Search results for: collective animal behavior algorithm

Authors: Piotr Podlipniak

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Social consolidation has often been indicated as an adaptive function of music which led to the evolution of music faculty. According to many scholars this function is possible thanks to musical rhythm that enables sensorimotor synchronization to a musical beat. The ability to synchronize to music allows performing music collectively which enhances social cohesion. However, the collective performance of music consists also in spectral synchronization that depends on musical pitch structure. Similarly to rhythmic synchronization, spectral synchronization is a result of ‘brain states alignment’ between people who collectively listen to or perform music. In order to successfully synchronize pitches performers have to adequately expect the pitch structure. The most common form of music which predominates among all human societies is tonal music. In fact tonality understood in the broadest sense as such an organization of musical pitches in which some pitch is more important than others is the only kind of musical pitch structure that has been observed in all currently known musical cultures. The perception of such a musical pitch structure elicits specific emotional reactions which are often described as tensions and relaxations. These facts provoke some important questions. What is the evolutionary reason that people use pitch structure as a form of vocal communication? Why different pitch structures elicit different emotional states independent of extra-musical context? It is proposed in the current presentation that in the course of evolution pitch structure became a human specific tool of communication the function of which is to induce emotional states such as uncertainty and cohesion. By the means of eliciting these emotions during collective music performance people are able to unconsciously give cues concerning social acceptance. This is probably one of the reasons why in all cultures people collectively perform tonal music. It is also suggested that tonal pitch structure had been invented socially before it became an evolutionary innovation of Homo sapiens. It means that a predisposition to tonally organize pitches evolved by the means of ‘Baldwin effect’ – a process in which natural selection transforms the learned response of an organism into the instinctive response. The hypothetical evolutionary scenario of the emergence of tonal pitch structure will be proposed. In this scenario social forces such as a need for closer cooperation play the crucial role.

Keywords: emotion, evolution, tonality, social consolidation

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Authors: Tomasz Grzes, Maciej Kopczynski, Jaroslaw Stepaniuk

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The rough sets theory developed by Prof. Z. Pawlak is one of the tools that can be used in the intelligent systems for data analysis and processing. Banking, medicine, image recognition and security are among the possible fields of utilization. In all these fields, the amount of the collected data is increasing quickly, but with the increase of the data, the computation speed becomes the critical factor. Data reduction is one of the solutions to this problem. Removing the redundancy in the rough sets can be achieved with the reduct. A lot of algorithms of generating the reduct were developed, but most of them are only software implementations, therefore have many limitations. Microprocessor uses the fixed word length, consumes a lot of time for either fetching as well as processing of the instruction and data; consequently, the software based implementations are relatively slow. Hardware systems don’t have these limitations and can process the data faster than a software. Reduct is the subset of the decision attributes that provides the discernibility of the objects. For the given decision table there can be more than one reduct. Core is the set of all indispensable condition attributes. None of its elements can be removed without affecting the classification power of all condition attributes. Moreover, every reduct consists of all the attributes from the core. In this paper, the hardware implementation of the two-stage greedy algorithm to find the one reduct is presented. The decision table is used as an input. Output of the algorithm is the superreduct which is the reduct with some additional removable attributes. First stage of the algorithm is calculating the core using the discernibility matrix. Second stage is generating the superreduct by enriching the core with the most common attributes, i.e., attributes that are more frequent in the decision table. Described above algorithm has two disadvantages: i) generating the superreduct instead of reduct, ii) additional first stage may be unnecessary if the core is empty. But for the systems focused on the fast computation of the reduct the first disadvantage is not the key problem. The core calculation can be achieved with a combinational logic block, and thus add respectively little time to the whole process. Algorithm presented in this paper was implemented in Field Programmable Gate Array (FPGA) as a digital device consisting of blocks that process the data in a single step. Calculating the core is done by the comparators connected to the block called 'singleton detector', which detects if the input word contains only single 'one'. Calculating the number of occurrences of the attribute is performed in the combinational block made up of the cascade of the adders. The superreduct generation process is iterative and thus needs the sequential circuit for controlling the calculations. For the research purpose, the algorithm was also implemented in C language and run on a PC. The times of execution of the reduct calculation in a hardware and software were considered. Results show increase in the speed of data processing.

Keywords: data reduction, digital systems design, field programmable gate array (FPGA), reduct, rough set

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Authors: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

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Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

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Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

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Authors: Jing Jiang, Yuguang Gao, Yang Yu

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Facing increasingly challenging insurance payment pressures, the Chinese healthcare system is undergoing significant transformations, akin to the implementation of DRG payment models by the United States' Medicare. Consequently, there is a pressing need for Chinese hospitals to establish optimizations in departmental operations tailored to the ongoing healthcare payment reforms. This abstract delineates the meticulous construction of a scientifically rigorous and comprehensive index system at the departmental level in China strategically aligned with the evolving landscape of healthcare payment reforms. Methodologically, it integrates key process areas and maturity assessment theories, synthesizing relevant literature and industry standards to construct a robust framework and indicator pool. Employing the Delphi method, consultations with 21 experts were conducted, revealing a collective demonstration of high enthusiasm, authority, and coordination in designing the index system. The resulting model comprises four primary indicators -technical capabilities, cost-effectiveness, operational efficiency, and disciplinary potential- supported by 14 secondary indicators and 23 tertiary indicators with varied coefficient adjustment for department types (platform or surgical). The application of this evaluation system in a Chinese hospital within the northeastern region yielded results aligning seamlessly with the actual operational scenario. In conclusion, the index system comprehensively considers the integrity and effectiveness of structural, process, and outcome indicators and stands as a comprehensive reflection of the collective expertise of the engaged experts, manifesting in a model designed to elevate the operational management of hospital departments. Its strategic alignment with healthcare payment reforms holds practical significance in guiding departmental development positioning, brand cultivation, and talent development.

Keywords: Chinese healthcare system, Delphi method, departmental management, evaluation indicators, hospital operations, weight coefficients

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: video tracking, particle filter, greedy snake, neural network

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Authors: M. M. Essa, S. Subash, M. Akbar, S. Al-Adawi, A. Al-Asmi, G. J. Guillemein

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Objective: Transgenic (tg) mice which contain an amyloid precursor protein (APP) gene mutation, develop extracellular amyloid beta (Aβ) deposition in the brain, and severe memory and behavioural deficits with age. These mice serve as an important animal model for testing the efficacy of novel drug candidates for the treatment and management of symptoms of Alzheimer's disease (AD). Several reports have suggested that oxidative stress is the underlying cause of Aβ neurotoxicity in AD. Pomegranates contain very high levels of antioxidants and several medicinal properties that may be useful for improving the quality of life in AD patients. In this study, we investigated the effect of dietary supplementation of Omani pomegranate extract on the memory, anxiety and learning skills along with inflammation in an AD mouse model containing the double Swedish APP mutation (APPsw/Tg2576). Methods: The experimental groups of APP-transgenic mice from the age of 4 months were fed custom-mix diets (pellets) containing 4% pomegranate. We assessed spatial memory and learning ability, psychomotor coordination, and anxiety-related behavior in Tg and wild-type mice at the age of 4-5 months and 18-19 months using the Morris water maze test, rota rod test, elevated plus maze test, and open field test. Further, inflammatory parameters also analysed. Results: APPsw/Tg2576 mice that were fed a standard chow diet without pomegranates showed significant memory deficits, increased anxiety-related behavior, and severe impairment in spatial learning ability, position discrimination learning ability and motor coordination along with increased inflammation compared to the wild type mice on the same diet, at the age of 18-19 months In contrast, APPsw/Tg2576 mice that were fed a diet containing 4% pomegranates showed a significant improvements in memory, learning, locomotor function, and anxiety with reduced inflammatory markers compared to APPsw/Tg2576 mice fed the standard chow diet. Conclusion: Our results suggest that dietary supplementation with pomegranates may slow the progression of cognitive and behavioural impairments in AD. The exact mechanism is still unclear and further extensive research needed.

Keywords: Alzheimer's disease, pomegranates, oman, cognitive decline, memory loss, anxiety, inflammation

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Authors: Heri Suryoatmojo, Adi Kurniawan, Feby A. Pamuji, Nursalim, Syaffaruddin, Herbert Innah

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Photovoltaic (PV) system hybrid with diesel system is utilized widely for electrification in remote area. PV output power fluctuates due to uncertainty condition of temperature and sun irradiance. When the penetration of PV power is large, the reliability of the power utility will be disturbed and seriously impact the unstable frequency of system. Therefore, designing a robust frequency controller in PV-diesel hybrid power system is very important. This paper proposes new method of frequency control application in hybrid PV-diesel system based on artificial neural network (ANN). This method can minimize the frequency deviation without smoothing PV output power that controlled by maximum power point tracking (MPPT) method. The neural network algorithm controller considers average irradiance, change of irradiance and frequency deviation. In order the show the effectiveness of proposed algorithm, the addition of battery as energy storage system is also presented. To validate the proposed method, the results of proposed system are compared with the results of similar system using MPPT only. The simulation results show that the proposed method able to suppress frequency deviation smaller compared to the results of system using MPPT only.

Keywords: energy storage system, frequency deviation, hybrid power generation, neural network algorithm

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Authors: Xiao Zhang, Wensheng Xiao, Junguo Cui, Hongmin Wang

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Submersible permanent magnet linear synchronous motors (SPMLSMs) are electromagnetic devices, which can directly drive plunger pump to obtain the crude oil. Those motors have been gradually applied in oil fields due to high thrust force density and high efficiency. Since the force performance closely depends on the concrete structural parameters, the seven different structural parameters are investigated in detail. This paper presents an optimum design of an SPMLSM to minimize the detent force and maximize the thrust by using design of experiment (DOE) and genetic algorithm (GA). The three significant structural parameters (air-gap length, slot width, pole-arc coefficient) are separately screened using 27 1/16 fractional factorial design (FFD) to investigate the significant effect of seven parameters used in this research on the force performance. Response surface methodology (RSM) is well adapted to make analytical model of thrust and detent force with constraints of corresponding significant parameters and enable objective function to be easily created, respectively. GA is performed as a searching tool to search for the Pareto-optimal solutions. By finite element analysis, the proposed PMLSM shows merits in improving thrust and reducing the detent force dramatically.

Keywords: optimization, force performance, design of experiment (DOE), genetic algorithm (GA)

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Authors: Elif Eroglu Hall, Nurdan Sevim

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The purposes of this research are to clarify the determinants of Muslim immigrants in consuming halal food by using components of Theory of Planned Behavior. The study was done by surveying Turkish immigrants living in Cologne Germany. The results of this study show that the intentions of Muslim Turkish immigrants in consuming halal food is influenced by attitude, subjective norms and perceived behavioral control.

Keywords: halal food, immigrants, religion, theory of planned behavior

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Toshinori Nawata

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In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of nonlinear systems with constrained input is presented. When designing the control, a constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. Parameters of the control are suboptimally selected by maximizing the stable region in the sense of Lyapunov with the aid of a genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.

Keywords: augmented automatic choosing control, nonlinear control, genetic algorithm, zero dynamics

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Authors: Fatna Benmessaoud, Mohammed Cheikh, Vencent Velay, Vanessa Vidal, Farhad Rezai-Aria, Christine Boher

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Ti-6Al-4V titanium alloy is one of the most widely used materials in aeronautical and aerospace industries. Because of its high specific strength, good fatigue, and corrosion resistance, this alloy is very suitable for moderate temperature applications. At room temperature, Ti-6Al-4V mechanical behavior is generally controlled by the behavior of alpha phase (beta phase percent is less than 8%). The plastic strain of this phase notably based on crystallographic slip can be hindered by various obstacles and mechanisms (crystal lattice friction, sessile dislocations, strengthening by solute atoms and grain boundaries…). The grains aspect of alpha phase (its morphology and texture) and the nature of its crystallographic lattice (which is hexagonal compact) give to plastic strain heterogeneous, discontinuous and anisotropic characteristics at the local scale. The aim of this work is to develop a multi-scale model for Ti-6Al-4V mechanical behavior using crystal plasticity approach; this multi-scale model is used then to investigate grains size, dispersion of grains size, crystallographic texture and slip systems activation effects on Ti-6Al-4V mechanical behavior under monotone quasi-static loading. Nine representative elementary volume (REV) are built for taking into account the physical elements (grains size, dispersion and crystallographic) mentioned above, then boundary conditions of tension test are applied. Finally, simulation of the mechanical behavior of Ti-6Al-4V and study of slip systems activation in alpha phase is reported. The results show that the macroscopic mechanical behavior of Ti-6Al-4V is strongly linked to the active slip systems family (prismatic, basal or pyramidal). The crystallographic texture determines which family of slip systems can be activated; therefore it gives to the plastic strain a heterogeneous character thus an anisotropic macroscopic mechanical behavior of Ti-6Al-4V alloy modeled. The grains size influences also on mechanical proprieties of Ti-6Al-4V, especially on the yield stress; by decreasing of the grain size, the yield strength increases. Finally, the grains' distribution which characterizes the morphology aspect (homogeneous or heterogeneous) gives to the deformation fields distribution enough heterogeneity because the crystallographic slip is easier in large grains compared to small grains, which generates a localization of plastic deformation in certain areas and a concentration of stresses in others.

Keywords: multi-scale modeling, Ti-6Al-4V alloy, crystal plasticity, grains size, crystallographic texture

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Authors: Debarun Dhar Purkayastha, Talinungsang

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SnO₂-TiO₂ nanocomposite thin films were prepared by the sol-gel method on borosilicate glass substrate. The films were annealed at a temperature of 300ᵒC, 400ᵒC, and 500ᵒC respectively for 2h in the air. The films obtained were further modified with stearic acid in order to decrease the surface energy. The X-ray diffraction patterns for the SnO₂-TiO₂ thin films after annealing at different temperatures can be indexed to the mixture of TiO₂ (rutile and anatase) and SnO₂ (tetragonal) phases. The average crystallite size calculated from Scherrer’s formula is found to be 6 nm. The SnO₂-TiO₂ thin films were hydrophilic which on modification with stearic acid exhibit superhydrophobic behavior. The increase in hydrophobicity of SnO₂ film with stearic acid modification is attributed to the change in surface energy of the film. The films exhibit superhydrophilic behavior under UV irradiation for 1h. Thus, it is observed that stearic acid modified surfaces are superhydrophobic but convert into superhydrophilic on being subjected to UV irradiation. SnO₂-TiO₂ thin films have potential for self-cleaning applications because of photoinduced hydrophilicity under UV irradiation.

Keywords: nanocomposite, self-cleaning, superhydrophobic, surface energy

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: S. A. Hashemi, A. Khoshraftar

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This paper presents the influence of the vertical seismic component on the non-linear dynamics analysis of three different structures. The subject structures were analyzed and designed according to recent codes. This paper considers three types of buildings: 5-, 10-, and 15-story buildings. The non-linear dynamics analysis of the structures with assuming elastic-perfectly-plastic behavior was performed using Ram Perform-3D software; the horizontal component was taken into consideration with and without the incorporation of the corresponding vertical component. Dynamic responses obtained for the horizontal component acting alone were compared with those obtained from the simultaneous application of both seismic components. The results show that the effect of the vertical component of the ground motion may increase the axial load significantly in the interior columns and consequently, the stories. The plastic mechanisms would be changed. The P-Delta effect is expected to increase. The punching base plate shear of the columns should be considered. Moreover, the vertical component increases the input energy when the structures exhibit inelastic behavior and are taller.

Keywords: inelastic behavior, non-linear dynamic analysis, steel structure, vertical component

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Authors: Carlos Joel Tchawouo Mbiada

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At the back of the apartheid era, South Africa embarked on a democratic drive of all its institution underpinned by a social justice perspective to eradicate past injustices. These democratic values based on fundamental human rights and equality informed all rights enshrined in the Constitution of the Republic of South Africa, 1996. This means that all rights are therefore infused by social justice perspective and labour rights are no exception. Labour law is therefore regulated to the extent that it is viewed as too rigid. Hence a call for more flexibility to enhance investment and boost job creation. This view articulated by the Free Market Foundation fell on deaf ears as the opponents believe in what is termed regulated flexibility which affords greater protection to vulnerable workers while promoting business opportunities and investment. The question that this paper seeks to examine is to what extent the regulation of labour law will go to protect employees. This question is prompted by the recent Constitutional Court’s judgment of National Union of Metalworkers of South Africa v Trenstar which barred the employer from employing labour replacement in response to the strike action by its employees. The question whether employers may use replacement labour and have recourse to lock-outs in response to strike action is considered in the context of the dichotomy between the Free market foundation and social justice perspectives which are at loggerheads in the South African collective bargaining process. With the current unemployment rate soaring constantly, the aftermath of the Covid 19 pandemic, the effects of the war in Ukraine and lately the financial burden of load shedding on companies to run their businesses, this paper argues for a policy shift toward deregulation or a lesser state and judiciary intervention. This initiative will relieve the burden on companies to run a viable business while at the same time protecting existing jobs.

Keywords: labour law, replacement labour, right to strike, free market foundation perspective, social justice perspective

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Authors: R. Dimitri Halley

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The research within Jungian Psychology presented here is on the wave state of Self. What has been discovered via shared dreaming, independently correlating dreams across dreamers, is beyond the Self stage into the deepest layer or the wave state Self: the very quantum ocean, the Self archetype is embedded in. A quantum wave or rhyming of meaning constituting synergy across several dreamers was discovered in dreams and in extensively shared dream work with small groups at a post therapy stage. Within the format of shared dreaming, we find synergy patterns beyond what Jung called the Self archetype. Jung led us up to the phase of Individuation and delivered the baton to Von Franz to work out the next synchronistic stage, here proposed as the finding of the quantum patterns making up the wave state of Self. These enfolded synchronistic patterns have been found in group format of shared dreaming of individuals approximating individuation, and the unfolding of it is carried by belief and faith. The reason for this format and operating system is because beyond therapy and of living reality, we find no science – no thinking or even awareness in the therapeutic sense – but rather a state of mental processing resembling more like that of spiritual attitude. Thinking as such is linear and cannot contain the deepest layer of Self, the quantum core of the human being. It is self reflection which is the container for the process at the wave state of Self. Observation locks us in an outside-in reactive flow from a first-person perspective and hence toward the surface we see to believe, whereas here, the direction of focus shifts to inside out/intrinsic. The operating system or language at the wave level of Self is thus belief and synchronicity. Belief has up to now been almost the sole province of organized religions but was viewed by Jung as an inherent property in the process of Individuation. The shared dreaming stage of the synchronistic patterns forms a larger story constituting a deep connectivity unfolding around individual Selves. Dreams of independent dreamers form larger patterns that come together as puzzles forming a larger story, and in this sense, this group work level builds on Jung as a post individuation collective stage. Shared dream correlations will be presented, illustrating a larger story in terms of trails of shared synchronicity.

Keywords: belief, shared dreaming, synchronistic patterns, wave state of self

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

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Authors: Jasbir Singh Gill, Baljit Singh

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Scheduling and mapping the application task graph on multiprocessor parallel systems is considered as the most crucial and critical NP-complete problem. Many genetic algorithms have been proposed to solve such problems. In this paper, two genetic approach based algorithms have been designed and developed with or without task duplication. The proposed algorithms work on two fitness functions. The first fitness i.e. task fitness is used to minimize the total finish time of the schedule (schedule length) while the second fitness function i.e. process fitness is concerned with allocating the tasks to the available highly efficient processor from the list of available processors (load balance). Proposed genetic-based algorithms have been experimentally implemented and evaluated with other state-of-art popular and widely used algorithms.

Keywords: parallel computing, task scheduling, task duplication, genetic algorithm

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Authors: Hooman Torabifard

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In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail.

Keywords: image summarization, particle swarm optimization, image threshold, image processing

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Authors: Mezigheche Nawel, Gouasmia Abdelhacine, Athmani Allaeddine, Merzoud Mouloud

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Masonry infill walls are inevitable in the self-supporting structures, but their contribution in the resistance of earthquake loads is generally neglected in the structural analyses. The principal aim of this work through a numerical study of the behavior of masonry infill walls in structures subjected to horizontal load is to propose by finite elements numerical modeling, a more reliable approach, faster and close to reality. In this study, 3D finite element analysis was developed to study the behavior of masonry infill walls in structures subjected to horizontal load: The finite element software being used was ABAQUS, it is observed that more rigidity of the masonry filling is significant, more the structure is rigid, so we can conclude that the filling brings an additional rigidity to the structure not to be neglected. It is also observed that when the framework is subjected to horizontal loads, the framework separates from the filling on the level of the tended diagonal.

Keywords: finite element, masonry infill walls, rigidity of the masonry, tended diagonal

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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Authors: Sarra Zaied, Arthur Bonpain, Pierre Yves Fravallo

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The permanent supply of marine litter implies their accumulation in the oceans, which causes the presence of more compact wastes layers. Their Spatio-temporal distribution is never homogeneous and depends mainly on the hydrodynamic characteristics of the environment and the size and location of the wastes. As part of optimizing collect of marine plastic wastes, it is important to measure and monitor their evolution over time. For this, many research studies have been dedicated to describing the wastes behavior in order to identify their accumulation in oceans areas. Several models are therefore developed to understand the mechanisms that allow the accumulation and the displacements of marine litter. These models are able to accurately simulate the drift of wastes to study their behavior and stranding. However, these works aim to study the wastes behavior over a long period of time and not at the time of waste collection. This work investigates the transport of floating marine litter (FML) to provide basic information that can help in optimizing wastes collection by proposing a model for predicting their behavior during collection. The proposed study is based on a Lagrangian modeling approach that uses the main factors influencing the dynamics of the waste. The performance of the proposed method was assessed on real data collected from the Copernicus Marine Environment Monitoring Service (CMEMS). Evaluation results in the Java Sea (Indonesia) prove that the proposed model can effectively predict the position and the velocity of marine wastes during collection.

Keywords: floating marine litter, lagrangian transport, particle-tracking model, wastes drift

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Authors: Roselyn Zaman, Mobarak Hossain

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A new mathematical model is developed to study crack breathing behavior considering effect of angular position of unbalanced force at different crack locations. Crack breathing behavior has been determined using effectual bending angle by studying the transient change of the crack area. Different crack breathing behavior of the unbalanced shaft has been observed for different combination of angular position of unbalanced force with crack location except crack locations 0.3L and 0.8335L, where L is the total length of the shaft, where unbalanced shaft behave completely like the balanced shaft. Based on different combination of angular position of unbalanced force with crack location, the stiffness of unbalanced shaft can be divided into three regions. An unbalanced shaft is overall stiffer than a balanced shaft when angular position of unbalance force is between 90° to 270° and crack located between 0.3L and 0.8335L, and it is overall flexible when the crack located in outside this crack region. On the other hand, it is overall flexible when angular position of unbalanced force is between 0° to 90° or 270° to 360° and crack located in middle region and it is overall stiffer for outside this crack region.

Keywords: cracked shaft, crack location, shaft stiffness, unbalanced force, and unbalanced force orientation

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Authors: Eleni Maria Pavlopoulou, Vasiliki N. Georgiannou, Filippos C. Chortis

Abstract:

The response of M31 sand stabilized with colloidal silica (CS) aqueous gel is investigated in the laboratory. CS is introduced in the water regime, forming a hydrosol. The low viscosity hydrosol thickens in a controllable manner to form a stable, non-toxic gel; the gel fills the pore space, retains the pore water, and supports the grain structure. The role of colloidal silica on subsequent sand behavior is examined with the aid of direct shear, triaxial, and normal compression tests. Under the examined loading modes, while the strength of the treated sand is enhanced, its stiffness may reduce, and its compressibility increase. However, in most geotechnical problems, the loading conditions are complex, involving changes in both stress magnitude and direction. Rotation of principal stresses (σ1, σ2, σ3) in varying amounts expressed as angle α, (from α=0° to 90°) in concurrence with increasing shear stress loading is commonly encountered in soil structures such as foundations, embankments, underwater slopes. To assess the influence of anisotropy on the response of sands before and after their stabilization, hollow cylinder tests were performed. The behavior of stabilized sand is compared with the characteristic sand behavior, i.e., a reduction in peak stress ratio associated with a softer stress-strain response with the increasing angle a. The influence of the magnitude of the intermediate principal stress (σ2) on the mechanical response of treated and untreated sand is also examined.

Keywords: anisotropy, colloidal silica, laboratory tests, sands, soil stabilization

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Authors: Chandra Mukherjee

Abstract:

The prevalent knowledge of the biological systems is based on the standard scientific perception of natural equilibrium, determination and predictability. Recently, a rethinking of concepts was presented and a new scientific perspective emerged that involves complexity theory with deterministic chaos theory, nonlinear dynamics and theory of fractals. The unpredictability of the chaotic processes probably would change our understanding of diseases and their management. The mathematical definition of chaos is defined by deterministic behavior with irregular patterns that obey mathematical equations which are critically dependent on initial conditions. The chaos theory is the branch of sciences with an interest in nonlinear dynamics, fractals, bifurcations, periodic oscillations and complexity. Recently, the biomedical interest for this scientific field made these mathematical concepts available to medical researchers and practitioners. Any biological network system is considered to have a nominal state, which is recognized as a homeostatic state. In reality, the different physiological systems are not under normal conditions in a stable state of homeostatic balance, but they are in a dynamically stable state with a chaotic behavior and complexity. Biological systems like heart rhythm and brain electrical activity are dynamical systems that can be classified as chaotic systems with sensitive dependence on initial conditions. In biological systems, the state of a disease is characterized by a loss of the complexity and chaotic behavior, and by the presence of pathological periodicity and regulatory behavior. The failure or the collapse of nonlinear dynamics is an indication of disease rather than a characteristic of health.

Keywords: HRV, HRVI, LF, HF, DII

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Authors: Zainal Abedin

Abstract:

Introduction: Reproductive health is one of the major priorities of global health and is a fundamental and inalienable part of women’s health due to childbearing, and it is closely associated with nutritional status. Objective: This study was done to assess reproductive health behavior and nutritional status of reproductive-age ethnic women residing in plain land. Method: It was a cross-sectional study conducted among conveniently selected 120 reproductive-aged ethnic women at three Upazila of Rajshahi District. Nutritional status was determined by the WHO cut-off value of BMI for the Asian population. Results: About 88% of respondents noticed that they seek treatment in response to disease, and most of them seek treatment from the pharmacy attendant. Two-thirds of women used contraceptives, and 76% of women received antenatal care visits from Govt health centers, private clinics, and NGO clinics, but 86% of respondents delivered at home. In terms of nutritional status, 70% were normal, 23% underweight, and 7% overweight. Conclusion: Though most of them were normal regarding nutritional status but one-fourth were still underweight. Local pharmacy/quack-dependent treatment should be reduced.

Keywords: reproductive health behavior, nutritional status, plain land, ethnic women

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Authors: Maria Tsompanoglou, Dimitris Armenis

Abstract:

Vessel behavior in different operational and weather conditions constitutes the main area of interest for the ship operator. Ship speed and fuel consumption are the most decisive parameters in this respect, as their correlation provides information about the economic and environmental efficiency of the vessel, becoming the basis of decision making in terms of maintenance and trading. In the analysis of vessel operational profile for the evaluation of fuel consumption and the equivalent CO2 emissions footprint, the indications of Speed Through Water are widely used. The seasonal and regional variations in seawater characteristics, which are available nowadays, can provide the basis for accurate estimation of the errors in Speed Through Water indications at any time. Accuracy in the speed value on a route basis can enable operator identify the ship fuel and propulsion efficiency and proceed with improvements. This paper discusses case studies, where the actual vessel speed was corrected by a post-processing algorithm. The effects of the vessel correction to standard Key Performance Indicators, as well as operational findings not identified earlier, are also discussed.

Keywords: data analytics, MATLAB, vessel performance monitoring, speed through water

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Authors: Crystal Shackleford, Michael Pasek, Allon Vishkin, Jeremy Ginges

Abstract:

In the Middle East, conflict is often viewed as religiously motivated. In this context, an important question is how we think the religion of the other drives their behavior. If people see conflicts as religious, they may expect the belief of the other to motivate intergroup bias. Beliefs about the motivations of the other impact how we engage with them. Conflict may result if actors believe the other’s religion promotes parochialism. To examine how actors on the ground in Israel-Palestine think about the God of the other as it relates to the other’s behavior towards them, we ran two studies in winter 2019 with an online sample of Jewish Israelis and fieldwork with Palestinians in the West Bank. We asked participants to predict the behavior of an outgroup member participating in an economic game task, dividing the money between themselves and another person, who is either an ingroup or outgroup member. Our experimental manipulation asks participants to predict the behavior of the other when the other is thinking of their God. Both Israelis and Palestinians believed outgroup members would show in-group favoritism, and that group members would give more to their in-group when thinking of their God. We also found that participants thought outgroup members would give more to their own ingroup when thinking of God. In other words, Palestinians predicted that Israelis would give more to fellow Israelis when thinking of God, but also more to Palestinians. Our results suggest that religious belief is seen to promote universal moral reasoning, even in a context with over 70 years of intense conflict. More broadly, this challenges the narrative that religion necessarily motivates intractable conflict.

Keywords: conflict, psychology, religion, meta-cognition, morality

Procedia PDF Downloads 123