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Nowadays, many online users find the selection of information and required products challenging due to the growing volume of data on the web. Recommender systems are introduced to deal with information overload. Cold start and data sparsity are the two primary issues in these systems, which lead to a decrease in the efficiency of recommender systems. To solve the problems, this paper proposes a novel method based on social network analysis. Our method leverages a multi-agent system for clustering users and items and predicting relationships between them simultaneously. The information on users and items is extracted from the user-item matrix as distinct graphs. Each of the graphs is then treated as a social network, which is further processed and analyzed by community detection and link prediction procedures. The users are grouped into several clusters by the community detection agent, which results in each cluster as a community. Then link prediction agent identifies the latent relationships between users and items. Simulation results show that the proposed method has significantly improved performance metrics as compared to recent techniques.

In recent years, we have seen an increase in the production of films in a variety of categoriesand genres. Many of these products contain concepts that are inappropriate for children andadolescents. Hence, parents are concerned that their children may be exposed to these products.As a result, a smart recommendation system that provides appropriate movies based on theuser’s age range could be a useful tool for parents. Existing movie recommender systems usequantitative factors and metadata that lead to less attention being paid to the content of themovies. This research is motivated by the need to extract movie features using informationretrieval methods in order to provide effective suggestions. The goal of this study is to propose amovie recommender system based on topic modeling and text-based age ratings. The proposedmethod uses latent Dirichlet allocation (LDA) modelling to identify hidden associations betweenwords, document topics, and the levels of expression of each topic in each document. Machinelearning models are then used to recommend age-appropriate movies. It has been demonstratedthat the proposed method can determine the user’s age and recommend movies based on theuser’s age with 93% accuracy, which is highly satisfactory.

In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user’s age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user’s age and recommend movies based on the user’s age with 93% accuracy, which is highly satisfactory.

Nowadays, with the advent of various communication technologies such as Internet of Things (IoT), a large volume of data is produced and needs to be processed in real-time. Fog computing is an appropriate solution to address the requirements of different types of IoT applications. In most cases, IoT applications consists a set of dependent task which can be separately processed in the heterogeneous Fog environment. Scheduling such dependent tasks in Fog environment is an NP-hard problem which needs a long time to be solved,making it unsuitable for real-time applications. In addition, reducing response time and energy consumption in Fog computing is an essential issue which should be taken into account in task scheduling algorithm. To address these challenges, we aim to propose a multiobjective task scheduling model to jointly improve energy efficiency and response time. To solve the model, we also propose an intelligent solution named IETIF which combines and levergaes the benefits of simulated annealing and NSGA-III algorithms. Simulation results show that IETIF outperforms the other state-of-the-art methods in terms of energy, response time and speedup.

The Levels of Personality Functioning Scale—Self-Report (LPFS-SR) is a freely available self-report instrument that corresponds directly in content to the DSM-5 Alternative Model of Personality Disorders (AMPD) Criterion A, which assesses global pathology of personality functioning. The present study translated the LPFS-SR into Persian and examined the psychometrics of the translated measure in a sample of 471 college students and 142 hospitalized patients with a diagnosed personality disorder (PD). Results indicate that the Persian translation of the LPFS-SR is highly internally consistent and concurrently valid with related indicators of personality pathology. Additionally, the LPFS-SR significantly discriminated between the college sample and clinical sample, while also demonstrating considerable incremental validity over the Structured Clinical Interview for DSM-IV-TR Axis II Personality Questionnaire (SCID-II-PQ) in the prediction of personality disorder diagnosis. The majority of indices supported a unidimensional structure for the LPFS-SR. Six items showed negative item-total correlations and may necessitate refinement in order to capture pathology-related content more adequately for Iranian culture. Overall, the findings of the present study support the use of the Persian translation of the LPFS-SR.

As a novel technology, the Internet of Things (IoT) has many applications in diverse fields, especially in smart homes. IoT includes a variety of communication networks and technologies which facilitate communication between heterogeneous devices. One of the primary challenges of IoT is energy consumption. This paper introduces a new Software Defined Network-based (SDN-based) clustering approach using intelligent algorithms for energy conservation in IoT. The proposed method uses an evolutionary algorithm to identify the required number of clusters and ensures their distribution in the environment. A virtual network is also employed to ensure network coverage and the formation of balanced clusters. Clustering, steady, and routing are the main steps of the proposed method that the clustering step is done in SDN. By expanding the steady phase and leveraging energy-based greedy routing, the network’s lifetime increases. After simulation in MATLAB, the proposed method is tested then the results are compared with other well-known algorithms. The evaluation results indicate that the proposed method has improved in terms of metrics such as energy consumption and network lifetime. The proposed approach improves energy consumption by 31%, 28%, 8% and 21% than FPA, MCFL, BEEG and NodeRanked respectively. The lifetime has been improved by 34% and 71% than BEEG and NodeRanked, respectively, and more than 100% for MCFL and FPA.

The current classification of personality disorder in ICD-11 includes a description of personality functioning, derived from a number of theoretical paradigms, but most notably consistent with the psychodynamic approach. Concurrently, an object-relations model of personality functioning in a dimensional assessment of severity is provided in the Structured Interview of Personality Organization-Revised (STIPO-R). To date, there are no published measures of International Classification of Diseases-11 (ICD-11) personality severity, though the construct is very comparable to the concepts assessed in the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) levels of personality functioning concept, which is measured by the Level of Personality Functioning Scale-Self-Report (LPFS-SR). This study examined the validity of ICD-11 personality functioning, as measured by the LPFS-SR, by evaluating its associations with the STIPO-R in Kurdistan region. The samples included 231 University students and 419 inpatient participants across four hospitals (267 with a diagnosed personality disorder). All the components of LPFS-SR and STIPO-R were positively and significantly intercorrelated. The components of each measure discriminated PD and non-PD patients from a University, non-clinical group adequately. Despite slightly better performance of the STIPO-R in this discrimination, the measures had a high congruence in predicting personality dysfunction. Overall, the findings of the present study support the validity of ICD-11 construct for evaluating personality functioning.

In this paper, by using an equivalent circuit, we examined V–I characteristic curve of an organic light-emitting diode. In this equivalent circuit, according to the multilayer structure of the organic light-emitting diode, intrinsic capacitance and ohmic resistance of layers’ junction are considered as the equivalent circuit elements. We investigated the impacts of temperature and equivalent circuit elements on the V–I characteristic curve. Furthermore, in this equivalent circuit, we have investigated voltage and current curve in term of frequency. Simulation has been done with the help of HSPICE and MATLAB software. The results can be used to improve the efficiency of the structure of organic light-emitting diode.

Polystyrene/poly(methyl methacrylate) (PS/PMMA) blends in 80:20, 50:50, and 20:80 ratios with and without calcium carbonate nanoparticles were prepared. n-Pentane was then used to foam the samples in an autoclave. After the diffusion of n-pentane gas into the polymer matrix, the samples and the gas were simultaneously cooled to obtain the liquid n-pentane phase. Phase change to liquid provided the required pressure drop for cell nucleation and consequent cell growth. The solubility of n-pentane in the samples was measured. Liquid n-pentane trapped inside created micro- and nanopores, forming a foam with closed cells. Experiments were carried out in different compositions of the materials, with and without nanoparticles, and the cell morphologies were characterized. The results of this work show that nanocellular structures can be achieved when calcium carbonate nanoparticles are added to PS/PMMA blends.

In recent years, the Underwater Internet of Things (IoUT) has become a popular technology for exploring the underwater environment. IoUT enables administrators to explore and monitor underwater environmental phenomena from anywhere in the world where there is Internet access. Due to the harsh underwater environment, the reliability of communication between sensor nodes deteriorates, causing certain performance issues such as higher packet loss rate and long end-to-end delay. Therefore, it is essential to manage the communications between the sensors to address these problems in order to improve the QoS. Software-defined networking (SDN) is one of the most promising architectures for providing efficient network management by decoupling the data plane from the control plane of the network. This paper proposes a new QoS routing technique for SDN-based IoUT aiming at improving QoS by establishing reliable paths between sensor nodes. To do this, the controller gathers the 3D coordinates of each underwater sensor in order to compute the distance between the nodes. Then, it estimates the reliability of each link by using underwater acoustic equations. Finally, it calculates the most reliable path with minimum delay and installs the path on the nodes located along it. The experimental results show that our mechanism significantly outperforms other non-SDN approaches in terms of several performance measures ranging from packet loss ratio and end-to-end delay to energy consumption.

Fog computing is a paradigm to overcome the cloud computing limitations which provides low latency to the users’ applications for the Internet of Things (IoT). Software-defined networking (SDN) is a practical networking infrastructure that provides a great capability in managing network flows. SDN switches are powerful devices, which can be used as fog devices/fog gateways simultaneously. Hence, fog devices are more vulnerable to several attacks. TCP SYN flood attack is one of the most common denial of service attacks, in which a malicious node produces many half-open TCP connections on the targeted computational nodes so as to break them down. Motivated by this, in this paper, we apply SDN concepts to address TCP SYN flood attacks in IoT–fog networks . We propose FUPE, a security-aware task scheduler in IoT–fog networks. FUPE puts forward a fuzzy-based multi-objective particle swarm Optimization approach to aggregate optimal computing resources and providing a proper level of security protection into one synthetic objective to find a single proper answer. We perform extensive simulations on IoT-based scenario to show that the FUPE algorithm significantly outperforms state-of-the-art algorithms. The simulation results indicate that, by varying the attack rates, the number of fog devices, and the number of jobs, the average response time of FUPE improved by 11% and 17%, and the network utilization of FUPE improved by 10% and 22% in comparison with Genetic and Particle Swarm Optimization algorithms, respectively.

A novel polyvinyl alcohol (PVA)/calcium carbonate-based double-layer cationic dye adsorbent was developed. Polyvinyl alcohol (50 wt %) and calcium carbonate (50 wt %) were used together with borax as a cross-linking agent. The nanocomposite was prepared using only water, without the need for any toxic solvent or hazardous chemical. The final samples were obtained by the solvent casting method. The nanocomposite adsorbent was characterized using a Fourier transform infrared (FTIR) spectroscope and a scanning electron microscope (SEM). The adsorption performance on two cationic dyes, i.e., methylene blue and safranin was studied. Dye adsorption was quantified by measuring the nanocomposite swelling, contact time, and dye concentration. Pseudo first-order and pseudo second-order kinetic models as well as intraparticle diffusion model were used to model the adsorption kinetics. Moreover, the isotherm dye adsorption was investigated by Langmuir and Freundlich models. The results revealed that the developed nanocomposite has relatively high adsorption efficiency and short adsorption time and retains its performance after several successive absorption&ndash, desorption processes. The results also showed that the pseudo-second-order model best describes the adsorption kinetics, and the Freundlich isotherm model has a better compatibility with the experimental data. Finally, an adsorption mechanism was proposed for the dye removal process. The developed PVA/CaCO3 nanocomposite can be potentially used for efficient dye removal in wastewater treatments.

Brown coal is a relatively abundant and low-cost material, which has been used as an effective ion-exchanger to remove ammonium from wastewater. In this study, the influences of pH, ammonium concentration and brown coal dose were investigated for removal of ammonium content from synthetic wastewater. Raw brown coal (RBC) treated with base solution has superior ammonium removal efficiency compared to RBC, which was due to chemical alterations and thus greater attachment of ammonium molecules to base-washed brown coal (BWBC), confirmed by Fourier transform infra-red spectroscopy. Scanning electron microscopy-electron diffraction scattering has identified the augmented sodium content in BWBC, which was subsequently replaced with nitrogen upon wastewater treatment. Crystallographic analysis showed a higher crystallinity formed in BWBC compared to RBC, which was likely due to formation of sodium salt crystals during NaOH treatment. Fitting batch experimental results to adsorption kinetic models suggested that the removal of ammonium was mainly governed by the reaction process rather than the physical diffusion mechanism. Both kinetic and isotherm studies confirmed higher adsorption capacity for BWBC compared to RBC. RBC in column mode was also experimented with to show organics removal from a secondary effluent. A comparatively lower removal of organics was obtained due to inability of charge neutralization as both brown coal and organics are positively charged.

The effects of using different seismic bearings were investigated to reduce the seismic response of buildings by assuming the vulnerability of 20-story regular RC building in this paper. The method of this study was that the studied building was studied in three different models in terms of its connection to the foundation. In the first model, the structures were placed on the rigid bearing and in the second and third models; lead-rubber bearings and friction pendulum bearings were placed at the counter between the structure and foundation, respectively. Then, the dynamic analysis was used to assess the behaviour and seismic response of the mentioned models. The results of the study showed that the structures in the first model functioned like cantilever column that would become uniaxial and biaxial bending under the effects of earthquake around the vertical axis of structure. Due to the tensile (tension) weakness in concrete, seismic loads caused major cracks in the tension part of the structures according to the place of the neutral axis that could lead to the collapse of structure. In addition, the use of mentioned seismic bearings under the earthquake caused the structure like a semi-rigid box slid on this equipment that reduced the structure's stiffness and increased the period of the structure in comparison with the first model. Using the studied seismic bearings caused the displacement of the roof of the first and twentieth stories of the structure become approximately equal and prevented the creation of the bending moment in the first model. The results of non-linear time history analysis showed that using the studied seismic bearings caused the response of the structure reduced significantly when the structure was placed on rigid bearings. It could be very valuable regarding the limitation of the capacity of the structure's members.

The virtual point detector concept is useful in gamma-ray spectroscopy. In this study, the virtual point detector, h0, was obtained for High Purity Germanium (HPGe) detectors of different sizes using MCNP5 Monte Carlo simulations. The HPGe detectors with different radii (rd), and height (hd), having aluminum, or Carbon windows, were simulated. A point photon source emitting several gammas with specific energies was defined at a distance x of the detectors. The pulse height distribution was scored using F8 tally. Finally, the artificial neural network was used for predicting the h0 values for every value of hd, rd, and x. Because of the high simulation duration of MCNP code, a trained ANN is used to predict the value of h0 for each detector size. The results indicate that the Artificial Neural Network (ANN) can predict the virtual point detector good accuracy.

In this paper, a dynamic analysis of annular laminate disk under radial thermal shock is carried out by employing a Galerkin Finite Element (GFE) approach. The governing equations, including the equation of the motion and energy equation are obtained based on Lord-Shulman theory. These two equations are solved simultaneously to obtain the displacement components and temperature distributions. A simply support boundary condition through outer edge is assumed for the annular disk. The inner radius is subjected to thermal shock and free of any traction. The outer edge is keeping at a constant temperature. Using Laplace transfer technique to transfer the governing equations into the space domain, where the Galerkin Finite Element Method is employed to obtain the solution in space domain. The inverse of Laplace transfer is performed numerically to achieve the final solution in the real time domain. The results are validated with the known data reported in the literature.

Today techniques of system simulation have become an important tool in the modern decision making science. Selecting an inappropriate simulation software package could lead to incorrect results and costly and adversely effects on organisational performance. The success of simulation depends on the ability to select a suitable simulation software package for an intended application. This study presents an integrated approach for evaluation and selection of best simulation software packages. Due to the strategic nature of this issue, the proposed approach includes: 1) criteria extracted from the literature on evaluating simulation software packages; 2) a framework based on fuzzy analytical hierarchy process (fuzzy AHP) and fuzzy technique for order performance by similarity to ideal solution (fuzzy TOPSIS), for selecting simulation software package; 3) an empirical study with data gathered from experts’ judgment to show applicability and superiority of the proposed model. In addition, the results are validated through sensitivity analysis.