Search results for: artificial neural network modeling

Authors: Amani Abdallah, Isam Shahrour

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The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration.

Keywords: distribution system, drinking water, refraction index, sensor, real-time

Procedia PDF Downloads 355

Authors: Farzingohar Mehrnaz, Yasemi Mehran, Esmaili Zinat, Baharlouian Maedeh

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Aboozar (Ardeshir) and Bahregansar are the two important Iranian oilfields in Persian Gulf waters. The operation activities cause to create spills which impacted on the marine environment. Assumed spills are molded by ADIOS II (Automated Data Inquiry for Oil Spills) which is NOAA’s weathering oil software. Various atmospheric and marine data with different oil types are used for the modeling. Numerous scenarios for 100 bbls with mean daily air temperature and wind speed are input for 5 days. To find the model sensitivity in each setting, one parameter is changed, but the others stayed constant. In both fields, the evaporated and dispersed output values increased hence the remaining rate is reduced. The results clarified that wind speed first, second air temperature and finally oil type respectively were the most effective factors on the oil weathering process. The obtained results can help the emergency systems to predict the floating (dispersed and remained) volume spill in order to find the suitable cleanup tools and methods.

Keywords: ADIOS, modeling, oil spill, sensitivity analysis

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Authors: Ameen Jameel Alawneh

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A Mobile ad-hoc network (MANET) is a collection of wireless mobile hosts that dynamically form a temporary network without the aid of a system administrator. It has neither fixed infrastructure nor wireless ad hoc sessions. It inherently reaches several nodes with a single transmission, and each node functions as both a host and a router. The network maybe represented as a set of clusters each managed by clusterhead. The cluster size is not fixed and it depends on the movement of nodes. We proposed a clusterhead size algorithm (CHSize). This clustering algorithm can be used by several routing algorithms for ad hoc networks. An elected clusterhead is assigned for communication with all other clusters. Analysis and simulation of the algorithm has been implemented using GloMoSim networks simulator, MATLAB and MAPL11 proved that the proposed algorithm achieves the goals.

Keywords: simulation, MANET, Ad-hoc, cluster head size, linked cluster algorithm, loss and dropped packets

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Authors: Ousmane Youme, Jean Marie Dembele, Eugene Ezin, Christophe Cambier

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In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

Keywords: CNN architecture, genetic algorithm, evolution algorithm, length constraints

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Authors: Ahmed Atef, Mohamed Alhasheem, Eman Beshr

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In this paper, an enhanced DC microgrid design is proposed using the DAB converter as a conversion unit in order to harvest the maximum power from the PV array. Each connected DAB converter is controlled with an enhanced control strategy. The controller is based on the artificial intelligence (AI) technique to regulate the terminal PV voltage through the phase shift angle of each DAB converter. In this manner, no need for a Maximum Power Point Tracking (MPPT) unit to set the reference of the PV terminal voltage. This strategy overcomes the stability issues of the DC microgrid as the response of converters is superior compared to the conventional strategies. The proposed PV interface system is modelled and simulated using MATLAB/SIMULINK. The simulation results reveal an accurate and fast response of the proposed design in case of irradiance changes.

Keywords: DC microgrid, DAB converter, parallel operation, artificial intelligence, fast response

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Authors: Laaziz El Hassan

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The Service Network Design Problem (SNDP) is a tactical issue in freight transportation firms. The existing formulations of the problem for intermodal rail-road transportation were not always adapted to the intermodality in terms of full asset utilization and modal shift reinforcement. The objective of the article is to propose a model having a more compliant formulation with intermodality, including constraints highlighting the imperatives of asset management, reinforcing modal shift from road to rail and reducing, by the way, road mode CO2 emissions. The model is a fixed charged, path based integer nonlinear program. Its objective is to minimize services total cost while ensuring full assets utilization to satisfy freight demand forecast. The model's main feature is that it gives as output both the train sizes and the services frequencies for a planning period. We solved the program using a commercial solver and discussed the numerical results.

Keywords: intermodal transport network, service network design, model, nonlinear integer program, path-based, service frequencies, modal shift

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Authors: Sandra Baez, Mariana Pino, Mildred Berrio, Hernando Santamaria-Garcia, Lucas Sedeno, Adolfo Garcia, Sol Fittipaldi, Agustin Ibanez

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Schadenfreude refers to the perceiver’s experience of pleasure at another’s misfortune. This is a multidetermined emotion which can be evoked by hostile feelings and envy. The experience of Schadenfreude engages mechanisms implicated in diverse social cognitive processes. For instance, Schadenfreude involves heightened reward processing, accompanied by increased striatal engagement and it interacts with mentalizing and perspective-taking abilities. Patients with Huntington's disease (HD) exhibit reductions of Schadenfreude experience, suggesting a role of striatal degeneration in such an impairment. However, no study has directly assessed the relationship between regional brain atrophy in HD and reduced Schadenfreude. This study investigated whether gray matter (GM) atrophy in HD patients correlates with ratings of Schadenfreude. First, we compared the performance of 20 HD patients and 23 controls on an experimental task designed to trigger Schadenfreude and envy (another social emotion acting as a control condition). Second, we compared GM volume between groups. Third, we examined brain regions where atrophy might be associated with specific impairments in the patients. Results showed that while both groups showed similar ratings of envy, HD patients reported lower Schadenfreude. The latter pattern was related to atrophy in regions of the reward system (ventral striatum) and the mentalizing network (precuneus and superior parietal lobule). Our results shed light on the intertwining of reward and socioemotional processes in Schadenfreude, while offering novel evidence about their neural correlates. In addition, our results open the door to future studies investigating social emotion processing in other clinical populations characterized by striatal or mentalizing network impairments (e.g., Parkinson’s disease, schizophrenia, autism spectrum disorders).

Keywords: envy, Gray matter atrophy, Huntigton's disease, Schadenfreude, social emotions

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Authors: Balamurugan L.

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Artificial intelligence (AI) and its applications in Intellectual Property Rights (IPR) has been significantly growing in recent years. In last couple of years, AI tools for Patent Research and Patent Analytics have been well-stabilized in terms of accuracy of references and representation of identified patent insights. However, AI tools for Patent Prosecution and Patent Litigation are still in the nascent stage and there may be a significant potential if such market is explored further. Our research is primarily focused on identifying potential whitespaces and schematic algorithms to automate the Patent Prosecution and Patent Litigation Process of the Intellectual Property. The schematic algorithms may assist leading AI tool developers, to explore such opportunities in the field of Intellectual Property. Our research is also focused on identification of pitfalls of the AI. For example, Information Security and its impact in IPR, and Potential remediations to sustain the IPR in the digital environment.

Keywords: artificial intelligence, patent analytics, patent drafting, patent litigation, patent prosecution, patent research

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Authors: Adil A. Mohammed, Seif-Eddeen K. Fateen, Tamer S. Ahmed, Tarek M. Moustafa

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Falling film were widely used for gas-liquid absorption and reaction process. Modeling of falling film for oligomerization of ethylene reaction to linear alpha olefins is developed. Although there are many researchers discuss modeling of falling film in many processes, there has been no publish study the simulation of falling film for the oligomerization of ethylene reaction to produce linear alpha olefins. The Comsol multiphysics software was used to simulate the mass transfer with chemical reaction in falling film absorption process. The effect of concentration profile absorption of the products through falling thickness is discussed. The effect of catalyst concentration, catalyst/co-catalyst ratio, and temperature is also studied. For the effect of the temperature, as it increase the concentration of C4 increase. For catalyst concentration and catalyst/co-catalyst ratio as they increases the concentration of C4 increases, till it reached almost constant value.

Keywords: falling film, oligomerization, comsol mutiphysics, linear alpha olefins

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Authors: Zhaohan Xie, Yining Yu, Mingliang Chen

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As research has shown a convergent tendency for aversion to AI recommendation, it is imperative to find a way to promote AI usage and better harness the technology. In the context of e-commerce, this study has found evidence that people show less avoidance of algorithms when recommending search products compared to experience products. This is due to people’s different attribution of mind to AI versus humans, as suggested by mind perception theory. While people hold the belief that an algorithm owns sufficient capability to think and calculate, which makes it competent to evaluate search product attributes that can be obtained before actual use, they doubt its capability to sense and feel, which is essential for evaluating experience product attributes that must be assessed after experience in person. The result of the behavioral investigation (Study 1, N=112) validated that consumers show low purchase intention to experience products recommended by AI. Further consumer neuroscience study (Study 2, N=26) using Event-related potential (ERP) showed that consumers have a higher level of cognitive conflict when faced with AI recommended experience product as reflected by larger N2 component, while the effect disappears for search product. This research has implications for the effective employment of AI recommenders, and it extends the literature on e-commerce and marketing communication.

Keywords: algorithm recommendation, consumer behavior, e-commerce, event-related potential, experience product, search product

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Authors: Angela U. Makolo, Temitayo A. Olagunju

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The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained using high throughput experimental procedures. However, these high throughput methods are known to produce very high rates of false positive and negative interactions. In order to construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed. A weighted interaction graph of the Saccharomyces cerevisiae (Baker’s Yeast) organism using the proteins as the nodes and interactions between them as edges was constructed. The weights were obtained using Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model. A pathway was formalized as a simple path in the interaction network from a starting protein and an ending protein of interest. We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. cerevisiae.

Keywords: Bayesian networks, protein interaction networks, Saccharomyces cerevisiae, signalling pathways

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Authors: Chia-Chen Chen, Mu-Yen Chen

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In recent years, with the vigorous growth of hardware and software technologies of smart mobile devices coupling with the rapid increase of social network influence, mobile commerce also presents the commercial operation mode of the future mainstream. For the time being, SoLoMo has become one of the very popular commercial models, its full name and meaning mainly refer to that users can obtain three key service types through smart mobile devices (Mobile) and omnipresent network services, and then link to the social (Social) web site platform to obtain the information exchange, again collocating with position and situational awareness technology to get the service suitable for the location (Local), through anytime, anywhere and any personal use of different mobile devices to provide the service concept of seamless integration style, and more deriving infinite opportunities of the future. The study tries to explore the use intention of users with SoLoMo mobile application formula, proposing research model to integrate TAM, ISSM, IDT and network externality, and with questionnaires to collect data and analyze results to verify the hypothesis, results show that perceived ease-of-use (PEOU), perceived usefulness (PU), and network externality have significant impact on the use intention with SoLoMo mobile application formula, and the information quality, relative advantages and observability have impacts on the perceived usefulness, and further affecting the use intention.

Keywords: SoLoMo (social, local, and mobile), technology acceptance model, innovation diffusion theory, network externality

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Authors: Ahmed Radwan, Ahmed Abdel Ghaney

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This paper delves into the transformative potential of artificial intelligence (AI) in fostering creativity within the domain of architecture education, especially with a specific emphasis on its implications within the Design Studios; the convergence of AI and architectural pedagogy has introduced avenues for redefining the boundaries of creative expression and problem-solving. By harnessing AI-driven tools, students and educators can collaboratively explore a spectrum of design possibilities, stimulate innovative ideation, and engage in multidimensional design processes. This paper investigates the ways in which AI contributes to architectural creativity by facilitating generative design, pattern recognition, virtual reality experiences, and sustainable design optimization. Furthermore, the study examines the balance between AI-enhanced creativity and the preservation of core principles of architectural design/education, ensuring that technology is harnessed to augment rather than replace foundational design skills. Through an exploration of Egypt's architectural heritage and contemporary challenges, this research underscores how AI can synergize with cultural context and historical insights to inspire cutting-edge architectural solutions. By analyzing AI's impact on nurturing creativity among Egyptian architecture students, this paper seeks to contribute to the ongoing discourse on the integration of technology within global architectural education paradigms. It is hoped that this research will guide the thoughtful incorporation of AI in fostering creativity while preserving the authenticity and richness of architectural design education in Egypt and beyond.

Keywords: architecture, artificial intelligence, architecture education, Egypt

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Authors: Jéssica Bernegossi, Lívia Nordi Dovigo, Marlus Chorilli

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Mucoadhesive liquid crystalline systems are emerging how delivery systems for oral cavity. These systems are interesting since they facilitate the targeting of medicines and change the release enabling a reduction in the number of applications made by the patient. The buccal mucosa is permeable besides present a great blood supply and absence of first pass metabolism, it is a good route of administration. It was developed two systems liquid crystals utilizing as surfactant the ethyl alcohol ethoxylated and propoxylated (30%) as oil phase the oleic acid (60%), and the aqueous phase (10%) dispersion of polymer polyethylenimine (0.5%) or dispersion of polymer poloxamer 407 (16%), with the intention of applying the buccal mucosa. Initially, was performed for characterization of systems the conference by polarized light microscopy and rheological analysis. For the preparation of the systems the components described was added above in glass vials and shaken. Then, 30 and 100% artificial saliva were added to each prepared formulation so as to simulate the environment of the oral cavity. For the verification of the system structure, aliquots of the formulations were observed in glass slide and covered with a coverslip, examined in polarized light microscope (PLM) Axioskop - Zeizz® in 40x magnifier. The formulations were also evaluated for their rheological profile Rheometer TA Instruments®, which were obtained rheograms the selected systems employing fluency mode (flow) in temperature of 37ºC (98.6ºF). In PLM, it was observed that in formulations containing polyethylenimine and poloxamer 407 without the addition of artificial saliva was observed dark-field being indicative of microemulsion, this was also observed with the formulation that was increased with 30% of the artificial saliva. In the formulation that was increased with 100% simulated saliva was shown to be a system structure since it presented anisotropy with the presence of striae being indicative of hexagonal liquid crystalline mesophase system. Upon observation of rheograms, both systems without the addition of artificial saliva showed a Newtonian profile, after addition of 30% artificial saliva have been given a non-Newtonian behavior of the pseudoplastic-thixotropic type and after adding 100% of the saliva artificial proved plastic-thixotropic. Furthermore, it is clearly seen that the formulations containing poloxamer 407 have significantly larger (15-800 Pa) shear stress compared to those containing polyethyleneimine (5-50 Pa), indicating a greater plasticity of these. Thus, it is possible to observe that the addition of saliva was of interest to the system structure, starting from a microemulsion for a liquid crystal system, thereby also changing thereby its rheological behavior. The systems have promising characteristics as controlled release systems to the oral cavity, as it features good fluidity during its possible application and greater structuring of the system when it comes into contact with environmental saliva.

Keywords: liquid crystal system, poloxamer 407, polyethylenimine, rheology

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Authors: Biruk Wolde, Atalay Ayele, Yonatan Garkabo, Trufat Hailmariam, Zemenu Germewu

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A comprehensive three-dimensional (3D) geological modeling and engineering geological characterization of shallow subsurface soils and rocks are essential for a wide range of geotechnical and seismological engineering applications, particularly in urban environments. The spatial distribution and geological variation of the shallow subsurface of Addis Ababa city have not been studied so far in terms of geological and geotechnical modeling. This study aims at the construction of a 3D geological model, as well as provides awareness into the engineering geological characteristics of shallow subsurface soil and rock of Addis Ababa city. The 3D geological model was constructed by using more than 1500 geotechnical boreholes, well-drilling data, and geological maps. A well-known geostatistical kriging 3D interpolation algorithm was applied to visualize the spatial distribution and geological variation of the shallow subsurface. Due to the complex nature of geological formations, vertical and lateral variation of the geological profiles horizons-solid command has been selected via the Groundwater Modelling System (GMS) graphical user interface software. For the engineering geological characterization of typical soils and rocks, both index and engineering laboratory tests have been used. The geotechnical properties of soil and rocks vary from place to place due to the uneven nature of subsurface formations observed in the study areas. The constructed model ascertains the thickness, extent, and 3D distribution of the important geological units of the city. This study is the first comprehensive research work on 3D geological modeling and subsurface characterization of soils and rocks in Addis Ababa city, and the outcomes will be important for further future research on subsurface conditions in the city. Furthermore, these findings provide a reference for developing a geo-database for the city.

Keywords: 3d geological modeling, addis ababa, engineering geology, geostatistics, horizons-solid

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Authors: Daneal Rorke, Gueguim Kana

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The shift towards renewable energy sources for biofuel production has received increasing attention. However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production.

Keywords: artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves

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Authors: Kate Benedicta Amenador, Dianjian Wang, Bright Nkrumah

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This extensive bibliometric study explores the dynamic influence of artificial intelligence in the field of English as a Foreign Language (EFL) between 2012 and 2024. The study, which examined 4,500 articles from Google Scholar, Modern Language Association Linguistics Abstracts, Web of Science, Scopus, Researchgate, and library genesis databases, indicates that AI integration in EFL is on the rise. This notable increase is ascribed to a variety of transformative events, including increased academic funding for higher education and the COVID-19 epidemic. The results of the study identify leading contributors, prominent authors, publishers and sources, with the United States, China and the United Kingdom emerging as key contributors. The co-occurrence analysis of key terms reveals five clusters highlighting patterns in AI-enhanced language instruction and learning, including evaluation strategies, educational technology, learning motivation, EFL teaching aspects, and learner feedback. The study also discusses the impact of various AIs in enhancing EFL writing skills with software such as Grammarly, Quilbot, and Chatgpt. The current study recognizes limitations in database selection and linguistic constraints. Nevertheless, the results provide useful insights for educators, researchers and policymakers, inspiring and guiding a cross-disciplinary collaboration and creative pedagogical techniques and approaches to teaching and learning in the future.

Keywords: artificial intelligence, bibliometrics study, VOSviewer visualization, English as a foreign language

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Authors: Chin-Chia Jane

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In a transportation network, travel time refers to the transmission time from source node to destination node, whereas reliability refers to the probability of a successful connection from source node to destination node. With an increasing emphasis on quality of service (QoS), both performance indexes are significant in the design and analysis of transportation systems. In this work, we extend the well-known flow network model for transportation networks so that travel time and reliability are integrated into the QoS measurement simultaneously. In the extended model, in addition to the general arc capacities, each intermediate node has a time weight which is the travel time for per unit of commodity going through the node. Meanwhile, arcs and nodes are treated as binary random variables that switch between operation and failure with associated probabilities. For pre-specified travel time limitation and demand requirement, the QoS of a transportation network is the probability that source can successfully transport the demand requirement to destination while the total transmission time is under the travel time limitation. This work is pioneering, since existing literatures that evaluate travel time reliability via a single optimization path, the proposed QoS focuses the performance of the whole network system. To compute the QoS of transportation networks, we first transfer the extended network model into an equivalent min-cost max-flow network model. In the transferred network, each arc has a new travel time weight which takes value 0. Each intermediate node is replaced by two nodes u and v, and an arc directed from u to v. The newly generated nodes u and v are perfect nodes. The new direct arc has three weights: travel time, capacity, and operation probability. Then the universal set of state vectors is recursively decomposed into disjoint subsets of reliable, unreliable, and stochastic vectors until no stochastic vector is left. The decomposition is made possible by applying existing efficient min-cost max-flow algorithm. Because the reliable subsets are disjoint, QoS can be obtained directly by summing the probabilities of these reliable subsets. Computational experiments are conducted on a benchmark network which has 11 nodes and 21 arcs. Five travel time limitations and five demand requirements are set to compute the QoS value. To make a comparison, we test the exhaustive complete enumeration method. Computational results reveal the proposed algorithm is much more efficient than the complete enumeration method. In this work, a transportation network is analyzed by an extended flow network model where each arc has a fixed capacity, each intermediate node has a time weight, and both arcs and nodes are independent binary random variables. The quality of service of the transportation network is an integration of customer demands, travel time, and the probability of connection. We present a decomposition algorithm to compute the QoS efficiently. Computational experiments conducted on a prototype network show that the proposed algorithm is superior to existing complete enumeration methods.

Keywords: quality of service, reliability, transportation network, travel time

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Authors: Tahammal Hussain, Khuram Zia, Mumammad Jalal Arif, Megha Parajulee, Abdul Hakeem

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The Bioassay on neonate, 2nd and 3rd instar larvae of Helicoverpa armigera (Hubner) were conducted against Bacillus thuringiensis proteins Cry1Ac. Cry1Ac was incorporated into an artificial diet and was serially diluted with distilled water and then mixed with diet at an appropriate temperature of diet. Toxins incorporated prepared diet was poured into Petri-dishes. For controls, distilled water was mixed with the diet. Five toxin doses 0.25, 0.5, 1, 2, and 4 ug / ml and one control were used for each instars of H. armigera 20 larvae were used in each replication and each treatment is replicated four times. LC50 of Cry1Ac against neonate, 2nd and 3rd instar larvae of H. armigera were 0.34, 0.81 and 1.46 ug / ml. So Cry1Ac is more effective against neonate larvae of H .armigera as compared to 2nd and 3rd instar larvae under laboratory conditions.

Keywords: B. thuringiensis, Cry1Ac, H. armigera, toxicity

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Authors: Erin Lynne Plettenberg, Jeremy Vickery

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In an access-control situation, the role of a user determines whether a data request is appropriate. This paper combines vetted web mining and logic modeling to build a lightweight system for determining the role of a health care provider based only on their prior authorized requests. The model identifies provider roles with 100% recall from very little data. This shows the value of vetted web mining in AI systems, and suggests the impact of the ICD classification on medical practice.

Keywords: electronic medical records, information extraction, logic modeling, ontology, vetted web mining

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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Authors: Omar Bouledroua, Djamel Zelmati, Zahreddine Hafsi, Milos B. Djukic

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Transporting hydrogen gas through the existing natural gas pipeline network offers an efficient solution for energy storage and conveyance. Hydrogen generated from excess renewable electricity can be conveyed through the API 5L steel-made pipelines that already exist. In recent years, there has been a growing demand for the transportation of hydrogen through existing gas pipelines. Therefore, numerical and experimental tests are required to verify and ensure the mechanical integrity of the API 5L steel pipelines that will be used for pressurized hydrogen transportation. Internal pressure loading is likely to accelerate hydrogen diffusion through the internal pipe wall and consequently accentuate the hydrogen embrittlement of steel pipelines. Furthermore, pre-cracked pipelines are susceptible to quick failure, mainly under a time-dependent cyclic pressure loading that drives fatigue crack propagation. Meanwhile, after several loading cycles, the initial cracks will propagate to a critical size. At this point, the remaining service life of the pipeline can be estimated, and inspection intervals can be determined. This paper focuses on the hydrogen embrittlement of API 5L steel-made pipeline under cyclic pressure loading. Pressurized hydrogen gas is transported through a network of pipelines where demands at consumption nodes vary periodically. The resulting pressure profile over time is considered a cyclic loading on the internal wall of a pre-cracked pipeline made of API 5L steel-grade material. Numerical modeling has allowed the prediction of fatigue crack evolution and estimation of the remaining service life of the pipeline. The developed methodology in this paper is based on the ASME B31.12 standard, which outlines the guidelines for hydrogen pipelines.

Keywords: hydrogen embrittlement, pipelines, transient flow, cyclic pressure, fatigue crack growth

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Authors: Md Yeasin, Ranjit Kumar Paul

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In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity.

Keywords: agriculture, casual inference, machine learning, recommendation system

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Authors: Coriolano Salvini, Ambra Giovannelli

Abstract:

The use of renewable energy sources for electric power production leads to reduced CO2 emissions and contributes to improving the domestic energy security. On the other hand, the intermittency and unpredictability of their availability poses relevant problems in fulfilling safely and in a cost efficient way the load demand along the time. Significant benefits in terms of “grid system applications”, “end-use applications” and “renewable applications” can be achieved by introducing energy storage systems. Among the currently available solutions, CAES (Compressed Air Energy Storage) shows favorable features. Small-medium size plants equipped with artificial air reservoirs can constitute an interesting option to get efficient and cost-effective distributed energy storage systems. The present paper is addressed to the design and off-design analysis of the compression system of small size CAES plants suited to absorb electric power in the range of hundreds of kilowatt. The system of interest is constituted by an intercooled (in case aftercooled) multi-stage reciprocating compressor and a man-made reservoir obtained by connecting large diameter steel pipe sections. A specific methodology for the system preliminary sizing and off-design modeling has been developed. Since during the charging phase the electric power absorbed along the time has to change according to the peculiar CAES requirements and the pressure ratio increases continuously during the filling of the reservoir, the compressor has to work at variable mass flow rate. In order to ensure an appropriately wide range of operations, particular attention has been paid to the selection of the most suitable compressor capacity control device. Given the capacity regulation margin of the compressor and the actual level of charge of the reservoir, the proposed approach allows the instant-by-instant evaluation of minimum and maximum electric power absorbable from the grid. The developed tool gives useful information to appropriately size the compression system and to manage it in the most effective way. Various cases characterized by different system requirements are analysed. Results are given and widely discussed.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), compressor design, compression system management.

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

Abstract:

This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: Hillary Mwongyera

Abstract:

The Semliki basin has become a frontier for petroleum exploration in recent years. Exploration efforts have resulted into extensive seismic data acquisition and drilling of three wells namely; Turaco 1, Turaco 2 and Turaco 3. A petrophysical analysis of the Turaco 1 well was carried out to identify two reservoir zones on which AVO modeling was performed. A combination of seismic modeling and rock physics modeling was applied during reservoir characterization and monitoring to determine variations of seismic responses with amplitude characteristics. AVO intercept gradient analysis applied on AVO synthetic CDP gathers classified AVO anomalies associated with both reservoir zones as Class 1 AVO anomalies. Fluid replacement modeling was carried out on both reservoir zones using homogeneous mixing and patchy saturation patterns to determine effects of fluid substitution on rock property interactions. For both homogeneous mixing and saturation patterns, density (ρ) showed an increasing trend with increasing brine substitution while Shear wave velocity (Vs) decreased with increasing brine substitution. A study of compressional wave velocity (Vp) with increasing brine substitution for both homogeneous mixing and patchy saturation gave quite interesting results. During patchy saturation, Vp increased with increasing brine substitution. During homogeneous mixing however, Vp showed a slightly decreasing trend with increasing brine substitution but increased tremendously towards and at full brine saturation. A sensitivity analysis carried out showed that density was a very sensitive rock property responding to brine saturation except at full brine saturation during homogeneous mixing where Vp showed greater sensitivity with brine saturation. Rock physics modeling was performed to predict diagnostics of reservoir quality using an inverse deterministic approach which showed low shale content and a high degree of shale stiffness within reservoir zones.

Keywords: Amplitude Versus Offset (AVO), fluid replacement modelling, reservoir characterization, AVO attributes, rock physics modelling, reservoir monitoring

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Authors: Mogeeb A. El-Sheikh

Abstract:

The study has developed the previous design of an artificial anthropomorphic humanoid hand and accustomed it as a prosthetic hand. The main specifications of this design are determined. The development of our previous design involves the main artificial hand’s parts and subassemblies, palm, fingers, and thumb. In addition, the study presents an adaptable socket design for a transradial amputee. This hand has 3 fingers and thumb. It is more reliable, cosmetics, modularity, and ease of assembly. Its size and weight are almost as a natural hand. The socket cavity has the capability for different sizes of a transradial amputee. The study implements the developed design by using rapid prototype and specifies its main specifications by using a data glove and finite element method.

Keywords: adaptable socket, prosthetic hand, transradial amputee, data glove

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Authors: Igwenagu Chinelo Mercy

Abstract:

This study is on the concept of Neurostatistics in relation to neuroscience. Neuroscience also known as neurobiology is the scientific study of the nervous system. In the study of neuroscience, it has been noted that brain function and its relations to the process of acquiring knowledge and behaviour can be better explained by the use of various interrelated methods. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. On the other hand, Neurostatistics based on this study is viewed as a statistical concept that uses similar techniques of neuron mechanisms to solve some problems especially in the field of life science. This study is imperative in this era of Artificial intelligence/Machine leaning in the sense that clear understanding of the technique and its proper application could assist in solving some medical disorder that are mainly associated with the nervous system. This will also help in layman’s understanding of the technique of the nervous system in order to overcome some of the health challenges associated with it. For this concept to be well understood, an illustrative example using a brain associated disorder was used for demonstration. Structural equation modelling was adopted in the analysis. The results clearly show the link between the techniques of statistical model and nervous system. Hence, based on this study, the appropriateness of Neurostatistics application in relation to neuroscience could be based on the understanding of the behavioural pattern of both concepts.

Keywords: brain, neurons, neuroscience, neurostatistics, structural equation modeling

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Authors: Jana Ismail, Layla Fallatah, Maha Alshmaisi

Abstract:

ChatGPT, a cutting-edge language model built on the GPT-3.5 architecture, has garnered attention for its profound natural language processing capabilities, holding promise for transformative applications in customer service and content creation. This study delves into ChatGPT's architecture, aiming to comprehensively understand its strengths and potential limitations. Through systematic experiments across diverse domains, such as general knowledge and creative writing, we evaluated the model's coherence, context retention, and task-specific accuracy. While ChatGPT excels in generating human-like responses and demonstrates adaptability, occasional inaccuracies and sensitivity to input phrasing were observed. The study emphasizes the impact of prompt design on output quality, providing valuable insights for the nuanced deployment of ChatGPT in conversational AI and contributing to the ongoing discourse on the evolving landscape of natural language processing in artificial intelligence.

Keywords: artificial Inelegance, chatGPT, open AI, NLP

Procedia PDF Downloads 77