Search results for: constriction factor based particle swarm optimization (CPSO)

Authors: Sinan Yapici, Hayrettin Eroglu

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The discharge of the aqueous radionuclides wastes used for the diagnoses of diseases and treatments of patients in nuclear medicine can cause fatal health problems when the radionuclides and its stable daughter component mix with underground water. Tl-201, which is one of the radionuclides commonly used in the nuclear medicine, is a toxic substance and is converted to its stable daughter component Hg-201, which is also a poisonous heavy metal: Tl201 → Hg201 + Gamma Ray [135-167 Kev (12%)] + X Ray [69-83 Kev (88%)]; t1/2 = 73,1 h. The purpose of the present work was to remove Tl-201 radionuclides from aqueous solution by biosorption on the solid bio wastes of food and cosmetic industry as bio sorbents of prina from an olive oil plant, rose residue from a rose oil plant and tea residue from a tea plant, and to make a comparison of the biosorption efficiencies. The effects of the biosorption temperature, initial pH of the aqueous solution, bio sorbent dose, particle size and stirring speed on the biosorption yield were investigated in a batch process. It was observed that the biosorption is a rapid process with an equilibrium time less than 10 minutes for all the bio sorbents. The efficiencies were found to be close to each other and measured maximum efficiencies were 93,30 percent for rose residue, 94,1 for prina and 98,4 for tea residue. In a temperature range of 283 and 313 K, the adsorption decreased with increasing temperature almost in a similar way. In a pH range of 2-10, increasing pH enhanced biosorption efficiency up to pH=7 and then the efficiency remained constant in a similar path for all the biosorbents. Increasing stirring speed from 360 to 720 rpm enhanced slightly the biosorption efficiency almost at the same ratio for all bio sorbents. Increasing particle size decreased the efficiency for all biosorbent; however the most negatively effected biosorbent was prina with a decrease in biosorption efficiency from about 84 percent to 40 with an increase in the nominal particle size 0,181 mm to 1,05 while the least effected one, tea residue, went down from about 97 percent to 87,5. The biosorption efficiencies of all the bio sorbents increased with increasing biosorbent dose in the range of 1,5 to 15,0 g/L in a similar manner. The fit of the experimental results to the adsorption isotherms proved that the biosorption process for all the bio sorbents can be represented best by Freundlich model. The kinetic analysis showed that all the processes fit very well to pseudo second order rate model. The thermodynamics calculations gave ∆G values between -8636 J mol-1 and -5378 for tea residue, -5313 and -3343 for rose residue, and -5701 and -3642 for prina with a ∆H values of -39516 J mol-1, -23660 and -26190, and ∆S values of -108.8 J mol-1 K-1, -64,0, -72,0 respectively, showing spontaneous and exothermic character of the processes. An empirical biosorption model in the following form was derived for each biosorbent as function of the parameters and time, taking into account the form of kinetic model, with regression coefficients over 0.9990 where At is biosorbtion efficiency at any time and Ae is the equilibrium efficiency, t is adsorption period as s, ko a constant, pH the initial acidity of biosorption medium, w the stirring speed as s-1, S the biosorbent dose as g L-1, D the particle size as m, and a, b, c, and e are the powers of the parameters, respectively, E a constant containing activation energy and T the temperature as K.

Keywords: radiation, diosorption, thallium, empirical modelling

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Authors: Amèdédjihundé H. J. Hounnou, Frédéric Dubas, François-Xavier Fifatin, Didier Chamagne, Antoine Vianou

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This paper presents the techno-economic evaluation of run-of-river small-hydropower plants. In this regard, a multi-objective optimization procedure is proposed for the optimal sizing of the hydropower plants, and NSGAII is employed as the optimization algorithm. Annual generated energy and investment cost are considered as the objective functions, and number of generator units (n) and nominal turbine flow rate (Q_T) constitute the decision variables. Site of Yeripao in Benin is considered as the case study. We have categorized the river of this site using its environmental characteristics: gross head, and first quartile, median, third quartile and mean of flow. Effects of each decision variable on the objective functions are analysed. The results gave Pareto Front which represents the trade-offs between annual energy generation and the investment cost of hydropower plants, as well as the recommended optimal solutions. We noted that with the increase of the annual energy generation, the investment cost rises. Thus, maximizing energy generation is contradictory with minimizing the investment cost. Moreover, we have noted that the solutions of Pareto Front are grouped according to the number of generator units (n). The results also illustrate that the costs per kWh are grouped according to the n and rise with the increase of the nominal turbine flow rate. The lowest investment costs per kWh are obtained for n equal to one and are between 0.065 and 0.180 €/kWh. Following the values of n (equal to 1, 2, 3 or 4), the investment cost and investment cost per kWh increase almost linearly with increasing the nominal turbine flowrate while annual generated. Energy increases logarithmically with increasing of the nominal turbine flowrate. This study made for the Yeripao river can be applied to other rivers with their own characteristics.

Keywords: hydropower plant, investment cost, multi-objective optimization, number of generator units

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Authors: S. J. Addinell, A. F. Grabsch, P. D. Fawell, B. Evans

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The Deep Exploration Technologies Cooperative Research Centre (DET CRC) is researching and developing a new coiled tubing based greenfields mineral exploration drilling system utilising down-hole water-powered percussive drill tooling. This new drilling system is aimed at significantly reducing the costs associated with identifying mineral resource deposits beneath deep, barren cover. This system has shown superior rates of penetration in water-rich, hard rock formations at depths exceeding 500 metres. With fluid flow rates of up to 120 litres per minute at 200 bar operating pressure to energise the bottom hole tooling, excessive quantities of high quality drilling fluid (water) would be required for a prolonged drilling campaign. As a result, drilling fluid recovery and recycling has been identified as a necessary option to minimise costs and logistical effort. While the majority of the cuttings report as coarse particles, a significant fines fraction will typically also be present. To maximise tool life longevity, the percussive bottom hole assembly requires high quality fluid with minimal solids loading and any recycled fluid needs to have a solids cut point below 40 microns and a concentration less than 400 ppm before it can be used to reenergise the system. This paper presents experimental results obtained from the research program during laboratory and field testing of the prototype drilling system. A study of the morphological aspects of the cuttings generated during the percussive drilling process shows a strong power law relationship for particle size distributions. This data is critical in optimising solids control strategies and cuttings dewatering techniques. Optimisation of deployable solids control equipment is discussed and how the required centrate clarity was achieved in the presence of pyrite-rich metasediment cuttings. Key results were the successful pre-aggregation of fines through the selection and use of high molecular weight anionic polyacrylamide flocculants and the techniques developed for optimal dosing prior to scroll decanter centrifugation, thus keeping sub 40 micron solids loading within prescribed limits. Experiments on maximising fines capture in the presence of thixotropic drilling fluid additives (e.g. Xanthan gum and other biopolymers) are also discussed. As no core is produced during the drilling process, it is intended that the particle laden returned drilling fluid is used for top-of-hole geochemical and mineralogical assessment. A discussion is therefore presented on the biasing and latency of cuttings representivity by dewatering techniques, as well as the resulting detrimental effects on depth fidelity and accuracy. Data pertaining to the sample biasing with respect to geochemical signatures due to particle size distributions is presented and shows that, depending on the solids control and dewatering techniques used, it can have unwanted influence on top-of-hole analysis. Strategies are proposed to overcome these effects, improving sample quality. Successful solids control and cuttings dewatering for water-powered percussive drilling is presented, contributing towards the successful advancement of coiled tubing based greenfields mineral exploration.

Keywords: cuttings, dewatering, flocculation, percussive drilling, solids control

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Authors: Sara Mohammad Atef Sabaika

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Background: The development of factor VIII (FVIII) inhibitor and hemophilic arthropathy in patients with hemophilia A (PWHA) are a great challenge for hemophilia care. Both genetic and environmental factors led to complications in PWHA. The development of inhibitory antibodies is usually induced by the immune response. Tumor necrosis factor α (TNF-α), one of the cytokines, might contribute to its polymorphism. Aim: Study the association between tumor necrosis alpha level and genotypes in pediatric patients with hemophilia A and its relation to inhibitor development and joint status. Methods: A cross-sectional study was conducted among a sufficient number of PWHA attending the Pediatric Hematology and Oncology Unit, Pediatric department in Menoufia University hospital. The clinical parameters, FVIII, FVIII inhibitor, and serum TNF-α level were assessed. The genotyping of −380G > A TNF-α gene polymorphism was performed using real time polymerase chain reaction. Results: Among the 50 PWHA, 28 (56%) were identified as severe PWHA. The FVIII inhibitor was identified in 6/28 (21.5%) of severe PWHA. There was a significant correlation between serum TNF-α level and the development of inhibitor (p = 0:043). There was significant correlation between polymorphisms of −380G > A TNF-α gene and hemophilic arthropathy development (p = 0:645). Conclusion: The prevalence of FVIII inhibitor in severe PWHA in Menoufia was 21.5%. The frequency of replacement therapy is a risk factor for inhibitor development. Serum TNF-α level and its gene polymorphism might be used to predict inhibitor development and joint status in pediatric patients with hemophilia A.

Keywords: hemophilic arthropathy, TNF alpha., patients witb hemophilia A PWHA, inhibitor

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Authors: Changiz Valmohammadi

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The main purpose of this study is to explore the perception of Iranian experts and executive managers of sample organizations on the benefits and barriers of Internet of Things (IoT) solutions implementation. Based on the review of the related literature and web sites, benefits and barriers of successful implementation to IoT solutions were identified. Through a self-administered questionnaire which was collected from 67 Iranian organizations the ranking and importance of benefits and barriers of IoT solutions implementation were determined based on the perception of the experts of the surveyed organizations. Analysis of data and the obtained results revealed that “improved customer experience” and “Supply chain optimization and responsiveness” are the most important benefits that the survey organizations expect to reap as a result of IoT solutions implementation. Also,” Integration challenges" and “cannot find right suppliers” were ranked as the most challenging barriers to IoT solutions implementation.

Keywords: internet of things (IoT), exploratory study, benefits, barriers, Iran

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Authors: Peter Unoh Bassey

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The study examined the influence of Costa and McCrae’s Neo-PI Factor and early adolescent’s school social adjustment in Cross River State, Nigeria. The research adopted the causal-comparative design also known as the ex-post facto with about one thousand and eighteen (1,018) students who were randomly selected from one stream of JSS 1 classes in 19 schools out of seventy-three (73) in the study area. Data were collected using two instruments one is the NEO-PI scale, and students school social adjustment questionnaire. Three research questions and three research hypotheses were postulated and tested at 0.05 level of significance. The analysis of data was carried out using both the independent t-test statistics and the one-way analysis of variance (ANOVA). The analyzed result indicated that the five dimensions had a significant influence on students school social adjustment. A post hoc was equally carried out to show the relative significant difference among the study variables. In view of the above, it was recommended that teachers, parents and educational psychologists should be involved to enhance students the confidence to overcome their social adjustment problem.

Keywords: Costa and McCrae’s NEO-PI Factor, early adolescents, school, social adjustment

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Authors: G. Lei, P. C. Dong, X. Q. Cen, S. Y. Mo

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One of the most significant parameters, describing the effect of water flooding in porous media, is flood-response time, and it is an important index in oilfield development. The responding time in low permeability reservoir is usually calculated by the method of stable state successive substitution neglecting the effect of medium deformation. Numerous studies show that the media deformation has an important impact on the development for low permeability reservoirs and can not be neglected. On the base of streamline tube model, we developed a method to interpret responding time with medium deformation factor. The results show that: the media deformation factor, threshold pressure gradient and well spacing have a significant effect on the flood response time. The greater the media deformation factor, threshold pressure gradient or well spacing is, the lower the flood response time is. The responding time of different streamlines varies. As the angle with the main streamline increases, the water flooding response time delays as a "parabola" shape.

Keywords: low permeability, flood-response time, threshold pressure gradient, medium deformation

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Authors: Hanjun Hu, Jinxiang Zhang

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With the decline of "growthism", China's urbanization process has shifted from the stage of spatial expansion to the stage of optimization of built-up spaces, and urban renewal has gradually become a new wave of China's urban movement in recent years. The ongoing urban renewal movement in China not only needs to generate new motivation for urban development but also solve the backlog of social problems caused by rapid urbanization, which provides an opportunity for the transformation of China's urban governance model. Unlike previous approaches that focused on physical space and functional renewal, such as urban reconstruction, redevelopment, and reuse, the key challenge of urban renewal in the post-growth era lies in coordinating the complex interest relationships between multiple stakeholders. The traditional theoretical frameworks that focus on the structural relations between social groups are insufficient to explain the behavior logic and mutual cooperation mechanism of various groups and individuals in the current urban renewal practices. Therefore, based on the long-term tracking of the urban renewal practices in the Old City of Nanjing (OCN), this paper introduces the "collective action" theory to deeply analyze changes in the urban renewal governance model in OCN and tries to summarize the governance strategies that promote the formation of collective action within recent practices from a micro-scale. The study found that the practice in OCN experienced three different stages "government-led", "growth coalition" and "asymmetric game". With the transformation of government governance concepts, the rise of residents' consciousness of rights, and the wider participation of social organizations in recent years, the urban renewal in OCN is entering a new stage of "collective renewal action". Through the establishment of the renewal organization model, incentive policies, and dynamic negotiation mechanism, urban renewal in OCN not only achieves a relative balance between individual interests and collective interests but also makes the willingness of residents the dominant factor in formulating urban renewal policies. However, the presentation of "collective renewal action" in OCN is still mainly based on typical cases. Although the government is no longer the dominant role, a large number of resident-led collective actions have not yet emerged, which puts forward new research needs for a sustainable governance policy innovation in this action.

Keywords: urban renewal, collective action theory, governance, cooperation mechanism, China

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Authors: Asawari Alok Pawaskar

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This study was aimed at investigating quality assurance (QA) done with IBA matrix, the discrepan­cies observed for helical tomotherapy plans. A selection of tomotherapy plans that initially failed the with Matrix process was chosen for this investigation. These plans failed the fluence analysis as assessed using gamma criteria (3%, 3 mm). Each of these plans was modified (keeping the planning constraints the same), beamlets rebatched and reoptimized. By increasing and decreasing the modula­tion factor, the fluence in a circumferential plane as measured with a diode array was assessed. A subset of these plans was investigated using varied pitch values. Factors for each plan that were examined were point doses, fluences, leaf opening times, planned leaf sinograms, and uniformity indices. In order to ensure that the treatment constraints remained the same, the dose-volume histograms (DVHs) of all the modulated plans were compared to the original plan. It was observed that a large increase in the modulation factor did not significantly improve DVH unifor­mity, but reduced the gamma analysis pass rate. This also increased the treatment delivery time by slowing down the gantry rotation speed which then increases the maximum to mean non-zero leaf open time ratio. Increasing and decreasing the pitch value did not substantially change treatment time, but the delivery accuracy was adversely affected. This may be due to many other factors, such as the complexity of the treatment plan and site. Patient sites included in this study were head and neck, breast, abdomen. The impact of leaf tim­ing inaccuracies on plans was greater with higher modulation factors. Point-dose measurements were seen to be less susceptible to changes in pitch and modulation factors. The initial modulation factor used by the optimizer, such that the TPS generated ‘actual’ modulation factor within the range of 1.4 to 2.5, resulted in an improved deliverable plan.

Keywords: dose volume histogram, modulation factor, IBA matrix, tomotherapy

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Authors: Simon J. Wilde, David M. Herold, Michael J. Bryant

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Social media networks have become the hotbed for advertising activities, due mainly to their increasing consumer/user base, and secondly, owing to the ability of marketers to accurately measure ad exposure and consumer-based insights on such networks. More than half of the world’s population (4.8 billion) now uses social media (60%), with 150 million new users having come online within the last 12 months (to June 2022). As the use of social media networks by users grows, key business strategies used for interacting with these potential customers have matured, especially social media advertising. Unlike other traditional media outlets, social media advertising is highly interactive and digital channel-specific. Social media advertisements are clearly targetable, providing marketers with an extremely powerful marketing tool. Yet despite the measurable benefits afforded to businesses engaged in social media advertising, recent controversies (such as the relationship between Facebook and Cambridge Analytica in 2018) have only heightened the role trust and privacy play within these social media networks. The purpose of this exploratory paper is to investigate the extent to which social media users trust social media advertising. Understanding this relationship will fundamentally assist marketers in better understanding social media interactions and their implications for society. Using a web-based quantitative survey instrument, survey participants were recruited via a reputable online panel survey site. Respondents to the survey represented social media users from all states and territories within Australia. Completed responses were received from a total of 258 social media users. Survey respondents represented all core age demographic groupings, including Gen Z/Millennials (18-45 years = 60.5% of respondents) and Gen X/Boomers (46-66+ years = 39.5% of respondents). An adapted ADTRUST scale, using a 20 item 7-point Likert scale, measured trust in social media advertising. The ADTRUST scale has been shown to be a valid measure of trust in advertising within traditional different media, such as broadcast media and print media, and more recently, the Internet (as a broader platform). The adapted scale was validated through exploratory factor analysis (EFA), resulting in a three-factor solution. These three factors were named reliability, usefulness and affect, and the willingness to rely on. Factor scores (weighted measures) were then calculated for these factors. Factor scores are estimates of the scores survey participants would have received on each of the factors had they been measured directly, with the following results recorded (Reliability = 4.68/7; Usefulness and Affect = 4.53/7; and Willingness to Rely On = 3.94/7). Further statistical analysis (independent samples t-test) determined the difference in factor scores between the factors when age (Gen Z/Millennials vs. Gen X/Boomers) was utilised as the independent, categorical variable. The results showed the difference in mean scores across all three factors to be statistically significant (p<0.05) for these two core age groupings: Gen Z/Millennials Reliability = 4.90/7 vs Gen X/Boomers Reliability = 4.34/7; Gen Z/Millennials Usefulness and Affect = 4.85/7 vs Gen X/Boomers Usefulness and Affect = 4.05/7; and Gen Z/Millennials Willingness to Rely On = 4.53/7 vs Gen X/Boomers Willingness to Rely On = 3.03/7. The results clearly indicate that older social media users lack trust in the quality of information conveyed in social media ads, when compared to younger, more social media-savvy consumers. This is especially evident with respect to Factor 3 (Willingness to Rely On), whose underlying variables reflect one’s behavioural intent to act based on the information conveyed in advertising. These findings can be useful to marketers, advertisers, and brand managers in that the results highlight a critical need to design ‘authentic’ advertisements on social media sites to better connect with these older users, in an attempt to foster positive behavioural responses from within this large demographic group – whose engagement with social media sites continues to increase year on year.

Keywords: social media advertising, trust, older consumers, online

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Authors: Varathagowry Vasudevan

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This paper attempts to demonstrate the importance of capacity building of the para-professionals in community based corrections for enhancing family and child welfare as a crucial factor in providing in-service training as a responsive methodology in community based corrections to enhance the best practices. The Diploma programme in community-based corrections initiated by the National Institute of Social Development has been engaged in this noble task of training quality personnel knowledgeable in the best practices and fieldwork skills on community-based correction and its best practice. To protect the families and children and enhance best practices, National Institute of Social Development with support from the department of community-based corrections initiated a Diploma programme in community-based corrections to enhance and update the knowledge, skills, attitudes with the right mindset of the work supervisors employed at the department of community-based corrections. This study based on reflective practice illustrated the effectiveness of curriculum of in-service training programme as a tool to enhance the capacities of the relevant officers in Sri Lanka. The data for the study was obtained from participants and coordinator through classroom discussions and key informant interviews. This study showed that use of appropriate tailor-made curriculum and field practice manual by the officers during the training was very much dependent on the provision of appropriate administrative facilities, passion, teaching methodology that promote capacity to involve best practices. It also demonstrated further the fact that professional social work response, strengthening families within legal framework was very much grounded in the adoption of proper skills imbibed through training in appropriate methodology practiced in the field under guided supervision.

Keywords: capacity building, community-based corrections, in-service training, paraprofessionals

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Authors: Meng-Yu Lin, Wan-Ju Lee

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Pore-water pressure, which mediates effective stress and shear strength at grain contacts, has a great influence on the motion of debris flow. The factors that control the diffusion of excess pore-water pressure play very important roles in the debris-flow motion. This research investigates these effects by solving the distribution of pore-water pressure numerically in an unsteady, surging motion of debris flow. The governing equations are the depth-averaged equations for the motion of debris-flow surges coupled with the one-dimensional diffusion equation for excess pore-water pressures. The pore-pressure diffusion equation is solved using a Fourier series, which may improve the accuracy of the solution. The motion of debris-flow surge is modelled using a Lagrangian particle method. From the computational results, the effects of pore-pressure diffusivities and the initial excess pore pressure on the formations of debris-flow surges are investigated. Computational results show that the presence of pore water can increase surge velocities and then changes the profiles of depth distribution. Due to the linear distribution of the vertical component of pore-water velocity, pore pressure dissipates rapidly near the bottom and forms a parabolic distribution in the vertical direction. Increases in the diffusivity of pore-water pressure cause the pore pressures decay more rapidly and then decrease the mobility of the surge.

Keywords: debris flow, diffusion, Lagrangian particle method, pore-pressure diffusivity, pore-water pressure

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Authors: Amari Khaoula, Berrahou Mohamed

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The topic of our article revolves around a numerical and comparative analysis between two notched Boron/epoxy plates that are U-shaped and the other V-shaped, cracked, and repaired by a rectangular patch of the same composite material; the finite element method was used for the analytical study and comparison of the results obtained for determining the optimal shape of notch which will give a longer life to the repair. In this context, we studied the variation of the stress intensity factor, the evolution of the damaged area, and the calculation of the ratio of the damaged area according to the crack length and the concentration of the Von Mises stresses as a function of the lengths of the paths. According to the results obtained, we conclude that the notch plate U is the optimal one than notch plate V because it has lower values either for the stress intensity factor (SIF), damaged area ratio (Dᵣ), or the Von Mises stresses.

Keywords: the notch U, the notch V, the finite element method FEM, comparison, rectangular patch, composite, stress intensity factor, damaged area ratio, Von Mises stresses

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Authors: Roman Snytsar

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Pseudo-random numbers are often used in scientific computing such as the Monte Carlo Simulations or the Quantum Inspired Optimization. Requirements for a parallel random number generator running in the modern multi-core vector environment are more stringent than those for sequential random number generators. As well as passing the usual quality tests, the output of the parallel random number generator must be verifiable and reproducible throughout the concurrent execution. We propose a family of vectorized Permuted Congruential Generators. Implementations are available for multiple modern vector modern computer architectures. Besides demonstrating good single core performance, the generators scale easily across many processor cores and multiple distributed nodes. We provide performance and parallel speedup analysis and comparisons between the implementations.

Keywords: pseudo-random numbers, quantum optimization, SIMD, parallel computing

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Authors: Huan Ting Liao

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In a typical manufacturing environment, computer numerical control (CNC) machining is essential for automating production through precise computer-controlled tool operations, significantly enhancing efficiency and ensuring consistent product quality. However, traditional CNC production lines often rely on manual loading and unloading, limiting operational efficiency and scalability. Although automated loading systems have been developed, they frequently lack sufficient intelligence and configuration efficiency, requiring extensive setup adjustments for different products and impacting overall productivity. This research addresses the job shop scheduling problem (JSSP) in CNC machining environments, aiming to minimize total completion time (makespan) and maximize CNC machine utilization. We propose a novel approach using reinforcement learning (RL), specifically the Q-learning algorithm, to optimize scheduling decisions. The study simulates the JSSP, incorporating robotic arm operations, machine processing times, and work order demand allocation to determine optimal processing sequences. The Q-learning algorithm enhances machine utilization by dynamically balancing workloads across CNC machines, adapting to varying job demands and machine states. This approach offers robust solutions for complex manufacturing environments by automating decision-making processes for job assignments. Additionally, we evaluate various layout configurations to identify the most efficient setup. By integrating RL-based scheduling optimization with layout analysis, this research aims to provide a comprehensive solution for improving manufacturing efficiency and productivity in CNC-based job shops. The proposed method's adaptability and automation potential promise significant advancements in tackling dynamic manufacturing challenges.

Keywords: job shop scheduling problem, reinforcement learning, operations sequence, layout optimization, q-learning

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Authors: Maíra O. Palm, Cintia Marangoni, Ozair Souza, Noeli Sellin

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Wastes from a vegetal extracts industry (cocoa, oak, Guarana and mate) were characterized by particle size, proximate and ultimate analysis, lignocellulosic fractions, high heating value, thermal analysis (Thermogravimetric analysis – TGA, and Differential thermal analysis - DTA) and energy density to evaluate their potential as biomass in the form of briquettes for power generation. All wastes presented adequate particle sizes to briquettes production. The wastes showed high moisture content, requiring previous drying for use as briquettes. Cocoa and oak wastes had the highest volatile matter contents with maximum mass loss at 310 ºC and 450 ºC, respectively. The solvents used in the aroma extraction process influenced in the moisture content of the wastes, which was higher for mate due to water has been used as solvent. All wastes showed an insignificant loss mass after 565 °C, hence resulting in low ash content. High carbon and hydrogen contents and low sulfur and nitrogen contents were observed ensuring a low generation of sulfur and nitrous oxides. Mate and cocoa exhibited the highest carbon and lignin content, and high heating value. The dried wastes had high heating value, from 17.1 MJ/kg to 20.8 MJ/kg. The results indicate the energy potential of wastes for use as fuel in power generation.

Keywords: agro-industrial waste, biomass, briquettes, combustion

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Sri Sharath Kulkarni, Pauline Janssen, Alberto Berardi, Bastiaan Dickhoff, Sander van Gessel

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Blending is a common process in the production of pharmaceutical dosage forms where the high shear is used to obtain a homogenous dosage. The shear required can lead to uncontrolled attrition of excipients and affect API’s. This has an impact on the performance of the formulation as this can alter the structure of the mixture. Therefore, it is important to understand the driving mechanisms for attrition. The aim of this study was to increase the fundamental understanding of the attrition behavior of excipients. Attrition behavior of the excipients was evaluated using a high shear blender (Procept Form-8, Zele, Belgium). Twelve pure excipients are tested, with morphologies varying from crystalline (sieved), granulated to spray dried (round to fibrous). Furthermore, materials include lactose, microcrystalline cellulose (MCC), di-calcium phosphate (DCP), and mannitol. The rotational speed of the blender was set at 1370 rpm to have the highest shear with a Froude (Fr) number 9. Varying blending times of 2-10 min were used. Subsequently, after blending, the excipients were analyzed for changes in particle size distribution (PSD). This was determined (n = 3) by dry laser diffraction (Helos/KR, Sympatec, Germany). Attrition was found to be a surface phenomenon which occurs in the first minutes of the high shear blending process. An increase of blending time above 2 mins showed no change in particle size distribution. Material chemistry was identified as a key driver for differences in the attrition behavior between different excipients. This is mainly related to the proneness to fragmentation, which is known to be higher for materials such as DCP and mannitol compared to lactose and MCC. Secondly, morphology also was identified as a driver of the degree of attrition. Granular products consisting of irregular surfaces showed the highest reduction in particle size. This is due to the weak solid bonds created between the primary particles during the granulation process. Granular DCP and mannitol show a reduction of 80-90% in x10(µm) compared to a 20-30% drop for granular lactose (monohydrate and anhydrous). Apart from the granular lactose, all the remaining morphologies of lactose (spray dried-round, sieved-tomahawk, milled) show little change in particle size. Similar observations have been made for spray-dried fibrous MCC. All these morphologies have little irregular or sharp surfaces and thereby are less prone to fragmentation. Therefore, products containing brittle materials such as mannitol and DCP are more prone to fragmentation when exposed to shear. Granular products with irregular surfaces lead to an increase in attrition. While spherical, crystalline, or fibrous morphologies show reduced impact during high shear blending. These changes in size will affect the functionality attributes of the formulation, such as flow, API homogeneity, tableting, formation of dust, etc. Hence it is important for formulators to fully understand the excipients to make the right choices.

Keywords: attrition, blending, continuous manufacturing, excipients, lactose, microcrystalline cellulose, shear

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Authors: Laurentiu Moruz, Jens Kitzhofer, Mircea Dinulescu

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The paper aims to extend the knowledge about jet behavior and jet interaction between five plane unventilated jets with large aspect ratio (AR). The distance between the single plane jets is two times the channel height. The experimental investigation applies 2D Particle Image Velocimetry (PIV) and static pressure measurements. Our study focuses on the influence of two different outlet nozzle geometries (triangular shape with 2 x 7.5° and blunt geometry) with respect to variation of Reynolds number from 5500 - 12000. It is shown that the outlet geometry has a major influence on the jet formation in terms of uniformity of velocity profiles downstream of the sudden expansion. Furthermore, we describe characteristic regions like converging region, merging region and combined region. The triangular outlet geometry generates most uniform velocity distributions in comparison to a blunt outlet nozzle geometry. The blunt outlet geometry shows an unstable behavior where the jets tend to attach to one side of the walls (ceiling) generating a large recirculation region on the opposite side. Static pressure measurements confirm the observation and indicate that the recirculation region is connected to larger pressure drop.

Keywords: 2D particle image velocimetry, parallel jet interaction, pressure drop, sudden expansion

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Authors: Meet Patel, Darshan Patel, Nilay Shah

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Excess growth of wind-based renewable energy sources is required to identify the optimal location and damping capacity of doubly fed induction-generator-based (DFIG) wind farms while it penetrates into the transmission network. In this analysis, various ratings of DFIG wind farms are penetrated into the Single Machine Infinite Bus (SMIB ) at a different distance of the transmission line. On the basis of detailed examinations, a prime position is evaluated to maximize the stability of overall systems. A damping controller is designed at an optimum location to mitigate the small oscillations. The proposed model was validated using eigenvalue analysis, calculation of the participation factor, and time-domain simulation.

Keywords: DFIG, small signal stability, eigenvalues, time domain simulation

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Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: Jiahui Yang, John Quigley, Lesley Walls

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In this paper, the authors develop a stochastic model regarding the investment in supplier delivery performance development from a buyer’s perspective. The authors propose a multivariate model through a Multinomial-Dirichlet distribution within an Empirical Bayesian inference framework, representing both the epistemic and aleatory uncertainties in deliveries. A closed form solution is obtained and the lower and upper bound for both optimal investment level and expected profit under uncertainty are derived. The theoretical properties provide decision makers with useful insights regarding supplier delivery performance improvement problems where multiple delivery statuses are involved. The authors also extend the model from a single supplier investment into a supplier portfolio, using a Lagrangian method to obtain a theoretical expression for an optimal investment level and overall expected profit. The model enables a buyer to know how the marginal expected profit/investment level of each supplier changes with respect to the budget and which supplier should be invested in when additional budget is available. An application of this model is illustrated in a simulation study. Overall, the main contribution of this study is to provide an optimal investment decision making framework for supplier development, taking into account multiple delivery statuses as well as multiple projects.

Keywords: decision making, empirical bayesian, portfolio optimization, supplier development, supply chain management

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Authors: C. E. Sutton, A. Varvani-Farahani

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Fatigue damage and life prediction of particle metal matrix composites (PMMCs) under uniaxial and multiaxial loading conditions were investigated. Three PMM composite materials of Al6061/Al2O3/20p-T6, Al6061/Al2O3/22p-T6 and Al6061/SiC/17w-T6 tested under tensile, torsion, and combined tension-torsion fatigue cycling were evaluated with various fatigue damage models. The fatigue damage models of Smith-Watson-Topper (S. W. T.), Ellyin, Brown-Miller, Fatemi-Socie, and Varvani were compared for their capability to assess the fatigue damage of materials undergoing various loading conditions. Fatigue life predication results were then evaluated by implementing material-dependent coefficients that factored in the effects of the particle reinforcement in the earlier developed Varvani model. The critical plane-energy approach incorporated the critical plane as the plane of crack initiation and early stage of crack growth. The strain energy density was calculated on the critical plane incorporating stress and strain components acting on the plane. This approach successfully evaluated fatigue damage values versus fatigue lives within a narrower band for both uniaxial and multiaxial loading conditions as compared with other damage approaches studied in this paper.

Keywords: fatigue damage, life prediction, critical plane approach, energy approach, PMM composites

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Authors: Wuthichai Wongthatsanekorn, Nuntana Matheekrieangkrai

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This research proposes Bee Algorithm (BA) to optimize Ready Mixed Concrete (RMC) truck scheduling problem from single batch plant to multiple construction sites. This problem is considered as an NP-hard constrained combinatorial optimization problem. This paper provides the details of the RMC dispatching process and its related constraints. BA was then developed to minimize total waiting time of RMC trucks while satisfying all constraints. The performance of BA is then evaluated on two benchmark problems (3 and 5construction sites) according to previous researchers. The simulation results of BA are compared in term of efficiency and accuracy with Genetic Algorithm (GA) and all problems show that BA approach outperforms GA in term of efficiency and accuracy to obtain optimal solution. Hence, BA approach could be practically implemented to obtain the best schedule.

Keywords: bee colony optimization, ready mixed concrete problem, ruck scheduling, multiple construction sites

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Authors: Chuan-Wen Liu, Min-Hsien Liu, Chung-Chieh Tai, Bing-Cheng Kuo, Cheng-Lung Chen, Huazhen Shen

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A recent surge in research on magnetic radar absorbing materials (RAMs) has presented researchers with new opportunities and challenges. This study was performed to gain a better understanding of the wave-absorbing phenomenon of magnetic RAMs. First, we hypothesized that the absorbing phenomenon is dependent on the particle shape. Using the Material Studio program and the micro-dot magnetic dipoles (MDMD) method, we obtained results from magnetic RAMs to support this hypothesis. The total MDMD energy of disk-like iron particles was greater than that of spherical iron particles. In addition, the particulate aggregation phenomenon decreases the wave-absorbance, according to both experiments and computational data. To conclude, this study may be of importance in terms of explaining the wave- absorbing characteristic of magnetic RAMs. Combining molecular dynamics simulation results and the theory of magnetization of magnetic dots, we investigated the magnetic properties of iron materials with different particle shapes and degrees of aggregation under external magnetic fields. The MDMD of the materials under magnetic fields of various strengths were simulated. Our results suggested that disk-like iron particles had a better magnetization than spherical iron particles. This result could be correlated with the magnetic wave- absorbing property of iron material.

Keywords: wave-absorbing property, magnetic material, micro-dot magnetic dipole, particulate aggregation

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Authors: Julio C. Ferreira, Maria T. A. Steiner

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The aim of this work is to present a solution procedure referred to here as the Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP) to provide solutions for a case study. The proposed methodology consists of three stages to resolve Scenario A. Stage 1 consists of the “treatment” of data; Stage 2 consists of applying mathematical models of the p-Median Capacitated Problem (with the objectives of minimization of distances and homogenization of demands between groups) and the Asymmetric Traveling Salesman Problem (with the objectives of minimizing distances and minimizing time). The weighted method was used as the multi-objective procedure. In Stage 3, an analysis of the results is conducted, taking into consideration the environmental aspects related to the case study, more specifically with regard to fuel consumption and air pollutant emission. This methodology was applied to a (partial) database that addresses newspaper distribution in the municipality of Curitiba, Paraná State, Brazil. The preliminary findings for Scenario A showed that it was possible to improve the distribution of the load, reduce the mileage and the greenhouse gas by 17.32% and the journey time by 22.58% in comparison with the current scenario. The intention for future works is to use other multi-objective techniques and an expanded version of the database and explore the triple bottom line of sustainability.

Keywords: Asymmetric Traveling Salesman Problem, Green Vehicle Routing Problem, Multi-objective Optimization, p-Median Capacitated Problem

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Authors: Itissam Abuiziah

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In several hydraulic systems, it is necessary to calculate the head losses which depend on the resistance flow friction factor in Darcy equation. Computing the resistance friction is based on implicit Colebrook-White equation which is considered as the standard for the friction calculation, but it needs high computational cost, therefore; several explicit approximation methods are used for solving an implicit equation to overcome this issue. It follows that the relative error is used to determine the most accurate method among the approximated used ones. Steel, cast iron and polyethylene pipe materials investigated with practical diameters ranged from 0.1m to 2.5m and velocities between 0.6m/s to 3m/s. In short, the results obtained show that the suitable method for some cases may not be accurate for other cases. For example, when using steel pipe materials, Zigrang and Silvester's method has revealed as the most precise in terms of low velocities 0.6 m/s to 1.3m/s. Comparatively, Halland method showed a less relative error with the gradual increase in velocity. Accordingly, the simulation results of this study might be employed by the hydraulic engineers, so they can take advantage to decide which is the most applicable method according to their practical pipe system expectations.

Keywords: Colebrook–White, explicit equation, friction factor, hydraulic resistance, implicit equation, Reynolds numbers

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Authors: P. Selva, B. Lorraina, J. Alexis, A. Seror, A. Longuet, C. Mary, F. Denard

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Over the past four decades, the fatigue behavior of nickel-based alloys has been widely studied. However, in recent years, significant advances in the fabrication process leading to grain size reduction have been made in order to improve fatigue properties of aircraft turbine discs. Indeed, a change in particle size affects the initiation mode of fatigue cracks as well as the fatigue life of the material. The present study aims to investigate the fatigue behavior of a newly developed nickel-based superalloy under biaxial-planar loading. Low Cycle Fatigue (LCF) tests are performed at different stress ratios so as to study the influence of the multiaxial stress state on the fatigue life of the material. Full-field displacement and strain measurements as well as crack initiation detection are obtained using Digital Image Correlation (DIC) techniques. The aim of this presentation is first to provide an in-depth description of both the experimental set-up and protocol: the multiaxial testing machine, the specific design of the cruciform specimen and performances of the DIC code are introduced. Second, results for sixteen specimens related to different load ratios are presented. Crack detection, strain amplitude and number of cycles to crack initiation vs. triaxial stress ratio for each loading case are given. Third, from fractographic investigations by scanning electron microscopy it is found that the mechanism of fatigue crack initiation does not depend on the triaxial stress ratio and that most fatigue cracks initiate from subsurface carbides.

Keywords: cruciform specimen, multiaxial fatigue, nickel-based superalloy

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Authors: Edafe Lucky Okotie, Emmanuel Osawaru Omosigho

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This paper examines the impacts of the introduction of distributed energy generation (DEG) technology into the Nigerian power system as an alternative means of energy generation at distribution ends using Otovwodo 15 MVA, 33/11kV injection substation as a case study. The overall idea is to increase the generated energy in the system, improve the voltage profile and reduce system losses. A photovoltaic-based distributed energy generator (PV-DEG) was considered and was optimally placed in the network using Genetic Algorithm (GA) in Mat. Lab/Simulink environment. The results of simulation obtained shows that the dynamic performance of the network was optimized with DEG-grid integration.

Keywords: distributed energy generation (DEG), genetic algorithm (GA), power quality, total load demand, voltage profile

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Authors: Einat Waizbard, Emilio Ferrer, Meghan Miller, Brianna Heath, Derek S. Andrews, Sally J. Rogers, Christine Wu Nordahl, Marjorie Solomon, David G. Amaral

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Background: Autism symptoms are comprised of social-communication deficits and restricted/repetitive behaviors (RRB). The severity of these symptoms can change during childhood, with differences between boys and girls. From the literature, it was found that young autistic girls show a stronger tendency to decrease and a weaker tendency to increase their overall autism symptom severity levels compared to young autistic boys. It is not clear, however, which symptoms are driving these sex differences across childhood. In the current study, we evaluated the trajectories of independent autism symptoms across childhood and compared the patterns of change in such symptoms between boys and girls. Method: The study included 183 children diagnosed with autism (55 girls) evaluated three times across childhood, at ages 3, 6 and 11. We analyzed 22 independent items from the Autism Diagnostic Observation Scheudule-2 (ADOS-2), the gold-standard assessment tool for autism symptoms, each item representing a specific autism symptom. First, we used latent growth curve models to estimate the trajectories for the 22 ADOS-2 items for each child in the study. Second, we extracted the factor scores representing the individual slopes for each ADOS-2 item (i.e., slope representing that child’s change in that specific item). Third, we used factor analysis to identify common patterns of change among the ADOS-2 items, separately for boys and girls, i.e., which autism symptoms tend to change together and which change independently across childhood. Results: The best-emerging patterns for both boys and girls identified four common factors: three factors representative of changes in social-communication symptoms and one factor describing changes in RRB. Boys and girls showed the same pattern of change in RRB, with four items (e.g., speech abnormalities) changing together across childhood and three items (e.g., mannerisms) changing independently of other items. For social-communication deficits in boys, three factors were identified: the first factor included six items representing initiating and engaging in social-communication (e.g., quality of social overtures, conversation), the second factor included five items describing responsive social-communication (e.g., response to name) and the third factor included three items related to different aspects of social-communication (e.g., level of language). Girls’ social-communications deficits also loaded onto three factors: the first factor included five items (e.g., unusual eye contact), the second factor included six items (e.g., quality of social response), and the third factor included four items (e.g., showing). Some items showed similar patterns of change for both sexes (e.g., responsive joint attention), while other items showed differences (e.g., shared enjoyment). Conclusions: Girls and boys had different patterns of change in autism symptom severity across childhood. For RRB, both sexes showed similar patterns. For social-communication symptoms, however, there were both similarities and differences between boys and girls in the way symptoms changed over time. The strongest patterns of change were identified for initiating and engaging in social communication for boys and responsive social communication for girls.

Keywords: autism spectrum disorder, autism symptom severity, symptom trajectories, sex differences

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