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2. Frost Prediction Using Machine Learning Methods in Fars Province

Author

Barooni, Milad, Ziarati, Koorush, and Barooni, Ali

Subjects
Computer Science - Machine Learning
Abstract

One of the common hazards and issues in meteorology and agriculture is the problem of frost, chilling or freezing. This event occurs when the minimum ambient temperature falls below a certain value. This phenomenon causes a lot of damage to the country, especially Fars province. Solving this problem requires that, in addition to predicting the minimum temperature, we can provide enough time to implement the necessary measures. Empirical methods have been provided by the Food and Agriculture Organization (FAO), which can predict the minimum temperature, but not in time. In addition to this, we can use machine learning methods to model the minimum temperature. In this study, we have used three methods Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) as deep learning methods, and Gradient Boosting (XGBoost). A customized loss function designed for methods based on deep learning, which can be effective in reducing prediction errors. With methods based on deep learning models, not only do we observe a reduction in RMSE error compared to empirical methods but also have more time to predict minimum temperature. Thus, we can model the minimum temperature for the next 24 hours by having the current 24 hours. With the gradient boosting model (XGBoost) we can keep the prediction time as deep learning and RMSE error reduced. Finally, we experimentally concluded that machine learning methods work better than empirical methods and XGBoost model can have better performance in this problem among other implemented., Comment: Accpeted by 28th International Computer Conference, Computer Society of Iran (CSICC)

Published
2024
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6. On Principal Curve-Based Classifiers and Similarity-Based Selective Sampling in Time-Series

Author

Hakimzadeh, Aref, Ziarati, Koorush, and Taheri, Mohammad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Considering the concept of time-dilation, there exist some major issues with recurrent neural Architectures. Any variation in time spans between input data points causes performance attenuation in recurrent neural network architectures. Principal curve-based classifiers have the ability of handling any kind of variation in time spans. In other words, principal curve-based classifiers preserve the relativity of time while neural network architecture violates this property of time. On the other hand, considering the labeling costs and problems in online monitoring devices, there should be an algorithm that finds the data points which knowing their labels will cause in better performance of the classifier. Current selective sampling algorithms have lack of reliability due to the randomness of the proposed algorithms. This paper proposes a classifier and also a deterministic selective sampling algorithm with the same computational steps, both by use of principal curve as their building block in model definition., Comment: 13 double column pages

Published
2022

7. Graph-based Local Climate Classification in Iran

Author

Akrami, Neda, Ziarati, Koorush, and Dev, Soumyabrata

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

In this paper, we introduce a novel graph-based method to classify the regions with similar climate in a local area. We refer our proposed method as Graph Partition Based Method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state-of-the-art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state-of-the-art climate classification techniques. The climate data is collected from 24 synoptic stations in Fars province in southern Iran. The data includes seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes. To the best of our knowledge, our proposed method is the first graph-based climate classification system., Comment: Accepted in International Journal of Climatology, 2021

Published
2021

18. On the Reverse Engineering of the Citadel Botnet

Author

Rahimian, Ashkan, Ziarati, Raha, Preda, Stere, and Debbabi, Mourad

Subjects
Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture, Computer Science - Operating Systems, Computer Science - Software Engineering, D.4.6, K.6.5, E.3, D.2.7
Abstract

Citadel is an advanced information-stealing malware which targets financial information. This malware poses a real threat against the confidentiality and integrity of personal and business data. A joint operation was recently conducted by the FBI and the Microsoft Digital Crimes Unit in order to take down Citadel command-and-control servers. The operation caused some disruption in the botnet but has not stopped it completely. Due to the complex structure and advanced anti-reverse engineering techniques, the Citadel malware analysis process is both challenging and time-consuming. This allows cyber criminals to carry on with their attacks while the analysis is still in progress. In this paper, we present the results of the Citadel reverse engineering and provide additional insight into the functionality, inner workings, and open source components of the malware. In order to accelerate the reverse engineering process, we propose a clone-based analysis methodology. Citadel is an offspring of a previously analyzed malware called Zeus; thus, using the former as a reference, we can measure and quantify the similarities and differences of the new variant. Two types of code analysis techniques are provided in the methodology, namely assembly to source code matching and binary clone detection. The methodology can help reduce the number of functions requiring manual analysis. The analysis results prove that the approach is promising in Citadel malware analysis. Furthermore, the same approach is applicable to similar malware analysis scenarios., Comment: 10 pages, 17 figures. This is an updated / edited version of a paper appeared in FPS 2013

Published
2014
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21. Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization

Author

Rastegarmoghadam, Mahin and Ziarati, Koorush

Abstract

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is personalized based on learning and solving problem styles. The purposed algorithm, based on ACO, generates the adaptive optimal learning path. The algorithm describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The algorithm parameters are tuned dynamically to conform to the actual pedagogical process. The article includes the results of implementation and experiment represent this algorithm is able to provide its main purpose which is finding optimal learning paths based on learning styles and improved performance of previous adaptive tutoring systems.

Published
2017
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35. The Algorithms Cruz Rodriguez (CR) are Proposing A Novel Vaccine RNA-Peptide against Breast, Ovarian, and Lung Cancers Disease: Exosomes as Carrier in Cancer Progression and Metastasis

Author

Cruz-Rodriguez L, Dilsiz N, Barea R, Ziarati P, Hochwimmer B, Zayas Tamayo A, and Lambert Brown D

Abstract

Exosomes are a novel horizon in modern therapy and open exciting new opportunities for advanced drug transport and targeted release. Exosomes are biological nanoparticles with dimensions ranging from 30 to 100 nm and are generated by all cell types in the human body. Exosomes-which are extracellular vesicles that function in intercellular communication-may play a key role in the dissemination of vaccine as well as host-derived molecules during cancer disease. We highlight the composition and function of exosomes produced during cancer disease in our work, how these vesicles could function as carriers of the RNA-peptide molecule. Finally, according to the algorithms CRUZ RODRIGUEZ (CR) we are proposing various methods to develop a novel vaccine against breast, ovarian, and lung cancers. Therefore, six RNA-peptides from our previous study were chosen based on the potential of inducing strong fusion stability (FS=58.50 cruz) and exosome affinity (EA=59.80 ro), Biological Action (BA= 1.0223 ro/cruz). This result may lead to the development of promising new therapeutic approaches in cancer management by using exosome-mediated miRNA-peptide vaccine therapy. Due to, according to the algorithm CR the Optimal Biological Action (OBA) value for antitumoral RNA-peptide with Exosome as carrier are: 0.8 < OBA < 1.3. These results suggest that the designed vaccine can elicit specific immune responses against three types of cancer; however, these results need experimental studies to confirm the efficacy and safety profile of the proposed vaccine structure.

Published
2021
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37. Municipal solid waste management using electric vehicle by variable neighbourhood search algorithm

Author

Zamanian, Ali, Khodaei, Zeynab, and Ziarati, Koorush

Abstract

Efficient solid waste management is essential for addressing the multifaceted challenges associated with waste disposal, including environmental, economic, technical, and social aspects. Municipal solid waste management (MSWM) incurs significant costs and contributes to environmental pollution primarily due to the collection process. This research presents a comprehensive case study that focuses on optimising the routing of electric vehicles for solid waste collection. Specifically, it addresses the Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem considering Battery Recharging and Swapping (HOTDMDEVRPBRS) in the context of waste collection. This problem aims to optimise the routes taken by electric vehicles across multiple depots, considering constraints such as limited battery capacity, multiple technologies for recharging battery capacity, the impact of truckload on energy consumption, and time-dependent travel times. To tackle this problem, we propose a highly efficient Variable Neighborhood Search (VNS) meta-heuristic algorithm, which outperforms the Simulated Annealing (SA) approach. The proposed algorithm achieves an average improvement of 11.88% in solution quality and reduces execution time by 73% for last-mile delivery. We evaluate the effectiveness of the proposed approach using real-world data from New York's recycling management system. The findings of this study emphasise the potential for enhancing solution quality and operational efficiency in solid waste management.

Published
2024
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38. A novel evolutionary strategy optimization algorithm for reliability redundancy allocation problem with heterogeneous components.

Author

Hesampour, A.D., Ziarati, K., and Zarezadeh, S.

Subjects
OPTIMIZATION algorithms, RELIABILITY in engineering, NP-hard problems, ALGORITHMS, DESIGN
Abstract

The reliability-redundancy allocation problem (RRAP) is an optimization problem that maximizes system reliability under some constraints. In most studies on the RRAP, either active redundant components or cold standby components are used in a subsystem. This paper presents a new model for the RRAP of a system with a mixed redundancy strategy, in which all components can be heterogeneous. This formulation leads to a more precise solution for the problem; however, RRAP is an np-hard problem, and the new mixed heterogeneous model will be more complicated to solve. After formulating the issue, a novel design of an evolutionary strategy optimization algorithm is proposed to solve that. The problem consists of discrete and continuous variables, and different mutation strategies are designed for each. The new formulation of the problem and the new method for solving it lead to better results than those reported in other recent papers. We implement the new suggested heterogeneous model with the PSO and SPSO algorithms to better compare the proposed algorithm. Results show improvement in both system reliability and fitness evaluation count. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Organ Donation‑Related Psychosocial Interventions: Towards a Research‑Based Guideline - A Prospective Observational Study.

Author

Maarefvand, Masoomeh, Poursaadati, Maryam Zabihi, Niyasar, Mahbubeh Ziarati, and Khubchandani, Jagdish

Subjects
BRAIN death, OCCUPATIONAL roles, PROFESSIONAL ethics, SCIENTIFIC observation, NURSES' attitudes, SOCIAL support, RESEARCH methodology, SOCIAL workers, INTERVIEWING, FAMILIES, MEDICAL protocols, QUALITATIVE research, INFORMED consent (Medical law), CONTENT analysis, JUDGMENT sampling, STATISTICAL sampling, EMOTIONS, ORGAN donation, PSYCHOTHERAPY, SOCIAL case work, LONGITUDINAL method, GROUP process
Abstract

Introduction: Lack of consent from brain dead individuals’ families is a major impediment for organ donation (OD) which is caused by several factors. Families need to receive information and support before, during, and after OD. Involving social workers in the transplantation team could be helpful. Methods: To develop a guideline for OD‑related social work interventions, we conducted an inductive content analysis on experiences of experts providing services for families of brain dead individuals and searched scientific documents to identify eligible social work guidelines and studies. The participants were invited through purposeful and convenience sampling. Sampling was terminated when no additional information was acquired, and data saturation occurred. Results: Eleven experts who had experience in interviewing families of brain dead individuals participated voluntarily. In‑depth and semi‑structured interviews were conducted individually for each participant. We shared the draft for the social work guidelines with experts and finalized the guidelines according to their comments. Recommendations for social work interventions for brain dead individuals’ families in three main phases were categorized; before donation (process initiation and family consent), during donation, and after donation (short‑term and long‑term interventions). Conclusion: Social workers can get involved in transplantation teams to improve family protection during the consent seeking process and after the family’s consent about donation. [ABSTRACT FROM AUTHOR]

Published
2022
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41. Pathogenic cis p-tau levels in CSF reflects severity of traumatic brain injury.

Author

Mohsenian Sisakht, Alireza, Karamzade-Ziarati, Najme, Jahanbakhshi, Amin, Shahpasand, Koorosh, Aghababaei, Samareh, Ahmadvand, Omid, Azar, Maziar, Fattahi, Arash, and Zamanzadeh, Selva

Subjects
BRAIN injuries, TAU proteins, YOUNG adults, TAUOPATHIES, CEREBROSPINAL fluid
Abstract

Traumatic brain injury (TBI) is the main cause of death and disability among young people. Following TBI, immune system activation and cytokine release induce kinase activity and hyperphosphorylation of tau protein, a structural molecule in axonal microtubules. The cis configuration of phosphorylated tau at Th231 is extremely neurotoxic and is having a prion nature, spreads to brain areas as well as CSF. We examined the cerebrospinal fluid (CSF) cis p-tau levels in 32 TBI patients and 5 non-TBI controls to find out the correlation with TBI severity. CSF samples were drained 5–7 days after TBI and subjected for ELISA analysis with anti cis p-tau and β-amyloid antibodies. We had no patients with mild TBI, two patients with moderate (6.2%), 23 patients with severe (71.9%), and 7 patients with critical TBI (21.9%). While mean CSF β-amyloid in TBI and control groups did not show a statistically significant difference, the mean CSF cis p-tau level was significantly higher in the TBI group than the control samples. Also, intergroup analysis demonstrated that CSF cis p-tau levels were statistically different according to the head injury severity. Although CSF cis p-tau increased in the TBI patients, β-amyloid did not show a significant difference between patients and controls. Also, we observed an obvious negative correlation between CSF cis p-tau levels and GCS scores. Therefore, future researches on suppression of cis P-tau production or removing previously produced cis P-tau could be a suitable approach in treating TBI in order to prevent tauopathies and future neurodegeneration. [ABSTRACT FROM AUTHOR]

Published
2022
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44. A New Perspective on Estimation of Gas Flaring Volume From Space: OLI/TIRS, VIIRS, and TROPOMI

Author

Asadi‐Fard, Elmira, Falahatkar, Samereh, Tanha Ziarati, Mahdi, and Zhang, Xiaodong

Abstract

Gas flaring (GF) has the negative impact on the environment, climate, and human health. So, regular monitoring of flares and quantification of their volume is necessary. Iran has many natural oil/gas processing plants and petrochemical companies which are concentrated in the southern region. Pars Special Economic Energy Zone (PSEEZ) is an industry part with different kinds of active flares, thus a significant potential source of environmental impacts due to gas flaring. Remotely sensed data are used in gas‐flaring detection, volume estimation, and pollution emission. In this study, we applied day/nighttime radiation and air pollutant data to estimate gas flaring volumes. We developed artificial neural network models (ANN) for finding the relationship between the field measurement of GF volume as the dependent variable and shortwave infrared and thermal infrared bands of Landsat 8, M10 band of Visible Infrared Imaging Radiometer Suite, and air pollutant (NO2, CO, O3, and SO2) of TROPOMI as independent variables. Results showed that R2values were 0.73 for the ANN model from 2018 to 2019. The sensitivity analysis demonstrated that the thermal infrared bands of B10 and B11 of Landsat 8 had the most important role in the estimation of gas flaring volume. In contrast, the SWIR bands of Landsat 8 and all TROPOMI products were insignificant. The findings of this research help to shed light on the use of remotely sensed data in estimating the volume of gas flaring at the regional/global scale by integration of the ANN model. Gas flaring (GF) is an essential process that used to dispose the unwanted gases in oil/gas processing plants and petrochemical companies. Obviously, burning gases has a huge and negative impact on the environment, climate, and human health. So, the best way to manage these effects are monitoring, detecting, characterizing, and estimating the volume of gas flaring. Remote sensing has enough potential to provide the useful data in all parts of the research on GF in industrial areas. In this study, we estimated the volume of gas flaring in one of the gas industries of Iran, the Pars Special Economic Energy Zone (PSEEZ) by using remotely sensed data (B6, B7, B10, B11/landsat8‐ M10/VIIRS‐ NO2, CO, O3, SO2/Sentinel‐5P) and ground data based on two kinds of models, artificial neural network (ANN), and multivariate regression. Since there was a non‐linear relationship between the variables and the volume of gas flaring, the ANN model showed the most acceptable results. Among all the variables, bands 10 and 11 of Landsat 8 played a very important role in estimating the gas flaring volume. Air pollutants and remote sensing radiation used for estimation of gas flaring volumeThermal infrared bands of Landsat 8 showed the most important role in GF volume estimationNO2showed more significant role in GF volume estimation compared to others pollutants Air pollutants and remote sensing radiation used for estimation of gas flaring volume Thermal infrared bands of Landsat 8 showed the most important role in GF volume estimation NO2showed more significant role in GF volume estimation compared to others pollutants

Published
2024
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48. 3D Yolk@Shell TiO2–x/LDH Architecture: Tailored Structure for Visible Light CO2Conversion

Author

Ziarati, Abolfazl, Badiei, Alireza, Grillo, Rossella, and Burgi, Thomas

Abstract

CO2photoconversion into hydrocarbon solar fuels by engineered semiconductors is considered as a feasible plan to address global energy requirements in times of global warming. In this regard, three-dimensional yolk@shell hydrogenated TiO2/Co–Al layered double hydroxide (3D Y@S TiO2–x/LDH) architecture was successfully assembled by sequential solvothermal, hydrogen treatment, and hydrothermal preparation steps. This architecture revealed a high efficiency for the photoreduction of CO2to solar fuels, without a noble metal cocatalyst. The time-dependent experiment indicated that the production of CH3OH was almost selective until 2 h (up to 251 μmol/gcat.h), whereas CH4was produced gradually by increasing the time of reaction to 12 h (up to 63 μmol/gcat.h). This significant efficiency can be ascribed to the engineering of 3D Y@S TiO2–x/LDH architecture with considerable CO2sorption ability in mesoporous yolk@shell structure and LDH interlayer spaces. Also, oxygen vacancies in TiO2–xcould provide excess sites for sorption, activation, and conversion of CO2. Furthermore, the generated Ti3+ions in the Y@S TiO2structure as well as connecting of structure with LDH plates can facilitate the charge separation and decrease the band gap of nanoarchitecture to the visible region.

Published
2019
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49. Engineering of highly active Au/Pd supported on hydrogenated urchin-like yolk@shell TiO2for visible light photocatalytic Suzuki couplingElectronic supplementary information (ESI) available. See DOI: 10.1039/c9cy00798a

Author

Rohani, Sahar, Ziarati, Abolfazl, Ziarani, Ghodsi Mohammadi, Badiei, Alireza, and Burgi, Thomas

Abstract

Efficient smart photocatalysts and their surface engineering are necessary for the effective conversion of light energy into chemical energy in photocatalyzed organic reactions. Herein, we designed a hydrogenated urchin-like yolk@shell TiO2structure decorated with Au and Pd nanoparticles (HUY@S-TOH@AuPd) as a robust photocatalyst for C–C coupling reactions. The resulting architecture exhibits considerable photocatalytic performance in Suzuki coupling reactions under visible light irradiation with a turnover frequency (TOF) value as high as 7095 h−1. The beauty of this engineered structure lies on the following four points: (I) the urchin-like structure provided a large accessible surface area for high light harvesting as well as high noble metal anchoring; (II) the yolk@shell mesoporous architecture improved the absorption of light by multiple scattering; (III) the presence of Ti3+species on the surface of TiO2decreased the band gap of the structure to the visible region; (IV) the enriched electron density of Pd through the injection of hot electrons from Au as well as the flow of electrons from the titanium dioxide semiconductor to the metals accelerated the rate-determining step. The merging of bimetallic plasmonic nanoparticles and urchin like yolk–shell hydrogenated titanium dioxide architecture can open an avenue for designing photocatalysts with high stability and promising activity as well as high direct harvesting of visible light for a broad range of photocatalytic organic reactions.

Published
2019
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50. Nano-NiZr4(PO4)6as a superior catalyst for the synthesis of propargylamines under ultrasound irradiation

Author

Shahbazi-Alavi, Hossein, Nazemzadeh, Seyed Hadi, Ziarati, Abolfazl, and Safaei-Ghomi, Javad

Abstract

An easy and rapid method for the synthesis of propargylamines has been achieved through a three-component reaction of phenylacetylene, aromatic aldehydes, and morpholine or piperidine using nano-NiZr4(PO4)6under ultrasound irradiation. Atom economy, a wide range of products, excellent yields in short times, reusability of the catalyst, and low catalyst loading are some of the important features of this protocol.

Published
2018
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