Search results for: sampling algorithms

Authors: Muhammad Umair, Syed Qasim Gilani

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A technique using Independent Component Analysis (ICA) for blind receiver signal processing is investigated. The problem of the receiver signal processing is viewed as of signal equalization and implementation imperfections compensation. Based on this, a model similar to a general ICA problem is developed for the received signal. Then, the use of ICA technique for blind signal equalization in the time domain is presented. The equalization is regarded as a signal separation problem, since the desired signal is separated from interference terms. This problem is addressed in the paper by over-sampling of the received signal. By using ICA for equalization, besides channel equalization, other transmission imperfections such as Direct current (DC) bias offset, carrier phase and In phase Quadrature phase imbalance will also be corrected. Simulation results for a system using 16-Quadraure Amplitude Modulation(QAM) are presented to show the performance of the proposed scheme.

Keywords: blind equalization, blind signal separation, equalization, independent component analysis, transmission impairments, QAM receiver

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Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

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This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: sound detection, impulsive signal, background noise, neural network

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Authors: Ali Hosseini

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Paralleling is a mechanism to decrease the time necessary to execute the programs. Sorting is one of the important operations to be used in different systems in a way that the proper function of many algorithms and operations depend on sorted data. CRCW_SORT algorithm executes ‘N’ elements sorting in O(1) time on SIMD (Single Instruction Multiple Data) computers with n^2/2-n/2 number of processors. In this article having presented a mechanism by dividing the input string by the hinge element into two less strings the number of the processors to be used in sorting ‘N’ elements in O(1) time has decreased to n^2/8-n/4 in the best state; by this mechanism the best state is when the hinge element is the middle one and the worst state is when it is minimum. The findings from assessing the proposed algorithm by other methods on data collection and number of the processors indicate that the proposed algorithm uses less processors to sort during execution than other methods.

Keywords: CRCW, SIMD (Single Instruction Multiple Data) computers, parallel computers, number of the processors

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Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

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Authors: Hsin-Yun Lee

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In this study, the pedestrian simulation VISWALK integration and application platform ant algorithms written program made to construct a renovation engineering schedule planning mode. The use of simulation analysis platform construction site when the user running the simulation, after calculating the user walks in the case of construction delays, the ant algorithm to find out the minimum delay time schedule plan, and add volume and unit area deactivated loss of business computing, and finally to the owners and users of two different positions cut considerations pick out the best schedule planning. To assess and validate its effectiveness, this study constructed the model imported floor of a shopping mall floor renovation engineering cases. Verify that the case can be found from the mode of the proposed project schedule planning program can effectively reduce the delay time and the user's walking mall loss of business, the impact of the operation on the renovation engineering facilities in the building to a minimum.

Keywords: pedestrian, renovation, schedule, simulation

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Authors: Nunna Venkata Prasad, Maryam Amoke Folarin, Muhammad Ali Shaukat Sham

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A quantitative investigation was conducted to explore the communication apprehension among undergraduate students of Abu Dhabi University. Communication apprehension (CA) is an individual’s level of fear or anxiety associated with either real or anticipated communication with another person or persons. All individuals experience some level of communication apprehension. A total of 100 participants selected through a stratified sampling method, which includes 50 males and 50 females participated in the study. The research was conducted by distributing the personal report of communication apprehension questionnaire, randomly amongst these students. Results were affirmative with previous researches conducted. Demographics, age, or college year did not make any significant differences amongst the undergraduate students. More students were found to have high CA with public speaking rather than other scenarios. And lesser students were found to have high CA level with one-on-one conversations although a significant number of them still tested to have high CA with interpersonal communications.

Keywords: communication apprehension, interpersonal communication, oral communication, public speaking

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Authors: Saba Moghaddam

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There is an ongoing and dynamic interplay between cultural environment and individuals’ psych, an interaction that starts at birth and continues throughout life. Cultural Unconscious affects the way people choose their partners and how they shape their relationships. The aim of this study is to identify cultural unconscious beliefs that play a decisive role in the relationship between couples. The study used the method of thematic analysis, and through purposeful sampling and semi-interviews, the themes regarding cultural unconscious in 17 married people between the ages of 24 and 40 years were identified. These themes are (1) Feminization-masculinization of post-marriage roles; (2) Subordinate Women – an intergenerational belief; (3) cultural standards affecting the choice of spouse; (4) primary family beliefs about marriage. Based on these findings, traditional beliefs continue to play a decisive function and effect on people’s unconscious, and in order to achieve a couple's relationship satisfaction, identifying their roles and becoming conscious of these unconscious cultural beliefs is very important.

Keywords: couple relationship, partner choice, thematic analysis, unconscious cultural believes

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Authors: Badr M. Alshammari, T. Guesmi

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In this paper, the concept of a non-dominated sorting multi-objective particle swarm optimization with local search (NSPSO-LS) is presented for the optimal design of multimachine power system stabilizers (PSSs). The controller design is formulated as an optimization problem in order to shift the system electromechanical modes in a pre-specified region in the s-plan. A composite set of objective functions comprising the damping factor and the damping ratio of the undamped and lightly damped electromechanical modes is considered. The performance of the proposed optimization algorithm is verified for the 3-machine 9-bus system. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and superiority of the NSPSO-LS algorithm in tuning PSSs over a wide range of loading conditions and large disturbance compared to the classic PSO technique and genetic algorithms.

Keywords: multi-objective optimization, particle swarm optimization, power system stabilizer, low frequency oscillations

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Authors: Ebrahim Farahmand, Ali Mahani

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Wireless sensor networks (WSNs) can facilitate continuous monitoring of patients and increase early detection of emergency conditions and diseases. High density WSNs helps us to accurately monitor a remote environment by intelligently combining the data from the individual nodes. Due to energy capacity limitation of sensors, enhancing the lifetime and the reliability of WSNs are important factors in designing of these networks. The clustering strategies are verified as effective and practical algorithms for reducing energy consumption in WSNs and can tackle WSNs limitations. In this paper, an Energy-efficient weight-based Clustering Protocol (EWCP) is presented. Artificial retina is selected as a case study of WSNs applied in body sensors. Cluster heads’ (CHs) selection is equipped with energy efficient parameters. Moreover, cluster members are selected based on their distance to the selected CHs. Comparing with the other benchmark protocols, the lifetime of EWCP is improved significantly.

Keywords: WSN, healthcare monitoring, weighted based clustering, lifetime

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Authors: Negar Mirzaie, Hedayet Allah Sharifi

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The present study was conducted with the aim of investigating the role of positive adolescent development on hope for the future in adolescents aged 14 to 18 with anxiety in the city of Ahvaz in the country of Iran in 2023. This research was descriptive and correlational. The statistical population of the present research was the adolescents aged 14 to 18 with anxiety of Ahvaz city in Iran in 2023. 150 people were selected by Purposeful sampling method and answered questionnaires of positive adolescent development and hope. The data were analyzed by Pearson correlation and multiple regression. The results showed that there is a significant relationship between the positive development of adolescence and hope for the future in anxious adolescents aged 14 to 18 in the city of Ahvaz in Iran in 2023 (p<0.05). Therefore, it is suggested to use the program of positive transformation of adolescents as hope in adolescents.

Keywords: positive adolescent development, hope, adolescents, anxiety

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Authors: L. N. A. Chandana Jayawardena, Ales Gregar

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Occupational Self Efficacy (OSE) reflects the conviction of a person’s ability to fulfill his job related behavior at a perfectly acceptable level to the employer. Transformational leadership improves followers’ commitment by influencing their needs, values, and self-esteem. Employees also develop a dyadic relationship with their immediate superiors. Study was conducted amongst one hundred and twenty two (122) bank managers in Sri Lanka. They were selected based on multi-stage (seniority in the hierarchy, gender, department-wise etc.) stratified random sampling. Major objectives of this study were to analyze the impact of transformational leadership style, and OSE along with socio-demographic factors, and career, job and organizational experience, to the career satisfaction of managers. SPSS software was used for parametric and non-parametric statistical analyses. Career satisfaction had positive impacts on their transformational leadership style, and their relationships with the immediate superior. Impact of socio-demographic factors, and career exposure to career satisfaction was assessed.

Keywords: career success, relationship with immediate superior, transformational leadership, occupational self efficacy (OSE)

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Authors: Thomas D. Collin, Rachel Cunningham, Bruce Jefferson, Raffaella Villa

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Fats, oils and grease (FOG) are by-products of food preparation and cooking processes. FOG enters wastewater systems through a variety of sources such as households, food service establishments, and industrial food facilities. Over time, if no source control is in place, FOG builds up on pipe walls, leading to blockages, and potentially to sewer overflows which are a major risk to the Environment and Human Health. UK water utilities spend millions of pounds annually trying to control FOG. Despite UK legislation specifying that discharge of such material is against the law, it is often complicated for water companies to identify and prosecute offenders. Hence, it leads to uncertainties regarding the attitude to take in terms of FOG management. Research is needed to seize the full potential of implementing current practices. The aim of this research was to undertake a comprehensive study to document the extent of FOG problems in sewer lines and reinforce existing knowledge. Data were collected to develop a model estimating quantities of FOG available for recovery within Thames Water wastewater catchments. Geographical Information System (GIS) software was used in conjunction to integrate data with a geographical component. FOG was responsible for at least 1/3 of sewer blockages in Thames Water waste area. A waste-based approach was developed through an extensive review to estimate the potential for FOG collection and recovery. Three main sources were identified: residential, commercial and industrial. Commercial properties were identified as one of the major FOG producers. The total potential FOG generated was estimated for the 354 wastewater catchments. Additionally, raw and settled sewage were sampled and analysed for FOG (as hexane extractable material) monthly at 20 sewage treatment works (STW) for three years. A good correlation was found with the sampled FOG and population equivalent (PE). On average, a difference of 43.03% was found between the estimated FOG (waste-based approach) and sampled FOG (raw sewage sampling). It was suggested that the approach undertaken could overestimate the FOG available, the sampling could only capture a fraction of FOG arriving at STW, and/or the difference could account for FOG accumulating in sewer lines. Furthermore, it was estimated that on average FOG could contribute up to 12.99% of the primary sludge removed. The model was further used to investigate the relationship between estimated FOG and number of blockages. The higher the FOG potential, the higher the number of FOG-related blockages is. The GIS-based tool was used to identify critical areas (i.e. high FOG potential and high number of FOG blockages). As reported in the literature, FOG was one of the main causes of sewer blockages. By identifying critical areas (i.e. high FOG potential and high number of FOG blockages) the model further explored the potential for source-control in terms of ‘sewer relief’ and waste recovery. Hence, it helped targeting where benefits from implementation of management strategies could be the highest. However, FOG is still likely to persist throughout the networks, and further research is needed to assess downstream impacts (i.e. at STW).

Keywords: fat, FOG, GIS, grease, oil, sewer blockages, sewer networks

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Authors: Volkan Kaya, Ersin Elbasi

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Watermarking and stenography are getting importance recently because of copyright protection and authentication. In watermarking we embed stamp, logo, noise or image to multimedia elements such as image, video, audio, animation and text. There are several works have been done in watermarking for different purposes. In this research work, we used watermarking techniques to embed patient information into the medical magnetic resonance (MR) images. There are two methods have been used; frequency domain (Digital Wavelet Transform-DWT, Digital Cosine Transform-DCT, and Digital Fourier Transform-DFT) and spatial domain (Least Significant Bits-LSB) domain. Experimental results show that embedding in frequency domains resist against one type of attacks, and embedding in spatial domain is resist against another group of attacks. Peak Signal Noise Ratio (PSNR) and Similarity Ratio (SR) values are two measurement values for testing. These two values give very promising result for information hiding in medical MR images.

Keywords: watermarking, medical image, frequency domain, least significant bits, security

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: Brian Ceh, Alison Jackson-Holland

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Food deserts are a reality in some cities. These deserts can be described as a shortage of healthy food options within close proximity of consumers. The shortage in this case is typically facilitated by a lack of stores in an urban area that provide adequate fruit and vegetable choices. This study explores new avenues to better understand food deserts by examining modes of transportation that are available to shoppers or consumers, e.g. walking, automobile, or public transit. Further, this study is unique in that it not only explores the location of large grocery stores, but small grocery and convenience stores too. In this study, the relationship between some socio-economic indicators, such as personal income, are also explored to determine any possible association with food deserts. In addition, to help facilitate our understanding of food deserts, complex network spatial models that are built on adequate algorithms are used to investigate the possibility of food deserts in the city of Hamilton, Canada. It is found that Hamilton, Canada is adequate serviced by retailers who provide healthy food choices and that the food desert phenomena is almost absent.

Keywords: Canada, desert, food, Hamilton, store

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Authors: Harisudha Kuresan, Dhanalakshmi Samiappan, T. Rama Rao

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In modern telecommunication applications, Glottal Closure Instants location finding is important and is directly evaluated from the speech waveform. Here, we study the GCI using Speech Event Detection using Residual Excitation and the Mean Based Signal (SEDREAMS) algorithm. Speech coding uses parameter estimation using audio signal processing techniques to model the speech signal combined with generic data compression algorithms to represent the resulting modeled in a compact bit stream. This paper proposes a sub-band coder SBC, which is a type of transform coding and its performance for GCI detection using SEDREAMS are evaluated. In SBCs code in the speech signal is divided into two or more frequency bands and each of these sub-band signal is coded individually. The sub-bands after being processed are recombined to form the output signal, whose bandwidth covers the whole frequency spectrum. Then the signal is decomposed into low and high-frequency components and decimation and interpolation in frequency domain are performed. The proposed structure significantly reduces error, and precise locations of Glottal Closure Instants (GCIs) are found using SEDREAMS algorithm.

Keywords: SEDREAMS, GCI, SBC, GOI

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Authors: Funmilayo L. Amao

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As a result of the high cost of housing, the increasing population is forced to live in substandard housing and unhealthy conditions giving rise to poor residential neighborhoods. The paper examines the causes and characteristics of poor residential neighborhood. The paper finds the problems that have influence poor neighborhoods to; poverty, growth of informal sector and housing shortage. The paper asserts that poor residential neighborhoods have adverse effects on the people. The secondary data was obtained from books, journals and seminar papers while primary data relating to building and environmental quality from structured questionnaire administered on sample of 500 household heads, from sampling frame of 5000 housing units. The study reveals that majority of the respondents are poor and employed in informal sector. The paper suggests urban renewal and slum upgrading programs as methods in dealing with the situation and an improvement in the socio-economic circumstances of the inhabitants.

Keywords: environmental degeneration, housing, poverty, quality of life, urban upgrading

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Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: Benedict Ita, Peter Okoi

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In this work, the concept of the extended Nikiforov-Uvarov technique is discussed and employed to obtain the exact bound state energy eigenvalues and the corresponding normalized eigenfunctions of the inverse square root potential. With expressions for the exact energy eigenvalues and corresponding eigenfunctions, the expressions for the expectation values of the inverse separation-squared, kinetic energy, and the momentum-squared of the potential are presented using the Hellmann Feynman theorem. For visualization, algorithms written and implemented in Python language are used to generate tables and plots for l-states of the energy eigenvalues and some expectation values. The results obtained here may find suitable applications in areas like atomic and molecular physics, chemical physics, nuclear physics, and solid-state physics.

Keywords: Schrodinger equation, Nikoforov-Uvarov method, inverse square root potential, diatomic molecules, Python programming, Hellmann-Feynman theorem, second order differential equation, matrix algebra

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Authors: Nga Yui Tong

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Mandarin, the official language of China, is based on the Beijing dialect and is spoken by more than one billion people from all over the world. To investigate the trends of Mandarin acquisition, 192 preschoolers are recruited by stratified random sampling. They are from 4 different districts in Beijing, 2 schools in each district, with 4 age groups, both genders, and 3 children in each stratum. The children are paired up to conduct semi-structured free play for 30 minutes. Their language output is videotaped, transcribed, and coded for the calculation of Mean Length of Utterance (MLU). Two-way ANOVA showed that the variation of MLU is significantly contributed by age, which is coherent to previous findings of other languages. This first large-scale study to investigate the developmental trend of Mandarin in young children in Beijing provides empirical evidence to the development of standards and curriculum planning for early Mandarin education. Interestingly, the gender effect in the study is insignificant, with boys showing a slightly higher MLU than girls across all age groups and settings, except the 4.5 years same-gender dyads. The societal factors in the Chinese context on parenting and gender bias are worth looking into.

Keywords: Beijing, language development, Mandarin, preschoolers

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Authors: Alexis Grandcoin, Stephanie Piel, Estelle Baures

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Brittany (France) is an agricultural region, where emerging pollutants are highly at risk to reach water bodies. Among them, pesticides metabolites are frequently detected in surface waters. The Vilaine watershed (11 000 km²) is of great interest, as a large drinking water treatment plant (100 000 m³/day) is located at the extreme downstream of it. This study aims to provide an evaluation of the pesticides metabolites pollution in the Vilaine watershed, and an understanding of their availability, in order to protect the water resource. Hydroclimatic conditions, land use, and water quality parameters controlling metabolites availability are emphasized. Later this knowledge will be used to understand the favoring conditions resulting in metabolites export towards surface water. 19 sampling points have been strategically chosen along the 220 km of the Vilaine river and its 3 main influents. Furthermore, the intakes of two drinking water plants have been sampled, one is located at the extreme downstream of the Vilaine river and the other is the riparian groundwater under the Vilaine river. 5 sampling campaigns with various hydroclimatic conditions have been carried out. Water quality parameters and hydroclimatic conditions have been measured. 15 environmentally relevant pesticides and metabolites have been analyzed. Also, these compounds are recalcitrant to classic water treatment that is why they have been selected. An evaluation of the watershed contamination has been done in 2016-2017. First observations showed that aminomethylphosphonic acid (AMPA) and metolachlor ethanesulfonic acid (MESA) are the most detected compounds in surface waters samples with 100 % and 98 % frequency of detection respectively. They are the main pollutants of the watershed regardless of the hydroclimatic conditions. AMPA concentration in the river strongly increases downstream of Rennes agglomeration (220k inhabitants) and reaches a maximum of 2.3 µg/l in low waters conditions. Groundwater contains mainly MESA, Diuron and metazachlor ESA at concentrations close to limits of quantification (LOQ) (0.02 µg/L). Metolachlor, metazachlor and alachlor due to their fast degradation in soils were found in small amounts (LOQ – 0.2 µg/L). Conversely glyphosate was regularly found during warm and sunny periods up to 0.6 µg/L. Soil uses (agricultural cultures types, urban areas, forests, wastewater treatment plants implementation), water quality parameters, and hydroclimatic conditions have been correlated to pesticides and metabolites concentration in waters. Statistical treatments showed that chloroacetamides metabolites and AMPA behave differently regardless of the hydroclimatic conditions. Chloroacetamides are correlated to each other, to agricultural areas and to typical agricultural tracers as nitrates. They are present in waters the whole year, especially during rainy periods, suggesting important stocks in soils. Also Chloroacetamides are negatively correlated with AMPA, the different forms of phosphorus, and organic matter. AMPA is ubiquitous but strongly correlated with urban areas despite the recent French regulation, restricting glyphosate to agricultural and private uses. This work helps to predict and understand metabolites present in the water resource used to craft drinking water. As the studied metabolites are difficult to remove, this project will be completed by a water treatment part.

Keywords: agricultural watershed, AMPA, metolachlor-ESA, water resource

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Authors: Abdelhadi Adel, Kadri Ouahab

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This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Keywords: multi-agent systems, emergence, genetic algorithm, makespan, systematic maintenance, scheduling, hybrid flow shop scheduling

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Authors: Nastaran Soltani

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Heavy metals such as Mercury are toxic elements that enter the environment through different ways and endanger the environment, plants, animals, and humans’ health. Microbial activities reduce the amount of heavy metals. Therefore, an effective mechanism to eliminate heavy metals in the nature and factory slops, is using bacteria living in polluted areas. Karun River in Khuzestan Province in Iran has been always polluted by heavy metals as it is located among different industries in the region. This study was performed based on the data from sampling water and sediments of four stations across the river during the four seasons of a year. The isolation of resistant bacteria was performed through enrichment and direct cultivation in a solid medium containing mercury. Various bacteria such as Pseudomonas sp., Serratia Marcescens, and E.coli were identified as mercury-resistant bacteria. The power of these bacteria to remove mercury varied from 28% to 86%, with strongest power belonging to Pseudomonas sp. isolated in spring making a good candidate to be used for mercury biological removal from factory slops.

Keywords: bacteria, Karun River, mercury, biological removal, mercury-resistant

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Authors: Paveenapat Nithitantiwat, Kwanjai Pataipakaipet

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The purpose of this research was to study the happiness level of nursing students, Boromarajonani College of nursing, Ratchaburi, Thailand. A purposive sampling of 652 first to four-year nursing students was used. This research is descriptive research. The instruments were questionnaires that developed by the researcher. It included the demographic data and nursing student’s perception about healthcare, safety, life security, family, proud of oneself, education and activities, dormitories and environment in college, and how to improve their happiness. Frequencies, percentage, mean, and T-test is used to analysis the data. The results of the research have shown that family and moral value was an important thing in nursing student’s life. In addition, the mean of the happiness level was a high level. The first year nursing students had the higher mean score of the happiness level than the fourth year, second year, and the third year, respectively. Therefore, nursing students would realize that the important things in their life are family and Buddhism’s teaching. In addition, dharma is guideline how to be both academic achievements and successful in life.

Keywords: happiness, nursing students, nursing students’ perceptions, bachelor program

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Haodong Huang, Zida Zhao, Shilong Sun, Chiyao Li, Wenfu Xu

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The control methods for legged robots based on deep reinforcement learning have seen widespread application; however, the inherent black-box nature of neural networks presents challenges in understanding the decision-making motives of the robots. To address this issue, we propose a fully interpretable deep reinforcement learning training method to elucidate the underlying principles of legged robot motion. We incorporate the dynamics of legged robots into the policy, where observations serve as inputs and actions as outputs of the dynamics model. By embedding the dynamics equations within the multi-layer perceptron (MLP) computation process and making the parameters trainable, we enhance interpretability. Additionally, Bayesian optimization is introduced to train these parameters. We validate the proposed fully interpretable motion control algorithm on a legged robot, opening new research avenues for motion control and learning algorithms for legged robots within the deep learning framework.

Keywords: deep reinforcement learning, interpretation, motion control, legged robots

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Authors: Mahdi Shamsi, Tohid Yousefi Rezaii, Siavash Eftekharifar

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Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising.

Keywords: compressed sensing, data denoising, model order selection, sparse representation

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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: Mobarok Hossain Bhuyain

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Video surveillance processing has attracted various security fields transforming it into one of the leading research fields. Today's demand for detection and tracking of human mobility for security is very useful for human security, such as in crowded areas. Accordingly, video surveillance technology has seen a rapid advancement in recent years, with algorithms analyzing the behavior of people under surveillance automatically. The main motivation of this research focuses on the detection and tracking of the elderly and socially vulnerable people in crowded areas. Degenerate people are a major health concern, especially for elderly people and socially vulnerable people. One major disadvantage of video surveillance is the need for continuous monitoring, especially in crowded areas. To assist the security monitoring live surveillance video, image processing, and artificial intelligence methods can be used to automatically send warning signals to the monitoring officers about elderly people and socially vulnerable people.

Keywords: human detection, target tracking, neural network, particle filter

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Authors: Jeyaseelan Maria Michael

Abstract:

This study investigated the prevalence of cyberbullying among college female students and its effects on their psychological well-being. The respondents were from the age group of 17 and 18, doing the first-year college in Tamilnadu, India. In this study, 110 participants were selected through simple random sampling. The standardized questionnaire of David Alvare-Garcia’s Cybervictimization Questionnaire for Adolescents (CYVIC) and Ryff’s Psychological Well-Being (PWB) were administered for data collection. CYVIC has four subdomains namely, impersonation, visual-sexual cybervictimization, written-verbal cybervictimization, online exclusion. Ryff’s PWB has six domains namely, autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self- acceptance. The collected data were analyzed by SPSS v.23. The results indicate that cyberbullying prevails among college female students (M=1.24, SD= .21). Among the participants, 17 are scored one standard deviation above the mean (1.45). Among the subdomains of the CYVIC, the respondents have the highest score (M=1.40, SD= .38) in written-verbal cybervictimization. Cyber victimization has a significant correlation at the 0.01 level with psychological well-being.

Keywords: college students, cyberbullying, cyber victimization, psychological well-being

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