Qazvin Islamic Azad University

Based on environmental, legal, social, and economic factors, reverse logistics and closed-loop supply chain issues have attracted attention among both academia and practitioners. This attention is evident by the vast number of publications in scientific journals which have been published in recent years. Hence, a comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework of the past, and to shed light on future directions. The aim of this paper is to review recently published papers in reverse logistic and closed-loop supply chain in scientific journals. A total of 382 papers published between January 2007 and March 2013 are selected and reviewed. The papers are then analyzed and categorized to construct a useful foundation of past research. Finally, gaps in the literature are identified to clarify and to suggest future research opportunities.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

The rapid attitude stabilization problem of flexible spacecraft with uncertain inertia and disturbances is investigated. In this article, a sliding mode-based fixed-time control approach is presented with a new fixed-time surface ensuring a faster convergence rate incorporated. This surface has no singularity and can guarantee the settling time to be independent of initial states. An adaptive fixed-time attitude control law is then synthesized, which is continuous and chattering free. It is rigorously proved that the states of the spacecraft attitude system can converge into a small neighborhood after fixed time. A numerical example is presented to validate that the designed scheme is efficient to perform attitude stabilization maneuvers rapidly, whereas high control accuracy is still provided.

This paper presents a new nondeterministic model for joint transmission and energy storage expansion planning along with optimal transmission switching in wind farm-integrated power systems. The proposed approach adopts the underlying idea of robust optimization to characterize the uncertainty sources pertaining to load demands and wind power productions through uncertainty sets. Accordingly, a tractable adaptive min-max-min cost model is introduced to find a robust optimal expansion plan for new lines and storages withstanding the worst-case realization of the uncertain variables. As the adaptive min-max-min cost model cannot be solved directly by the commercial off-the-shelf software packages, a decomposition algorithm using primal cutting planes is introduced to obtain the optimal solution. The proposed approach has been implemented on the IEEE 24-bus and the IEEE 73-bus test systems. Also, the robustness of optimal expansion plans under different circumstances is evaluated through a post-optimization procedure simulating different realizations of the uncertainty sources. Case studies justify the efficiency of the proposed RO-based model.

The influence of Nano particles on mechanical properties and durability of concrete has been investigated. For this purpose, constant content of Nano-ZrO2 (NZ), Nano-Fe3O4 (NF), Nano TiO2 (NT) and Nano-Al2O3 (NA) have been added to concrete mixtures. Mechanical properties have been investigated through the compressive and indirect tensile strength and durability has been investigated through chloride penetration test and concrete permeability. Results of this study showed that Nano particles can be very effective in improvement of both mechanical properties and durability of concrete. Results of this study seem to indicate that the Nano-Al2O3 is most effective nano-particle of examined nano materials in improvement of mechanical properties of high performance concrete.

The present study investigates the effects of participation of managers in budgeting on their management performance in the Tehran regional electricity company. Variables of this study are participation in budgeting, performance of managers, adequacy of funding and resource allocation, job satisfaction, organizational commitment and organizational trust. The type of research is survey. The study population consists of all middle level managers of Tehran Regional Electricity Company who according to the approved organizational chart were 34 in 2012. The research tool is a questionnaire. Analysis of data collected is accomplished by using the SPSS and smartPLS. The results show a significant correlation between the variables of managers' participation in budgeting and organizational trust, adequacy of funding and resource allocation and job satisfaction, organizational trust of managers on organizational commitment, organizational commitment and competence in the absorption of funding and optimal resource allocation and finally the competence in funding and optimal resource allocation of managers as well as job satisfaction and management performance.

Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize firefly's movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed algorithm was tested on five standard functions that have ever used for testing the static optimization algorithms. Experimental results show better performance and more accuracy than standard Firefly algorithm.

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research—the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset (including 150,000 instances of people detection) with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the spatio-temporal data from people’s moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention.

This study presents a new adaptive data-hiding method based on least-significant-bit (LSB) substitution and pixel-value differencing (PVD) for grey-scale images. The proposed method partition the cover image into some non-overlapping blocks having three consecutive pixels and select the second pixel of each block as the central pixel (called base-pixel). Then, k-bits of secret data are embedded in the base pixel by using LSB substitution and optimal pixel adjustment process (OPAP). The difference between the base-pixel value and other pixel values in the block are utilised to determine how many secret bits can be embedded in the two pixels. Also, the method divides all possible differences into lower level and higher level with a number of ranges. Then, it obtains the number of the secret bits that will be embedded into each block depending on the range which the difference values belong to. The experimental results show that the proposed method can embed a large amount of secret data while maintaining a high visual quality of the stego-images. The peak signal-to-noise ratio (PSNR) values and the embedding capacity of our method are higher than those of three other data-hiding methods which are investigated in this study.

Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms.

Fused deposition modeling (FDM) is the trendiest three‐dimensional (3D) printing method among additive manufacturing technologies. In this process, the final parts are constructed through layer‐by‐layer adhesion of thermoplastic polymers. Amorphous thermoplastic polymers have better printability compared to semicrystalline ones; so, they are most popular with FDM users. Generally, the overall mechanical properties of FDM 3D printed parts are weaker in comparison to the traditional methods (such as injection molding) due to the weak bonds between the deposited rasters and layers. Therefore, the introduction of new materials with higher mechanical properties and easy printing process of the semicrystalline polymers has always been challenging to progress the mechanical properties of the products. In this study by the FDM process, the effect of nozzle temperature and heat treatment (annealing) on the mechanical properties of high‐temperature polylactic acids is investigated. The increase in the nozzle temperature develops the rasters and layers bonding, and the heat treatment of the parts after printing rises the crystallinity percentage, which is crucial for the improvement of mechanical properties. Experimental results show that an increase in the nozzle temperature raises the tensile strength and modulus to 65.7 MPa and 4.97 GPa, respectively. Furthermore, the heat treatment process increases the tensile strength and modulus up to 67.4 MPa and 5.65 GPa. The final tensile modulus values are the highest ones reported for pure materials printed by the FDM process. POLYM. ENG. SCI., 60:979–987, 2020. © 2020 Society of Plastics Engineers

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research—the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset (including 150,000 instances of people detection) with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the spatio-temporal data from people’s moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention.

This paper describes a prototype and analytical studies of a spherical rolling robot, a new design of an omnidirectional robot system. The robot can arbitrarily begin to move in any direction to the target, and autonomously roll and reach any desired position. Our design considers a spherical robot with an internal mechanism for propulsion. The propulsion mechanism distributes weights radially along spokes fixed inside the sphere and enables the robot to accelerate, decelerate, and move with constant velocity. A mathematical model of the robot's dynamic and motion is instructed. An algorithmic motion planning is developed and, partly, pseudo-code of that is presented. For a number of missions, it is shown experimentally that the model agrees well with the results.

Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

This paper studies a multi-trip vehicle routing problem with time windows specifically related to urban waste collection. Urban waste collection is one of the municipal activities with large costs and has many practical difficulties. In other words, waste collection and disposal is a costly task due to high operating expenses (fuel, maintenance, recycling, manpower, etc.) and small improvements in this field can result in tremendous savings on municipal expenditure. In the raised problem, the goal is to minimize total cost including traversing cost, vehicle employment cost, and exit penalty from permissible time windows. In this problem, the waste is deposited at the points indicating the demand nodes, in which each demand shows the volume of generated waste. Considering multiple trips for vehicles and time windows are the most critical features of the problem, so that the priorities of serving some specific places such as hospitals can be observed. Since vehicle routing problems (VRP) belongs to NP-hard problems, an efficient simulated annealing (SA) is proposed to solve the problem. The computational results show that our proposed algorithm has a great performance in a short computational time in comparison with the CPLEX solver. Finally, in order to demonstrate the applicability of the model, a case study is analyzed in Iran, and the optimal policies are presented.

In this article, a new feature selection (FS) algorithm, called simple, fast, and efficient (SFE), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using a search agent and two operators: 1) nonselection and 2) selection. It comprises two phases: 1) exploration and 2) exploitation. In the exploration phase, the nonselection operator performs a global search in the entire problem search space for the irrelevant, redundant, trivial, and noisy features and changes the status of the features from selected mode to nonselected mode. In the exploitation phase, the selection operator searches the problem search space for the features with a high impact on the classification results and changes the status of the features from nonselected mode to selected mode. The proposed SFE is successful in FS from high-dimensional datasets. However, after reducing the dimensionality of a dataset, its performance cannot be increased significantly. In these situations, an evolutionary computational method could be used to find a more efficient subset of features in the new and reduced search space. To overcome this issue, this article proposes a hybrid algorithm, SFE-PSO (particle swarm optimization) to find an optimal feature subset. The efficiency and effectiveness of the SFE and the SFE-PSO for FS are compared on 40 high-dimensional datasets. Their performances were compared with six recently proposed FS algorithms. The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.

This study evaluates the impact of Memorable Tourism Experiences (MTEs) on destination satisfaction, revisit intentions, and tourists’ positive word-of-mouth in the Iranian eco-tourism context. The sample consists of 389 Iranian tourists who travelled to two eco-tourism destinations: Deylaman and Rig-e-Jen. We analysed our data using Partial Least Squares Structural Equation Modelling (PLS–SEM). Nearly all MTE dimensions influenced destination satisfaction. Destination satisfaction mediated the relationship between MTEs, positive word-of-mouth, and revisit intentions. However, we could not find support for local culture in both mediation and direct impact. Our research is the first to apply MTE to eco-tourism in Iran.

Supplier selection is the process of finding the right suppliers, at the right price, at the right time, in the right quantities, and with the right quality. The aim of this paper, is supplier selection in the context of supply chain risk management. Thus nine criteria of quality, on time delivery and performance history and six risks in the supply chain including supply risk, demand risk, manufacturing risk, logistics risk, information risk and environmental risk considered for evaluating suppliers. Shannon entropy is used for weighing criteria and fuzzy TOPSIS is applied for ranking suppliers. Findings show that, in the spare parts supplier selection problem, demand risk is the most important factor.

Security in transmission storage of digital images has its importance in today's image communications and confidential video conferencing. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. Advanced Encryption Standard (AES) is a well known block cipher that has several advantages in data encryption. However, it is not suitable for real-time applications. In this paper, we analyze and present a modification to the Advanced Encryption Standard (MAES) to reflect a high level security and better image encryption. The modification is done by adjusting the ShiftRow Transformation. Detailed results in terms of security analysis and implementation are given. Experimental results verify and prove that the proposed modification to image cryptosystem is highly secure from the cryptographic viewpoint. The results also prove that with a comparison to original AES encryption algorithm the modified algorithm gives better encryption results in terms of security against statistical attacks.