Search results for: artificial intelligence in medicine

Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: Mingxi Xiao

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Children’s experiences in the early years build and shape the brain. The early years learning environment plays a significantly important role in children’s development. A well-constructed environment will facilitate children’s physical and mental well-being. This case study used an early learning centre in Australia called SDN Hurstville as an example, describing the learning environment in the centre, as well as analyzing the functions of the affordances. In addition, this report talks about the sustainability of learning in the centre, and how the environment supports cultural diversity and indigenous learning. The early years for children are significant. Different elements in the early childhood centre should work together to help children develop better. This case study found that the natural environment and the artificial environment are both critical to children; only when they work together can children have better development in physical and mental well-being and have a sense of belonging when playing and learning in the centre.

Keywords: early childhood center, early childhood education, learning environment, Australia

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Authors: Coriolano Salvini

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The increasing utilization of renewable energy sources for electric power production calls for the introduction of energy storage systems to match the electric demand along the time. Although many countries are pursuing as a final goal a “decarbonized” electrical system, in the next decades the traditional fossil fuel fed power plant still will play a relevant role in fulfilling the electric demand. Presently, such plants provide grid ancillary services (frequency control, grid balance, reserve, etc.) by adapting the output power to the grid requirements. An interesting option is represented by the possibility to use traditional plants to improve the grid storage capabilities. The present paper is addressed to small-medium size systems suited for distributed energy storage. The proposed Energy Storage System (ESS) is based on a Compressed Air Energy Storage (CAES) integrated into a Gas-Steam Combined Cycle (GSCC) or a Gas Turbine based CHP plants. The systems can be incorporated in an ex novo built plant or added to an already existing one. To avoid any geological restriction related to the availability of natural compressed air reservoirs, artificial storage is addressed. During the charging phase, electric power is absorbed from the grid by an electric driven intercooled/aftercooled compressor. In the course of the discharge phase, the compressed stored air is sent to a heat transfer device fed by hot gas taken upstream the Heat Recovery Steam Generator (HRSG) and subsequently expanded for power production. To maximize the output power, a staged reheated expansion process is adopted. The specific power production related to the kilogram per second of exhaust gas used to heat the stored air is two/three times larger than that achieved if the gas were used to produce steam in the HRSG. As a result, a relevant power augmentation is attained with respect to normal GSCC plant operations without additional use of fuel. Therefore, the excess of output power can be considered “fuel free” and the storage system can be compared to “pure” ESSs such as electrochemical, pumped hydro or adiabatic CAES. Representative cases featured by different power absorption, production capability, and storage capacity have been taken into consideration. For each case, a technical optimization aimed at maximizing the storage efficiency has been carried out. On the basis of the resulting storage pressure and volume, number of compression and expansion stages, air heater arrangement and process quantities found for each case, a cost estimation of the storage systems has been performed. Storage efficiencies from 0.6 to 0.7 have been assessed. Capital costs in the range of 400-800 €/kW and 500-1000 €/kWh have been estimated. Such figures are similar or lower to those featuring alternative storage technologies.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), gas steam combined cycle (GSCC), techno-economic analysis

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Authors: Benyapa Thitimapong, Najwa Niyomdecha

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This qualitative descriptive research aimed to explore the self-care experiences during pregnancy of Muslim adolescents. Twenty participants were first-time Muslim mothers who had pregnancy experienceห under 20 years of age in three Southern border provinces of Thailand. Participants were selected by purposive sampling with inclusion criteria. Data were collected from the in-depth interview and analyzed using content analysis. The findings revealed that Muslim pregnant adolescents take care of themselves in the context of combining self-care in an Islamic way and conventional medicine. There are 2 subthemes: 1) antenatal care with Tok Bidan and 2) health promotion during pregnancy. The finding will help to understand self-care during pregnancy of Muslim adolescents among three Southern border provinces and can apply to nurse educators as a guide to educate and manage an appropriate self-care program for Muslim pregnant adolescents based on cultural diversity.

Keywords: adolescents, muslim, pregnancy, selfcare

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Authors: Ali Atashbar Orang, Carlo Massimo Casciola

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A novel numerical approach for the steady incompressible turbulent flows is presented in this paper. The artificial compressibility method (ACM) is applied to the Reynolds Averaged Navier-Stokes (RANS) equations. A new Characteristic-Based Turbulent (CBT) scheme is developed for the convective fluxes. The well-known Spalart–Allmaras turbulence model is employed to check the effectiveness of this new scheme. Comparing the proposed scheme with previous studies, it is found that the present CBT scheme demonstrates accurate results, high stability and faster convergence. In addition, the local time stepping and implicit residual smoothing are applied as the convergence acceleration techniques. The turbulent flows past a backward facing step, circular cylinder, and NACA0012 hydrofoil are studied as benchmarks. Results compare favorably with those of other available schemes.

Keywords: incompressible turbulent flow, method of characteristics, finite volume, Spalart–Allmaras turbulence model

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Authors: Nadia el Atlas, Mohammed el Aroussi, Mohammed Wahbi

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In this paper a novel technique of mass characterization based on robust features-fusion is presented. The proposed method consists of mainly four stages: (a) the first phase involves segmenting the masses using edge information’s. (b) The second phase is to calculate and fuse the most relevant morphological features. (c) The last phase is the classification step which allows us to classify the images into benign and malignant masses. In this step we have implemented Support Vectors Machines (SVM) and Artificial Neural Networks (ANN), which were evaluated with the following performance criteria: confusion matrix, accuracy, sensitivity, specificity, receiver operating characteristic ROC, and error histogram. The effectiveness of this new approach was evaluated by a recently developed database: INBREAST database. The fusion of the most appropriate morphological features provided very good results. The SVM gives accuracy to within 64.3%. Whereas the ANN classifier gives better results with an accuracy of 97.5%.

Keywords: breast cancer, mammography, CAD system, features, fusion

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Authors: Mohammad Ridzwan Bin Abd Rahim, Siegfried Schmauder, Yupiter HP Manurung, Peter Binkele, Meor Iqram B. Meor Ahmad, Kiarash Dogahe

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This paper deals with the short crack initiation of the material P91 under cyclic loading at two different temperatures, concluded with the estimation of the short crack initiation Wöhler (S/N) curve. An artificial but representative model microstructure was generated using Voronoi tessellation and the Finite Element Method, and the non-uniform stress distribution was calculated accordingly afterward. The number of cycles needed for crack initiation is estimated on the basis of the stress distribution in the model by applying the physically-based Tanaka-Mura model. Initial results show that the number of cycles to generate crack initiation is strongly correlated with temperature.

Keywords: short crack initiation, P91, Wöhler curve, Voronoi tessellation, Tanaka-Mura model

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Muhammad Nouman Qureshi, Muhammad Hanif

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Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper.

Keywords: auxiliary information, adaptive cluster sampling, clustered populations, Hansen-Hurwitz estimation

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Authors: Shin-Yi Mao, Jiashing Yu

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Decellularized adipose tissue (DAT) has received lots of attention as biological scaffolds recently. DAT that extracted from the extracellular matrix (ECM) of adipose tissues holds great promise as a xenogeneic biomaterial for tissue engineering and regenerative medicine. In our study, 2-D DATsol film was fabricated to enhance cell adhesion, proliferation, and differentiation of ASCs in vitro. DAT was also used to modify alginate for improvement of cell adhesion. Alginate microspheres modified with DAT were prepared by Nisco. These microspheres could provide a highly supportive 3-D environment for ASCs. In our works, ASCs were immobilized in alginate microspheres modified with DAT to promoted cell adhesion and adipogenic differentiation. Accordingly, we hypothesize that tissue regeneration in vivo could be promoted with the aid of modified microspheres in future.

Keywords: adipose stem cells, decellularize adipose tissue, Alginate, microcarries

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Authors: Sahar Heidary

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Radiography and radiotherapy have emerged as crucial pillars of modern medical practice, revolutionizing diagnostics and treatment for a myriad of health conditions. This abstract highlights the pivotal role of radiography and radiotherapy in favor of healthcare and society. Radiography, a non-invasive imaging technique, has significantly advanced medical diagnostics by enabling the visualization of internal structures and abnormalities within the human body. With the advent of digital radiography, clinicians can obtain high-resolution images promptly, leading to faster diagnoses and informed treatment decisions. Radiography plays a pivotal role in detecting fractures, tumors, infections, and various other conditions, allowing for timely interventions and improved patient outcomes. Moreover, its widespread accessibility and cost-effectiveness make it an indispensable tool in healthcare settings worldwide. On the other hand, radiotherapy, a branch of medical science that utilizes high-energy radiation, has become an integral component of cancer treatment and management. By precisely targeting and damaging cancerous cells, radiotherapy offers a potent strategy to control tumor growth and, in many cases, leads to cancer eradication. Additionally, radiotherapy is often used in combination with surgery and chemotherapy, providing a multifaceted approach to combat cancer comprehensively. The continuous advancements in radiotherapy techniques, such as intensity-modulated radiotherapy and stereotactic radiosurgery, have further improved treatment precision while minimizing damage to surrounding healthy tissues. Furthermore, radiography and radiotherapy have demonstrated their worth beyond oncology. Radiography is instrumental in guiding various medical procedures, including catheter placement, joint injections, and dental evaluations, reducing complications and enhancing procedural accuracy. On the other hand, radiotherapy finds applications in non-cancerous conditions like benign tumors, vascular malformations, and certain neurological disorders, offering therapeutic options for patients who may not benefit from traditional surgical interventions. In conclusion, radiography and radiotherapy stand as indispensable tools in modern medicine, driving transformative improvements in patient care and treatment outcomes. Their ability to diagnose, treat, and manage a wide array of medical conditions underscores their favor in medical practice. As technology continues to advance, radiography and radiotherapy will undoubtedly play an ever more significant role in shaping the future of healthcare, ultimately saving lives and enhancing the quality of life for countless individuals worldwide.

Keywords: radiology, radiotherapy, medical imaging, cancer treatment

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Authors: Meng-Hui Chen, Chiao-Wei Yu, Pei-Chann Chang

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Imperial competitive algorithm (ICA) simulates a multi-agent algorithm. Each agent is like a kingdom has its country, and the strongest country in each agent is called imperialist, others are colony. Countries are competitive with imperialist which in the same kingdom by evolving. So this country will move in the search space to find better solutions with higher fitness to be a new imperialist. The main idea in this paper is using the peculiarity of ICA to explore the search space to solve the kinds of combinational problems. Otherwise, we also study to use the greed search to increase the local search ability. To verify the proposed algorithm in this paper, the experimental results of traveling salesman problem (TSP) is according to the traveling salesman problem library (TSPLIB). The results show that the proposed algorithm has higher performance than the other known methods.

Keywords: traveling salesman problem, artificial chromosomes, greedy search, imperial competitive algorithm

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Tuğrul Varol, Halil Barış Özel

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In this study which has been conducted in Akçasu Forest Range District of Devrek Forest Directorate; 3 methods (cover removal with human force, cover removal with Hitachi F20 Excavator, and cover removal with agricultural equipment mounted on a Ferguson 240S agriculture tractor) utilized in weed control efforts in regeneration of degraded oriental beech forests have been compared. In this respect, 3 methods have been compared by determining certain work hours and standard durations of unit areas (1 hectare). For this purpose, evaluating the tasks made with human and machine force from the aspects of duration, productivity and costs, it has been aimed to determine the most productive method in accordance with the actual ecological conditions of research field. Within the scope of the study, the time studies have been conducted for 3 methods used in weed control efforts. While carrying out those studies, the performed implementations have been evaluated by dividing them into business stages. Also, the actual data have been used while calculating the cost accounts. In those calculations, the latest formulas and equations which are also used in developed countries have been utilized. The variance of analysis (ANOVA) was used in order to determine whether there is any statistically significant difference among obtained results, and the Duncan test was used for grouping if there is significant difference. According to the measurements and findings carried out within the scope of this study, it has been found during living cover removal efforts in regeneration efforts in demolished oriental beech forests that the removal of weed layer in 1 hectare of field has taken 920 hours with human force, 15.1 hours with excavator and 60 hours with an equipment mounted on a tractor. On the other hand, it has been determined that the cost of removal of living cover in unit area (1 hectare) was 3220.00 TL for man power, 788.70 TL for excavator and 2227.20 TL for equipment mounted on a tractor. According to the obtained results, it has been found that the utilization of excavator in weed control effort in regeneration of degraded oriental beech regions under actual ecological conditions of research field has been found to be more productive from both of aspects of duration and costs. These determinations carried out should be repeated in weed control efforts in degraded forest fields with different ecological conditions, it is compulsory for finding the most efficient weed control method. These findings will light the way of technical staff of forestry directorate in determination of the most effective and economic weed contol method. Thus, the more actual data will be used while preparing the weed control budgets, and there will be significant contributions to national economy. Also the results of this and similar studies are very important for developing the policies for our forestry in short and long term.

Keywords: artificial regeneration, weed control, oriental beech, productivity, mechanization, man power, cost analysis

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Authors: A. P. Mikhailovich, V. V. Fomin, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Woody vegetation among the upper limit of its habitat is a sensitive indicator of biota reaction to regional climate changes. Quantitative assessment of temporal and spatial changes in the distribution of trees and plant biocenoses calls for the development of new modeling approaches based upon selected data from measurements on the ground level and ultra-resolution aerial photography. Statistical models were developed for the study area located in the Polar Urals. These models allow obtaining probabilistic estimates for placing Siberian Larch trees into one of the three age intervals, namely 1-10, 11-40 and over 40 years, based on the Weilbull distribution of the maximum horizontal crown projection. Authors developed the distribution map for larch trees with crown diameters exceeding twenty centimeters by deciphering aerial photographs made by a UAV from an altitude equal to fifty meters. The total number of larches was equal to 88608, forming the following distribution row across the abovementioned intervals: 16980, 51740, and 19889 trees. The results demonstrate that two processes can be observed in the course of recent decades: first is the intensive forestation of previously barren or lightly wooded fragments of the study area located within the patches of wood, woodlands, and sparse stand, and second, expansion into mountain tundra. The current expansion of the Siberian Larch in the region replaced the depopulation process that occurred in the course of the Little Ice Age from the late 13ᵗʰ to the end of the 20ᵗʰ century. Using data from field measurements of Siberian larch specimen biometric parameters (including height, diameter at root collar and at 1.3 meters, and maximum projection of the crown in two orthogonal directions) and data on tree ages obtained at nine circular test sites, authors developed a model for artificial neural network including two layers with three and two neurons, respectively. The model allows quantitative assessment of a specimen's age based on height and maximum crone projection values. Tree height and crown diameters can be quantitatively assessed using data from aerial photographs and lidar scans. The resulting model can be used to assess the age of all Siberian larch trees. The proposed approach, after validation, can be applied to assessing the age of other tree species growing near the upper tree boundaries in other mountainous regions. This research was collaboratively funded by the Russian Ministry for Science and Education (project No. FEUG-2023-0002) and Russian Science Foundation (project No. 24-24-00235) in the field of data modeling on the basis of artificial neural network.

Keywords: treeline, dynamic, climate, modeling

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Authors: Chien-Liang Chao, Yun-Sheng Lin

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Secondary metabolites are applied in the human life of the Chinese herbal medicine. Many drugs are originally extracted from natural products with combination of pharmaceutical and chemical studies. Crude extract of the leaves from Zamia furfureace L. has been shown to exhibit anticancer activities. The first chemical investigation of this plant was carried out by our group. In this study, four known compounds were isolated from Zamia furfureace L. with three lignins (Sesamin (1), Wodeshiol (2) and Paulownin (3)), and one dipeptide (Aurantiamide acetate (4)). The structures of these compounds were analyzed through the 1D-NMR(1H-NMR,13C-NMR)、2D-NMR(COSY、HMQC、HMBC、NOESY) spectroscopic analysis, and by comparison of variety of physical data (IR, mass spectrometry, ultraviolet, optical rotation). Among them, Aurantiamide acetate (4) exhibited weak cytotoxic activity against human gastric cancer cells.

Keywords: Zamia furfureace L., AGS, sesamin, Aurantiamide acetate, secondary metabolites

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Authors: Guofan Shao, Lina Tang, Hao Zhang

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The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified.

Keywords: accuracy assessment, efficacy, image classification, machine learning, uncertainty

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Authors: Sean Hall

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Prior to COVID-19, the world was preoccupied with opioids, effectiveness versus risk, and specifically toxicity versus abuse. Historically underpinning opioid use was a concept of safety. As use over time and real-world data evolved, a pursuit for efficacy associated with non-opioid alternatives became mainstream. On January 8, 2021, the US signed back into the opioid problem, with these two fundamental questions still unresolved. The author will share the current progression of a lead non-opioid cancer bone pain candidate, NanaBis™. NanaBis™ represents two innovative factors: The active ingredients are from cannabinoids; these ingredients are in a proprietary sub-micron delivery platform, NanoCelle®. The author will offer an opinion piece, potentiating the future role of delivery platforms in medicine to increase both patient safety and compliance.

Keywords: NanaBis, nanoCelle, opioids, toxicity

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Authors: Mohammadhadi Mirmohammadi, Reza Safian, Hossein Haddad

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This paper presents an innovative solution for complex multi-objective optimization problem which is a part of efforts toward maximizing rolling mill throughput and minimizing processing costs in tandem cold rolling. This computational intelligence based optimization has been applied to the rolling schedules of tandem cold rolling mill. This method involves the combination of two derivative-free optimization procedures in the form of nested loops. The first optimization loop is based on Improving Hit and Run method which focus on balance of power, force and reduction distribution in rolling schedules. The second loop is a real-coded genetic algorithm based optimization procedure which optimizes energy consumption and productivity. An experimental result of application to five stand tandem cold rolling mill is presented.

Keywords: derivative-free optimization, Improving Hit and Run method, real-coded genetic algorithm, rolling schedules of tandem cold rolling mill

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Authors: Rizwana Amin, Afshan Afroze Bhatti

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The study was a quasi-experimental pre-post test design including two groups. Data was collected from 127 students through non-probability random sampling from Bahaudin Zakariya University Multan. The groups were given pre-test using perceived stress scale and information about academic achievement was taken by self-report. After screening, 27 participants didn’t meet the criterion. Remaining 100 participants were divided into two groups (experimental and control). Further, 4 students of experimental group denied taking intervention. Then 46 understudies were separated into three subgroups (16, 15 and 15 in each) for training. The experimental groups were given the stress management training, each of experimental group attended one 3-hour training sessions separately while the control group was only given pre-post assessment. The data were analyzed using ANCOVA method (analysis of covariance) t–test. Results of the study indicate that stress training will lead to increased emotional intelligence and academic achievement of students.

Keywords: stress, stress management, academic achievement, students

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Authors: Farzaneh Badinloo, Niloufar Jalali-Moghadam, Reza Kormi-Nouri

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The aim of this study was to explore the short-term and working memory performances across age and gender in school aged children. Most of the studies have been interested in looking into memory changes in adult subjects. This study was instead focused on exploring both short-term and working memories of children over time. Totally 410 school child participants belonging to four age groups (approximately 8, 10, 12 and 14 years old) among which were 201 girls and 208 boys were employed in the study. digits forward and backward tests of the Wechsler children intelligence scale-revised were conducted respectively as short-term and working memory measures. According to results, there was found a general increment in both short-term and working memory scores across age (p ˂ .05) by which whereas short-term memory performance was shown to increase up to 12 years old, working memory scores showed no significant increase after 10 years old of age. No difference was observed in terms of gender (p ˃ .05). In conclusion, this study suggested that both short-term and working memories improve across age in children where 12 and 10 years of old are likely the crucial age periods in terms of short-term and working memories development.

Keywords: age, gender, short-term memory, working memory

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Authors: Alexandra Leukauf, Alexander Schirrer, Emir Talic

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Numerical computation of wave propagation in a large domain usually requires significant computational effort. Hence, the considered domain must be truncated to a smaller domain of interest. In addition, special boundary conditions, which absorb the outward travelling waves, need to be implemented in order to describe the system domains correctly. In this work, the linear one dimensional wave equation is approximated by utilizing the Fourier Galerkin approach. Furthermore, the artificial boundaries are realized with absorbing boundary conditions. Within this work, a systematic work flow for setting up the wave problem, including the absorbing boundary conditions, is proposed. As a result, a convenient modal system description with an effective absorbing boundary formulation is established. Moreover, the truncated model shows high accuracy compared to the global domain.

Keywords: absorbing boundary conditions, boundary control, Fourier Galerkin approach, modal approach, wave equation

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Authors: Kamel Laidi, Khelifa Benmansour, Ouahid Bouchhida

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Neural networks-based approach for 2-cells serial converter has been developed and implemented. The approach is based on a behavioural description of the different operating modes of the converter. Each operating mode represents a well-defined configuration, and for which is matched an operating zone satisfying given invariance conditions, depending on the capacitors' voltages and the load current of the converter. For each mode, a control vector whose components are the control signals to be applied to the converter switches has been associated. Therefore, the problem is reduced to a classification task of the different operating modes of the converter. The artificial neural nets-based approach, which constitutes a powerful tool for this kind of task, has been adopted and implemented. The application to a 2-cells chopper has allowed ensuring efficient and robust control of the load current and a high capacitors voltages balancing.

Keywords: neural nets, control, multicellular converters, 2-cells chopper

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Authors: Shubhakar Gupta, Dhruv Prakash, Apoorv Mehta

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In this project, we design a modular robot capable of using and switching between multiple methods of propulsion and classifying terrain, based on an Inertial Measurement Unit (IMU) input. We wanted to make a robot that is not only intelligent in its functioning but also versatile in its physical design. The advantage of a modular robot is that it can be designed to hold several movement-apparatuses, such as wheels, legs for a hexapod or a quadpod setup, propellers for underwater locomotion, and any other solution that may be needed. The robot takes roughness input from a gyroscope and an accelerometer in the IMU, and based on the terrain classification from an artificial neural network; it decides which method of propulsion would best optimize its movement. This provides the bot with adaptability over a set of terrains, which means it can optimize its locomotion on a terrain based on its roughness. A feature like this would be a great asset to have in autonomous exploration or research drones.

Keywords: modular robotics, terrain detection, terrain classification, neural network

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Authors: Praveen Kumar, R. Uma, R. P. Sharma

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This study investigates the generation of turbulence in a deep-fluid environment using a damped 1D-modified nonlinear Schrödinger equation model. The well-known damped modified nonlinear Schrödinger equation (d-MNLS) is solved using numerical methods. Artificial damping is added to the MNLS equation, and turbulence generation is investigated through a numerical simulation. The numerical simulation employs a finite difference method for temporal evolution and a pseudo-spectral approach to characterize spatial patterns. The results reveal a recurring periodic pattern in both space and time when the nonlinear Schrödinger equation is considered. Additionally, the study shows that the modified nonlinear Schrödinger equation disrupts the localization of structure and the recurrence of the Fermi-Pasta-Ulam (FPU) phenomenon. The energy spectrum exhibits a power-law behavior, closely following Kolmogorov's spectra steeper than k⁻⁵/³ in the inertial sub-range.

Keywords: water waves, modulation instability, hydrodynamics, nonlinear Schrödinger's equation

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Igor A. Krichtafovitch

Abstract:

The evolutionary processes are not linear. Long periods of quiet and slow development turn to rather rapid emergences of new species and even phyla. During Cambrian explosion, 22 new phyla were added to the previously existed 3 phyla. Contrary to the common credence the natural selection or a survival of the fittest cannot be accounted for the dominant evolution vector which is steady and accelerated advent of more complex and more intelligent living organisms. Neither Darwinism nor alternative concepts including panspermia and intelligent design propose a satisfactory solution for these phenomena. The proposed hypothesis offers a logical and plausible explanation of the evolutionary processes in general. It is based on two postulates: a) the Biosphere is a single living organism, all parts of which are interconnected, and b) the Biosphere acts as a giant biological supercomputer, storing and processing the information in digital and analog forms. Such supercomputer surpasses all human-made computers by many orders of magnitude. Living organisms are the product of intelligent creative action of the biosphere supercomputer. The biological evolution is driven by growing amount of information stored in the living organisms and increasing complexity of the biosphere as a single organism. Main evolutionary vector is not a survival of the fittest but an accelerated growth of the computational complexity of the living organisms. The following postulates may summarize the proposed hypothesis: biological evolution as a natural life origin and development is a reality. Evolution is a coordinated and controlled process. One of evolution’s main development vectors is a growing computational complexity of the living organisms and the biosphere’s intelligence. The intelligent matter which conducts and controls global evolution is a gigantic bio-computer combining all living organisms on Earth. The information is acting like a software stored in and controlled by the biosphere. Random mutations trigger this software, as is stipulated by Darwinian Evolution Theories, and it is further stimulated by the growing demand for the Biosphere’s global memory storage and computational complexity. Greater memory volume requires a greater number and more intellectually advanced organisms for storing and handling it. More intricate organisms require the greater computational complexity of biosphere in order to keep control over the living world. This is an endless recursive endeavor with accelerated evolutionary dynamic. New species emerge when two conditions are met: a) crucial environmental changes occur and/or global memory storage volume comes to its limit and b) biosphere computational complexity reaches critical mass capable of producing more advanced creatures. The hypothesis presented here is a naturalistic concept of life creation and evolution. The hypothesis logically resolves many puzzling problems with the current state evolution theory such as speciation, as a result of GM purposeful design, evolution development vector, as a need for growing global intelligence, punctuated equilibrium, happening when two above conditions a) and b) are met, the Cambrian explosion, mass extinctions, happening when more intelligent species should replace outdated creatures.

Keywords: supercomputer, biological evolution, Darwinism, speciation

Procedia PDF Downloads 164

Authors: Edita Baltrėnaitė, Pranas Baltrėnas, Eglė MarčIulaitienė, Mantas PranskevičIus, Valeriia Chemerys

Abstract:

The growing demand for organic products in the market promotes their use in various fields. One of such products is biochar. Among the innovative environmental applications, biochar has the potential as an adsorbent for retaining contaminants in environmental engineering and agrotechnical systems. Artificial modification of biochar can improve its adsorption capacity. However, indirect/natural change of biochar composition (e.g., contaminated biomass) based on syngenetic elements provides prospects for new applications of biochar as well as decreases the modification costs. Natural lignocellulosic and biochar composition variations would lead to a new field of application of biochar and reduce resources for biochar modifications. The aim of this study was to determine the influence of syngenetic elements of biochar’s feedstock on the physicochemical properties of lignocellulosic biochar. Syngenetic elements (e.g., Zn, Cu, Ni, Pb, Mg) and other intrinsic properties (e.g., lignin, COHN, moisture, ash) of indifferent types of lignocellulosic feedstock on the physicochemical characteristics of biochar are discussed.

Keywords: adsorption, lignocellulosic biochar, instrinsic properties, syngenetic elements

Procedia PDF Downloads 198

Authors: M. H. Korayem, Sh. Ameri, N. Yousefi Lademakhi

Abstract:

To ensure the stability of closed-loop nonlinear model predictive control (NMPC) within a finite horizon, there is a need for appropriate design terminal ingredients, which can be a time-consuming and challenging effort. Otherwise, in order to ensure the stability of the control system, it is necessary to consider an infinite predictive horizon. Increasing the prediction horizon increases computational demand and slows down the implementation of the method. In this study, a new technique has been proposed to ensure system stability without terminal ingredients. This technique has been employed in the design of the NMPC algorithm, leading to a reduction in the computational complexity of designing terminal ingredients and computational burden. The studied system is a wheeled mobile robot (WMR) subjected to non-holonomic constraints. Simulation has been investigated for two problems: trajectory tracking and adjustment mode.

Keywords: wheeled mobile robot, nonlinear model predictive control, stability, without terminal ingredients

Procedia PDF Downloads 89