In particular, antimicrobial properties of CS can be significantly increased when is blended with nanostructured materials. Silver-based nanocomposites have gained attention in medicine due to their high antibacterial properties against pathogens, related to their high surface area/volume ratio at nanomolar concentrations. Silver nanocomposites can be blended or synthesized with chitin-derivative biopolymers in order to obtain a biodegradable/antimicrobial hybrid with improved physic-mechanical properties. In this study, nanocomposites based on chitosan/silver nanoparticles (CS/nAg) were synthesized by the in situ chemical reduction method, improving their antibacterial properties against pathogenic bacteria and enhancing the healing process in thermal burn injuries produced in an animal model. CS/nAg was prepared in solution by the chemical reduction method, using AgNO₃ as precursor. CS was dissolved in acetic acid and mixed with different molar concentrations of AgNO₃: 0.01, 0.025, 0.05 and 0.1 M. Solutions were stirred at 95°C during 20 hours, in order to promote the nAg formation. CS/nAg solutions were placed in Petri dishes and dried, to obtain films. Structural analyses confirm the synthesis of silver nanoparticles (nAg) by means of UV-Vis and TEM, with an average size of 7.5 nm and spherical morphology. FTIR analyses showed the complex formation by the interaction of hydroxyl and amine groups with metallic nanoparticles, and surface chemical analysis (XPS) shows low concentration of Ag⁰/Ag⁺ species. Topography surface analyses by means of AFM shown that hydrated CS form a mesh with an average diameter of 10 µm. Antibacterial activity against S. aureus and P. aeruginosa was improved in all evaluated conditions, such as nAg loading and interaction time. CS/nAg nanocomposites films did not show Ag⁰/Ag⁺ release in saline buffer and rat serum after exposition during 7 days. Healing process was significantly enhanced by the presence of CS/nAg nanocomposites, inducing the production of myofibloblasts, collagen remodelation, blood vessels neoformation and epidermis regeneration after 7 days of injury treatment, by means of histological and immunohistochemistry assays. The present work suggests that hydrated CS/nAg nanocomposites can be formed a mesh, improving the bacterial penetration and the contact with embedded nAg, producing complete growth inhibition after 1.5 hours. Furthermore, CS/nAg nanocomposites improve the cell tissue regeneration in thermal burn injuries induced in rats. Synthesis of antibacterial, non-toxic, and biocompatible nanocomposites can be an important issue in tissue engineering and health care applications.

Keywords: antibacterial, chitosan, healing process, nanocomposites, silver

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Authors: Karim Hrratha, Khaled Essalahb, Christophe Morellc, Henry Chermettec, Salima Boughdiria

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Among the carbon-carbon bond formation types, allylation of active methylene compounds with cyclic Baylis-Hillman (BH) alcohols is a reliable and widely used method. This reaction is a very attractive tool in organic synthesis of biological and biodiesel compounds. Thus, in view of an insistent and peremptory request for an efficient and straightly method for synthesizing the desired product, a thorough analysis of various aspects of the reaction processes is an important task. The product afforded by the reaction of active methylene with BH alcohols depends largely on the experimental conditions, notably on the catalyst properties. All experiments reported that catalysis is needed for this reaction type because of the poor ability of alcohol hydroxyl group to be as a suitable leaving group. Within the catalysts, several transition- metal based have been used such as palladium in the presence of acid or base and have been considered as reliable methods. Furthemore, acid catalysts such as BF3.OEt2, BiX3 (X= Cl, Br, I, (OTf)3), InCl3, Yb(OTf)3, FeCl3, p-TsOH and H-montmorillonite have been employed to activate the C-C bond formation through the alkylation of active methylene compounds. Interestingly a report of a smoothly process for the ability of 4-imethyaminopyridine(DMAP) to catalyze the allylation reaction of active methylene compounds with cyclic Baylis-Hillman (BH) alcohol appeared recently. However, the reaction mechanism remains ambiguous, since the C- allylation process leads to an unexpected product (noted P1), corresponding to a direct allylation instead of conjugate allylation, which involves the most electrophilic center according to the electron withdrawing group CO effect. The main objective of the present theoretical study is to better understand the role of the DMAP catalytic activity as well as the process leading to the end- product (P1) for the catalytic reaction of a cyclic BH alcohol with active methylene compounds. For that purpose, we have carried out computations of a set of active methylene compounds varying by R1 and R2 toward the same alcohol, and we have attempted to rationalize the mechanisms thanks to the acid–base approach, and conceptual DFT tools such as chemical potential, hardness, Fukui functions, electrophilicity index and dual descriptor, as these approaches have shown a good prediction of reactions products.The present work is then organized as follows: In a first part some computational details will be given, introducing the reactivity indexes used in the present work, then Section 3 is dedicated to the discussion of the prediction of the selectivity and regioselectivity. The paper ends with some concluding remarks. In this work, we have shown, through DFT method at the B3LYP/6-311++G(d,p) level of theory that: The allylation of active methylene compounds with cyclic BH alcohol is governed by orbital control character. Hence the end- product denoted P1 is generated by direct allylation.

Keywords: DFT calculation, gas phase pKa, theoretical mechanism, orbital control, charge control, Fukui function, transition state

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Authors: Piyush Sharma, Laltu Chandra, P. S. Ghoshdastidar, Rajiv Shekhar

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Metals processing operations such as melting and heat treatment of metals are energy-intensive, requiring temperatures greater than 500oC. The desired temperature in these industrial furnaces is attained by circulating electrically-heated air. In most of these furnaces, electricity produced from captive coal-based thermal power plants is used. Solar thermal energy could be a viable heat source in these furnaces. A retrofitted solar convective furnace (SCF) concept, which uses solar thermal generated hot air, has been proposed. Critical to the success of a SCF is the design of an open volumetric air receiver (OVAR), which can heat air in excess of 800oC. The OVAR is placed on top of a tower and receives concentrated solar radiation from a heliostat field. Absorbers, mixer assembly, and the return air flow chamber (RAFC) are the major components of an OVAR. The absorber is a porous structure that transfers heat from concentrated solar radiation to ambient air, referred to as primary air. The mixer ensures uniform air temperature at the receiver exit. Flow of the relatively cooler return air in the RAFC ensures that the absorbers do not fail by overheating. In an earlier publication, the detailed design basis, fabrication, and characterization of a 2 kWth open volumetric air receiver (OVAR) based laboratory solar air tower simulator was presented. Development of an experimentally-validated, CFD based mathematical model which can ultimately be used for the design and scale-up of an OVAR has been the major objective of this investigation. In contrast to the published literature, where flow and heat transfer have been modeled primarily in a single absorber module, the present study has modeled the entire receiver assembly, including the RAFC. Flow and heat transfer calculations have been carried out in ANSYS using the LTNE model. The complex return air flow pattern in the RAFC requires complicated meshes and is computational and time intensive. Hence a simple, realistic 1-D mathematical model, which circumvents the need for carrying out detailed flow and heat transfer calculations, has also been proposed. Several important results have emerged from this investigation. Circumferential electrical heating of absorbers can mimic frontal heating by concentrated solar radiation reasonably well in testing and characterizing the performance of an OVAR. Circumferential heating, therefore, obviates the need for expensive high solar concentration simulators. Predictions suggest that the ratio of power on aperture (POA) and mass flow rate of air (MFR) is a normalizing parameter for characterizing the thermal performance of an OVAR. Increasing POA/MFR increases the maximum temperature of air, but decreases the thermal efficiency of an OVAR. Predictions of the 1-D mathematical are within 5% of ANSYS predictions and computation time is reduced from ~ 5 hours to a few seconds.

Keywords: absorbers, mixer assembly, open volumetric air receiver, return air flow chamber, solar thermal energy

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Authors: Leo Nnamdi Ozurumba-Dwight

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Single-cell genomic analytical systems have proved to be a platform to isolate bulk cells into selected single cells for genomic, proteomic, and related metabolomic studies. This is enabling systematic investigations of the level of heterogeneity in a diverse and wide pool of cell populations. Single cell technologies, embracing techniques such as high parameter flow cytometry, single-cell sequencing, and high-resolution images are playing vital roles in these investigations on messenger ribonucleic acid (mRNA) molecules and related gene expressions in tracking the nature and course of disease conditions. This entails targeted molecular investigations on unit cells that help us understand cell behavoiur and expressions, which can be examined for their health implications on the health state of patients. One of the vital good sides of single-cell RNA sequencing (scRNA seq) is its probing capacity to detect deranged or abnormal cell populations present within homogenously perceived pooled cells, which would have evaded cursory screening on the pooled cell populations of biological samples obtained as part of diagnostic procedures. Despite conduction of just single-cell transcriptome analysis, scRNAseq now permits comparison of the transcriptome of the individual cells, which can be evaluated for gene expressional patterns that depict areas of heterogeneity with pharmaceutical drug discovery and clinical treatment applications. It is vital to strictly work through the tools of investigations from wet lab to bioinformatics and computational tooled analyses. In the precise steps for scRNAseq, it is critical to do thorough and effective isolation of viable single cells from the tissues of interest using dependable techniques (such as FACS) before proceeding to lysis, as this enhances the appropriate picking of quality mRNA molecules for subsequent sequencing (such as by the use of Polymerase Chain Reaction machine). Interestingly, scRNAseq can be deployed to analyze various types of biological samples such as embryos, nervous systems, tumour cells, stem cells, lymphocytes, and haematopoietic cells. In haematopoietic cells, it can be used to stratify acute myeloid leukemia patterns in patients, sorting them out into cohorts that enable re-modeling of treatment regimens based on stratified presentations. In immunotherapy, it can furnish specialist clinician-immunologist with tools to re-model treatment for each patient, an attribute of precision medicine. Finally, the good predictive attribute of scRNAseq can help reduce the cost of treatment for patients, thus attracting more patients who would have otherwise been discouraged from seeking quality clinical consultation help due to perceived high cost. This is a positive paradigm shift for patients’ attitudes primed towards seeking treatment.

Keywords: immunotherapy, transcriptome, re-modeling, mRNA, scRNA-seq

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Authors: Aastha Arora, Vikas Bhuria, Saurabh Singh, Uma Pathak, Shweta Mathur, Puja P. Hazari, Rajat Sandhir, Ravi Soni, Anant N. Bhatt, Bilikere S. Dwarakanath

Abstract:

Radiotherapy is an effective curative and palliative option for patients with thoracic malignancies. However, lung injury, comprising of pneumonitis and fibrosis, remains a significant clin¬ical complication of thoracic radiation, thus making it a dose-limiting factor. Also, injury to the lung is often reported as part of multi-organ failure in victims of accidental radiation exposures. Radiation induced inflammatory response in the lung, characterized by leukocyte infiltration and vascular changes, is an important contributing factor for the injury. Therefore, countermeasure agents to attenuate radiation induced inflammatory response are considered as an important approach to prevent chronic lung damage. Although Amifostine, the widely used, FDA approved radio-protector, has been found to reduce the radiation induced pneumonitis during radiation therapy of non-small cell lung carcinoma, its application during mass and field exposure is limited due to associated toxicity and ineffectiveness with the oral administration. The amifostine analogue (DRDE-30) overcomes this limitation as it is orally effective in reducing the mortality of whole body irradiated mice. The current study was undertaken to investigate the potential of DRDE-30 to ameliorate radiation induced lung damage. DRDE-30 was administered intra-peritoneally, 30 minutes prior to 13.5 Gy thoracic (60Co-gamma) radiation in C57BL/6 mice. Broncheo- alveolar lavage fluid (BALF) and lung tissues were harvested at 12 and 24 weeks post irradiation for studying inflammatory and fibrotic markers. Lactate dehydrogenase (LDH) leakage, leukocyte count and protein content in BALF were used as parameters to evaluate lung vascular permeability. Inflammatory cell signaling (p38 phosphorylation) and anti-oxidant status (MnSOD and Catalase level) was assessed by Western blot, while X-ray CT scan, H & E staining and trichrome staining were done to study the lung architecture and collagen deposition. Irradiation of the lung increased the total protein content, LDH leakage and total leukocyte count in the BALF, reflecting endothelial barrier dysfunction. These disruptive effects were significantly abolished by DRDE-30, which appear to be linked to the DRDE-30 mediated abrogation of activation of the redox-sensitive pro- inflammatory signaling cascade, the MAPK pathway. Concurrent administration of DRDE-30 with radiation inhibited radiation-induced oxidative stress by strengthening the anti-oxidant defense system and abrogated p38 mitogen-activated protein kinase activation, which was associated with reduced vascular leak and macrophage recruitment to the lungs. Histopathological examination (by H & E staining) of the lung showed radiation-induced inflammation of the lungs, characterized by cellular infiltration, interstitial oedema, alveolar wall thickening, perivascular fibrosis and obstruction of alveolar spaces, which were all reduced by pre-administration of DRDE-30. Structural analysis with X-ray CT indicated lung architecture (linked to the degree of opacity) comparable to un-irradiated mice that correlated well with the lung morphology and reduced collagen deposition. Reduction in the radiation-induced inflammation and fibrosis brought about by DRDE-30 resulted in a profound increase in animal survival (72 % in the combination vs 24% with radiation) observed at the end of 24 weeks following irradiation. These findings establish the potential of the Amifostine analogue, DRDE-30, in reducing radiation induced pulmonary injury by attenuating the inflammatory and fibrotic responses.

Keywords: amifostine, fibrosis, inflammation, lung injury radiation

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Authors: Yixin Zhu, Lishengsa Yue, Jian Sun, Lanyue Tang

Abstract:

Modeling driving distraction in microscopic traffic simulation is crucial for enhancing simulation accuracy. Current driving distraction models are mainly derived from physical motion constraints under distracted states, in which distraction-related error terms are added to existing microscopic driver models. However, the model accuracy is not very satisfying, due to a lack of modeling the cognitive mechanism underlying the distraction. This study models driving distraction based on the Queueing Network Human Processor model (QN-MHP). This study utilizes the queuing structure of the model to perform task invocation and switching for distracted operation and control of the vehicle under driver distraction. Based on the assumption of the QN-MHP model about the cognitive sub-network, server F is a structural bottleneck. The latter information must wait for the previous information to leave server F before it can be processed in server F. Therefore, the waiting time for task switching needs to be calculated. Since the QN-MHP model has different information processing paths for auditory information and visual information, this study divides driving distraction into two types: auditory distraction and visual distraction. For visual distraction, both the visual distraction task and the driving task need to go through the visual perception sub-network, and the stimuli of the two are asynchronous, which is called stimulus on asynchrony (SOA), so when calculating the waiting time for switching tasks, it is necessary to consider it. In the case of auditory distraction, the auditory distraction task and the driving task do not need to compete for the server resources of the perceptual sub-network, and their stimuli can be synchronized without considering the time difference in receiving the stimuli. According to the Theory of Planned Behavior for drivers (TPB), this study uses risk entropy as the decision criterion for driver task switching. A logistic regression model is used with risk entropy as the independent variable to determine whether the driver performs a distraction task, to explain the relationship between perceived risk and distraction. Furthermore, to model a driver’s perception characteristics, a neurophysiological model of visual distraction tasks is incorporated into the QN-MHP, and executes the classical Intelligent Driver Model. The proposed driving distraction model integrates the psychological cognitive process of a driver with the physical motion characteristics, resulting in both high accuracy and interpretability. This paper uses 773 segments of distracted car-following in Shanghai Naturalistic Driving Study data (SH-NDS) to classify the patterns of distracted behavior on different road facilities and obtains three types of distraction patterns: numbness, delay, and aggressiveness. The model was calibrated and verified by simulation. The results indicate that the model can effectively simulate the distracted car-following behavior of different patterns on various roadway facilities, and its performance is better than the traditional IDM model with distraction-related error terms. The proposed model overcomes the limitations of physical-constraints-based models in replicating dangerous driving behaviors, and internal characteristics of an individual. Moreover, the model is demonstrated to effectively generate more dangerous distracted driving scenarios, which can be used to construct high-value automated driving test scenarios.

Keywords: computational cognitive model, driving distraction, microscopic traffic simulation, psychological-physical constraints

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Authors: Mohammed Makki, Milad Showkatbakhsh, Aiman Tabony

Abstract:

Computational applications of natural evolutionary processes as problem-solving tools have been well established since the mid-20th century. However, their application within architecture and design has only gained ground in recent years, with an increasing number of academics and professionals in the field electing to utilize evolutionary computation to address problems comprised from multiple conflicting objectives with no clear optimal solution. Recent advances in computer science and its consequent constructive influence on the architectural discourse has led to the emergence of multiple algorithmic processes capable of simulating the evolutionary process in nature within an efficient timescale. Many of the developed processes of generating a population of candidate solutions to a design problem through an evolutionary based stochastic search process are often driven through the application of both environmental and architectural parameters. These methods allow for conflicting objectives to be simultaneously, independently, and objectively optimized. This is an essential approach in design problems with a final product that must address the demand of a multitude of individuals with various requirements. However, one of the main challenges encountered through the application of an evolutionary process as a design tool is the ability for the simulation to maintain variation amongst design solutions in the population while simultaneously increasing in fitness. This is most commonly known as the ‘golden rule’ of balancing exploration and exploitation over time; the difficulty of achieving this balance in the simulation is due to the tendency of either variation or optimization being favored as the simulation progresses. In such cases, the generated population of candidate solutions has either optimized very early in the simulation, or has continued to maintain high levels of variation to which an optimal set could not be discerned; thus, providing the user with a solution set that has not evolved efficiently to the objectives outlined in the problem at hand. As such, the experiments presented in this paper seek to achieve the ‘golden rule’ by incorporating a mathematical fitness criterion for the development of an urban tissue comprised from the superblock as its primary architectural element. The mathematical value investigated in the experiments is the standard deviation factor. Traditionally, the standard deviation factor has been used as an analytical value rather than a generative one, conventionally used to measure the distribution of variation within a population by calculating the degree by which the majority of the population deviates from the mean. A higher standard deviation value delineates a higher number of the population is clustered around the mean and thus limited variation within the population, while a lower standard deviation value is due to greater variation within the population and a lack of convergence towards an optimal solution. The results presented will aim to clarify the extent to which the utilization of the standard deviation factor as a fitness criterion can be advantageous to generating fitter individuals in a more efficient timeframe when compared to conventional simulations that only incorporate architectural and environmental parameters.

Keywords: architecture, computation, evolution, standard deviation, urban

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Authors: Oleg Reva, Ilya Korotetskiy, Marina Lankina, Murat Kulmanov, Aleksandr Ilin

Abstract:

Molecular docking approaches are widely used for design of new antibiotics and modeling of antibacterial activities of numerous ligands which bind specifically to active centers of indispensable enzymes and/or key signaling proteins of pathogens. Widespread drug resistance among pathogenic microorganisms calls for development of new antibiotics specifically targeting important metabolic and information pathways. A generally recognized problem is that almost all molecular targets have been identified already and it is getting more and more difficult to design innovative antibacterial compounds to combat the drug resistance. A promising way to overcome the drug resistance problem is an induction of reversion of drug resistance by supplementary medicines to improve the efficacy of the conventional antibiotics. In contrast to well established computer-based drug design, modeling of drug resistance reversion still is in its infancy. In this work, we proposed an approach to identification of compensatory genetic variants reducing the fitness cost associated with the acquisition of drug resistance by pathogenic bacteria. The approach was based on an analysis of the population genetic of Mycobacterium tuberculosis and on results of experimental modeling of the drug resistance reversion induced by a new anti-tuberculosis drug FS-1. The latter drug is an iodine-containing nanomolecular complex that passed clinical trials and was admitted as a new medicine against MDR-TB in Kazakhstan. Isolates of M. tuberculosis obtained on different stages of the clinical trials and also from laboratory animals infected with MDR-TB strain were characterized by antibiotic resistance, and their genomes were sequenced by the paired-end Illumina HiSeq 2000 technology. A steady increase in sensitivity to conventional anti-tuberculosis antibiotics in series of isolated treated with FS-1 was registered despite the fact that the canonical drug resistance mutations identified in the genomes of these isolates remained intact. It was hypothesized that the drug resistance phenotype in M. tuberculosis requires an adjustment of activities of many genes to compensate the fitness cost of the drug resistance mutations. FS-1 cased an aggravation of the fitness cost and removal of the drug-resistant variants of M. tuberculosis from the population. This process caused a significant increase in genetic heterogeneity of the Mtb population that was not observed in the positive and negative controls (infected laboratory animals left untreated and treated solely with the antibiotics). A large-scale search for linkage disequilibrium associations between the drug resistance mutations and genetic variants in other genomic loci allowed identification of target proteins, which could be influenced by supplementary drugs to increase the fitness cost of the drug resistance and deprive the drug-resistant bacterial variants of their competitiveness in the population. The approach will be used to improve the efficacy of FS-1 and also for computer-based design of new drugs to combat drug-resistant infections.

Keywords: complete genome sequencing, computational modeling, drug resistance reversion, Mycobacterium tuberculosis

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