University of Tehran

Toxic elements released due to mining activities are of the most important environmental concerns, characterised not only by their concentration, but also by their distribution among different chemical species, known as speciation. These are conventionally determined using chemical analysis and sequential extraction, which are expensive and time-demanding. In this study, the possibility of using visible-near-infrared-shortwave infrared (VNIR-SWIR) reflectance spectroscopy was investigated as an alternative technique to quantify the contents of cobalt (Co) and nickel (Ni) in soil samples collected from Sarcheshmeh copper mine waste dump surface, in Iran. As a novel approach, the capability of VNIR-SWIR spectroscopy was also investigated in speciation of those elements. Three machine learning (ML) techniques (i.e., extreme gradient boosting (EGB), random forest (RF) and support vector regression (SVR)) were used to make relationships between soil spectral responses and Co and Ni contents of the samples. For all ML algorithms, the best prediction accuracies were obtained by the models developed on the first derivative (FD) spectra (for Co: RMSEp values of 7.82, 8.03 and 9.22 mg·kg-1, and for Ni: RMSEp values of 9.88, 10.32 and 11.02 mg·kg-1, using EGB, RF and SVR, respectively). Spatial variability maps of elements showed relatively similar patterns between observed and predicted values. Correlation and ML (EGB, RF, SVR)-based methods revealed that the most important wavelengths for Co and Ni prediction were those related to iron oxides/hydroxides and clay minerals, as two main soil properties responsible for controlling their speciation. This study demonstrated that the EGB technique was successful at indirect quantification and spatial variability mapping of Co and Ni on the mine waste dump surface. In addition, it provided an inspiration for implementation of the VNIR-SWIR reflectance spectroscopy as a potentially fast and cost-effective method for speciation studies of toxic elements, specially in heterogeneous soil environments.

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

Fault type classification is a critical stage in modern distance relays for activating the effective phase and ground distance elements. It is also used for some supplementary functions such as single-pole tripping and reclosing, teleprotection, and fault location. Traditional fault type classifiers are based on the symmetrical or superimposed components of the current signal. However, owing to the unconventional behavior of inverter-based resources (IBRs) compared to the synchronous generators, traditional methods are not reliable in the presence of IBRs. For example, modern grid codes impose some restrictions in terms of injected current during asymmetrical fault which put the accuracy of conventional fault classification methods in danger. Fault resistance could also affect the accuracy of fault type classification method. In this paper, first, the fault current signature of IBRs is analytically derived. Then, impacts of modern grid code and fault resistance on the conventional fault classification are properly evaluated by executing some simulation studies. Finally, a new fault classifier is proposed which is based on symmetrical components of the local voltage and current during asymmetrical faults. The proposed classifier presents a satisfactory degree of security in the faulted phase identification in the presence of IBRs under different host grid codes. Performance of the proposed method is properly analysed with different simulation scenarios such as different fault locations, fault resistances, pre-fault condition, test systems, and host grid codes in PSCAD/EMTDC software.

Hand hygiene plays a crucial role in healthcare environments which can cease infections and diseases from spreading. It is also regarded as the second most effective way to control the transmission of COVID-19. The World Health Organization (WHO) recommends a 12-step guideline for alcohol-based hand rubbing. Compliance with this guideline is vital in order to clean the hands thoroughly. Hence, an automated system can help to improve the quality of this procedure. In this study, a large-scale and diverse dataset for both real and fake hand rubbing motions is collected as the first stage of building a reliable hand hygiene system. In the next stage, various pre-trained networks were analyzed and compared using a swift version of the Separation Index (SI) method. The proposed Swift SI method facilitates choosing the best pre-trained network without fine-tuning them on the whole dataset. Accordingly, the Inception-ResNet architecture achieved the highest SI among Inception, ResNet, Xception, and MobileNet networks. Fine-tuning the Inception-ResNet model led to an accuracy of 98% on the test dataset, which is the highest score in the literature. Therefore, from the proposed approach, a lightweight version of this model with fewer layers but almost the same accuracy is produced and examined. In the final stage, a novel metric, called Feature-Based Confidence (FBC), is devised for estimating the confidence of models in prediction. The proposed confidence measure is able to profoundly differentiate models with similar accuracy and determine the superior one. Based on the metrics results, the Inception-ResNet model is about 2x slower but 5% more confident than its lightweight version. Putting all together, by addressing the real-time application concerns, a Deep Learning based method is offered to qualify the hand rubbing process. The model is also employed in a commercial machine, called DeepHARTS, to estimate the quality of the hand rubbing procedure in different organizations and healthcare environments.

This paper proposes a new non-isolated DC-DC buck-boost converter with low voltage stress on components and enhanced voltage gain. This converter has a high voltage gain in boost mode and is capable of reducing the voltage level significantly in the buck mode, which makes it suitable for renewable energy and other industrial applications. Moreover, the number of components in this converter is low and the input current is continuous. Operational principles of the proposed converter in both CCM and DCM are presented, efficiency analysis is discussed, and small-signal model is derived. Comparisons between the presented converter and related buckboost converters are given in terms of voltage gain, voltage stress on components, number of elements, etc. Besides, a singleinput multi-output converter based on the proposed topology is presented. Experimental results from a lab-scale prototype are given for both buck and boost modes in order to verify the performance of the converter.

Re-curing partly recovers the mechanical and durability properties of fire-damaged concretes. This inexpensive method can be used to reduce the cost of rehabilitation and probably prevent the demolition of fire-damaged concrete infrastructures. This paper investigates the macro- and microstructure of zeolite containing concretes after heating and re-curing. In this research, two mixtures including OPC concrete and zeolite concrete were studied. The specimens were heated up to 800 °C and then re-cured in moist air or water for 28, 56 or 90 days. Macrostructure testing including compressive strength, water absorption, surface resistivity and gas permeability was conducted on the specimens. A set of rehydration products including portlandite, C–S–H gel, ettringite, etc. regenerated during the re-curing period, which was identified by SEM and XRD analyses. These products improved the properties of the heated concretes, especially the OPC concrete. The recovery of properties was more pronounced in the water re-cured specimens. Formation of needle-shaped crystals in the zeolite concrete, which appears to be thaumasite-ettringite solid solution, led to a decrease in compressive strength at the early ages of re-curing. However, the durability properties were improved at the early ages of re-curing due to the expansion of these needle-shaped crystals during the formation. In the zeolite concrete, strength recovery occurred after 56 days of re-curing. Although the unheated specimens of the zeolite concrete had better mechanical and durability properties compared to the OPC concrete, the properties of the zeolite concrete were inferior after heating and re-curing in moist air or water.

In this paper, a formal transition system model is presented called Linear Temporal Public Announcement Logic (LTPAL) to extract knowledge in a classification process. The model combines Public Announcement Logic (PAL) and Linear Temporal Logic (LTL). For this purpose, first, an epistemic logic model is created to capture information gathered by classifiers in single-framed data input. Next, using LTL, classifiers are considered for data stream inputs. Then, a verification method is proposed for such data streams. Finally, we formalize natural language properties in LTPAL with a video-stream object detection sample.

The Choghart and Chadormalu iron deposits in Bafq metallogenic zone in Central Iran are classified as Kiruna-type deposits and surrounded by metasomatic rocks which Russian experts named them metasomatite. These rocks include different types of acidic to mafic igneous rocks and sediment and metamorphic rocks known as metasomatic bedrocks. The enrichment pattern of LREEs compared to HREEs in the spider diagrams is consistent with the felsic and alkaline nature of magma in the Choghart and Chadormalu deposits. Also, the negative anomaly of Nb and Ta and the positive anomaly of Th could be an important geochemical indicators for magmas produced in subduction zones. These metasomatic rocks formed as a result of the earliest and the deepest sodic metasomatism, multi-stage calcic metasomatism, and younger potassic metasomatism. The origin of the fluids responsible for metasomatism and alteration is magmatic, and these processes occur at a pressure of 40 MPa and a maximum depth of 2 km. The hydrothermal fluids have had an intermediate temperature and low salinity resulted in the sodic and primary calcic alteration and originated from the gabbro-diorite intrusive masses. The circulation of the high-salinity and high-temperature fluids has caused the next calcic alteration which can be related to a magmatic-hydrothermal origin which is associated with felsic magmatism in the region. The high-temperature potassium-containing fluids derived from intermediate to felsic magmas flowed towards shallow depths and caused potassic alteration. In the last stage of alteration, the release of calcium-rich hydrothermal fluids with low temperature and salinity created subsequent alteration after the main mineralization phases.

Accurate and comprehensive modelling aimed at investigating the impact of climate change on rainfed crop yields is of great importance due to the interconnected issues of water scarcity and food security. Because the process-based and statistical approaches to simulating crop yields are different in nature, a comparison between them is needed. This study investigates the accuracy of crop yield simulations in the historical period as well as future projections using two modelling approaches: 1) a process-based approach employing the Soil and Water Assessment Tool+ (SWAT+) model, and 2) a statistical approach employing a data-driven model, Feed Forward Back Propagation Neural Network (FFBPNN) over a medium-sized catchment in northwestern Poland. The application of two potential evapotranspiration methods (Penman-Monteith and Hargreaves) in SWAT+ permitted calibration (2004-2011) and validation (2012-2019) of runoff and yields of winter wheat and spring barley. Different combinations of climatic parameters with a drought index based on Joint Deficit Index were applied to simulate and project rainfed crop yields (winter wheat, barley, potato, rye, rapeseed, sugar beets, cereals, maize for grain, maize for green forage, pulses) with FFBPNN. The results reveal that adding the new drought index helped increase the FFBPNN performance. This approach showed that future yields of the studied crops would slightly increase under RCP8.5 by 2060. Winter wheat and spring barley projections from SWAT+ showed very small changes using both the Penman-Monteith and Hargreaves method. Policy-wise, the results should be of interest to climate change adaptation practitioners and food security experts. Future studies should aim at more thorough investigation of the role of the downscaling technique and extreme events, as well as the effect of elevated CO 2 on future crop yields.

Modeling of karstic basins can provide a better understanding of the interactions between surface water and groundwater, a more accurate estimation of infiltrated water amount, and a more reliable water balance calculation. In this study, the hydrological simulation of a karstic basin in a semiarid region in Iran was performed in three different stages. In the first stage, the original SWAT model was used to simulate surface-water flow. Then, the SWAT-MODFLOW conjunctive model was implemented according to the groundwater characteristics of the study area. Finally, due to the karstic characteristics of the region and using the CrackFlow (CF) package, the SWAT-MODFLOW-CF conjunctive model was developed to improve the simulation results. The coefficient of determination (R2) and the Nash-Sutcliffe efficiency coefficient (NSE) as error evaluation criteria were calculated for the models, and their average values were 0.63 and 0.57 for SWAT, 0.68 and 0.61 for SWAT-MODFLOW, 0.73 and 0.7 for SWAT-MODFLOW-CF, respectively. Moreover, the mean absolute error (MAE) and root mean squared error (RMSE) of the calibration for groundwater simulation using the SWAT-MODFLOW model were 1.23 and 1.77 m, respectively. These values were 1.01 and 1.33 m after the calibration of the SWAT-MODFLOW-CF model. After modifying the CF code and keeping the seams and cracks open in both dry and wet conditions, the amount of infiltrated water increased and the aquifer water level rose. Therefore, the SWAT-MODFLOW-CF conjunctive model can be proposed for use in karstic areas containing a considerable amount of both surface water and groundwater resources.

The spike protein of coronavirus is crucial in binding and arrival of the virus to the human cell via binding to the human ACE2 receptor. In this study, at first 25 antiviral phytochemicals were docked into the RBD domain of spike protein, and then all complexes and free RBD domains were separately subjected to molecular dynamics simulation for 100 ns and MM/PBSA binding free energy calculation. In this phase, four ligands were chosen as hit compounds and a natural compound database (NPASS) was screened based on high similarity with these ligands, and 367 ligands were found. Then the same previous procedure was repeated for these ligands and ADME properties were investigated. Finally, virtual screening and 4400 ns MD simulation and MM/PBSA calculation revealed that new ligands including NPC67959, NPC157855, NPC248793, and NPC216361 can inhibit the RBD domain of spike protein and we propose them as potential drugs for experimental studies. Communicated by Ramaswamy H. Sarma

Increasing the price of fossil fuels, unreliability of fossil fuels for a secure supply of demand in future and their relevant environmental concerns provide an attitude toward substituting renewable energies with fossil fuels for reaching sustainable development in societies. Biofuels as a type of renewable energies can be easily transferred between supply chain’s centers and do not have limitation for transportation after their production. Among various types of biofuels, biodiesel, which can be mainly produced from the non-edible feedstocks, such as Jatropha Curcas L. (JCL), is preferred to other biofuels because biodiesel production from JCL which can be cultivated in marginal lands, improves three pillars of sustainability. Since biofuel supply chain’s costs can mainly be affected by its feedstock location optimization, this paper used a common weight data envelopment analysis (CWDEA) method for location optimization of feedstock cultivation for a biodiesel supply chain by considering a comprehensive set of sustainability criteria for investigating locations. A case study of Iran is provided for assessing the model’s application, and its results in ranking potential locations for JCL cultivation are validated by a numerical taxonomy (NT) approach. In fact, this paper not only specifies the optimum locations for the feedstock cultivation of a biofuel supply chain regarding to sustainability criteria, but also discuss the balanced socioeconomic development and environmental benefits which can be attained by JCL cultivation in marginal and mostly underdeveloped lands. Provided results imply that vast area of Iran’s marginal lands has suitable climate for JCL cultivation and policy makers can address all aspect of sustainability simultaneously by investment on those specified lands as well as supplying countries’ demand for biodiesel which will be produced from this feedstock. Graphical abstract

Progress of highly sensitive analytical methods for illicit drugs is one of the serious topics in addressing new challenges based on the consumption of these drugs worldwide. Electrochemical aptamer-based sensors have been widely considered as potent analytical tools providing valuable portability, quick response, sensitivity and specificity in addition to lower charge and simplicity. Herein, a novel aptasensor is presented to determine methamphetamine (METH) via electrochemical impedance spectroscopy (EIS). Nanocerium oxide (CeO2NPs) decorated on reduced graphene oxide was fabricated to modify glassy carbon electrode (GCE) for METH determination. EIS using [Fe(CN)6]−3/−4 redox probe was exploited as a precise detection method for METH determination. The proposed label-free aptasensor was able to detect METH from 0.5 to 250 nM (limit of detection of 0.16 nM). The proposed aptasensor exhibits excellent repeatability and selectivity as well as was effectively employed to determine METH in a spiked urine sample. Graphical abstract

Background Adult T-cell Leukemia/Lymphoma (ATLL) is a rapidly progressing type of T-cell non-Hodgkin lymphoma that is developed after the infection by human T-cell leukemia virus type 1 (HTLV-1). It could be categorized into four major subtypes, acute, lymphoma, chronic, and smoldering. These different subtypes have some shared clinical manifestations, and there are no trustworthy biomarkers for diagnosis of them. Methods We applied weighted-gene co-expression network analysis to find the potential gene and miRNA biomarkers for various ATLL subtypes. Afterward, we found reliable miRNA-gene interactions by identifying the experimentally validated-target genes of miRNAs. Results The outcomes disclosed the interactions of miR-29b-2-5p and miR-342-3p with LSAMP in ATLL_acute, miR-575 with UBN2, miR-342-3p with ZNF280B, and miR-342-5p with FOXRED2 in ATLL_chronic, miR-940 and miR-423-3p with C6orf141, miR-940 and miR-1225-3p with CDCP1, and miR-324-3p with COL14A1 in ATLL_smoldering. These miRNA-gene interactions determine the molecular factors involved in the pathogenesis of each ATLL subtype and the unique ones could be considered biomarkers. Conclusion The above-mentioned miRNAs-genes interactions are suggested as diagnostic biomarkers for different ATLL subtypes.

Polyester-urethanes as the most widely used polyurethanes (PUs) are among the most recalcitrant plastics in natural conditions. Among existing approaches for managing and reducing plastic waste, biodegradation as a promising approach to reduce plastic waste pollution has drawn scientific society's attention in recent years. In this study, two polyester–polyether urethane degrading yeasts were isolated and identified as two new strains of Exophilia sp. NS-7 and Rhodotorula sp. NS-12. The results showed that Exophilia sp. NS-7 is esterase, protease, and urease positive, and Rhodotorula sp. NS-12 can produce esterase and urease. Both strains can degrade Impranil® as the sole carbon source with the highest growth rate in 4–6 and 8–12 days, respectively. SEM micrograph revealed PU degradation ability in both strains by showing so many pits and holes in treated films. The Sturm test showed that these two isolates can mineralize PU to CO2, and significant decreases in N–H stretching, C–H stretching, C=O stretching, and N–H/C=O bending absorption in the molecular structure of PU were revealed by the FT-IR spectrum. The detection of the deshielding effect in chemical shifts of the H-NMR spectrum after the treatment also confirmed the destructive effects of both strains on PU films.

Background and aims: Artemisia is a mega-diverse genus consisting of ca. 400 species. Despite its medicinal importance and ecological significance, a well-resolved phylogeny for global Artemisia, a natural generic delimitation and infrageneric taxonomy heretofore remain missing, owing to the obstructions from limited taxon sampling and insufficient information of DNA markers. Its morphological characters, like capitulum, life form, and leaf show marked variations, and are widely employed in its infrageneric taxonomy. However, their evolution within Artemisia are poorly understood. Here, we aim to reconstruct a well-resolved phylogeny for global Artemisia using phylogenomic approach, to infer the evolutionary patterns of its key morphological characters, and to update its circumscription and infrageneric taxonomy. Methods: We sampled 228 species (258 samples) of Artemisia and its allies from both fresh and herbarium collections covering all the subgenera and its main geographic areas, and conducted a phylogenomic analysis based on nuclear single nucleotide polymorphism (SNP) obtained from genome skimming data. Base on the phylogenetic framework, we inferred the possible evolutionary patterns of six key morphological characters widely used in its previous taxonomy. Key results: The genus Kaschgaria was revealed to be nested in Artemisia with strong support. A well-resolved phylogeny of Artemisia consisting of eight highly-supported clades was recovered, two of which were first identified. Most of the previously recognized subgenera were not supported as monophyletic. Evolutionary inferences of the six morphological characters showed that different states of these characters independently originated more than one time. Conclusions: The circumscription of Artemisia is enlarged to include the genus Kaschgaria. The morphological characters traditionally used for the infrageneric taxonomy of Artemisia do not match the new phylogenetic tree. They experienced more complex evolutionary history than previously thought. We propose a revised infrageneric taxonomy of the newly circumscribed Artemisia with eight recognized subgenera to accommodate the new results.

In this communication, it is mathematically proved that the modified couple stress theory-based stability trend (buckling-post-buckling) of the size-dependent porous functionally graded (metal-ceramic) shear deformable microplates with fully simply supported edges was wrongly predicted in the aforementioned paper.

Mites and insects have different kinds of relationships with each other, which is very important to be studied. One of these relationships is parasitism, which is seen between larvae of terrestrial Parasitengona mites (Acari: Trombidiformes) and different active stages of insects. This study aimed to determine parasitic mites associated with the solitary wasps of the family Crabronidae. The presence of mites was investigated on 3247 wasp individuals belonging to 250 species from 53 genera. Among them, 32 specimens representing 23 species were parasitized by Leptus Latreille mite larvae. As a result, a total of 58 mite larvae were detected and two species, Leptus rosellae Haitlinger, 1999 and L. tridentatus Saboori, Hakimitabar & Khademi, 2018, were identified. The latter one has been reported for the first time from Türkiye and all mite-parasitized wasp species are new host records of both mite species. This study is the first record of the parasite-host relationship between the family Crabronidae and the genus Leptus.

Trueperella pyogenes (T. pyogenes) is a zoonotic pathogen that is cause a variety of pyogenic diseases in animals. The complex pathogenicity and various virulence factors are important challenges to produce an effective vaccine. According to previous trials, inactivated whole-cell bacteria or recombinant vaccines were unsuccessful in preventing disease. Thus, this study aims to introduce a new vaccine candidate based on a live-attenuated platform. For this purpose, first T. pyogenes was subjected to sequential passage (SP) and antibiotic treatment (AT) to lose their pathogenicity. Second, Plo and fimA expressions as virulence genes were evaluated by qPCR and then mice were challenged with bacteria from SP and AT culture by intraperitoneal route. Compared to the control group (T. pyogenes-wild type), plo and fimA gene expressions were downregulated and vaccinated mice have a normal spleen appearance in contrast to the control group. In addition, there was no significant difference between bacterial count from spleen, liver, heart and peritoneal fluid in vaccinated mice and the control group. In conclusion, this study introduces a new T. pyogenes vaccine candidate based on a live-attenuated strategy that mimics natural infection without pathogenicity for further investigation on vaccines against T. pyogenes infections.Graphical abstract

Cast Mg–8 wt% Mg2Si in situ composites containing 0, 0.5, 1, 2 and 3 wt% Gd were studied for their microstructure and high-temperature behaviour. Mechanical properties of the composites were investigated in the temperature range 25–250°C. Gd addition resulted in the modification of the primary Mg2Si particles, reduction of their average size and volume fraction, and formation of new Mg3Si2Gd2 and Mg5SiGd intermetallic phases and solid solution of Gd in the Mg matrix. The composite with 1% Gd addition showed the highest strength due to the presence of finer Mg2Si particles with polygonal morphology. Higher Gd contents decreased the strength, due to the coarsening of the Mg2Si particles, caused by the over-modification effect of Gd.