Search results for: computational linguistics

Authors: Vinko Lešić, Mario Vašak, Anita Martinčević, Marko Gulin, Antonio Starčić, Hrvoje Novak

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With 40% of total world energy consumption, building systems are developing into technically complex large energy consumers suitable for application of sophisticated power management approaches to largely increase the energy efficiency and even make them active energy market participants. Centralized control system of building heating and cooling managed by economically-optimal model predictive control shows promising results with estimated 30% of energy efficiency increase. The research is focused on implementation of such a method on a case study performed on two floors of our faculty building with corresponding sensors wireless data acquisition, remote heating/cooling units and central climate controller. Building walls are mathematically modeled with corresponding material types, surface shapes and sizes. Models are then exploited to predict thermal characteristics and changes in different building zones. Exterior influences such as environmental conditions and weather forecast, people behavior and comfort demands are all taken into account for deriving price-optimal climate control. Finally, a DC microgrid with photovoltaics, wind turbine, supercapacitor, batteries and fuel cell stacks is added to make the building a unit capable of active participation in a price-varying energy market. Computational burden of applying model predictive control on such a complex system is relaxed through a hierarchical decomposition of the microgrid and climate control, where the former is designed as higher hierarchical level with pre-calculated price-optimal power flows control, and latter is designed as lower level control responsible to ensure thermal comfort and exploit the optimal supply conditions enabled by microgrid energy flows management. Such an approach is expected to enable the inclusion of more complex building subsystems into consideration in order to further increase the energy efficiency.

Keywords: price-optimal building climate control, Microgrid power flow optimisation, hierarchical model predictive control, energy efficient buildings, energy market participation

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Authors: Zainab S. Ahmed, Mohammed S. Ali, Nadia A. Elshiekh, Sami Adam Ibrahim, Ghada M. El-Tayeb, Ahmed H. Elsadig, Rihab A. Omer, Sofia B. Mohamed

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Maple syrup urine (MSUD) is an autosomal recessive disease that causes a deficiency in the enzyme branched-chain alpha-keto acid (BCKA) dehydrogenase. The development of disease has been associated with SNPs in the DBT gene. Despite that, the computational analysis of SNPs in coding and noncoding and their functional impacts on protein level still remains unknown. Hence, in this study, we carried out a comprehensive in silico analysis of missense that was predicted to have a harmful influence on DBT structure and function. In this study, eight different in silico prediction algorithms; SIFT, PROVEAN, MutPred, SNP&GO, PhD-SNP, PANTHER, I-Mutant 2.0 and MUpo were used for screening nsSNPs in DBT including. Additionally, to understand the effect of mutations in the strength of the interactions that bind protein together the ELASPIC servers were used. Finally, the 3D structure of DBT was formed using Mutation3D and Chimera servers respectively. Our result showed that a total of 15 nsSNPs confirmed by 4 software (R301C, R376H, W84R, S268F, W84C, F276C, H452R, R178H, I355T, V191G, M444T, T174A, I200T, R113H, and R178C) were found damaging and can lead to a shift in DBT gene structure. Moreover, we found 7 nsSNPs located on the 2-oxoacid_dh catalytic domain, 5 nsSNPs on the E_3 binding domain and 3 nsSNPs on the Biotin Domain. So these nsSNPs may alter the putative structure of DBT’s domain. Furthermore, we detected all these nsSNPs are on the core residues of the protein and have the ability to change the stability of the protein. Additionally, we found W84R, S268F, and M444T have high significance, and they affected Leucine, Isoleucine, and Valine, which reduces or disrupt the function of BCKD complex, E2-subunit which the DBT gene encodes. In conclusion, based on our extensive in-silico analysis, we report 15 nsSNPs that have possible association with protein deteriorating and disease-causing abilities. These candidate SNPs can aid in future studies on Maple Syrup Urine Disease type II base in the genetic level.

Keywords: DBT gene, ELASPIC, in silico analysis, UCSF chimer

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Authors: Xiang Shijie, Zhou Dong, Tian Dan

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This paper focuses on the application of real-time semantic segmentation technology in complex road condition recognition, aiming to address the critical issue of how to improve segmentation accuracy while ensuring real-time performance. Semantic segmentation technology has broad application prospects in fields such as autonomous vehicle navigation and remote sensing image recognition. However, current real-time semantic segmentation networks face significant technical challenges and optimization gaps in balancing speed and accuracy. To tackle this problem, this paper conducts an in-depth study and proposes an innovative Guided Image Reconstruction Module. By resampling high-resolution images into a set of low-resolution images, this module effectively reduces computational complexity, allowing the network to more efficiently extract features within limited resources, thereby improving the performance of real-time segmentation tasks. In addition, a dual-branch network structure is designed in this paper to fully leverage the advantages of different feature layers. A novel Hybrid Attention Mechanism is also introduced, which can dynamically capture multi-scale contextual information and effectively enhance the focus on important features, thus improving the segmentation accuracy of the network in complex road condition. Compared with traditional methods, the proposed model achieves a better balance between accuracy and real-time performance and demonstrates competitive results in road condition segmentation tasks, showcasing its superiority. Experimental results show that this method not only significantly improves segmentation accuracy while maintaining real-time performance, but also remains stable across diverse and complex road conditions, making it highly applicable in practical scenarios. By incorporating the Guided Image Reconstruction Module, dual-branch structure, and Hybrid Attention Mechanism, this paper presents a novel approach to real-time semantic segmentation tasks, which is expected to further advance the development of this field.

Keywords: hybrid attention mechanism, image reconstruction, real-time, road status recognition

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Authors: Heiko Diefenbach, Christoph H. Glock

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Order picking, i.e., the order-based retrieval of items in a warehouse, is an important time- and cost-intensive process for many logistic systems. Despite the ongoing trend of automation, most order picking systems are still manual picker-to-parts systems, where human pickers walk through the warehouse to collect ordered items. Human work in warehouses is not free from errors, and order pickers may at times pick the wrong or the incorrect number of items. Errors can cause additional costs and significant correction efforts. Moreover, age might increase a person’s likelihood to make mistakes. Hence, the negative impact of picking errors might increase for an aging workforce currently witnessed in many regions globally. A significant amount of research has focused on making order picking systems more efficient. Among other factors, storage assignment, i.e., the assignment of items to storage locations (e.g., shelves) within the warehouse, has been subject to optimization. Usually, the objective is to assign items to storage locations such that order picking times are minimized. Surprisingly, there is a lack of research concerned with picking errors and respective prevention approaches. This paper hypothesize that the storage assignment of items can affect the probability of pick errors. For example, storing similar-looking items apart from one other might reduce confusion. Moreover, storing items that are hard to count or require a lot of counting at easy-to-access and easy-to-comprehend self heights might reduce the probability to pick the wrong number of items. Based on this hypothesis, the paper discusses how to incorporate error-prevention measures into mathematical models for storage assignment optimization. Various approaches with respective benefits and shortcomings are presented and mathematically modeled. To investigate the newly developed models further, they are compared to conventional storage assignment strategies in a computational study. The study specifically investigates how the importance of error prevention increases with pickers being more prone to errors due to age, for example. The results suggest that considering error-prevention measures for storage assignment can reduce error probabilities with only minor decreases in picking efficiency. The results might be especially relevant for an aging workforce.

Keywords: an aging workforce, error prevention, order picking, storage assignment

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Authors: Wesley Teskey, Vedran Glavas, Julian Wegener

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Generating battery cathode microstructures is an important area of research, given the proliferation of the use of automotive batteries. Currently, finite element analysis (FEA) is often used for simulations of battery cathode microstructures before physical batteries can be manufactured and tested to verify the simulation results. Unfortunately, a key drawback of using FEA is that this method of simulation is very slow in terms of computational runtime. Generative AI offers the key advantage of speed when compared to FEA, and because of this, generative AI is capable of evaluating very large numbers of candidate microstructures. Given AI generated candidate microstructures, a subset of the promising microstructures can be selected for further validation using FEA. Leveraging the speed advantage of AI allows for a better final microstructural selection because high speed allows for the evaluation of many more candidate microstructures. For the approach presented, battery cathode 3D candidate microstructures are generated using Gaussian Mixture Models (GMMs) and pix2pix. This approach first uses GMMs to generate a population of spheres (representing the “active material” of the cathode). Once spheres have been sampled from the GMM, they are placed within a microstructure. Subsequently, the pix2pix sweeps over the 3D microstructure (iteratively) slice by slice and adds details to the microstructure to determine what portions of the microstructure will become electrolyte and what part of the microstructure will become binder. In this manner, each subsequent slice of the microstructure is evaluated using pix2pix, where the inputs into pix2pix are the previously processed layers of the microstructure. By feeding into pix2pix previously fully processed layers of the microstructure, pix2pix can be used to ensure candidate microstructures represent a realistic physical reality. More specifically, in order for the microstructure to represent a realistic physical reality, the locations of electrolyte and binder in each layer of the microstructure must reasonably match the locations of electrolyte and binder in previous layers to ensure geometric continuity. Using the above outlined approach, a 10x to 100x speed increase was possible when generating candidate microstructures using AI when compared to using a FEA only approach for this task. A key metric for evaluating microstructures was the battery specific power value that the microstructures would be able to produce. The best generative AI result obtained was a 12% increase in specific power for a candidate microstructure when compared to what a FEA only approach was capable of producing. This 12% increase in specific power was verified by FEA simulation.

Keywords: finite element analysis, gaussian mixture models, generative design, Pix2Pix, structural design

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Authors: Munirajulu M, Srikanth Modem

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A large cricket stadium with the capacity to accommodate several thousands of spectators has the seating arena consisting of a two-tier arrangement with an upper and a lower bowl and an intermediate concourse podium level for pedestrian movement to access the bowls. The uniqueness of the stadium is that spectators can have an unobstructed view from all around the podium towards the field of play. Upper and lower bowls are connected by stairs. The stairs landing is a precast slab supported by cantilevered steel beams. These steel beams are fixed to precast columns supporting the stadium structure. The stair slabs are precast concrete supported on a landing slab and cantilevered steel beams. During an event of a fire at podium level between two staircases, fire resistance of steel beams is very critical to life safety. If the steel beam loses its strength due to lack of fire resistance, it will be weak in supporting stair slabs and may lead to a hazard in evacuating occupants from the upper bowl to the lower bowl. In this study, to ascertain fire rating and life safety, a performance-based design using CFD analysis is used to evaluate the steel beams' fire resistance. A fire size of 3.5 MW (convective heat output of fire) with a wind speed of 2.57 m/s is considered for fire and smoke simulation. CFD results show that the smoke temperature near the staircase/ around the staircase does not exceed 1500 C for the fire duration considered. The surface temperature of cantilevered steel beams is found to be less than or equal to 1500 C. Since this temperature is much less than the critical failure temperature of steel (5200 C), it is concluded that the design of structural steel supports on the staircase is adequate and does not need additional fire protection such as fire-resistant coating. CFD analysis provided an engineering basis for the performance-based design of steel structural elements and an opportunity to optimize fire protection requirements. Thus, performance-based design using CFD modeling and simulation of fire and smoke is an innovative way to evaluate fire rating requirements, ascertain life safety and optimize the design with regard to fire protection on structural steel elements.

Keywords: fire resistance, life safety, performance-based design, CFD analysis

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Authors: Daniela Brioschi, Rovilson Mafalda, Silvia Titotto

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In the present days, packages are considered a fundamental element in the commercialization of products and services. A good package is capable of helping to attract new customers and also increasing a product’s purchase intent. In this scenario, packaging design emerges as an important tool, since products and design of their packaging are so interconnected that they are no longer seen as separate elements. Packaging design is, in fact, capable of generating desire for a product. The packaging market for cosmetics, especially makeup market, has also been experiencing an increasing level of sophistication and requirements. Considering packaging represents an important link of communication with the final user and plays a significant role on the sales process, it is of great importance that packages accomplish not only with functional requirements but also with the visual appeal. One of the possibilities for the design of packages and, in this context, packages for make-up, is the bioinspired design – or biomimicry. The bio-inspired design presents a promising paradigm for innovation in both design and sustainable design, by using biological system analogies to develop solutions. It has gained importance as a widely diffused movement in design for environmentally conscious development and is also responsible for several useful and innovative designs. As eye mascara packages are also part of the constant evolution on the design for cosmetics area and the traditional packages present the disadvantage of product drying along time, this project aims to develop a new and innovative package for this product, by using a selected bioinspired design methodology during the development process and also suitable computational tools. In order to guide the development process of the package, it was chosen the spiral methodology, conceived by The Biomimicry Institut, which consists of a reliable tool, since it was based on traditional design methodologies. The spiral design comprises identification, translation, discovery, abstraction, emulation and evaluation steps, that can work iteratively as the process develops as a spiral. As support tool for packaging, 3D modelling is being used by the software Inventor Autodesk Inventor 2018. Although this is an ongoing research, first results showed that spiral methodology design, together with Autodesk Inventor, consist of suitable instruments for the bio-inspired design process, and also nature proved itself to be an amazing and inexhaustible source of inspiration.

Keywords: bio-inspired design, design methodology, packaging, cosmetics

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Authors: Thiago Ribeiro de Alencar, Jacyro Gramulia Junior, Patricia Teixeira Leite

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The area of the electricity sector that deals with energy needs by the hydroelectric in a coordinated manner is called Operation Planning of Hydrothermal Power Systems (OPHPS). The purpose of this is to find a political operative to provide electrical power to the system in a given period, with reliability and minimal cost. Therefore, it is necessary to determine an optimal schedule of generation for each hydroelectric, each range, so that the system meets the demand reliably, avoiding rationing in years of severe drought, and that minimizes the expected cost of operation during the planning, defining an appropriate strategy for thermal complementation. Several optimization algorithms specifically applied to this problem have been developed and are used. Although providing solutions to various problems encountered, these algorithms have some weaknesses, difficulties in convergence, simplification of the original formulation of the problem, or owing to the complexity of the objective function. An alternative to these challenges is the development of techniques for simulation optimization and more sophisticated and reliable, it can assist the planning of the operation. Thus, this paper presents the development of a computational tool, namely Hydro-IA for solving optimization problem identified and to provide the User an easy handling. Adopted as intelligent optimization technique is Genetic Algorithm (GA) and programming language is Java. First made the modeling of the chromosomes, then implemented the function assessment of the problem and the operators involved, and finally the drafting of the graphical interfaces for access to the User. The results with the Genetic Algorithms were compared with the optimization technique nonlinear programming (NLP). Tests were conducted with seven hydroelectric plants interconnected hydraulically with historical stream flow from 1953 to 1955. The results of comparison between the GA and NLP techniques shows that the cost of operating the GA becomes increasingly smaller than the NLP when the number of hydroelectric plants interconnected increases. The program has managed to relate a coherent performance in problem resolution without the need for simplification of the calculations together with the ease of manipulating the parameters of simulation and visualization of output results.

Keywords: energy, optimization, hydrothermal power systems, artificial intelligence and genetic algorithms

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Authors: Debamitra Chakravorty, Pratap K. Parida

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Cold-adapted proteases enhance wash performance in low-temperature laundry resulting in a reduction in energy consumption and wear of textiles and are also used in the dehairing process in leather industries. Unfortunately, the possible drawbacks of using cold-adapted proteases are their instability at higher temperatures. Therefore, proteases with broad temperature stability are required. Unfortunately, wild-type cold-adapted proteases exhibit instability at higher temperatures and thus have low shelf lives. Therefore, attempts to engineer cold-adapted proteases by protein engineering were made previously by directed evolution and random mutagenesis. The lacuna is the time, capital, and labour involved to obtain these variants are very demanding and challenging. Therefore, rational engineering for cold stability without compromising an enzyme's optimum pH and temperature for activity is the current requirement. In this work, mutations were rationally designed with the aid of high throughput computational methodology of network analysis, evolutionary conservation scores, and molecular dynamics simulations for Savinase from Bacillus lentus with the intention of rendering the mutants cold stable without affecting their temperature and pH optimum for activity. Further, an attempt was made to incorporate a mutation in the most stable mutant rationally obtained by this method to introduce oxidative stability in the mutant. Such enzymes are desired in detergents with bleaching agents. In silico analysis by performing 300 ns molecular dynamics simulations at 5 different temperatures revealed that these three mutants were found to be better in cold stability compared to the wild type Savinase from Bacillus lentus. Conclusively, this work shows that cold adaptation without losing optimum temperature and pH stability and additionally stability from oxidative damage can be rationally designed by in silico enzyme engineering. The key findings of this work were first, the in silico data of H5 (cold stable savinase) used as a control in this work, corroborated with its reported wet lab temperature stability data. Secondly, three cold stable mutants of Savinase from Bacillus lentus were rationally identified. Lastly, a mutation which will stabilize savinase against oxidative damage was additionally identified.

Keywords: cold stability, molecular dynamics simulations, protein engineering, rational design

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Authors: Mohanad Rezeq, Tarik Aouam, Frederik Gailly

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In times of armed conflicts, various security checkpoints are placed by authorities to control the flow of merchandise into and within areas of conflict. The flow of humanitarian trucks that is added to the regular flow of commercial trucks, together with the complex security procedures, creates congestion and long waiting times at the security checkpoints. This causes distribution costs to increase and shortages of relief aid to the affected people to occur. Our research proposes a decision-support tool to assist planners and policymakers in building efficient plans for the distribution of relief aid, taking into account congestion at security checkpoints. The proposed tool is built around a multi-item humanitarian distribution planning model based on multi-phase design science methodology that has as its objective to minimize distribution and back ordering costs subject to capacity constraints that reflect congestion effects using nonlinear clearing functions. Using the 2014 Gaza War as a case study, we illustrate the application of the proposed tool, model the underlying relief-aid humanitarian supply chain, estimate clearing functions at different security checkpoints, and conduct computational experiments. The decision support tool generated a shipment plan that was compared to two benchmarks in terms of total distribution cost, average lead time and work in progress (WIP) at security checkpoints, and average inventory and backorders at distribution centers. The first benchmark is the shipment plan generated by the fixed capacity model, and the second is the actual shipment plan implemented by the planners during the armed conflict. According to our findings, modeling and optimizing supply chain flows reduce total distribution costs, average truck wait times at security checkpoints, and average backorders when compared to the executed plan and the fixed-capacity model. Finally, scenario analysis concludes that increasing capacity at security checkpoints can lower total operations costs by reducing the average lead time.

Keywords: humanitarian distribution planning, relief-aid distribution, congestion, clearing functions

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Authors: Chen Wang, Jared Evans, Yan Asmann

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With the quick evolvement of next-generation sequencing techniques, whole-exome or exome-panel data have become a cost-effective way for detection of small exonic mutations, but there has been a growing desire to accurately detect copy number variations (CNVs) as well. In order to address this research and clinical needs, we developed a sequencing coverage pattern-based method not only for copy number detections, data integrity checks, CNV calling, and visualization reports. The developed methodologies include complete automation to increase usability, genome content-coverage bias correction, CNV segmentation, data quality reports, and publication quality images. Automatic identification and removal of poor quality outlier samples were made automatically. Multiple experimental batches were routinely detected and further reduced for a clean subset of samples before analysis. Algorithm improvements were also made to improve somatic CNV detection as well as germline CNV detection in trio family. Additionally, a set of utilities was included to facilitate users for producing CNV plots in focused genes of interest. We demonstrate the somatic CNV enhancements by accurately detecting CNVs in whole exome-wide data from the cancer genome atlas cancer samples and a lymphoma case study with paired tumor and normal samples. We also showed our efficient reuses of existing exome sequencing data, for improved germline CNV calling in a family of the trio from the phase-III study of 1000 Genome to detect CNVs with various modes of inheritance. The performance of the developed method is evaluated by comparing CNV calling results with results from other orthogonal copy number platforms. Through our case studies, reuses of exome sequencing data for calling CNVs have several noticeable functionalities, including a better quality control for exome sequencing data, improved joint analysis with single nucleotide variant calls, and novel genomic discovery of under-utilized existing whole exome and custom exome panel data.

Keywords: bioinformatics, computational genetics, copy number variations, data reuse, exome sequencing, next generation sequencing

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Authors: Ji Zhou, Jung Hee Seo, Rajat Mittal

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Fish move in groups for foraging, reproduction, predator protection, and hydrodynamic efficiency. Schooling's predator protection involves the "many eyes" theory, which increases predator detection probability in a group. Reduced visual signature in a group scales with school size, offering per-capita protection. The ‘confusion effect’ makes it hard for predators to target prey in a group. These benefits, however, all focus on vision-based sensing, overlooking sound-based detection. Fish, including predators, possess sophisticated sensory systems for pressure waves and underwater sound. The lateral line system detects acoustic waves, while otolith organs sense infrasound, and sharks use an auditory system for low-frequency sounds. Among sound generation mechanisms of fish, the mechanism of dipole sound relates to hydrodynamic pressure forces on the body surface of the fish and this pressure would be affected by group swimming. Thus, swimming within a group could affect this hydrodynamic noise signature of fish and possibly serve as an additional protection afforded by schooling, but none of the studies to date have explored this effect. BAUVs with fin-like propulsors could reduce acoustic noise without compromising performance, addressing issues of anthropogenic noise pollution in marine environments. Therefore, in this study, we used our in-house immersed-boundary method flow and acoustic solver, ViCar3D, to simulate fish schools consisting of four swimmers in the classic ‘diamond’ configuration and discussed the feasibility of yielding higher swimming efficiency and controlling far-field sound signature of the school. We examine the effects of the relative phase of fin flapping of the swimmers and the simulation results indicate that the phase of the fin flapping is a dominant factor in both thrust enhancement and the total sound radiated into the far-field by a group of swimmers. For fish in the “diamond” configuration, a suitable combination of the relative phase difference between pairs of leading fish and trailing fish can result in better swimming performance with significantly lower hydroacoustic noise.

Keywords: fish schooling, biopropulsion, hydrodynamics, hydroacoustics

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Authors: Tommaso Adamo, Gianpaolo Ghiani, Antonio Domenico Grieco, Emanuela Guerriero

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Batch production plants provide a wide range of scheduling problems. In pharmaceutical industries a batch process is usually described by a recipe, consisting of an ordering of tasks to produce the desired product. In this research work we focused on pharmaceutical production processes requiring the culture of a microorganism population (i.e. bacteria, yeasts or antibiotics). Several sources of uncertainty may influence the yield of the culture processes, including (i) low performance and quality of the cultured microorganism population or (ii) microbial contamination. For these reasons, robustness is a valuable property for the considered application context. In particular, a robust schedule will not collapse immediately when a cell of microorganisms has to be thrown away due to a microbial contamination. Indeed, a robust schedule should change locally in small proportions and the overall performance measure (i.e. makespan, lateness) should change a little if at all. In this research work we formulated a constraint programming optimization (COP) model for the robust planning of antibiotics production. We developed a discrete-time model with a multi-criteria objective, ordering the different criteria and performing a lexicographic optimization. A feasible solution of the proposed COP model is a schedule of a given set of tasks onto available resources. The schedule has to satisfy tasks precedence constraints, resource capacity constraints and time constraints. In particular time constraints model tasks duedates and resource availability time windows constraints. To improve the schedule robustness, we modeled the concept of (a, b) super-solutions, where (a, b) are input parameters of the COP model. An (a, b) super-solution is one in which if a variables (i.e. the completion times of a culture tasks) lose their values (i.e. cultures are contaminated), the solution can be repaired by assigning these variables values with a new values (i.e. the completion times of a backup culture tasks) and at most b other variables (i.e. delaying the completion of at most b other tasks). The efficiency and applicability of the proposed model is demonstrated by solving instances taken from Sanofi Aventis, a French pharmaceutical company. Computational results showed that the determined super-solutions are near-optimal.

Keywords: constraint programming, super-solutions, robust scheduling, batch process, pharmaceutical industries

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Authors: Jaime Leavitt, Katy Haralampides

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Numerical modelling has been used to investigate the long-term impact of a large dam on downstream wetland areas, specifically in terms of changing sediment dynamics in the system. The Mactaquac Generating Station (MQGS) is a 672MW run-of-the-river hydroelectric facility, commissioned in 1968 on the mainstem of the Wolastoq|Saint John River in New Brunswick, Canada. New Brunswick Power owns and operates the dam and has been working closely with the Canadian Rivers Institute at UNB Fredericton on a multi-year, multi-disciplinary project investigating the impact the dam has on its surrounding environment. With focus on the downstream river, this research discusses the initialization, set-up, calibration, and preliminary results of a 2-D hydrodynamic model using the Delft3d Flexible Mesh Suite (successor of the Delft3d 4 Suite). The flexible mesh allows the model grid to be structured in the main channel and unstructured in the floodplains and other downstream regions with complex geometry. The combination of grid types improves computational time and output. As the movement of water governs the movement of sediment, the calibrated and validated hydrodynamic model was applied to sediment transport simulations, particularly of the fine suspended sediments. Several provincially significant Protected Natural Areas and federally significant National Wildlife Areas are located 60km downstream of the MQGS. These broad, low-lying floodplains and wetlands are known as the Grand Lake Meadows Complex (GLM Complex). There is added pressure to investigate the impacts of river regulation on these protected regions that rely heavily on natural river processes like sediment transport and flooding. It is hypothesized that the fine suspended sediment would naturally travel to the floodplains for nutrient deposition and replenishment, particularly during the freshet and large storms. The purpose of this research is to investigate the impacts of river regulation on downstream environments and use the model as a tool for informed decision making to protect and maintain biologically productive wetlands and floodplains.

Keywords: hydrodynamic modelling, national wildlife area, protected natural area, sediment transport.

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Authors: Yasser Ahmed Mahmoud Ali Helal

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The use of CAD (Computer Aided Design) technology is ubiquitous in the architecture, engineering and construction (AEC) industry. This has led to its inclusion in the curriculum of architecture schools in Nigeria as an important part of the training module. This article examines the ethical issues involved in implementing CAD (Computer Aided Design) content into the architectural education curriculum. Using existing literature, this study begins with the benefits of integrating CAD into architectural education and the responsibilities of different stakeholders in the implementation process. It also examines issues related to the negative use of information technology and the perceived negative impact of CAD use on design creativity. Using a survey method, data from the architecture department of University was collected to serve as a case study on how the issues raised were being addressed. The article draws conclusions on what ensures successful ethical implementation. Millions of people around the world suffer from hepatitis C, one of the world's deadliest diseases. Interferon (IFN) is treatment options for patients with hepatitis C, but these treatments have their side effects. Our research focused on developing an oral small molecule drug that targets hepatitis C virus (HCV) proteins and has fewer side effects. Our current study aims to develop a drug based on a small molecule antiviral drug specific for the hepatitis C virus (HCV). Drug development using laboratory experiments is not only expensive, but also time-consuming to conduct these experiments. Instead, in this in silicon study, we used computational techniques to propose a specific antiviral drug for the protein domains of found in the hepatitis C virus. This study used homology modeling and abs initio modeling to generate the 3D structure of the proteins, then identifying pockets in the proteins. Acceptable lagans for pocket drugs have been developed using the de novo drug design method. Pocket geometry is taken into account when designing ligands. Among the various lagans generated, a new specific for each of the HCV protein domains has been proposed.

Keywords: drug design, anti-viral drug, in-silicon drug design, hepatitis C virus (HCV) CAD (Computer Aided Design), CAD education, education improvement, small-size contractor automatic pharmacy, PLC, control system, management system, communication

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Authors: Maciej Zasuwa, Grzegorz Ptasinski, Antoni Kopyt

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Nowadays flight simulators offer tremendous possibilities for safe and cost-effective pilot training, by utilization of powerful, computational tools. Due to technology outpacing methodology, vast majority of training related work is done by human instructors. It makes assessment not efficient, and vulnerable to instructors’ subjectivity. The research presents an Objective Assessment Tool (gOAT) developed at the Warsaw University of Technology, and tested on SW-4 helicopter flight simulator. The tool uses database of the predefined manoeuvres, defined and integrated to the virtual environment. These were implemented, basing on Aeronautical Design Standard Performance Specification Handling Qualities Requirements for Military Rotorcraft (ADS-33), with predefined Mission-Task-Elements (MTEs). The core element of the gOAT enhanced algorithm that provides instructor a new set of information. In details, a set of objective flight parameters fused with report about psychophysical state of the pilot. While the pilot performs the task, the gOAT system automatically calculates performance using the embedded algorithms, data registered by the simulator software (position, orientation, velocity, etc.), as well as measurements of physiological changes of pilot’s psychophysiological state (temperature, sweating, heart rate). Complete set of measurements is presented on-line to instructor’s station and shown in dedicated graphical interface. The presented tool is based on open source solutions, and flexible for editing. Additional manoeuvres can be easily added using guide developed by authors, and MTEs can be changed by instructor even during an exercise. Algorithm and measurements used allow not only to implement basic stress level measurements, but also to reduce instructor’s workload significantly. Tool developed can be used for training purpose, as well as periodical checks of the aircrew. Flexibility and ease of modifications allow the further development to be wide ranged, and the tool to be customized. Depending on simulation purpose, gOAT can be adjusted to support simulator of aircraft, helicopter, or unmanned aerial vehicle (UAV).

Keywords: automated assessment, flight simulator, human factors, pilot training

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Authors: Yena Lee, Vassilis M. Charitopoulos, Karthik Thyagarajan, Ian Morris, Jose M. Pinto, Lazaros G. Papageorgiou

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With globalisation, the need to have better coordination of production and distribution decisions has become increasingly important for industrial gas companies in order to remain competitive in the marketplace. In this work, we investigate a problem that integrates production, inventory, and routing decisions in a liquid oxygen supply chain. The oxygen supply chain consists of production facilities, external third-party suppliers, and multiple customers, including hospitals and industrial customers. The product produced by the plants or sourced from the competitors, i.e., third-party suppliers, is distributed by a fleet of heterogenous vehicles to satisfy customer demands. The objective is to minimise the total operating cost involving production, third-party, and transportation costs. The key decisions for production include production and inventory levels and product amount from third-party suppliers. In contrast, the distribution decisions involve customer allocation, delivery timing, delivery amount, and vehicle routing. The optimisation of the coordinated production, inventory, and routing decisions is a challenging problem, especially when dealing with large-size problems. Thus, we present a two-stage procedure to solve the integrated problem efficiently. First, the problem is formulated as a mixed-integer linear programming (MILP) model by simplifying the routing component. The solution from the first-stage MILP model yields the optimal customer allocation, production and inventory levels, and delivery timing and amount. Then, we fix the previous decisions and solve a detailed routing. In the second stage, we propose a column generation scheme to address the computational complexity of the resulting detailed routing problem. A case study considering a real-life oxygen supply chain in the UK is presented to illustrate the capability of the proposed models and solution method. Furthermore, a comparison of the solutions from the proposed approach with the corresponding solutions provided by existing metaheuristic techniques (e.g., guided local search and tabu search algorithms) is presented to evaluate the efficiency.

Keywords: production planning, inventory routing, column generation, mixed-integer linear programming

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Authors: Zeina Bou Diab, Arij Daou

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The neural mechanisms of sequential behaviors are intensively studied, with songbirds a focus for learned vocal production. We are studying the premotor nucleus HVC at a nexus of multiple pathways contributing to song learning and production. The HVC consists of multiple classes of neuronal populations, each has its own cellular, electrophysiological and functional properties. During singing, a large subset of motor cortex analog-projecting HVCRA neurons emit a single 6-10 ms burst of spikes at the same time during each rendition of song, a large subset of basal ganglia-projecting HVCX neurons fire 1 to 4 bursts that are similarly time locked to vocalizations, while HVCINT neurons fire tonically at average high frequency throughout song with prominent modulations whose timing in relation to song remains unresolved. This opens the opportunity to define models relating explicit HVC circuitry to how these neurons work cooperatively to control learning and singing. We developed conductance-based Hodgkin-Huxley models for the three classes of HVC neurons (based on the ion channels previously identified from in vitro recordings) and connected them in several physiologically realistic networks (based on the known synaptic connectivity and specific glutaminergic and gabaergic pharmacology) via different architecture patterning scenarios with the aim to replicate the in vivo firing patterning behaviors. We are able, through these networks, to reproduce the in vivo behavior of each class of HVC neurons, as shown by the experimental recordings. The different network architectures developed highlight different mechanisms that might be contributing to the propagation of sequential neural activity (continuous or punctate) in the HVC and to the distinctive firing patterns that each class exhibits during singing. Examples of such possible mechanisms include: 1) post-inhibitory rebound in HVCX and their population patterns during singing, 2) different subclasses of HVCINT interacting via inhibitory-inhibitory loops, 3) mono-synaptic HVCX to HVCRA excitatory connectivity, and 4) structured many-to-one inhibitory synapses from interneurons to projection neurons, and others. Replication is only a preliminary step that must be followed by model prediction and testing.

Keywords: computational modeling, neural networks, temporal neural sequences, ionic currents, songbird

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Authors: Jose Daniel Giraldo Arias, Camilo Rojas Gomez, David Villegas Delgado, Gullermo Idarraga Alarcon, Juan Meza Meza

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The reverse engineering processes are widely used in the industry with the main goal to determine the materials and the manufacture used to produce a component. There are a lot of characterization techniques and computational tools that are used in order to get this information. A study case of a reverse engineering applied to a secondary sandwich- hybrid type structure used in a helicopter is presented. The methodology used consists of five main steps, which can be applied to any other similar component: Collect information about the service conditions of the part, disassembly and dimensional characterization, functional characterization, material properties characterization and manufacturing processes characterization, allowing to obtain all the supports of the traceability of the materials and processes of the aeronautical products that ensure their airworthiness. A detailed explanation of each step is covered. Criticality and comprehend the functionalities of each part, information of the state of the art and information obtained from interviews with the technical groups of the helicopter’s operators were analyzed,3D optical scanning technique, standard and advanced materials characterization techniques and finite element simulation allow to obtain all the characteristics of the materials used in the manufacture of the component. It was found that most of the materials are quite common in the aeronautical industry, including Kevlar, carbon, and glass fibers, aluminum honeycomb core, epoxy resin and epoxy adhesive. The stacking sequence and volumetric fiber fraction are a critical issue for the mechanical behavior; a digestion acid method was used for this purpose. This also helps in the determination of the manufacture technique which for this case was Vacuum Bagging. Samples of the material were manufactured and submitted to mechanical and environmental tests. These results were compared with those obtained during reverse engineering, which allows concluding that the materials and manufacture were correctly determined. Tooling for the manufacture was designed and manufactured according to the geometry and manufacture process requisites. The part was manufactured and the mechanical, and environmental tests required were also performed. Finally, a geometric characterization and non-destructive techniques allow verifying the quality of the part.

Keywords: reverse engineering, sandwich-structured composite parts, helicopter, mechanical properties, prototype

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Authors: William Waddington, M. Jahir Rizvi

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Sail used to propel a vessel functions in a similar way to an aircraft wing. Traditionally, cloth and ropes were used to produce sails. However, there is one major problem with traditional sail design, the increase in turbulence and flow separation when compared to that of an aircraft wing with the same camber. This has led to the development of the solid wing sail focusing mainly on the sail shape. Traditional cloth sails are manufactured as a single element whereas solid wing sail is made of two segments. To the authors’ best knowledge, the phenomena behind the performances of this type of sail at various angles of wind direction with respect to a sailing vessel’s direction (known as the angle of attack) is still an area of mystery. Hence, in this study, the thrusts of a sailing vessel produced by wing sails constructed with various angles (22°, 24°, 26° and 28°) between the two segments have been compared to that of a traditional cloth sail made of carbon-fiber material. The reason for using carbon-fiber material is to achieve the correct and the exact shape of a commercially available mainsail. NACA 0024 and NACA 0016 foils have been used to generate two-segment wing sail shape which incorporates a flap between the first and the second segments. Both the two-dimensional and the three-dimensional sail models designed in commercial CAD software Solidworks have been analyzed through Computational Fluid Dynamics (CFD) techniques using Ansys CFX considering an apparent wind speed of 20.55 knots with an apparent wind angle of 31°. The results indicate that the thrust from traditional sail increases from 8.18 N to 8.26 N when the angle of attack is increased from 5° to 7°. However, the thrust value decreases if the angle of attack is further increased. A solid wing sail which possesses 20° angle between its two segments, produces thrusts from 7.61 N to 7.74 N with an increase in the angle of attack from 7° to 8°. The thrust remains steady up to 9° angle of attack and drops dramatically beyond 9°. The highest thrust values that can be obtained for the solid wing sails with 22°, 24°, 26° and 28° angle respectively between the two segments are 8.75 N, 9.10 N, 9.29 N and 9.19 N respectively. The optimum angle of attack for each of the solid wing sails is identified as 7° at which these thrust values are obtained. Therefore, it can be concluded that all the thrust values predicted for the solid wing sails of angles between the two segments above 20° are higher compared to the thrust predicted for the traditional sail. However, the best performance from a solid wing sail is expected when the sail is created with an angle between the two segments above 20° but below or equal to 26°. In addition, 1/29th scale models in the wind tunnel have been tested to observe the flow behaviors around the sails. The experimental results support the numerical observations as the flow behaviors are exactly the same.

Keywords: CFD, drag, sailing vessel, thrust, traditional sail, wing sail

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Authors: Ruchi Pathania, Ahmad Ahmad, Shireesh Srivastava

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Cyanobacteria is a promising microbe that can capture and convert atmospheric CO₂ and light into valuable industrial bio-products like biofuels, biodegradable plastics, etc. Among their most attractive traits are faster autotrophic growth, whole year cultivation using non-arable land, high photosynthetic activity, much greater biomass and productivity and easy for genetic manipulations. Cyanobacteria store carbon in the form of glycogen which can be hydrolyzed to release glucose and fermented to form bioethanol or other valuable products. Marine cyanobacterial species are especially attractive for countries with scarcity of freshwater. We recently identified a marine native cyanobacterium Synechococcus sp. BDU 130192 which has good growth rate and high level of polyglucans accumulation compared to Synechococcus PCC 7002. In this study, firstly we sequenced the whole genome and the sequences were annotated using the RAST server. Genome scale metabolic model (GSMM) was reconstructed through COBRA toolbox. GSMM is a computational representation of the metabolic reactions and metabolites of the target strain. GSMMs construction through the application of Flux Balance Analysis (FBA), which uses external nutrient uptake rates and estimate steady state intracellular and extracellular reaction fluxes, including maximization of cell growth. The model, which we have named isyn942, includes 942 reactions and 913 metabolites having 831 metabolic, 78 transport and 33 exchange reactions. The phylogenetic tree obtained by BLAST search revealed that the strain was a close relative of Synechococcus PCC 7002. The flux balance analysis (FBA) was applied on the model iSyn942 to predict the theoretical yields (mol product produced/mol CO₂ consumed) for native and non-native products like acetone, butanol, etc. under phototrophic condition by applying metabolic engineering strategies. The reported strain can be a viable strain for biotechnological applications, and the model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for enhanced production of various bioproducts.

Keywords: cyanobacteria, flux balance analysis, genome scale metabolic model, metabolic engineering

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Authors: Jean Yves Trepanier, Sami Ammar, Sagnik Banik

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Lattice Boltzmann Method has been advantageous in simulating complex boundary conditions and solving for fluid flow parameters by streaming and collision processes. This paper includes the study of three different test cases in a confined domain using the method of the Lattice Boltzmann model. 1. An SRT (Single Relaxation Time) approach in the Lattice Boltzmann model is used to simulate Lid Driven Cavity flow for different Reynolds Number (100, 400 and 1000) with a domain aspect ratio of 1, i.e., square cavity. A moment-based boundary condition is used for more accurate results. 2. A Thermal Lattice BGK (Bhatnagar-Gross-Krook) Model is developed for the Rayleigh Benard convection for both test cases - Horizontal and Vertical Temperature difference, considered separately for a Boussinesq incompressible fluid. The Rayleigh number is varied for both the test cases (10^3 ≤ Ra ≤ 10^6) keeping the Prandtl number at 0.71. A stability criteria with a precise forcing scheme is used for a greater level of accuracy. 3. The phase change problem governed by the heat-conduction equation is studied using the enthalpy based Lattice Boltzmann Model with a single iteration for each time step, thus reducing the computational time. A double distribution function approach with D2Q9 (density) model and D2Q5 (temperature) model are used for two different test cases-the conduction dominated melting and the convection dominated melting. The solidification process is also simulated using the enthalpy based method with a single distribution function using the D2Q5 model to provide a better understanding of the heat transport phenomenon. The domain for the test cases has an aspect ratio of 2 with some exceptions for a square cavity. An approximate velocity scale is chosen to ensure that the simulations are within the incompressible regime. Different parameters like velocities, temperature, Nusselt number, etc. are calculated for a comparative study with the existing works of literature. The simulated results demonstrate excellent agreement with the existing benchmark solution within an error limit of ± 0.05 implicates the viability of this method for complex fluid flow problems.

Keywords: BGK, Nusselt, Prandtl, Rayleigh, SRT

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Authors: Ella Sèdé Maforikan

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Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested.

Keywords: land cover mapping, Google Earth Engine, random forest, Beterou catchment

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Authors: Lucas Caldas, Pablo Paulse, Karla Hora

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Shipping containers are present in different Brazilian cities, firstly used for transportation purposes, but which become waste materials and an environmental burden in their end-of-life cycle. In the last decade, in Brazil, some buildings made partly or totally from shipping containers started to appear, most of them for commercial and office uses. Although the use of a reused container for buildings seems a sustainable solution, it is very important to measure the thermal and energy aspects when they are used as such. In this context, this study aims to evaluate the thermal and energy performance of an office building totally made from a 12-meter-long, High Cube 40’ shipping container in different Brazilian Bioclimatic Zones. Four different constructive solutions, mostly used in Brazil were chosen: (1) container without any covering; (2) with internally insulated drywall; (3) with external fiber cement boards; (4) with both drywall and fiber cement boards. For this, the DesignBuilder with EnergyPlus was used for the computational simulation in 8760 hours. The EnergyPlus Weather File (EPW) data of six Brazilian capital cities were considered: Curitiba, Sao Paulo, Brasilia, Campo Grande, Teresina and Rio de Janeiro. Air conditioning appliance (split) was adopted for the conditioned area and the cooling setpoint was fixed at 25°C. The coefficient of performance (CoP) of air conditioning equipment was set as 3.3. Three kinds of solar absorptances were verified: 0.3, 0.6 and 0.9 of exterior layer. The building in Teresina presented the highest level of energy consumption, while the one in Curitiba presented the lowest, with a wide range of differences in results. The constructive option of external fiber cement and drywall presented the best results, although the differences were not significant compared to the solution using just drywall. The choice of absorptance showed a great impact in energy consumption, mainly compared to the case of containers without any covering and for use in the hottest cities: Teresina, Rio de Janeiro, and Campo Grande. This study brings as the main contribution the discussion of constructive aspects for design guidelines for more energy-efficient container buildings, considering local climate differences, and helps the dissemination of this cleaner constructive practice in the Brazilian building sector.

Keywords: bioclimatic zones, Brazil, shipping containers, thermal and energy performance

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Authors: Peter Edwar Mortada Nasif

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The use of CAD (Computer Aided Design) technology is ubiquitous in the architecture, engineering and construction (AEC) industry. This has led to its inclusion in the curriculum of architecture schools in Nigeria as an important part of the training module. This article examines the ethical issues involved in implementing CAD (Computer Aided Design) content into the architectural education curriculum. Using existing literature, this study begins with the benefits of integrating CAD into architectural education and the responsibilities of different stakeholders in the implementation process. It also examines issues related to the negative use of information technology and the perceived negative impact of CAD use on design creativity. Using a survey method, data from the architecture department of Chukwuemeka Odumegwu Ojukwu Uli University was collected to serve as a case study on how the issues raised were being addressed. The article draws conclusions on what ensures successful ethical implementation. Millions of people around the world suffer from hepatitis C, one of the world's deadliest diseases. Interferon (IFN) is treatment options for patients with hepatitis C, but these treatments have their side effects. Our research focused on developing an oral small molecule drug that targets hepatitis C virus (HCV) proteins and has fewer side effects. Our current study aims to develop a drug based on a small molecule antiviral drug specific for the hepatitis C virus (HCV). Drug development using laboratory experiments is not only expensive, but also time-consuming to conduct these experiments. Instead, in this in silicon study, we used computational techniques to propose a specific antiviral drug for the protein domains of found in the hepatitis C virus. This study used homology modeling and abs initio modeling to generate the 3D structure of the proteins, then identifying pockets in the proteins. Acceptable lagans for pocket drugs have been developed using the de novo drug design method. Pocket geometry is taken into account when designing ligands. Among the various lagans generated, a new specific for each of the HCV protein domains has been proposed.

Keywords: drug design, anti-viral drug, in-silicon drug design, hepatitis C virus, computer aided design, CAD education, education improvement, small-size contractor automatic pharmacy, PLC, control system, management system, communication

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Authors: Bastian Vollrath, Hartwig Hubel

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In case of over-elastic and cyclic loading, strain may accumulate due to a ratcheting mechanism until the state of shakedown is possibly achieved. Load history dependent numerical investigations by a step-by-step analysis are rather costly in terms of engineering time and numerical effort. In the case of multi-parameter loading, where various independent loadings affect the final state of shakedown, the computational effort becomes an additional challenge. Therefore, direct methods like the Simplified Theory of Plastic Zones (STPZ) are developed to solve the problem with a few linear elastic analyses. Post-shakedown quantities such as strain ranges and cyclic accumulated strains are calculated approximately by disregarding the load history. The STPZ is based on estimates of a transformed internal variable, which can be used to perform modified elastic analyses, where the elastic material parameters are modified, and initial strains are applied as modified loading, resulting in residual stresses and strains. The STPZ already turned out to work well with respect to cyclic loading between two states of loading. Usually, few linear elastic analyses are sufficient to obtain a good approximation to the post-shakedown quantities. In a multi-dimensional load domain, the approximation of the transformed internal variable transforms from a plane problem into a hyperspace problem, where time-consuming approximation methods need to be applied. Therefore, a solution restricted to structures with four stress components was developed to estimate the transformed internal variable by means of three-dimensional vector algebra. This paper presents the extension to cyclic multi-parameter loading so that an unlimited number of load cases can be taken into account. The theoretical basis and basic presumptions of the Simplified Theory of Plastic Zones are outlined for the case of elastic shakedown. The extension of the method to many load cases is explained, and a workflow of the procedure is illustrated. An example, adopting the FE-implementation of the method into ANSYS and considering multilinear hardening is given which highlights the advantages of the method compared to incremental, step-by-step analysis.

Keywords: cyclic loading, direct method, elastic shakedown, multi-parameter loading, STPZ

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Authors: Mariam Matiashvili

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Argumentation is an integral part of our daily communications - formal or informal. Argumentative reasoning, techniques, and language tools are used both in personal conversations and in the business environment. Verbalization of the opinions requires the use of extraordinary syntactic-pragmatic structural quantities - arguments that add credibility to the statement. The study of argumentative structures allows us to identify the linguistic features that make the text argumentative. Knowing what elements make up an argumentative text in a particular language helps the users of that language improve their skills. Also, natural language processing (NLP) has become especially relevant recently. In this context, one of the main emphases is on the computational processing of argumentative texts, which will enable the automatic recognition and analysis of large volumes of textual data. The research deals with the linguistic analysis of the argumentative structures of Georgian political speeches - particularly the linguistic structure, characteristics, and functions of the parts of the argumentative text - claims, support, and attack statements. The research aims to describe the linguistic cues that give the sentence a judgmental/controversial character and helps to identify reasoning parts of the argumentative text. The empirical data comes from the Georgian Political Corpus, particularly TV debates. Consequently, the texts are of a dialogical nature, representing a discussion between two or more people (most often between a journalist and a politician). The research uses the following approaches to identify and analyze the argumentative structures Lexical Classification & Analysis - Identify lexical items that are relevant in argumentative texts creating process - Creating the lexicon of argumentation (presents groups of words gathered from a semantic point of view); Grammatical Analysis and Classification - means grammatical analysis of the words and phrases identified based on the arguing lexicon. Argumentation Schemas - Describe and identify the Argumentation Schemes that are most likely used in Georgian Political Speeches. As a final step, we analyzed the relations between the above mentioned components. For example, If an identified argument scheme is “Argument from Analogy”, identified lexical items semantically express analogy too, and they are most likely adverbs in Georgian. As a result, we created the lexicon with the words that play a significant role in creating Georgian argumentative structures. Linguistic analysis has shown that verbs play a crucial role in creating argumentative structures.

Keywords: georgian, argumentation schemas, argumentation structures, argumentation lexicon

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Authors: Daphne Meza, Louie Abejar, David A. Rubenstein, Wei Yin

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Endothelial cell (ECs) morphology and function is highly impacted by the mechanical stresses these cells experience in vivo. Any change in the mechanical environment can trigger pathological EC responses. A detailed understanding of EC morphological response and function upon subjection to individual and simultaneous mechanical stimuli is needed for advancement in mechanobiology and preventive medicine. To investigate this, a programmable device capable of simultaneously applying physiological fluid shear stress (FSS) and cyclic strain (CS) has been developed, characterized and validated. Its validation was performed both experimentally, through tracer tracking, and theoretically, through the use of a computational fluid dynamics model. The effectiveness of the device was evaluated through EC morphology changes under mechanical loading conditions. Changes in cell morphology were evaluated through: cell and nucleus elongation, cell alignment and junctional actin production. The results demonstrated that the combined FSS-CS stimulation induced visible changes in EC morphology. Upon simultaneous fluid shear stress and biaxial tensile strain stimulation, cells were elongated and generally aligned with the flow direction, with stress fibers highlighted along the cell junctions. The concurrent stimulation from shear stress and biaxial cyclic stretch led to a significant increase in cell elongation compared to untreated cells. This, however, was significantly lower than that induced by shear stress alone, indicating that the biaxial tensile strain may counteract the elongating effect of shear stress to maintain the shape of ECs. A similar trend was seen in alignment, where the alignment induced by the concurrent application of shear stress and cyclic stretch fell in between that induced by shear stress and tensile stretch alone, indicating the opposite role shear stress and tensile strain may play in cell alignment. Junctional actin accumulation was increased upon shear stress alone or simultaneously with tensile stretch. Tensile stretch alone did not change junctional actin accumulation, indicating the dominant role of shear stress in damaging EC junctions. These results demonstrate that the shearing-stretching device is capable of applying well characterized dynamic shear stress and tensile strain to cultured ECs. Using this device, EC response to altered mechanical environment in vivo can be characterized in vitro.

Keywords: cyclic stretch, endothelial cells, fluid shear stress, vascular biology

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Jillian M. Hagel, Joseph E. Tucker, Peter J. Facchini

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With the aim of establishing a holistic approach for the treatment of central nervous system (CNS) disorders, we are pursuing a drug development program rapidly progressing through discovery and characterization phases. The drug candidates identified in this program are referred to as neuroplastogens owing to their ability to mediate neuroplasticity, which can be beneficial to patients suffering from anxiety, depression, or posttraumatic stress disorder. These and other related neuropsychiatric conditions are associated with the onset of neuronal atrophy, which is defined as a reduction in the number and/or productivity of neurons. The stimulation of neuroplasticity results in an increase in the connectivity between neurons and promotes the restoration of healthy brain function. We have synthesized a substantial catalogue of proprietary indolethylamine derivatives based on the general structures of serotonin (5-hydroxytryptamine) and psychedelic molecules such as N,N-dimethyltryptamine (DMT) and psilocin (4-hydroxy-DMT) that function as neuroplastogens. A primary objective in our screening protocol is the identification of derivatives associated with a significant reduction in hallucination, which will allow administration of the drug at a dose that induces neuroplasticity and triggers other efficacious outcomes in the treatment of targeted CNS disorders but which does not cause a psychedelic response in the patient. Both neuroplasticity and hallucination are associated with engagement of the 5HT2A receptor, requiring drug candidates differentially coupled to these two outcomes at a molecular level. We use novel and proprietary artificial intelligence algorithms to predict the mode of binding to the 5HT2A receptor, which has been shown to correlate with the hallucinogenic response. Hallucination is tested using the mouse head-twitch response model, whereas mouse marble-burying and sucrose preference assays are used to evaluate anxiolytic and anti-depressive potential. Neuroplasticity is assays using dendritic outgrowth assays and cell-based ELISA analysis. Pharmacokinetics and additional receptor-binding analyses also contribute the selection of lead candidates. A summary of the program is presented.

Keywords: neuroplastogen, non-hallucinogenic, drug development, anxiety, depression, PTSD, indolethylamine derivatives, psychedelic-inspired, 5-HT2A receptor, computational chemistry, head-twitch response behavioural model, neurite outgrowth assay

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