امیرهومن حمصی

Acoustic signals are used for seafloor mapping, marine communications, detecting obstacles and barriers to sound propagation which have reflective or absorptive effects. This can be used to explore oil slicks or objects on the water. In this paper, both laboratory experiments and computer simulations are used to identify the material of the surface layer. For this purpose, a sound wave with a frequency of 59 kHz was emitted in a homogeneous environment under laboratory conditions and the data were collected. The experiment was performed using a tank with free surface and oil cover and with transmitter being positioned at different angles. Ray theory is used to verify the experimental laboratory results with computer simulation results. The experiments were performed in a 300 cm long, 50 cm wide and 80 cm deep glass tank. According to the simulation results, the accumulation of rays occurs at depths above 50 cm. By considering the temporal differences that appear in different patterns that are due to different surface layers and stratification, it is possible to determine the material of the surface layer and how sound moves in water. Also, the angle of the transmitter has a great effect on the sound signal. According to the results, sound is better emitted when the transmitter angle is 45 degrees.

The importance of organizational resilience in relief organizations and especially firefighting organizations as the first responders in the face of fire and accidents, the quality of services and organizational resilience in evaluating citizens' safety services is an important and effective issue in these organizations. The purpose of this research is to increase the resilience and reduce the flammability of the Tehran organization by identifying the indicators that affect resilience.
Material and

The nature of this research is of a scientific research type and the method of this study is applied based on library studies as well as field methods such as preparing a researcher questionnaire. In this regard, a mathematical decision-making model and statistical calculations have been used. Questionnaire questions were extracted from this research using theoretical bases, experts' opinions and using NFPA standards in different steps and distributed and completed among 150 employees of Tehran fire department in the second 6 months of 1400, and then by analyzing the data using Criteria were identified and prioritized using ANP method and EXCEL, SPSS22 and Super Decisions software.

According to the research findings, the highest priority among 3 criteria, 7 sub-criteria and 30 indicators, including: intra-organizational resilience (D) with a weight of 0.6601, downward resilience (D1) with a weight of 0.8019 and team skill (e12) with a weight of 0.6418 and the lowest priority is, respectively, extra-organizational resilience (B) with a weight of 0.0889, personnel skill (E1) with a weight of 0.0911, and the index of low-risk places (b14) with a weight of 0.0457.

The research results show that to increase organizational resilience and reduce vulnerability, intra-organizational resilience (criterion) and downward resilience (sub-criterion) and finally team skill (indicator) should be prioritized in the organization's policies and goals.

In this study, films consisting of TEMPO-oxidized cellulose nanofibers (TOCNF) prepared from eucalyptus and graphene oxide (GO) were prepared by casting-evaporation method. The morphological structure, thermal stability and mechanical properties of the nanocomposite films were investigated by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and tensile mechanical tests. The results obtained from the X-ray diffraction spectrum, FTIR spectrum and SEM observations revealed that TOCNF and graphene oxide were able to form nanocomposite films with homogeneous structure in low-weight percent of graphene oxide. Compared with films of Tempo-oxidized cellulose nanofibers (TOCNF/GO0%), the mean tensile strength of nanocomposite films consisted of 1.5 wt% of graphene oxide (TOCNF/GO1.5%) increased to 19%, which was not statistically significant. In addition, the mean tensile strength of nanocomposite films consisted of 3wt% of graphene oxide (TOCNF/GO3%) decreased to 14.5%, which was not statistically significant. The results of TGA showed that the thermal degradation temperature of TOCNF/GO0.5%, TOCNF/GO1.5% and TOCNF/GO3% nanocomposite films compared to the film of TOCNF/GO0% slightly changed towards lower temperatures.

The shortage and restriction of drinking water and even water consumption in many water-scarce countries of the globe, including Iran, has become an essential and vital issue. This is one of the key reasons that people and communities maximize the development of crops. The best management of crop production, taking into account the requirement for water in the agricultural sector, is one of the acceptable options in nations like Iran, which is also active in the field of agriculture. On the basis of this, it is vital to look at various analytical, planning, and decision-making techniques. Additionally, taking into account and calculating virtual water can significantly improve the cultivation pattern and decrease the amount of water currently consumed; as a result, optimizing the cultivation pattern using virtual water calculations can be highly beneficial. Coherent management and accurate decision-making in a variety of areas will result from the usage and implementation of spatial information systems, and taking into account virtual water will enhance navigation patterns and decrease water consumption. In order to achieve the best allocation of the cultivation pattern and as a result, the correct use of water in the agricultural lands of Ben-Rood district in the operations of Varzaneh city, located in the southeast of Isfahan province, was achieved in the current research by using the decision-making system that was created with the capabilities of GIS and artificial intelligence, along with the virtual water calculations of some agricultural plants. Using the collective intelligence algorithm of the ant community (ACO) in conjunction with the spatial information system, it was possible to achieve this goal after examining the optimization methods in decision-making using environmental parameters. This was done by taking into account the virtual water of cultivation and the growth of plants in the irrigation networks of the agricultural lands of the mentioned sector. Finally, the findings of this study demonstrate that the amount of water consumed may be decreased to 37% of the initial amount after optimizing the allocation of land for the cultivation of specific crops in that area, based on the virtual water of the crops.

Traditional methods of irrigation and resource utilization lack an organized strategy for managing water resources. There are often inefficiencies in the way irrigation water is used in fields and plains, leading to irrigation waste and inefficient water utilization. Considering the size and dispersion of the land, the variety of crops, the time and location at which they are planted, and the distance between the source of water and the point where it is consumed, irrigation and drainage networks are essential for the best management of water resources. Therefore, in the present study, we have attempted to optimize the use of water in the agricultural sector by taking into account both the virtual water of plant cultivation and growth as well as the environmental conditions relevant to it, both geographically and temporally. Based on the calculations presented in this research and comparisons with the existing traditional method, the amount of water consumed in agriculture in the study area, which is part of Isfahan Province's southern part in the Gavkhoni lagoon, is estimated to be approximately 70 percent reduced if the solution presented in this study is implemented. It would be equivalent to using about 30% of the amount of water currently consumed and something equivalent to about 300 thousand cubic meters of water consumption reduction, which is a significant amount.

The growing concern about environmental pollution has increased the demand for biodegradable materials worldwide. Natural polymers are used in composite manufacturing to prevent environmental damage, reduce costs, and replace synthetic polymers. In this research, the biodegradability and thermal properties of the wood-plastic composite were investigated. Beechwood flour with a weight ratio of 40 % and a particle size of 80 mesh was used to make the samples as a reinforcement of the matrix. The studied polymer to make the composite biodegradable was polyhydroxy butyrate (PHB). Starch with a weight percentage of 8 and 12 % was used as copolymer, nanoclay was used to improve properties, and maleic anhydride was used as a coupling agent. The samples were mixed using a twin-screw extruder and made with an injection machine. Then, they were subjected to thermogravimetric analysis, differential scanning calorimetry, and biodegradability tests. In terms of thermal properties, a degradation temperature of about 250 ºC was achieved with the presence of wood flour as a filler and other additives, which showed improvement compared to PHB biopolymer which is in the range of 95 ºC. The glass transition temperature increased and this value in the pure polymer was 5 and it showed an increase up to 68 degrees in the results obtained from the composite, however, the melting and crystallinity were decreased in both. In the biodegradable test, the samples containing PHB showed good degradability properties. The weight loss occurred due to the destruction of wood flour, starch, and natural polymer by soil microorganisms. The produced composite samples in this research are completely biodegradable.

In this study, the effect of the chemical treatment of OCC pulp and polypropylene molecular weight on the physical and morphological properties of cellulosic biocomposite was studied. For this purpose, OCC fibers were treated with acetic anhydride and then mixed with polypropylene and the compatibilizing agent (0 and 3%). The ratio of fibers and polymer materials was considered 30 to 70, which was manufactured by the hot-pressing method at 180 °C. The ratio of fibers and polymer materials was considered 30 to 70, which was manufactured by the hot-pressing method at 180 °C. The water absorption, volumetric swelling, and contact angle were measured on each sample according to the ASTM standards. Fourier transform spectroscopy (FTIR) and scanning electron microscopy (SEM) were used to investigate the chemical modification of fibers and the morphology of biocomposites, respectively. The results of the measurements showed that the effect of chemical treatment and polypropylene on contact angle and water absorption was significant and chemical modification of the fibers reduced the water absorption and volumetric swelling. Finally, FTIR analysis showed that hydroxyl groups were decreased and the SEM images indicated the bond between fibers and polypropylene was improved through chemical modification of OCC fibers.

Drought is one of the most important natural disasters with devastating and harmful effects in various economic, social, and environmental fields. Due to the repetitive behavior of this phenomenon, if the appropriate solutions are not implemented, its destructive effects can remain in the region for years after its occurrence. Most natural disasters, such as floods, earthquakes, hurricanes, and landslides in the short term, can cause severe financial and human damage to society, but droughts are slow-moving and creepy in nature, and their devastating effects appear gradually and over a longer period of time. Therefore, by modeling drought, it is possible to provide plans for drought preparation and reduce the damage caused by it. In this study, computational intelligence algorithms of Multi-Layer Perceptron neural network, Generalized Regression Neural Network, Support Vector Regression with support kernel, and Support Vector regression with the proposed kernel (Support Vector) Regression New kernel has been used to model the drought using the Standardized Precipitation Index. The modeling results, in most cases, showed better performance of the proposed SVR_N model than other models. The values of RMSE and R2 were 0.093 and 0.991, respectively, and the GRNN, MLP, and SVR models performed better in modeling after SVR_N, respectively. Modeling of drought phenomenon in modeling is supported by vector regression method.

 In this study, black poplar (Populus nigra) cut from Zanjan province was used in two age groups of 18 and 38- year-old. Timbers with thicknesses of 4, 5 and 6 cm were prepared from each age group. The prepared timbers were modified during the heat treatment process (Thermowood) at a constant temperature of 212 °C. Experimental samples were obtained from the surface and middle depth of heat-treated timbers. Heat-treated and control wood samples were subjected to accelerated weathering for 150 hours. Color lightness (L*), color difference (ΔΕ*) and water droplet contact angle were measured for heat-treated and control samples before and after weathering. In order to investigation of the wettability content, the method of measuring the contact angle of the water drop - static according to the ASTM D 5946 standard was used. In general, before weathering, the brightness of the heat-treated samples decreased compared to the control sample, but after weathering, the color lightness of the control samples decreased and the color lightness of the heat-treated samples increased. Also, the content of color difference of samples after weathering compared to before weathering showed a significant decrease statistically. Heat treatment increased the contact angle of water droplets of the samples 60.31% and the contact angle of water droplets of the heat-treated and control samples increased significantly after weathering compared to before weathering.

A drought crisis is a dry period of climate that can occur anywhere globally and with any climate. Although this crisis starts slowly, it can have a serious impact on health, agricultural products, the economy, energy, and the environment for a long time to come. Drought severely threatens human livelihood and health and increases the risk of various diseases. Therefore, modeling and predicting drought is one of the most important and serious issues in the scientific community. In the past, mathematical and statistical models such as simple regression, Auto-regression (AR), moving average (MA), and ARIMA were used to model the drought. In recent years, machine learning methods and computational intelligence to model and predict drought have been of great interest to scientists. Computational intelligence algorithms that have been previously considered by scientists to model drought include multilayer perceptron neural network, RBF neural network, support vector machine, fuzzy, and ANFIS methods. In this research, the purpose of modeling and predicting drought is by using three neural network algorithms, including multilayer perceptron, RBF neural network, and generalized regression neural. The drought index used in this research is the standardized precipitation index (SPI). In this research, the wavelet technique in combination with artificial neural network algorithms for modeling and predicting drought in 10 synoptic stations in Iran (Abadan, Babolsar, Bandar Abbas, Kerman, Mashhad, Rasht, Saqez, Tehran, Tabriz, and Zahedan) have been used in different climates and with suitable spatial distribution throughout Iran.

This study, initially using monthly precipitation data between 1961 and 2017, SPI drought index in time scales of 3, 6, 12, 18, 24, and 48 months through programming in soft environment MATLAB software implemented. The results of this step were validated using the available scientific software MDM and Drinc. Then, prediction models were designed using the Markov chain. In this study, a total of six computational intelligence models, including three single models of multilayer perceptron neural network (MLP), radial basis function neural network (RBF), and generalized regression neural network (GRNN), and three hybrids wavelet models with these three models (WMLP-WRBF-WGRNN) have been used to model and predict the SPI index in 10 stations of this research. In implementing all these six models, the MATLAB software programming environment has been used. In this study, four types of discrete wavelets were used, including Daubechies, Symlets, Coiflets, and Biorthogonal. Due to the better performance of the Dobbies wavelet, this type of wavelet was used as a final option in the research. In the Daubechies wavelet used between levels 1 to 45, level 3 showed the best performance among different SPI time scales; therefore, the Daubechies level 3 wavelet was used in all hybrid models of this study. After training all six algorithms used, the evaluation criteria of coefficient of determination (R2) and root mean square error (RMSE) was used to measure the difference between actual and estimated values.

The results of this study showed that computational intelligence methods have high accuracy in modeling and predicting the SPI drought index. In the first stage, the results showed that the individual MLP, RBF, and GRNN models, if properly trained, have close results in modeling and predicting the SPI drought index. In the next step, it was observed that the wavelet technique would improve the modeling results. In using the wavelet technique in combination with three single models MLP, RBF, and GRNN, the choice of wavelet type is also more effective in modeling, so in this research, the first of the four types of discrete wavelets Daubechies, Symlet, Qoiflet, and Biorthogonal in combination with Three single models of this research were used and the results of these four types of wavelets showed the relative superiority of the Daubechies wavelet over the other three wavelets. In using the Daubechies wavelet, since this wavelet has 45 times and the choice of order was also effective in modeling, it was observed by testing the wavelet 45 times that the 3rd wavelet, in general, has higher accuracy in all time scales of SPI index, 3, 6, 12, 18, 24 and 48 months and also in all three algorithms MLP, RBF, and GRNN. Therefore, in this research, the third-order Daubechies wavelet was used in all three algorithms of this research, as well as in all time scales. The results showed that combining the wavelet technique with all three models MLP, RBF, and GRNN will improve the results. The research graphs showed that for the quarterly time scale, the values obtained from the single model prediction in MLP and RBF modeling have a somewhat one-month phase difference compared to the hybrid model, while in the GRNN model, this prediction difference is negligible. The modeling results for both single and hybrid modeling modes indicate that there is no phase difference between the single and hybrid modeling methods in time scales of 6, 12, 18, 24, and 48. For the 12- and 24-month time scales, the single GRNN model had more fluctuations and errors in SPI monthly modeling and forecasting, while the hybrid model in these two-time scales had much better behavior in monthly modeling and forecasting. Distribution diagrams of data related to observational SPI of Abadan station showed that the modeling results for single and hybrid modes in 3 and 6-month time scales are less accurate than other time scales and fit line separation, and its uncertainty is higher than others. However, in all neural network models and in all time scales, the hybrid method has shown more accuracy. The numerical results of the study indicate that in all SPIs and stations under study, the differential values of R2 are positive, which indicates higher values of R2 in the hybrid model than in single neural network modeling, which indicates an improvement in hybrid modeling compared to individual models. Also, the differential values of RMSE are negative in all studied models and stations, which indicates that the amount of RMSE in predicting hybrid models is lower than individual neural network models. In the research graphs, it can be seen that the amount of differences in RMSE and R2 indicates a greater difference in time scales 3 and 6 than the time scales 12, 18, 24, and 48, which somehow goes back to the nature of the data of these time scales. The most significant improvement in R2 and RMSE is from the 3-month low to the 48-month high, respectively.

 From the findings of this study, it can be concluded that artificial neural network algorithms are efficient methods for modeling and predicting the SPI drought index. The use of wavelets in all three models of artificial neural networks will also improve the results. It can also be concluded that for better modeling of the SPI drought index, it is necessary to select the optimal wavelet type and order. From the results of this study, it can be concluded that the wavelet technique has a greater impact on the lower time scales, i.e., 3 and 6 months, than the higher scales, i.e., 24 and 48 months.

The main purpose of this study was to determine the level of occupational stress and job satisfaction in faculty members among university faculty members in Tehran and to investigate the relationship between these two indicators.

Present study is practical, observational, cross-sectional, descriptive, and analytical research. The statistical sample included 52 faculty board members. The research methods were the Demographic, Job Descriptive Index (JDI) and Job Stress (Steinmetz) Questionnaire. In order to data analysis, descriptive statistics and analytical statistics have been used.

The findings showed that there was a significant negative correlation between job satisfaction and job stress. This means that if job satisfaction increases, stress decreases and vice versa. The results indicated moderate job satisfaction. The faculty board was significantly satisfied with the nature of the job, Direct supervisor, and colleagues, but they were significantly unsatisfied with income. The range of Job stress in the faculty board was almost low. Faculty board with Academic Rank of “Full Professor”, have shown more job satisfaction and the faculty board with Academic Rank of “Associate Professor”, have shown less job satisfaction.

Originality/Value: 

The faculty board of the university have a great role in cultural development promoting the knowledge level of the future workforces. Thus, we must try in order to promote job satisfaction indicators, and also reduce job stress in their community and by implementing the identified solutions, Organizational Excellence through consideration to the Academic board, which are the assets of the country, are provided.

Drought is one of the natural hazards that have random and nonlinear behavior due to its various climatic parameters. SPI index is the most common index extracted from rainfall that has been used in modeling drought by various researchers.

The use of computational intelligence methods to model drought in recent years has been much considered by researchers in the field of water resources. In this research, SVR and GPR algorithms individually and also the combination of these algorithms with wavelet algorithms have been modeled and predicted by SPI index, and the purpose was to evaluate the improvement of computational intelligence algorithms in combination with wavelet. In this research, the time series data of 10 synoptic stations in Iran in the period 1961 to 2017 have been used on a monthly basis for modeling the drought as the input of the studied algorithms.

The results of this study showed that the use of the wavelet method in combination with SVR and GPR computational intelligence algorithms improved the results in all time scales. Also, the modeling improvement is due to the use of wavelet in combination with the SVR model with an average RMSE difference of -0.1540 and R2 difference of 0.1491 and the GPR model with an average RMSE difference of -0.1554 and R2 difference of 0.1530 Compared to the single SVR and GPR models showed that the GPR model in general (all time scales and all stations) had a better improvement in the hybrid model than the single model.

Soil contamination with heavy metals is the most important challenge and common environmental, economic, and public health issue in the world. Therefore, this research was conducted to evaluate the contamination and source identification of Fe, Zn, Cd, and Cr in the surface soils of Khorramabad county, west of Iran in 2020.

In this descriptive study, after dividing the study area into 11 homogeneous units, a total of 65 surface soil samples were collected. After preparing the samples, the content of the elements was determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES). Also, enrichment factor (EF), pollution factor (CF), and Nemrow integrated pollution index (NIPI) were calculated. Statistical analyses of the data were performed using SPSS statistical software.

The results showed that the average content of Fe, Zn, Cd, and Cr (mg/kg) in soil samples were 3.14, 1.13, 0.021, and 0.529, respectively. The EF values showed that the enrichment of Zn, Cd, and Cr was "extremely severe" and the average values of EF for the elements followed the descending order of Cd > Cr > Zn. The average values of CF and NIPI varied from 3.30×10-5 to 0.182 and 0.043-0.136, respectively, indicating the level of "low pollution" and quality conditions of "no pollution" in all the studied stations. Based on the results of multivariate statistical analysis (PCC, PCA and HCA), Fe has a geological origin; while, Zn, Cd, and Cr mainly originated from a combination of geological processes and anthropogenic activities.

Although the average values of CF and NIPI showed that the study area has an acceptable soil quality, the values of EF indicated the impact of anthropogenic activities on soil contamination. Therefore, regular and periodic monitoring of soil samples as well as management and control of pollutant emission sources is recommended for maintaining environmental and human health.

The most important result of the production of aggregate concrete materials from the process of recycling rubber waste and the production of widely used products is to turn the problem into a solution. In this research, a multi-criteria decision model has been used to select the best product from aerated concrete. Accordingly, the multi-criteria decision model is collected step by step by AHP hierarchical method by preparing and arranging two separate series of questionnaires in two separate stages. After reviewing and analyzing the data obtained from the first stage of the questionnaire, the data in the second stage are based. Modeling and analyzing the information of the questionnaire using EXCEL and EXPERT CHOICE software has been studied and final prioritized. The weights of the main decision criteria including technical, environmental, managerial and executive, economic, insulation and insulation criteria were determined by scoring civil and environmental experts and the relevant matrix was formed. Using EC software, the relative weight of the main criteria relative to the main goal, which is the optimal product selection of aerated concrete, as well as the relative weight of the indicators related to each criterion to the relevant criterion were calculated. After two stages of reviewing the results of two series of questionnaires and analyzing the results and based on the results of combining the results of EC software (second stage of research) and ranking the options based on criteria and sub-criteria, the weights of the best product options made from Asphalt concrete as described in Asphalt Asphalt Concrete (crac) 14135, Highway Guardrail Asphalt Concrete Block 14119, Non-Load Wall Panel Asphalt Partition 14196, Asphalt Concrete Street Table Block 14179 Asphalt Particleboard Urban facilities were set at 1417 .Therefore, the production of asphalt crumb concrete product (crac) as the best product in all respects in this study was qualified and selected.

Utilization of ionizing rays like gamma-ray in polymers to correct their properties has attracted so much attention from researchers. Therefore, in this study, polypropylene as the composite matrix, maleic anhydride (MAPP) as the compatibilizer material, and poplar wood as the filler were placed under the radiation of gamma-ray with different doses from 0 to 75 kGy. The white rot fungi (Trametes versicolor) and, brown rot fungi (Gloeophyllum Trabeum) were used in fungal decay test. The main purpose of this study is to investigate the probable benefits of gamma-ray radiation for improvement of some of the composites physical and mechanical properties, and also, to investigate the biodegradation in fungal decay and thermal properties. The results indicates that radiation of gamma-ray improves the physical and mechanical properties of some structures, and the decay resistance of the WPCs against brown and white rot fungus according to their weight losses. The uniformity of the micrographs obtained from the scanning electron microscope (SEM) indicated that the interaction between the polymer and wood fibers.

By considering the high consumption of acoustic panels in the related industries and the importance of natural fibers’ consumption as proper sound absorber replacement for synthetic fibers, the probability of producing acoustic panels made of date-palm-trunk fibers and sodium silicate adhesive is assessed. The effect of thickness, density, particle length, adhesive percentage and air-gap on sound absorption coefficient are measured according to ASTM 10534-2 ISO at 250, 500, 1000, 2000 and 4000 Hz frequencies. The results reveal that an increase in thickness shifts the sound absorption coefficient peak towards lower frequencies. An increase in density increases the sound absorption coefficient and at high frequencies it decreases. An increase in particle length increases sound absorption coefficient and at 4000 Hz this process is reversed. An increase in adhesive percentage decrease sound absorption coefficient at high frequencies. The effect of 25 and 41 mm air-gap on sound absorption frequency on the best produced panels of 32 and 16 mm thick, respectively, is assessed.  Panels of low thickness at higher air-gap enhance sound absorption coefficient at all frequencies. This fact can be highly contributive in reducing production cost of environment-friendly acoustic panels where, silicate sodium adhesive is consumed.

Today, informal settlements are a major indicator of the spread of unsustainable urbanization in the world. The environmental problems of these settlements are seriously intertwined with the economic and social aspects of human activities; DPSIR framework is a flexible approach that considers both economic and social factors as well as environmental factors in assessing environmental pollution. In this research with field survey based observation method, Meta-analysis of previous studies, Library studies and review of upstream documents, the main components of DPSIR were identified in the research topic; Then, using the semi-structured interview method with panel of experts based on the main extracted indicators, Their opinions are collected and then by extracting and categorizing the factors proposed by the experts and removing the overlaps with the primary factors extracted by the authors and combining synonymous items, final effective factors in environmental pollution of informal settlements of Mashhad were identified. Then, by analyzing the final factors, an integrated conceptual model of environmental pollution management in informal settlements of Mashhad was obtained. Population growth due to high Immigration rate to Mashhad and unbalanced spatial-physical development of informal settlements, social, cultural and economic characteristics of these settlements, along with low environmental literacy of residents and lack of good urban governance as the most important driving forces in creating the effects of environmental pollution in informal settlements of Mashhad were identified. In accordance with these works, policy making and planning responses were provided in order to create a situation of sustainable development of these settlements.

Iran is ranked as the ninth largest greenhouse gas producer in the world, with an annual emission of nearly 180 million tons of CO2 by thermal power plants.. The purpose of this research is to simulate and optimize the optimal economic and environmental load dispatch and a portfolio of efficiency improvement strategies on both supply and demand sides in MT1, MT2, and EEP scenarios in using the Long-range Energy Alternative Planning (LEAP) modeler and its OSeMOSYS optimizer system. We do this using the time series data from 2008 to 2018 of the country's energy balance. Our modelling allowed us to estimate the current situation and forecast of supply and demand of thermal electricity, types of costs, and the amount of greenhouse gas emissions  for the whole country over a 20-year period (2011-2030). We identified the most efficient policy for improving the environmental economic efficiency of the thermal power sector in line with the Paris Agreement in the form of the best scenario through cost-benefit analysis by determining the optimal dispatching of thermal power plants. The results showed that all alternative scenarios with a reduction of more than 15% emissions and environmental economic benefits of more than $ 8 billion compared to the BAU scenario would achieve Iran's INDC goals and meet our commitments under either a conditional or an unconditional agreement.

In recent years, the development of submerged membrane systems has led to the significant development of ultrafiltration/water purification markets. The study aimed to investigate the efficiency of Poly-Aluminum Chloride coagulant in removing turbidity using a submerged membrane reactor for simultaneous coagulation and flocculation of filtration and determine the optimal values of its performance parameters. In this study, the Poly-Aluminum Chloride coagulant along with lime and polyelectrolyte for water coagulation and flocculation was firstly evaluated by the Jar test in different turbidities. Also, pH (5-10), Poly-Aluminum Chloride (1-50 mg/L), and coagulant aids of lime (0.5-15 mg/L) and polyelectrolyte (0.1 to 2 mg/L) were examined. Then, a pilot-scale submerged membrane reactor was designed for coagulation, flocculation, and membrane filtration processes. Pilot experiments were used as closed systems, and then different parameters of flux, aluminum concentration in the treated water, and membrane fouling were investigated. Jar test results showed that Poly-Aluminum Chloride had a great performance in removing turbidity. In addition, the use of lime and polyelectrolyte coagulant aids improved the turbidity removal process by 3%. Furthermore, pH=8 was selected as the optimal range, and the best flux performance was obtained at turbidity less than 100NTU in a submerged membrane pilot. The flux reduction in eight hours of operation time was only 5% while this increased to 50% in turbidity above 200NTU. The turbidity removal percentages were reported to be constant and higher than 99.5%. The removal rate of total aluminum by the membrane process has been over 99%, and the type of membrane fouling is surface sediment and is reversible. Results indicated that the submerged membrane reactor along with coagulation and flocculation could be applied as an efficient method in water treatment with different turbidity.

The mechanical (tensile strength and modulus, bending strength and modulus), humidity (water absorption and thickness swelling) and biodegradability properties of wood-plastics produced from polypropylene and poplar wood-flour (wood flour in three levels of 0, 20 and 25 percentage) in the presence of starch powder (at four levels of 0, 5, 10 and 15 percentage) are assessed as a bio strength agent. The material mixing process is run in two stages of applying an extruder machine and applying an internal mixer haake machine. The mechanical and humidity tests specimens are produced by mold injection method and the biodegradability test specimens are formed by hot press method. The tests are run according to standards and procedures. The results indicate that an increase in weight percentage of wood-flour in the composition, increase the mechanical strength, water absorption, thickness swelling and biodegradability of composites. An increase in weight percentage of starch powder in the composition, increase the bending strength and modulus, tensile modulus, water absorption, thickness swelling and biodegradability of composites, while decreasing the tensile strength. Additional studies run through the scanning electron microscopy indicate that by adding wood-flour and starch powder to the composition, the intensity of biodegradation increase at the surface of the specimens. By adding starch powder to the composition, the starch powder particles fill the cavities in the composite structure, thus a reduction in the volume of the pores in the composite structure.

One of the problems of non-destructive tests in the detection of the inhomogeneities in wood and wood products is their location relative to the vibrating node and antinode. Thus, the hidden inhomogeneities located at the vibration node in such materials may not have any effects on the dynamic modulus of elasticity and may not be detectable. In this study, we investigated the development of simple end-to-end joint in oak beams (Quercus castaneifolia) with dimensions of 72×4×2 cm (longitudinal×Radial×Tangential), with blocks removed from the areas of 0.06 and 0.50 cm of the beam length. The blocks were removed from the same beam as the joint was developed. The results indicated that developing a joint in 0.50 cm of the beam length has no significant effect on values obtained from the dynamic modulus of elasticity while creating this joint in 0.06 cm of the beam length has a significant effect on values obtained from the dynamic modulus of elasticity. Also, in both joints, the damping and acoustic converting efficiency values were increased and decreased, respectively. The amount of these changes was more in the area of 0.06 cm of beam length. In general, it can be said that due to the effect of vibrating node and antinode and on the other hand, the location of the joint in both areas, the acoustic conversion efficiency factor is a suitable factor to estimate the location of the joint or the inhomogeneity itself.

The paper aims to study differential, dynamic-mechanical and biodegradability analysis of polylactic acid/wood fiber (PLA) composites using three levels of nano-graphene (0.75, 1.5 and 3%). In order to mix the materials together and to make standard specimens, an internal mixer and pressure press were used. In the analysis of differential polishing calorimeter, the addition of fibers as well as nano-graphene had a positive effect on the glass transition temperature and the degree of crystallinity, which indicates a change from softness and flexibility to hardness and hardness. Dynamic-mechanical analysis has shown that the addition of wood fibers to pure polylactic acid can case an increase in the storage modulus of the composite and with the addition of nano-graphene to increasing composition, while the highest amount of storage modulus was related to polylactic acid composites / 30% of fibers and 30% of fibers. Results of tan δ peak showed that with the presence of wood fibers and nano-graphene, the temperature was transferred to a higher temperature and the limited movement of molecules due to the improvement of the fiber reaction in the PLA polymer. The results of biodegradability test also showed that the addition of fibers to pure PLA caused a significant increase in the weight loss of the composite. Additionally, nano-graphene to PLA composites decreases with the less weight in composites.

Considering the increase of environmental pollutant resulted from agglomerating rubbish and wastes containing stable petroleum-based polymers, substitution of these polymers with bio-polymers and solving their problems and defects in the production process as well as the final products is an important topic. In this study, the possibility of the production of poly lactic acid (PLA)-rice husk composite with acceptable properties, and also the effect of using flame retardant mineral fillers of aluminum three hydrates (ATH) and magnesium di-hydroxide (MDH) on physical and thermal properties of the produced composite were investigated. Results indicated that the density of PLA-rice husk composite was higher than petroleum-based polymers composites (PP/HDPE-rice husk composites); and flame retardant mineral fillers addition led to an increase in the composite density. Also, flame retardant mineral fillers addition decreased the composite water absorption and thickness swelling, in a way that they were comparable with petroleum-based polymers composites. Results of thermal gravimetric analysis (TGA) showed that flame retardant mineral fillers addition also decreased the temperature of the composite weight loss curve peaks, and the weight loss rate toward temperature rising.

The purpose of this study was to investigate the composition and biodiversity of herbaceous cover and regeneration in natural forest (dominated by hornbeam-iron tree), plantations (pure and mixed alder and maple) and degraded forest areas (without tree cover) in the Kelarabad of Chalus. A total of 16 large sample plots (20 × 20 m2) to record the vegetative characteristics of the trees and 144 small sample plots (1 × 1 m2) to record the number of regenerated woody species, type and percentage of herbaceous species. The variety of indices were used to compare biodiversity. The highest percentage of herbaceous species belonged to the Brachypodim pinnatum L., while in the degraded area the percentage of Rubus caesius L. was dominant. Among the understory herbaceous plants, hemicryptophytes and cryptophytes were the most abundant biological form. Also, according to the results, the highest percentage of regeneration species was related to the dominant tree species in each forest stands, and among regeneration species, Phanerophytes were the most abundant biological form. Investigation of biodiversity indices in both herbaceous and regeneration layers indicates the negative effects of forest degradation as well as the positive role of natural forest in the study area. As a general conclusion, the present study demonstrates the protective role of temperate broadleaf mixed forest stands for the richness and diversity of herbaceous plant species and the sustainable regeneration and conservation of understory communities.

In this study, the effect of cellulose nanofibers and silicon nanoparticles on the properties of nanocomposites made with polyvinyl alcohol was studied and the physical, mechanical and morphological properties of nanocomposites were investigated. Samples were prepared by casting with different ratios of 0, 5 and 10% by weight. The morphology of nanocomposites was examined by scanning electron microscopy. Observations of water vapor permeability and water uptake by adding cellulose and nanoxide oxide nanofibers in pure polyvinyl alcohol control film and nanocomposite films with different compositions were significantly different from each other. Tensile strength of nanocomposites showed that increasing the amount of cellulose nanofibers and silicon nanoparticles increased the tensile strength and the percentage of elongation increased with the addition of cellulose nanofibers and silicon nanoparticles decreased. The results of the present study show that the addition of small amounts of nanocellulose fibers and silicon nanoparticles strengthens the polyvinyl alcohol polymer and improves the physical and mechanical properties and increases the performance of nanocomposites.