فهرست مطالب s parsa

Several service identification methods have been proposed to identify services using a business process-based strategy. However, these methods are still not accurate enough and adequately automated and thus need improvements. The present study addresses this gap by proposing a new semi-automated combinational method that applies process mining techniques and simultaneously considers different aspects of the business domain (e.g., goal and data). We argue that this method facilitates service identification more comprehensively and accurately and helps enhance organizational performance and lower cost structure. Our method includes three Phases. In the first phase, the system log is inspected, and the running business process is extracted using process mining techniques. After validating this model, we create a goal and data model in the next phase. In the third phase, we establish connections between the introduced models by defining some matrices. These connections are of two types: structural and conceptual. Finally, we propose a couple of algorithms that lead to the identification of services. We evaluate the utility of our proposed method by conducting a case study and using the experts’ opinions from different perspectives as follows: (1) assessing the accuracy and reusability of the identified services, (2) appraising the efficiency of employing this method in more complex processes, (3) calculating the cohesion to coupling ratio, and (4) assessing the performance of the method and other service quality measures. The results indicate that the average accuracy of this method is about 12 % higher than the previously identified methods for both simple and complex processes. Additionally, it empirically proves that using the process mining techniques improves the service identification considerably (8%). Moreover, according to the experts’ opinions, the combination of goal and data model and process mining has increased the performance by 8%. In comparison, the cohesion to coupling ratio demonstrated a 7% increase compared to other methods. In sum, we conclude that this method is an advanced and reliable way of service identification regardless of the process size and the complexity. The findings reveal that considering different aspects of business processes together and using process mining techniques improves the ratio of cohesion to coupling and accuracy of the identified services. Adherence to this approach enables companies to mine their business processes, modify them, and quickly identify services with higher performance. Besides, using this method provides a semi-automated and more effective way of service identification

BlockChain (BC) has gotten a lot of interest because of its immutability and accompanying security and privacy benefits. BC has the potential to address the Internet of Things' security and privacy issues (IoT). BC, on the other hand, is computationally expensive, has limited scalability, and incurs significant bandwidth overheads and delays, making it unsuitable for use in the IoT. In this study, we propose a method that optimizes BC for use in IOT environment. we present a hierarchical structure that uses a private BC to increase scalability, reduce network overhead and delay. In the proposed method, devices with high-level resources build a network referred to as the overlay network on devices with low-level resources. The members of the overlay network manage the BC. The arrangement of the block managers at the overlay network level is two-dimensional which removes the focus from IOT management. We use trust technique and voting from direct neighbors to reduce network traffic and overhead costs. The use of factors such as trust, encouragement and penalty of block managers in the overlay network ensures accurate transactions in IOT. We use the new block manager authentication algorithm for authentication in the overlay network. The simulation results show that the proposed algorithm reduces packet overhead and delay in service delivery and increases the scalability of the BC in comparison to the system that uses the base BC. Furthermore, because in the proposed algorithm, the number of effective block managers in voting is limited to direct neighbors, the average time to confirm a block is significantly reduced.

Diesel engines in both automotive and industrial types are one of the most efficient engines in the world. Improving emissions and reducing fuel consumption is of great importance in the automotive industry. There are various methods in order to reduce emissions and fuel consumption of diesel engines. In this paper, at first it is tried to use the common rail fuel injection system to provide high pressure fuel behind the injectors and improve combustion inside the combustion chamber. It will decrease, soot, HC and CO emissions and fuel consumption while maintaining the performance of the OM355 diesel engine. It should be noted that by using the common rail system and better evaporation of fuel by the injection system and increasing the combustion efficiency, while reducing soot, HC and CO, the amount of NOX also increases. Therefore, after this stage, using a system to reduce the amount of NOX is necessary to create a trade-off between soot, HC, CO and NOX. So, with the use of Selective catalytic reduction (SCR) catalyst, which is one of the efficient technologies for controlling and reducing NOX, the amount of NOX in the post-combustion phase is significantly reduced. The experimental results of the OM355 engine test show the proper operation of the common rail and SCR systems.

Botnet is a group of hosts infected with the same malicious code and managed by an attacker or Botmaster through one or more command and control (C&C) servers. The new generation of Botnets generates C&C domain name  server’s list dynamically. This dynamic list created by a domain generation algorithm helps an attacker to periodically change its C&C servers and prevent their addresses from being blacklisted. Each infected host generates a large   number of domain names using a predefined algorithm and attempts to map them to their corresponding addresses by sending queries to the domain server. In this paper, the deep autoencoder neural network is used to identify domains without any knowledge of their generating algorithm, and the performance of the proposed method is compared with the performance of machine learning algorithms. Initially, a new dataset is created by combining a data set with normal domains and two datasets containing malicious and abnormal domains and both manual and automated   methods are used to extract the features of the new dataset. Deep autoencoder neural network is applied to new and pre-processed datasets and the results are compared with machine learning algorithms. Based on the obtained results, it is possible to identify the malicious domains generated by domain generating algorithms using the deep autoencoder neural network with a higher speed and an accuracy rate larger than 98.61%.

Domain generation algorithms (DGAs) are used in Botnets as rendezvous points to their command and control (C&C) servers, and can continuously provide a large number of domains which can evade detection by traditional methods such as Blacklist. Internet security vendors often use blacklists to detect Botnets and malwares, but the DGA can    continuously update the domain to evade blacklist detection. In this paper, first, using features engineering; the three types of structural, statistical and linguistic features are extracted for the detection of DGAs, and then a new dataset is produced by using a dataset with normal DGAs and two datasets with malicious DGAs. Using supervised machine learning algorithms, the classification of DGAs has been performed and the results have been compared to determine a DGA detection model with a higher accuracy and a lower error rate. The results obtained in this paper show that the random forest algorithm offers accuracy rate, detection rate and receiver operating characteristic (ROC) equal to 89.32%, 91.67% and 0.889, respectively. Also, compared to the results of the other investigated algorithms, the random forest algorithm presents a lower false positive rate (FPR) equal to 0.373.

Increasing freshwater consumption caused to reduce renewable freshwater resources in recent years, and one of the basic strategies would be use of non-conventional water resources. Arsenic is one of the natural elements widely distributed in the Earth’s crust. It is commonly found in compounds with oxygen, chlorine, or sulfur, which generally contain inorganic arsenic compounds. Arsenic organic compounds also contain hydrogen and arsenic carbon. There are several methods that can limit the amount of arsenic in water and wastewater; one of these methods is surface adsorption. In this process, any solid that tends to absorb the fluid environment on its surface is considered as an adsorbent. Absorption capacity, selectivity, reproducibility, kinetics, compatibility, and cost are the most essential characteristics of the adsorbent.

In this study, activated carbon derived from agricultural waste was used as tertiary treatment. The heated coal powder used in this study was obtained from the almond and walnut peel waste (from Tuyserkan city of Hamedan province). Activated carbon powder was used in laboratory-scale experiments and was performed for arsenic removal from synthetics samples. Physically activated carbon was obtained and then chemically activated by acidification. Characterization tests (i.e., XRD, FT-IR, BETand SEM tests) were carried out on both types of the adsorbent. Arsenic removal was carried out in batch experiments. The effect of laboratory parameters (i.e., contact time, pH, adsorbent dose, and initial concentration) on the removal process was studied. Experiments are carried out step by step, and after optimizing each parameter and keeping the other parameters constant, all the parameters are optimized accordingly.

The contact time for the adsorption process was considerably decreased in comparison with previous studies. Kinetic and equilibrium studies showed that the adsorption process followed by Langmuir isotherm and second-degree kinetic models. Chemical activation, improved performance, and characteristics of the adsorbent. Acidified charcoal and raw charcoal were compared, and it was found that acidic charcoal had the finest cavities and had a uniform distribution. Although the volume of the cavities has not changed significantly, the structure of the cavities has changed substantially, with the most enormous volume of cavities (0.5 cm3 / g) being less than 5 nm in diameter and the average diameter of the cavities Decreased by 2 nm. According to the results of the Coal Structure Morphological Survey (SEM), in crude coal, the cavities have large openings. Their number is small, but in acidified coal, the number of cavities is increased, and the surface area of the coal is high. The high internal surface area and the presence of microstructural cavities lead to high adsorption of arsenic at the acid-adsorbed sites. With increasing contact time from 0 to 3 minutes, the removal rate of arsenic increases, and after 3 minutes to 10 minutes, it grows with a low gradient and then the removal percentage slightly. In other words, after 10 minutes, there is a balance between the solid and liquid phases. The arsenic removal rate reaches 90% at the third minute and 100% in approximately 13 minutes. As the retention time increases, the contact time of the arsenic with the adsorbent increases, and the adsorption rate increases as the opportunity for contact with the adsorption sites increases. Due to the high specific surface area of the adsorbent and its morphological characteristics, the removal rate reaches 100% with time.

The XRD experiment shows that improved coal is closer to the stable structure than the raw coal. According to the FT-IR experiment, the acidified charcoal decreased the oxygen and aliphatic functional groups and increased the hydrophobicity of the charcoal. The BET experiment revealed that the cavity surface size increased, and the cavity diameter decreased. The cavity distribution was such that the largest volume of cavities was in the range of nanomolecular size. The SEM image also shows an increase in the fine cavities. As a result, the adsorbent has a good morphology and reduces the adsorption time. Also, its special surface is high and has uniform cavities distribution, which can be one of the main causes of high removal percentage. The results showed that at concentrations of less than 120 µg / L and 10 min retention time, the removal rate was 100%. At higher concentrations, drinking water standards can be reached by increasing the retention time or adsorbent dose. The rate of uptake depends on both the concentration of arsenic and the amount of the adsorbent

A Botnet is a set of infected computers and smart devices on the Internet that are controlled remotely by a Botmaster to perform various malicious activities like distributed denial of service attacks(DDoS), sending spam, click-fraud and etc. When a Botmaster communicates with its own Bots, it generates traffic that  analyzing this traffic to detect the traffic of the Botnet can be one of the influential factors for intrusion  detection systems (IDS). In this paper, the long short term memory (LSTM) method is proposed to classify P2P Botnet activities. The proposed approach is based on the characteristics of the transfer control protocol (TCP) packets and the performance of the method is evaluated using both ISCX and ISOT datasets. The experimental results show that our proposed approach has a high capability in identifying P2P network activities based on evaluation criteria. The proposed method offers a 99.65% precision rate, a 96.32% accuracy rate and a recall rate of 99.63% with a false positive rate (FPR) of 0.67%.

Fuzzing is a dynamic software testing technique. In this technique with repeated generation and injection of malformed test data to the software under test (SUT), we are looking for the possible errors and vulnerabilities. Files are significant inputs to most real-world applications. Many of test data which are generated for fuzzing such programs are rejected by the parser because they are not in the acceptable format and this results in a low code coverage in the process of fuzz testing. Using the grammatical structure of input files to generate test data leads to increase code coverage. However, often, the grammar extraction is performed manually, which is a time consuming, costly and error-prone task. In this paper, a new method, based on deep neural language models (NLMs), is proposed for automatically learning the file structure and then generating and fuzzing test data. Our experiments demonstrate that the data produced by this method leads to an increase in the code coverage compared to previous test data generation methods. For MuPDF software, which accepts the PDF complex file format as an input, we have more than 1.30 to 12 percent improvement in code coverage than both the intelligence and random methods.

Availability of nutrients needed by plants and ambient light are important in the growth and synthesis of active substance of medicinal plants. Various nutritional combinations have been assessed to study their effect on improving field management and production of medicinal plants. Nitrogen is often a limiting nutrient in plant growth in most agricultural soils. Nitrogen is a major component of chlorophyll, proteins, nucleic acid, vitamins and other organic compounds by which plants use sunlight energy to produce sugars from water and carbon dioxide (i.e., photosynthesis). It is also a major component of amino acids, the building units of proteins. Phosphorus is another element which has an important role in nearly every plant process that involves energy transfer. High-energy phosphate, contained in the chemical structures of adenosine diphosphate (ADP) and ATP, is the source of energy that drives the variety of chemical reactions within the plant. Nitrogen and phosphorus can be supplied through fertilizers or animal manure. Various studies show that combined use of manure and chemical fertilizers (as N, P and …) have positive effects on soil fertility and growth of plant as well as protecting the environment. Studies also show the effect of adequate nutrients on plant growth parameters, light absorption and biosynthesis in medicinal plants. Therefore, the present study was conducted to evaluate the effect of organic amendments enriched with chemical fertilizers (nitrogen and phosphorus) on effective traits in light absorption and alcoholic extract of hemp in two consecutive years in Birjand. To study the effect of different levels of animal manure and chemical fertilizers, a split factorial experiment, based on complete randomized blocks design with three replications was conducted at the research Farm of Faculty of agriculture, University of Birjand, during 2013- 2014 growing seasons. Experimental factors include application of animal manure (well-rotted farmyard manure) at a rate of 0, 10, 20 and 30 ton ha-1 to the main plot. The sub plots were received nitrogen at rate of 0, 50 or 100 kg ha-1 (nitrogen in form of Urea) and phosphorus at rate of 0 or 80 kg ha-1 (P2O5 as triple superphosphate). Measured traits were shoot height and diameter, leaf area and leaf number, nitrogen percentage of leaf, canopy light absorption, shoot and leaf dry weights, percentage of extract of leaves and seeds. Nitrogen content of plants was determined by Kjeldahl method and light Absorption was measured by Sun scan. The data were statistically analyzed by SAS software (V. 9.1). Comparison of the means was performed using FLSD test at the 0.05 level of significant. The results showed that canopy light absorption highly correlated with the leaf numbers (0.676**), shoot height (0.621**), nitrogen percentage of leaf (0.543**) and shoot diameter (0.481**) respectively. Application of 30 ton ha-1 of animal manure resulted in less efficiency in increasing shoot height and diameter, leaf area and number and nitrogen percentage of leaf. The absorption of light by plant was higher in treatments with higher nitrogen content in leaf. The study showed that weight of shoot and leaf following treatment increased by animal manure, nitrogen and phosphorus supplements, which subsequently increased final weight of extract from leaves and seeds.
Application of 30 ton ha-1 of animal manure and 100 kg ha-1 of nitrogen resulted in 20.1% and 20.2% increase in content of leaf extract respectively, while application of 100 kg ha-1 of nitrogen resulted in 20% increase in total weight of extracts from seeds. Seed extracts were also highly correlated with the dry weight of leaves. Therefore, the study suggests that adequate nutritional supplement to soil can increase plant growth and final weight of the leaf and seed extracts from plants. Most seed extract estimated in combined treatment of 30 ton ha-1 animal manure and 80 kg ha-1 of phosphorus. The rate of plant growth was lower in the first year compare to the second year, which can be due to higher salinity of soil and animal manure and temperature of germination in the first year.
Overall, the findings of this study showed that application of 30 ton ha-1 of animal manure can provide the plants with adequate level of nitrogen and phosphorus resulting in increased level of extracts of leaf and seed in hemp plant and can reduce the need for nitrogen and phosphorus fertilizers use