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Background: PET/CT imaging using [18F]-FDG is utilized in clinical oncology for tumor detecting, staging and responding to therapy procedures. Essential consideration must be taken for radiation staff due to high gamma radiation in PET/CT and cyclotron center. The aim of this study was to assess the staff exposure regarding whole body and organ dose and to evaluate environment dose in PET/CT and cyclotron center. Materials and Methods: 80 patients participated in this study. Thermoluminescence, electronic personal dosimeter and Geiger–Muller dosimeter were also utilized for measurement purpose. Results: The mean annual equivalent organ dose for scanning operator with regard to lens of eyes, thyroid, breast and finger according to mean±SD value, were 0.262±0.044, 0.256±0.046, 0.257±0.040 and 0.316±0.118, respectively. The maximum and minimum estimated annual whole body doses were observed for injector and the chemist group with values of (3.98±0.021) mSv/yr and (1.64±0.014) mSv/yr, respectively. The observed dose rates were 5.67 µSv/h in uptake room at the distance of 0.5 meter from the patient whereas the value 4.94 and 3.08 µSv/h were recorded close to patient’s head in PET/CT room and 3.5 meter from the reception desk. Conclusion: In this study, the injector staff and scanning operator received the first high level and second high level of radiation. This study confirmed that low levels of radiation dose were received by all radiation staff during PET/CT procedure using 18F-FDG due to efficient shielding and using trained radiation staff in PET/CT and cyclotron center of Masih Daneshvari hospital.

In this work, an approach for optimum placement of on-board decoupling capacitors (decaps) is presented, which aims at reducing transient noise in power delivery networks (PDNs). This approach is based on a genetic algorithm (GA) and accelerated by the use of an artificial neural network (ANN) as surrogate model to efficiently determine the fitness of a decap design, i.e., of a particular choice for the position and the type of the decaps to integrate in the printed circuit board (PCB). The ANN is trained by a suitable set of reference designs labeled by the impedance at the power pin of the integrated circuit (IC), which is computed by commercial simulation software. Several iterative runs of the GA are performed with an increasing number of decaps to identify a design with the least number of decaps necessary to reduce the distance of the frequency domain input impedance of the considered point-to-point connection from a desired target impedance as far as possible. This approach is successfully applied to generate an optimum decap design for a PDN with 52 possible decap positions and decaps chosen from three types.

Background & aim: Thalassemia major is a chronic genetic blood disease. This disease causes fatigue in people and affects different aspects of their lives, therefore the purpose of the present study was to determine the effect of intervention based on collaborative care model on the fatigue of patients with thalassemia major. Methods: The present randomized controlled and blinded clinical trial study was conducted in 2019 on 90 patients with thalassemia major referred to the thalassemia department of Shahid Beheshti Hospital of Yasuj, Iran. The random sampling was simple and they were divided into two groups of 45 people, intervention and control. First, a test to determine the fatigue status was taken from the two groups. The data collection tool included demographic information and the Multidimensional Fatigue Inventory (MFI) questionnaire. And then for the intervention group, training based on the collaborative care model was held during 8 sessions of 90 minutes. The collected data were analyzed using chi-square, independent t, and paired t tests. Results: The results indicated that the average age of the patients was 22.2 ± 6.73 years. Demographically, there was no significant difference between the groups. There was no significant change in the mean score of fatigue between the two intervention and control groups after the intervention compared to before the intervention, and after the intervention, no significant difference was observed in the score of the dimensions of fatigue between the two test and control groups(p>0.05). Conclusion: The results of the present study indicated that after the completion of the research interventions in the experimental and control groups, the changes in the overall average score of the fatigue status of patients with thalassemia major are not statistically significant. Based on this, the collaborative care model did not improve the fatigue of patients with thalassemia major.

Abstract Background Infectious keratitis is a serious ocular condition, which can lead to corneal scarring, vision loss, and even blindness. Pediatric infectious keratitis accounts for about 13% of all cases, although there is a lack of comprehensive data regarding keratitis in less than two years of age population group. This study was aimed to determine predisposing factors, clinical characteristics, microbial profile, and management of infectious keratitis in a population of children aged less than two years. Materials and methods A retrospective study was carried out in a tertiary eye institute over a period of 18 years from July 2005 to December 2022. Collected data was analyzed for demographics, predisposing factors, clinical features, and treatment methods. Results Fifty-seven cases of keratitis were identified. Age of the patients ranged from 1 to 24 months (Median: 6, interquartile range: 2–10). Thirty cases were male (52.6%). Predisposing factors were identified in 39 cases (68.4%): consisting of prior ocular trauma (n = 15), previous intraocular surgery (n = 11), ocular surface disease (n = 10), nasolacrimal duct obstruction (n = 4), prematurity (n = 3), developmental delay (n = 2), TORCH infection (n = 1), and contact lens (n = 1). Corneal thinning was observed in 29 eyes (50.9%), which progressed to perforation in 13 eyes (22.8%). Three patients developed endophthalmitis (95% CI, 1.5–13.4%). Most eyes had negative smear (60.4%) and culture (59.6%) results. Pseudomonas aeruginosa was the most common microorganism (11 of 21). Candida albicans was isolated in one case. In vitro susceptibility results showed good coverage of the combined ceftazidime and vancomycin regimen (100%). Surgical procedures were carried out in 35 eyes (61.4%) and 15 eyes required tectonic procedures (26.3%). Conclusion Despite good coverage of medical treatment over cultured isolates, surgical tectonic intervention was required in nearly a quarter of cases to resolve the corneal infection. This finding indicates the necessity of prompt patient referring, corneal sampling and initiation of the treatment.

Background and Aim: Bacterial co-infections have emerged as significant contributors to mortality in patients with viral respiratory infections, particularly during the COVID-19 pandemic. This study aimed to determine the frequency of bacterial co-infections and the antibiotic sensitivity patterns of strains isolated from tracheal cultures of COVID-19 patients with ventilator-associated pneumonia (VAP) admitted to the intensive care unit (ICU). Methods: In this cross-sectional study, we examined 57 COVID-19 patients diagnosed with VAP by an infectious disease specialist at Shahid Beheshti Hospital in Kashan, Iran, during 2021-2022. Respiratory secretion samples were collected 48 hours after hospitalization. Following Gram staining, microbial tests and cultures were conducted on blood agar and MacConkey agar (MAC) media. The isolated bacterial colonies were identified using specific tests and cultivation in differential and selective media. Antibiotic susceptibility testing was performed according to CLSI guidelines, with sensitivity patterns determined using the agar diffusion method. Results: Among the 57 COVID-19 patients with VAP, 31 (54.4%) were women, and 77% of the patients died. Hypertension and diabetes were the most frequently reported underlying conditions. The predominant bacteria isolated from tracheal cultures were Acinetobacter (50.9%), Klebsiella (33.3%), and coagulase-negative Staphylococcus (7%). Antibiogram results revealed that Acinetobacter exhibited the highest resistance to cotrimoxazole, meropenem, piperacillin, tazobactam, and ciprofloxacin, while Klebsiella showed the greatest resistance to piperacillin, tazobactam, ceftriaxone, ciprofloxacin, and ceftazidime. Conclusion: The high prevalence of Gram-negative bacteria such as Klebsiella and Acinetobacter, along with significant antibiotic resistance in COVID-19 patients with VAP, poses a critical challenge for ICU personnel. Therefore, investigating the antimicrobial resistance of strains isolated from these patients can aid in timely treatment and rational antibiotic prescription.

The availability of continuous spatiotemporal land surface temperature (LST) with high resolution is critical for many disciplines including hydrology, meteorology, ecology, and geology. Like other remote sensing data, satellite–based LST is also encountered with the cloud issue. In this research, over 5000 daytime and nighttime MODIS–LST images are utilized during 2014–2020 for Yazd–Ardakan plain in Yazd, Iran. The multi–channel singular spectrum analysis (MSSA) model is employed to reconstruct missing values due to dusts, clouds, and sensor defect. The selection of eigenvalues is based on the Monte Carlo test and the spectral analysis of eigenvalues. It is found that enlarging the window size has no effect on the number of significant components of the signal which account for the most variance of the data. However, data variance changes for all the three components. Employing two images per day, window sizes 60, 180, 360, and 720 are examined for reconstructing one year LST, where these selections are based on monthly, seasonal, semi-annual, and annual LST cycles, respectively. The results show that window size 60 had the least computational cost and the highest accuracy with RMSE (root mean square error) of 2.6 °C for the entire study region and 1.4 °C for a selected pixel. The gap–filling performance of MSSA is also compared with the one by the harmonic analysis of time series (HANTS) model, showing the superiority of MSSA with an improved RMSE of about 2.7 °C for the study region. In addition, daytime and nighttime LST series for different land covers are compared. Lastly, the maximum, minimum, and average LST for each day and night as well as average and standard deviation of LST images in the seven-year-long time series are also computed.

Thermal monitoring of different regions is usually limited to meteorological data in ground stations. Meteorological networks are limited in arid and semi-arid areas, where monitoring climatic conditions is not possible. The aim of this study is to estimate the land surface temperature (LST) hourly for Yazd-Ardakan plain by modeling the diurnal temperature cycle (DTC) using LST imagery of moderate resolution imaging spectroradiometer (MODIS). First, MODIS imagery are reconstructed using the multi-channel singular spectrum analysis, and the complete time series without missing values are created. Then, six DTC models are compared. The accuracy of DTC models is examined by ground LST measurements, air temperature, humidity, and wind speed. In addition, the results of examining the root mean square error (RMSE) images obtained from cross-validation based on MODIS LST imagery show that DTC2 has the highest error, where 73% of the area has RMSE greater than $3^{\circ} \text{C}$ . In DTC1 and DTC2, 64% and 5.8% of the study region has RMSE less than $2^{\circ} \text{C}$ . In general, DTC1, DTC6, DTC5, DTC4, DTC3, and DTC2 models have shown the highest to lowest accuracy in modeling the LST diurnal cycle. In addition, the difference between LST in mountain and plain lands is greater at the time of maximum temperature than at other hours of the day and night. The findings of this research are crucial in studies concerning climate change and land environmental monitoring in arid/semi-arid regions.

The effects of parameters affecting the input impedance of a power delivery network (PDN) are investigated. It is considered that the size of the power plane and the number of associated planes in the PCB layout, apart from the decoupling capacitor, have an effect on the impedance behavior within a certain frequency range. An artificial neural network (ANN) is trained using the generated data utilizing a process to generate suitable input for training a machine learning (ML) module, which is able to predict the impedance profile of the PDN. In order to obtain a more accurate prediction, Bayesian optimization is implemented and the results are compared to commercial power integrity (PI) software.

Complications of the rhinoplasty surgery may involve the orbital contents due to the anatomical proximity to the surgical site. In this article, we report an inadvertent complication of the rhinoplasty in a 32-year-old woman who presented to our clinic with reduced vision of her left eye following the surgery. With diagnosis of a penetrating globe injury, the patient underwent an anterior segment surgery with general anesthesia which included both corneal laceration repair and lensectomy. We suggest that surgeons consider using protective eye shields for their patients during these surgeries depending on their experience and technique.

Objective: The pathogenesis of metabolic syndrome (MetS) complications involves the excessive production ofreactive oxygen species, inflammation, and endothelial dysfunction. Due to Lycopene, a highly unstable structure andits significant effects on modulating the metabolic system, there is a strong need for a formula that can increase itsstability. The aim of this study was to develop an approach for encapsulating Lycopene and investigate its effects oninflammatory markers, oxidative stress, and liver enzymes in patients with MetS.Materials and Methods: This study is a simple randomized, double-blind, objective-based clinical trial that involvedeighty subjects with MetS, who were equally and randomly assigned to two groups: one group received 20 mg ofLycopene per day for 8 weeks, and the Placebo group followed the same protocol as the Lycopene group but receiveda placebo instead of Lycopene. They were called Lycopene and placebo, respectively. During follow-up visits after 4and 8 weeks, 20 ml of blood was collected for evaluation of liver enzymes and some inflammatory related markers.Results: Prior to the assignment of volunteers to their respective groups, there were no notable differences in C-reactiveprotein (CRP), serum liver enzymes, systolic and diastolic blood pressure, or pro-oxidant-antioxidant balance (PAB)between the Lycopene and placebo groups. However, our subsequent analysis revealed a significant reduction in theserum levels of CRP (P=0.001) and PAB (P=0.004) in the group that received Lycopene. Our encapsulated Lycopenetreatment was not associated with a significant difference in serum levels of alanine aminotransferase (ALT), aspartatetransferase (AST), or alkaline phosphatase (ALP) between our two groups.Conclusion: This study investigated the impact of Lycopene on individuals with MetS, revealing a noteworthymodulation effect on PAB and inflammation linked to MetS. However, no significant differences was demonstrated inserum levels of ALT, AST and ALP between the studied group (registration number: IRCT20130507013263N3).

Abstract Background The Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (β) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies. Methods 18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (β value of 100–500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM. Results OSEM had 11.5–18.6% higher BV than Q.Clear using β value of 500. Conversely, RC from OSEM to Q.Clear using β value of 500 decreased by 3.3–7.7% for a sphere with a diameter of 10 mm and 2.5–5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a β value of 500 was 5.2–8.1% in the smallest spheres compared to OSEM. When the β value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between β value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as β increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions. Conclusions Implementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal β value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal β value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended β value of 300 for small lesions and β value of 500 for large lesions in clinical study.

Land Surface Temperature (LST) is one of the most important parameters in land-atmosphere energy exchange that is applicable to many sciences such as climatology, hydrology, agriculture, ecology, etc. One of the most significant limitations of using remote sensing for estimation of LST is the presence of clouds, which remarkably affects the energy reflected from the surface and disrupts the reading ability of the optical and thermal sensors. In the present study, 23 Landsat 8 images in 2015 were used as an annual time series to estimate LST in a part of the pistachio farms of Yazd, Iran. LST in the 23 images was estimated by generalized split-window algorithm. The results showed in the estimated (23 images) LST time series, the minimum, maximum, and mean missing data due to cloud cover were 17%, 28%, and 19%, respectively. SSA algorithm was used to solve the problem of missing data. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) between the original and reconstructed data at the data points in the studied LST time series were 3.4 and 2.5 K, respectively. Moreover, the gap-filling error was estimated by extracting five random images with four iterations of time series and comparing the reconstructed images with the extracted spatio-temporal images. RMSE and MAE were estimated to be 4.4 and 3.6 K in reconstruction of temporal artificial gaps and 3.7 and 2.8 K in the spatial artificial gaps, respectively. Based on the findings, SSA algorithm can be effectively used to fill the problem of missing data due to cloud cover in Landsat 8 LST time series.

The corneal epithelium (CE) is the outermost layer of the cornea with constant turnover, relative stability, remarkable plasticity, and compensatory properties to mask alterations in the underlying stroma. The advent of quantitative imaging modalities capable of producing epithelial thickness mapping (ETM) has made it possible to characterize better the different patterns of epithelial remodeling. In this comprehensive synthesis, we reviewed all available data on ETM with different methods, including very high-frequency ultrasound (VHF-US) and spectral-domain optical coherence tomography (SD-OCT) in normal individuals, corneal or systemic diseases, and corneal surgical scenarios. We excluded OCT studies that manually measured the corneal epithelial thickness (CET) (e.g., by digital calipers) or the CE (e.g., by confocal scanning or handheld pachymeters). A comparison of different CET measuring technologies and devices capable of producing thickness maps is provided. Normative data on CET and the possible effects of gender, aging, diurnal changes, refraction, and intraocular pressure are discussed. We also reviewed ETM data in several corneal disorders, including keratoconus, corneal dystrophies, recurrent epithelial erosion, herpes keratitis, keratoplasty, bullous keratopathy, carcinoma in situ, pterygium, and limbal stem cell deficiency. The available data on the potential role of ETM in indicating refractive surgeries, planning the procedure, and assessing postoperative changes are reviewed. Alterations in ETM in systemic and ocular conditions such as eyelid abnormalities and dry eye disease and the effects of contact lenses, topical medications, and cataract surgery on the ETM profile are discussed.

Introduction: Dapoxetine is a novel therapeutic agent employed in treating specific diseases. However, its potential impact on renal excretion processes has yet to be thoroughly investigated, necessitating further exploration in this study. Objectives: This research aimed to assess the effects of dapoxetine on renal function and explore any potential disturbances in kidney excretion processes. Materials and Methods: In this study, 32 male Albino rats weighing between 200-250 g were utilized. The rats were randomly divided into four groups. Group one served as the control and received a normal diet, while groups two to four were administered dapoxetine through gavage at doses of 1 mg/kg, 2 mg/kg, and 4 mg/kg, respectively. The study evaluated blood urea nitrogen (BUN), and serum creatinine levels and examined renal pathological changes in the rats. Results: The results demonstrated a significant increase in average BUN levels in group four compared to other groups (P0.05). Importantly, no indications of apoptosis, necrosis, edema, hydropic degeneration, or glomerular changes were observed in any of the renal cells from the rat groups. Conclusion: Dapoxetine administration led to changes in BUN and creatinine levels; however, it did not adversely affect the renal cells’ pathological outcomes. These results suggest that dapoxetine could be considered for use in the future treatment of certain diseases, considering its minimal impact on renal function. Further investigations and clinical trials are warranted to corroborate these findings and inform medical decision-making.

IntroductionOver the past 60 years, the use of plastic covers as a tool to increase the harvest and increase the yield of horticultural crops has steadily increased worldwide. Plastic greenhouses have turned desert areas into areas with modern agricultural development, the province of Almera in the south of Spain is a good example. It now has many greenhouses, making it a global model of agricultural development; but greenhouses contain large quantities of phthalates and cause harm to people, like hormonal disturbances, heart problems, cancer, etc. However, plastic greenhouses are widely built to produce vegetables and fruits near cities. As a result, several remote sensing methods have been developed to identify and monitor the distribution of plastic covered greenhouses in order to manage water resources, identify sites and quantities of plastic greenhouses. Remote detection is the only practical method to monitor plastic greenhouses in a vast geographic area. In the past few years, there have been few studies using high spatial resolution images. In one study, three main absorption ranges were identified that are unaffected by dust, washing and surface factors. In Spain, an artificial intelligence neural network has been proposed to identify greenhouse using Quick Bird images with a resolution of 1.5m. Studies based on medium spatial resolution imagery were also conducted, resulting in different results. In search on land cover classification using Landsat TM images, no favorable results were found. Research has proposed a new method for mapping greenhouses using Sentinel-2 dual-time images and 1D-CNN deep learning. The aim of the current research is to identify the length of the cultivation period of greenhouse crops using Sentinel-2 satellite images. Researchers used a variety of satellite imagery to identify and classify greenhouses. The innovative objective of the current research is to evaluate the Sentinel-2 satellite images in determining the length of the cultivation period of greenhouse crops; which was done for the first time within the country. Material and MethodsIn the first step, the cultivated area of greenhouses was identified using an aerial photograph. Then, useful bands were extracted in greenhouse studies using Sentinel-2 time series images. Next, vegetation and plastic cover indices were calculated for the greenhouse growing period. 1) Identify and determine the area under cultivation in greenhouses: By comparing the pixel-based classification algorithms and the object-oriented classification algorithms, the cultivated area of greenhouses was identified. ENVI and eCognition software were used for pixel-based classification and object-oriented classification, respectively. It should be noted that only one aerial photograph with three bands, blue, green and red, has been used in object-oriented classification. In order to classify the base pixel, learning samples were selected using expert knowledge from the study area. In some instances, Google Earth was used as well. Learning samples were selected scattered across the image to improve accuracy. For validation of the maps obtained from these algorithms, ground control points were used. To reduce human error, these points were also entered into Google Earth. 2) Identifying functional bands in the greenhouse study: The spectral behavior curve of different earth surface covers in Blue, Green, Red, SWIR, NIR bands was drawn in an image of the Sentinel-2 satellite. These spectral signatures were compared on May 20, 2020 i.e., when outdoor vegetation and greenhouse cultivation were at their peak. 3) Determining the duration of the greenhouse growing season: Vegetation indices of the NDVI, SAVI, OSAVI, MSAVI, GNDVI, RVI, DVI, RGVI, IPVI and plastic cover indices PGI, RPGI, PI, PMLI were compared using Sentinel-2 satellite time series images. Results and DiscussionGreenhouse cultivation is used to improve quality and increase food production. However, their development has many negative effects on the environment. Therefore, obtaining accurate and timely information on the distribution of greenhouses and the time of planting and harvesting crops under plastic covers can make a significant contribution to agricultural management, water management and soil protection. The present study is the first study to identify the length of the greenhouse cultivation period using Sentinel-2 satellite time series images. The results showed that among the object-oriented classification algorithms, two classification algorithms, Bayes and KNN, were more precise for the identification and determination of the cultivated area of greenhouses. The reflection of the plant below the plastic cover of the greenhouse in the NIR, Narrow NIR, Red Edge and SWIR bands increases by an equal amount in comparison with the reflection from the vegetation in the open space. Comparing vegetation cover and greenhouse cover indices showed that the indices designed based on SWIR and Red bands showed greater reflection during the hot season of the year. Indices based on NIR, Narrow NIR, and Red Edge bands were more reflective from October to late February. Based on the results obtained from the ground truth data, greenhouses are plastered during the warm season. This caused an increase in the reflectance in indices designed based on SWIR and Red bands and a decrease in reflectance in indices designed based on NIR, Narrow NIR, Red Edge bands, during the hot season. Therefore, combining all indices, two crop periods were observed: the first started in early March, peak cover was reached in April, and harvest continued. Until the middle of August. The second harvest started at the end of August and peaked in December, until the end of harvest in mid-February.

Water scarcity is a critical abiotic stress factor for plants in arid and semi-arid regions, impacting crop development and production yield and quality. Monitoring water stress at finer scales (e.g., farm and plant), requires multispectral imagery with thermal capabilities at centimeter resolution. This study investigates drought stress in pistachio trees in a farm located in Yazd province, Iran, by using Unmanned Aerial Vehicle (UAV) images to quantify evapotranspiration and assess drought stress in individual trees. Images were captured on 10 July 2022, using a Matrix 300 UAV with a MicaSense Altum multispectral sensor. By employing the Surface Energy Balance Algorithm for Land (SEBAL), actual field evapotranspiration was accurately calculated (10 cm spatial resolution). Maps of the optimum crop coefficient (Kc) were developed from the Normalized Difference Vegetation Index (NDVI) based on standard evapotranspiration using the Food and Agriculture Organization (FAO) 56 methodology. The comparison between actual and standard evapotranspiration allowed us to identify drought-stressed trees. Results showed an average and maximum daily evaporation of 4.3 and 8.0 mm/day, respectively, in pistachio trees. The real crop coefficient (Kc) for pistachio was 0.66, contrasting with the FAO 56 standard of 1.17 due to the stress factor (Ks). A significant correlation was found between Kc and NDVI (R2 = 0.67, p < 0.01). The regression model produced a crop coefficient map, valuable to support precise irrigation management and drought prevention, considering the heterogeneity at the farm scale.

Vehicular motion simulators have evolved to become an important contributor to major industries, including defense, aerospace, and vehicle manufacturing. During the past few decades, many efforts have been made towards developing robust, adaptive motion simulators with the highest level of fidelity and realism. Thus, it is important to recollect, in order to evaluate the current state of complex robotic systems, encouraging careful planning of further improvements in the future. This article focuses on the current motion simulators’ structural designs and working principles alongside the currently developed motion control algorithms to achieve the highest fidelity. Furthermore, within the era of industry 4.0 and the fast-paced merging of technologies into key industries, some suggestions are made for future works which it is believed are worth investigating to provide robustness and adaptivity to the control of simulation systems, improving their fidelity and realism alongside reducing motion sickness experienced by the simulator operator.

Background: The results of previous studies indicated that increasing self-compassion is associated with increased ability to regulate emotion. Objective: The aim of this study was to investigate the mediating role of difficulty in emotional regulation in the relationship between self-compassion and anxiety among university students. Materials & Methods: The current cross-sectional study was performed on male and female undergraduate, graduate and doctoral students in Tehran from January to September 2022. After obtaining official permission, 208 students from Amir Kabir University, Shahid Beheshti University, Islamic Azad University, North branch, Central branch, Research Science branch and Rodehen branch were selected using a convenience sampling method. Self-compassion scale of Neff, difficulty in emotion regulation of Graz and Roemer, and Beck anxiety scale were used to collect data. Data were then analyzed using structural equation modeling. Results: The mean age of participants was 27.63 (standard deviation= 9.13), 143 were female and 145 were single. the direct effect of self-compassion on anxiety is not significant (β =0.261, P = 0.109), but the indirect effect of self-compassion on anxiety with mediating role in difficulty of emotional regulation is significant (β = -0.674, P = 0.001). Thus, self-compassion reduces anxiety by reducing the difficulty in emotional regulation. The results also show that self-compassion alone explains 17% of the variance of anxiety. Self-compassion and difficulty in emotional regulation account for 34% of the variance of anxiety. Conclusion: The findings revealed that difficulty in emotional regulation plays a full mediating role in the relationship between self-compassion and anxiety. This study highlights the role of self-compassion and difficulty in emotional regulation in reducing anxiety, But to use self-compassion-based education and emotional regulation in anxiety management, more research is needed to examine their effect in the form of empirical research.

The preparation of land cover maps provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land cover maps using the classification of Normalized Difference Vegetation Index (NDVI) values ​​and prepare land cover maps from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the map produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values ​​was extracted in order to classify it and the obtained map was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0.75 and overall accuracy of 80.86%. The results of evaluating the accuracy of the NDVI Based land cover Classification (NBC) index also indicated that this map has extracted the land cover map with an overall accuracy of 83% and a Kappa coefficient of 0.77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values ​​for the production of natural land cover maps and can be used in order to prepare these maps for geographic monitoring and achieving sustainable environmental development.

Background: The present study aims to assess the impact of various image reconstruction methods in 18F-FDG PET/CT imaging on the quantification performance of the proposed technique for joint compensation of respiratory motion and partial volume effects (PVEs) in patients with non-small cell lung cancer. Materials and Methods: An image-based deconvolution technique was proposed, incorporating wavelet-based denoising within the Lucy-Richardson algorithm to jointly compensate for PVEs and respiratory motion. The method was evaluated using data from 15 patients with 60 non-small cell lung cancer. In these patients, the lesions were classified by size, location and Signal-to-Background Ratios (SBR). In each study, PET images were reconstructed using four different methods: OSEM with time- of-flight (TOF) information, OSEM with point spread function modelling (PSF), OSEM with both TOF and PSF (TOFPSF), and OSEM without PSF or TOF (OSEM). The Contrast to Noise Ratio (CNR), Coefficient of Variation (COV) and Standardized Uptake Values (SUV) were measured within the lesions and compared to images that were not processed using the joint-compensation technique. Furthermore, variabilities arising due to the choice of the reconstruction methods were assessed. Results: Processing the images using the proposed technique yielded significantly higher CNR and SUV, particularly in small spheres, for all the reconstruction methods and all the SBRs (P

The preparation of geological maps based on field data and the application of aerial photographs have always been an error because of the structural diversity of the earth and the difficulty of accessing certain regions. But in recent decades, the use of satellite imagery has but in recent decades, the use of satellite imagery has gone a long way to increasing the accuracy and timeliness of geological mapping. The purpose of the current study is to investigate the applicability of Landsat 8 satellite images and object-oriented pixel classification methods in mapping the geological formations of some part of the Shirkuh Mountain range in Yazd province. This area is part of the scattered mountain range of Central Iran with a dry climate and minimal vegetation. Initially, enhanced operations were performed to identify geological information’s using MNF, PCA and FCC processing. Then, the images were classified using object-oriented algorithms (BAYES, SVM, KNN, Decision Tree and Random Forest), neural network (ARTMAP, RBF, MLP and SOM), and base pixels (Maximum Likelihood, Minimum Distance, Mahalanobis and SAM). Next, the error rate of each method was calculated using Boolean logic and kappa coefficient. The results showed that the maximum probability classification with kappa coefficient of 75% in the base pixel category, Fuzzy ARTMAP classification in neural network method with kappa coefficient of 72% and Bayesian classification in object-oriented method with kappa coefficient of 82% have the best results among other methods. These results show that the methods mentioned in the identification and separation of geological formations are effective. The SAM of pixel-based methods, SOM of neural network methods and RF of object-oriented methods with 49%, 64% and 61%, respectively, showed the lowest accuracy in each category.

Climate change affects plant dynamics and functioning of terrestrial ecosystems. This study aims to investigate temporal changes in global vegetation coverage and biomes during the past three decades. We compared historic annual NDVI time series (1982, 1983, 1984 and 1985) with recent ones (2015, 2016, 2017 and 2018), captured from NOAA-AVHRR satellite observations. To correct the NDVI time series for missing data and outliers, we applied the Harmonic Analysis of Time Series (HANTS) algorithm. The NDVI time series were decomposed in their significant amplitude and phase given their periodic fluctuation, except for ever green vegetation. Our findings show that the average NDVI values in most biomes have increased significantly (F-value

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

Land surface temperature (LST) is a significant environmental factor in many studies. LST estimation methods require various parameters, such as emissivity, temperature, atmospheric transmittance and water vapor. Uncertainty in these parameters can cause error in LST estimation. The present study shows how the moderate resolution imaging spectroradiometer (MODIS) water vapor imagery can improve the accuracy of Landsat 8 LST in different land covers of arid regions of Yazd province in Iran. For this purpose, water vapor variation is analyzed for different land covers within different seasons. Validation is performed using T-based and cross-validation methods. The image of atmospheric water vapor is estimated using the MODIS sensor, and its changes are investigated in different land covers. The bare lands and sparse vegetation show the highest and lowest accuracy levels for T-based validation, respectively. The root mean square error (RMSE) is also calculated as 0.57 °C and 1.41 °C for the improved and general split-window (SW) algorithms, respectively. The cross-validation results show that the use of the MODIS water vapor imagery in the SW algorithm leads to a reduction of about 2.2% in the area where the RMSE group is above 5 °C.

Investigating vegetation changes, especially plant phenology, can yield valuable information about global warming and climate change. Time series satellite observations and remote sensing methods offer a great source of information on distinctions and changing aspects of vegetation. The current study aimed to determine the trend and rate of changes in some phenological components of Iran’s vegetation. In this regard, the current study employed the daily NDVI (Normalized Difference Vegetation Index) product of the AVHRR sensor with a spatial resolution of 0.05° × 0.05°, named AVH13C1. Then, using the HANTS algorithm, images of amplitude zero, annual amplitude, and annual phase were prepared annually from 1982 to 2019. Using TIMESAT software, the starting, end, and length of time of growing season were calculated for each pixel time series to prepare annual maps. The Mann–Kendall statistical test was used to investigate the significance of changes during the study period. On average in the entire area of Iran, the annual phase was declining with a trend of −0.6° per year, and the time for the start and end of the season was declining by −0.3 and −0.65 days per year, respectively. Major changes were noticed in the northeast, west, and northwest regions of Iran, where the annual phase declined with a trend of −0.9° per year. Since the annual growth cycle of the plant (equivalent to 356 days) was in the form of a sinusoidal signal, and the angular changes in the sine wave were between zero and 360°, each degree of change was equivalent to 1.01 days per year. Therefore, the reduction in the annual phase by −0.9 degrees almost means a change in the time (due to the earlier negative start phase) of the start of the annual growth signal by −0.9 days per year. The time of the start and end of the growing season declined by −0.6 and −1.33 days per year, respectively. The reduction in annual phase and differences in time of the starting season from 1982 to 2019 indicate the acceleration and earlier initiation of various phenological processes in the area.

The aim of this study was to develop a coronary anxiety model in students based on the dimensions of positive psychology. The research method was descriptive correlational studies and structural equation modeling. The statistical population included all high school students in Mashhad in the year 2021-2022 with 983 people who were selected using Cochran's formula and simple random sampling method of 276 people. The data collection tools were corona anxiety and positive psychology questionnaires. The validity of the questionnaires was confirmed using content and face validity. Its reliability was obtained through Cronbach's alpha coefficient of 0.77 and 0.89, respectively. In addition to descriptive statistics, statistical techniques such as Pearson correlation coefficient, multiple regression and structural equation modeling were used to analyze the data. The results showed that the dimensions of positive psychology (happiness, mental well-being, optimism, self-worth, hope, interpersonal skills and responsibility) have a direct and significant effect on coronary anxiety in students. The results of the fit model indices indicated a good fit of the model, and in fact the general result is that the designed model of students' coronary anxiety based on the dimensions of positive psychology has a good fit.

Introduction: The growth of urbanization and the expansion of cities have caused significant changes in the surrounding land cover. Changes in land surface characteristics as a result of rapid urbanization can affect climatic conditions in seasonal and long-term periods on a local, regional and global scales. Land surface change (LST) change is directly related to land cover changes, so that land cover change causes changes in the land surface temperature. Yazd plain as the study area, has witnessed many changes in land cover during the past two decades due to immigrants, so studying its temperature changes is very important. The aim of this study was to investigate the changes in land surface temperature of the Yazd plain over an 18-year period and compare it with land cover changes in order to determine the effect of these land cover changes on land surface temperature. The distinction between the current study and previous studies is the effect of land cover change on land surface temperature in different months of the year. Methodology: In this study, Landsat 5 and 8 satellite images were used to investigate the effect of land cover changes on land surface temperature in 2001 and 2019. Land surface temperature map were estimated for Landsat 5 satellite images using single band method, and for Landsat 8 images using split-window algorithm. The land cover maps for the studied years were prepared using the supervised classification method of maximum likelihood. Changes in land cover made from 2001 to 2019 and its effect on land surface temperature were examined. Results and Discussion: In the land cover map of the study area, six land cover types of mountainous rangelands, plain rangelands, agriculture, sand dunes, barren lands and residential areas were identified. For better separation of land cover, images related to the peak of vegetation period were used in the classification processes. After determining the degree of class segregation, the supervised classification was performed by the maximum likelihood method, and then land cover changes were detected in the area. In order to investigate the changes made in the study area, the area and changes of each land cover were also extracted. Urban areas with an increase of 12.1% area had the highest growth, and agricultural lands with 28% reduction had the highest reduction in area. At the same time, the study period with increasing migration from villages to urban areas, the phenomenon of urbanization and industrialization of the study area can be the reason for this issue. Also, the 24.5% increase in the area of sand dunes is due to recent droughts and the placing of the study area in the sedimentation site of the wind erosion process. Although little vegetation in sand dunes has stabilized them, in recent years with the loss of this cover, the area of wind erosion process has increased. Land cover changes map was extracted from the area in 18-year period. The Land surface temperature maps for four months of the year were prepared using the single channel method in 2001, and the split window algorithm in 2019. For better comparison, the relevant Land surface temperature indexes per month was classified into 10 categories at 2 °C intervals and the areas of the classes were compared with each other. In order to investigate the effect of land cover change on land surface temperature, the mean, minimum and maximum land surface temperatures in each land cover on the mentioned dates were examined. The results show that the land surface temperature in each area of the earth is influenced by surface factors and its characteristics and the temperature obtained in different land cover is different. Land surface temperatures in bare lands and sand dunes that have been converted into other land covers have dropped significantly, so that, in July and September it dropped about 2°C. Most of the temperature increase is related to the conversion of agricultural lands to bare lands, sand dunes and mountainous rangelands. Contrary to the existing hypotheses that the greatest increase in temperature is related to changing agricultural to residential land cover, in this area located in arid regions of central Iran, the conversion of agricultural lands to bare lands and sand dunes has led to higher temperatures. The change in land surface temperatures in the changed coverage in February was higher than in other months and less in September.Conclusion: The results of this study showed that during 2001-2019, the area of residential lands and sand dunes increased and the area of agricultural lands, bare lands, and rangelands decreased. The results of land surface temperature assessment in changed land cover showed that the land surface temperature in bare lands and sand dunes that have been converted to other coatings has decreased significantly. Conversion of agricultural lands to bare lands and sand dunes has led to higher temperatures than other land cover type and physical development has created numerous environmental problems.