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Radiation therapy is the primary method used to treat cancer in the clinic. Its goal is to deliver a precise dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). However, the traditional workflow used by dosimetrists to plan the treatment is time-consuming and subjective, requiring iterative adjustments based on their experience. Deep learning methods can be used to predict dose distribution maps to address these limitations. The study proposes a cascade model for organs at risk segmentation and dose distribution prediction. An encoder-decoder network has been developed for the segmentation task, in which the encoder consists of transformer blocks, and the decoder uses multi-scale convolutional blocks. Another cascade encoder-decoder network has been proposed for dose distribution prediction using a pyramid architecture. The proposed model has been evaluated using an in-house head and neck cancer dataset of 96 patients and OpenKBP, a public head and neck cancer dataset of 340 patients. The segmentation subnet achieved 0.79 and 2.71 for Dice and HD95 scores, respectively. This subnet outperformed the existing baselines. The dose distribution prediction subnet outperformed the winner of the OpenKBP2020 competition with 2.77 and 1.79 for dose and DVH scores, respectively. The predicted dose maps showed good coincidence with ground truth, with a superiority after linking with the auxiliary segmentation task. The proposed model outperformed state-of-the-art methods, especially in regions with low prescribed doses.

Recent research shows the eye has a paravascular solute transport pathway driven by physiological pulsations resembling the brain. we developed synchronous multimodal imaging tools aimed at measuring the driving pulsations of the human eye. We used an eye-tracking functional eye camera (FEC) compatible with magnetic resonance imaging (MRI) for measuring eye surface pulsations. Special optics enabled the integration of the FEC with a magnetic resonance compatible video ophthalmoscopy (MRcVO) for simultaneous retinal imaging along with functional eye MRI imaging (fMREye) reflecting BOLD (blood oxygen level dependent) contrast. Upon optimizing the fMREye parameters, we thus measured the power of the physiological (vasomotor, respiratory, and cardiac) eye and brain pulsations by fast Fourier transform (FFT) power analysis. The human eye proved to pulsate in all three physiological pulse bands, most prominently in the respiratory (RESP) band. The FFT power means of physiological pulsation for two adjacent slices was significantly higher than in one-slice scans (RESP1 .vs RESP2; df = 5, p = 0.0174). FEC and MRcVO confirmed the respiratory pulsations at the eye surface and retina. we conclude that the human eye has three pulsation mechanisms, and multimodal imaging offers non-invasive monitoring of their effects in driving eye fluidics.

Background Fatigue is one of the most common problems in patients with multiple sclerosis (MS) and has adverse effects on their sleep status and self-efficacy. This study aimed to determine the effect of distance nurse-led fatigue management on fatigue, sleep quality, and self-efficacy in patients with MS. Methods This quasi-experimental study was performed on 60 patients with MS in Arak, Iran. Subjects were randomly assigned into intervention and control groups. The intervention group received eight sessions of nurse-led fatigue management training through the Skyroom platform. The control group received only the usual programs. Data were collected before and two months after the intervention using the Fatigue Severity Scale, the Pittsburgh Sleep Quality Index, and the Multiple Sclerosis Self-Efficacy Scale. The significance level in this study was determined 0.05. Results After the intervention, the mean score of fatigue severity in the intervention group was significantly lower than the control group (2.52 ± 0.40 vs 5.65 ± 0.52) (P P P Conclusion Distance nurse-led fatigue management improved fatigue, sleep quality, and self-efficacy in patients with MS. We recommend the use of these courses as an important step toward improving fatigue, sleep quality, and self-efficacy among these patients.

In this paper, we aimed to investigate the linear and nonlinear optical properties of GO-ZnO and RGO-ZnO nanocomposites in comparison with pure GO and reduced graphene oxide (RGO). For this purpose, GO, RGO, GO-ZnO, and RGO-ZnO were synthesized and characterized by Fourier transform infrared (FT-IR), Ultraviolet–Visible (UV–Vis) absorption, X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDX). XRD and EDX analysis indicated the reduction of GO as well as the successful synthesis of GO-ZnO and RGO-ZnO nanocomposites. The FT-IR spectroscopy showed that absorption bands were at 3340 cm−1, 1630 cm−1, 1730 cm−1 and 480 cm−1 related to OH, C=C, C=O, and Zn–O stretching vibrations, respectively. The direct band gaps of GO, RGO, GO-ZnO and RGO-ZnO from UV–Vis spectra were at 3.36, 3.18, 3.63 and 3.25 eV, sequentially. Moreover, the third-order nonlinear optical properties were investigated using a z-scan technique with Nd: YAG laser (532 nm, 70 mW). It can be seen that the nonlinear absorption coefficient value $$(\upbeta )$$ ( β ) increased from 5.3 × 10–4 (GO) to 8.4 × 10–3 cm/W (RGO-ZnO). In addition, nonlinear refractive index (n2) of the GO, RGO, GO-ZnO, and RGO-ZnO was obtained as 10.9 × 10–10, 14.3 × 10–10, 22.9 × 10–10, and 31.9 × 10–10 cm2/W respectively.

Purpose Predicting the progression of MCI to Alzheimer's disease is an important step in reducing the progression of the disease. Therefore, many methods have been introduced for this task based on deep learning. Among these approaches, the methods based on ROIs are in a good position in terms of accuracy and complexity. In these techniques, some specific parts of the brain are extracted as ROI manually for all of the patients. Extracting ROI manually is time-consuming and its results depend on human expertness and precision. Method To overcome these limitations, we propose a novel smart method for detecting ROIs automatically based on Explainable AI using Grad-Cam and a 3DCNN model that extracts ROIs per patient. After extracting the ROIs automatically, Alzheimer's disease is predicted using extracted ROI-based 3D CNN. Results We implement our method on 176 MCI patients of the famous ADNI dataset and obtain remarkable results compared to the state-of-the-art methods. The accuracy acquired using 5-fold cross-validation is 98.6 and the AUC is 1. We also compare the results of the ROI-based method with the whole brain-based method. The results show that the performance is impressively increased. Conclusion The experimental results show that the proposed smart ROI extraction, which extracts the ROIs automatically, performs well for Alzheimer's disease prediction. The proposed method can also be used for Alzheimer's disease classification and diagnosis.

DeepFake involves the use of deep learning and artificial intelligence techniques to produce or change video and image contents typically generated by GANs. Moreover, it can be misused and leads to fictitious news, ethical and financial crimes, and also affects the performance of facial recognition systems. Thus, detection of real or fake images is significant specially to authenticate originality of people's images or videos. One of the most important challenges in this topic is obstruction that decreases the system precision. In this study, we present a deep learning approach using the entire face and face patches to distinguish real/fake images in the presence of obstruction with a three-path decision: first entire-face reasoning, second a decision based on the concatenation of feature vectors of face patches, and third a majority vote decision based on these features. To test our approach, new datasets including real and fake images are created. For producing fake images, StyleGAN and StyleGAN2 are trained by FFHQ images and also StarGAN and PGGAN are trained by CelebA images. The CelebA and FFHQ datasets are used as real images. The proposed approach reaches higher results in early epochs than other methods and increases the SoTA results by 0.4\%-7.9\% in the different built data-sets. Also, we have shown in experimental results that weighing the patches may improve accuracy.

Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are among the things that reveal the necessity of automatic image quality assessment (IQA). However, automated IQA requires access to bulk annotated datasets for training deep learning (DL) models. Labelling medical images is a tedious, costly and time-consuming process, which creates a fundamental challenge in proposing DL-based methods for medical applications. This study aims to present a new method for CMR IQA when there is limited access to annotated datasets. Methods: The proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks. This model was evaluated on the data of over 6,000 subjects from the UK Biobank for five defined tasks, including detecting respiratory motion, cardiac motion, Aliasing and Gibbs ringing artefacts and images without artefacts. Results: The results of extensive experiments show the superiority of the proposed model. Besides, comparing the model's accuracy with the domain adaptation model indicates a significant difference by using only 64 annotated images related to the desired tasks. Conclusion: The proposed model can identify unknown artefacts in images with acceptable accuracy, which makes it suitable for medical applications and quality assessment of large cohorts., Comment: 16 pages, 1 figure, 2 tables

Most qualitative characteristics cannot be easily reported numerically. In such cases, each product is inspected and usually divided into two conforming and nonconforming groups based on their qualitative characteristics. Since nowadays products and processes have generally several interdependent qualitative characteristics, it is necessary to use multivariate quality control methods to make the relationship between variables and their variations. To do this, the sampling of the considered qualitative characteristic is done at the specified time intervals to check the control of the process over time after drawing the considered statistic on the control chart. A common problem while sampling is measurement error. It affects the performance of control charts, impairs their ability to detect changes in the process, and increases the cost and time to search for out-of-control situations. In this paper, the effect of measurement error on the performance of the Multivariate Nonconforming Proportion (MNP) control chart has been evaluated based on the criterion of Average Run Length (ARL) for the first time. The results imply that the measurement error has a considerable impact on the performance of this chart. Also, the results indicate that if the defective items have been wrongly considered as correct items, we would have a higher ARL compared to an ideal and accurate system. On the other hand, if the system considers right items as defective, we will have a lower ARL than the ideal and accurate system. It is proved that if both errors (considering faulty items as correct ones and vice versa) occur simultaneously, the ARL will be reduced like the previous case.

After harvesting of Sepestan (Cordia myxa) fruit, it immediately undergoes the physical and chemical spoilage. Therefore, Sepestan fruit has a limited shelf life and some procedures should be considered so that it could be stored and consumed for a longer period of time. One of the practical methods to enhance shelf life of the fruits and vegetables is their conversion into various products such as pickles. In this study, the effect of ripening extent with three different ripening degrees: unripe/green fruit, semi-ripe and ripe were evaluated on the quality of Sepestan pickles during 90 days of storage at the ambient temperature. For pickle preparation, an identical amount of 6% vinegar solution (about 25% of fruit weight) was used and physicochemical, textural, microbial, and sensorial properties of samples were evaluated during the storage period. The results showed that during the storage period, amounts of pH, hardness, cohesiveness, and all the sensory attributes of all the pickle samples decreased while acidity, adhesiveness, and the counts of lactic acid bacteria and molds and yeasts increased. Furthermore, the lowest number of lactic acid bacteria and molds and yeasts count at the end of the keeping period was related to the unripe sample and the highest amount was related to the ripe sample. Throughout the storage period, particularly at the end of 90 days, pickle sample prepared from unripe fruit had significantly (P

Infective endocarditis is rare in children but can cause significant morbidity and mortality. Streptococcus and Staphylococcus species are the leading causes of this disease. Staphylococcus is more common in people with underlying heart disease, and Streptococcus viridans is more common in people who have had a dental procedure. In general, any fever of unknown origin in children with an underlying heart problem should be carefully evaluated for endocarditis, and empiric therapy should be performed. The main symptoms of the disease include fever, new murmur, deterioration of the previous murmur, hematuria, embolic events, splenomegaly, bleeding splinter, Osler's nodes, Janeway lesion, and Roth spots. One of the important complications of infective endocarditis is cerebrovascular event and stroke. Herein, we describe a 6-year-old girl presented with fever and skin lesions and no history of underlying heart problem or dental procedure. The patient expired after three days of mitral valve infection with S. aureus.

Background: The present study investigated sports knowledge, attitude, and practice in adolescent athletes in Tehran, Iran. Methods: Data were collected using the Sports Nutritional Knowledge, Attitudes, and Behaviors questionnaire (SNKABQ), completed by 174 high school students in Tehran aged 15 - 18 years (85 females and 89 males). The original version of SNKABQ was designed in English and, in this study, was translated into Persian and adapted to the Iranian nutritional culture. The SNKABQ has devoted five sections: Demographic information, eating and drinking habits, attitudes toward nutrition, knowledge of nutrition, and resource nutritional information. Data analysis was performed using descriptive statistics, the Mann-Whitney U test, the Kruskal-Wallis test, and Pearson's correlation coefficient. Results: The mean total score was 59%, while the mean scores of 66%, 63.4%, and 48.71% were recorded for sports nutrition knowledge, attitude, and practice, respectively. No significant differences were observed in the sports nutrition knowledge and attitude between the male and female students (P ≥ 0.05). However, the male students selected more appropriate feeding methods than the females (P = 0.009). In addition, school area and sports type affected the level of sports nutrition knowledge, attitude, and selection of proper nutritional behaviors. Conclusions: According to the results, the adolescent athletes aged 15 - 18 years in Tehran do not have sufficient sports nutrition knowledge, attitude, and practice, or if they have some correct expertise in some parts, they usually cannot apply their information. Therefore, it is suggested that effective measures be taken to increase the nutritional knowledge of adolescent athletes to maintain their health, ensure their growth, and facilitate their athletic progress.

Purpose Artificial intelligence (AI)-based systems can save the lives of many people by assessing the safety of flight paths. Unfortunately, the world witnessed a horrible event in January 2020 with the case of flight 752 of Ukrainian International Airlines from Tehran to Kiev and it has prompted us to ask how AI can prevent such events by warning to flight path planners. This paper aims to propose a framework for assessing the safety of flight paths from a shooting of an airplane by air defense systems installed on the path. Unlike the existing studies, this study takes a new look at pre-flight risk assessment by using textual information in social and news networks. To this end, the authors use existing information retrieval techniques to identify high flight risk areas by examining the news articles, comments, posts, tweets, etc., in social media and then estimate the probability of targeting a passenger aircraft by the air defense systems probably installed on high-risk areas with the help of a statistical model. This estimation can then be used by fight planners to avoid such events. Design/methodology/approach To design a framework for estimating the probability of a fatal shooting of an airplane by air defense systems installed on its flight path, the authors have used the idea of information retrieval in conjunction with statistical methods. The authors have extracted some significant variables in the shooting of flights and proposed an AI-based framework to estimate the probability of a fatal shooting of an airplane during its flight and sketched a case study for using machine learning approaches to assist with flight path planning. As a case study, the authors covered flight 752 to explain the usefulness of the proposed framework in this context. Findings Unlike the existing methods, this study investigates flight path safety assessment from the social media and crowdsourcing perspective. In this study, the authors proposed an AI-based framework to avoid aviation hazards by estimating the probability of a shooting of an airplane by air defense systems installed on its flight path. Moreover, this study was designed to show how estimating the safety of flight paths by using AI-based methods can help flight planners to avoid such events and gain further insights into the use of AI-based systems in pre-flight risk assessment. Originality/value The idea behind the proposed method is original and as the authors’ best knowledge, there is no similar framework using social media for flight path safety assessment.

Design concept evaluation is a key process in the new product development process with a significant impact on the product's success and total cost over its life cycle. This paper is motivated by two limitations of the state-of-the-art in concept evaluation: (1) The amount and diversity of user feedback and insights utilized by existing concept evaluation methods such as quality function deployment are limited. (2) Subjective concept evaluation methods require significant manual effort which in turn may limit the number of concepts considered for evaluation. A Deep Multimodal Design Evaluation (DMDE) model is proposed in this paper to bridge these gaps by providing designers with an accurate and scalable prediction of new concepts' overall and attribute-level desirability based on large-scale user reviews on existing designs. The attribute-level sentiment intensities of users are first extracted and aggregated from online reviews. A multimodal deep regression model is then developed to predict the overall and attribute-level sentiment values based on the features extracted from orthographic product images via a fine-tuned ResNet-50 model and from product descriptions via a fine-tuned BERT model, and aggregated using a novel self-attention-based fusion model. The DMDE model adds a data-driven, user-centered loop within the concept development process to better inform the concept evaluation process. Numerical experiments on a large dataset from an online footwear store indicate a promising performance by the DMDE model with 0.001 MSE loss and over 99.1% accuracy.

New coronavirus disease 2019 (COVID-19), as a pandemic disaster, has drawn the attention of researchers in various fields to discover suitable therapeutic approaches for the management of COVID-19 patients. Currently, there are many worries about the rapid spread of COVID-19; there is no approved treatment for this infectious disease, despite many efforts to develop therapeutic procedures for COVID-19. Emerging evidence shows that mesenchymal stromal/stem cell (MSC) therapy can be a suitable option for the management of COVID-19. These cells have many biological features (including the potential of differentiation, high safety and effectiveness, secretion of trophic factors and immunoregulatory features) that make them suitable for the treatment of various diseases. However, some studies have questioned the positive role of MSC therapy in the treatment of COVID-19. Accordingly, in this paper, we will focus on the therapeutic impacts of MSCs and their critical role in cytokine storm of COVID-19 patients.

In this study, Citrus sinensis essential oil was extracted by hydrodistillation method and the results of gas chromatography-mass spectrometry showed that the oil is rich in limonene (87.86%). Total phenol and flavonoid contents of the CSEO were 125.41 mg GAE/g and 32.50 mg QE/g, respectively. Its antioxidant activity was found to be 39.75 and 52.43%, based on DPPH and ABTS radical scavenging activity, respectively. The presence of limonene in the CSEO was confirmed by furrier transform infrared spectroscopy. The CSEO was then combined with Cordia myxa fruit mucilage to produce an edible coating in order to improve the microbial and oxidative stability of buffalo meat during storage (10 days, 4 °C). Results showed that the growth of total viable count, psychrotrophic bacteria, E. coli, Staphylococcus aureus, and fungi were significantly inhibited in the buffalo meat samples coated with CMFM+CSEO. The CMFM+CSEO edible coating was also more effective in inhibiting the oxidation progression and moisture and texture losses, in comparison with control sample. According to the sensory evaluation results, the CMFM+CSEO edible coating increased the shelf-life of buffalo meat samples; the samples were acceptable even at the end of storage period (10 days), but the control sample was unacceptable after 7 days of storage. Therefore, the edible coating based on Cordia myxa fruit mucilage and Citrus sinensis essential oil could be used as a natural preservative to increase the shelf-life of buffalo meat and other meat products.

Background and Objectives: Asthma is one of the most common respiratory diseases. Inhaled particles that may trigger allergic reactions and air routes are important factors contributing to asthma. This study aimed to determine the abundance of allergens in children with asthma in Gorgan, Iran. Methods: In this retrospective study, we randomly collected records of all asthmatic patients over five years of age referred to asthma and allergy clinic in Gorgan, Iran. Furthermore, data related to skin prick test were recorded. Results: The severity of asthma differed significantly between males and females. There was a direct relationship between age and reaction to one allergen. In addition, 18 of 25 (72 %) patients with severe asthma were allergic to more than two allergens. Moreover, the most common allergen was mite (56%). Conclusion: Mites are the most common allergen among children with asthma in the study area.

Objective: In this regard, the purpose of this study is to identify, rank and weight the factors affecting the bias of tax auditors or compliance auditors. Methods: The present research is an applied one in terms of purpose and its nature is exploratory. In the qualitative section, by reviewing the theoretical foundations and conducting semi-structured interviews with 17 members of the Supreme Tax Council, affecting components the bias of tax officials was identified. The statistical population of the study is the members of the Supreme Tax Council. After gathering the effective components, in order to rank and weight them, a questionnaire based on fuzzy technique and hierarchical analysis was designed and distributed among experts. After collecting information, the components were ranked using fuzzy technique and their weight was determined based on Analytical Hierarchy process in Expert Choice 11 software. Results: Based on the research findings, 15 components were identified as the main affecting factors on bias of tax officials, which are: the concept of bias, philosophy and mission of taxation, different motives, acceptance of biased tax, inconsistency of goals, borderless judgment, Conflicting rather than interactive perspectives, shortcomings of tax investigation, behavioral controls, bigotry, lack of knowledge, understanding of the concept of tax justice, avoidance and context, personal characteristics and structure. Among these, the results of fuzzy TOPSIS and Analytical Hierarchy process techniques showed that lack of knowledge of the philosophy and mission of taxation, acceptance of biased taxation and lack of proper understanding of the concept of bias are the most important factors. Conclusion: The research findings indicate that tax laws and regulations alone cannot meet the goals of legislators. Fair and equitable tax regulations require the supervision and care of professional executives, and without considering these issues, the mission of taxation and taxation will not be achieved

Addiction medicine is a dynamic field that encompasses clinical practice and research in the context of societal, economic, and cultural factors at the local, national, regional, and global levels. This field has evolved profoundly during the past decades in terms of scopes and activities with the contribution of addiction medicine scientists and professionals globally. The dynamic nature of drug addiction at the global level has resulted in a crucial need for developing an international collaborative network of addiction societies, treatment programs and experts to monitor emerging concerns at national, regional, and global levels. In this protocol, methodological details of running longitudinal surveys at national, regional, and global levels through the Global Expert Network of the International Society of Addiction Medicine (ISAM-GEN) are presented. The surveys will be developed by global experts in addiction medicine on treatment services, service coverage, comorbidities, treatment standards and barriers, emerging drug addictions and/or dynamic changes in treatment needs across the world. Survey participants in categories of (1) addiction societies/associations, (2) addiction treatment programs, (3) addiction experts/clinicians and (4) related stakeholders will respond to these longitudinal global surveys. The results will be analyzed and cross-examined with available data and peer-reviewed for publication.

Currently, anti-leishmanial drugs have been developed. However, the available compounds have several side effects such as drug resistance and toxicity that cause some limitation for use. The development of nanoparticles (NPs) use in biological research and the proven effectiveness of CaONPs and MgONPs on bacteria and fungi, along with the lack of information about its antileishmanial effects, have motivated this study. CaO and MgONPs possess considerable antibacterial effects because of their alkalinity and active oxygen species. This study has taken into account the impacts of these two NPs on the L. major in vitro and in vivo.To evaluate the antileishmanial activity of NPs, the cytotoxic effect of CaONPs, MgONPs, and MgOCaONPs against L. major amastigotes, promastigotes, as well as macrophages, was evaluated using counting or MTT assay. The possible apoptosis of L. major by CaONPs, MgONPs, and MgOCaONPs was evaluated via flow cytometry assay. For in vivo study, BALB/c mice were allocated to five groups and the lesions of infected mice with L. major promastigotes were treated with a 200 μg/mL concentration CaONPs, MgONPs, and MgOCaONPs, then the mice underwent a 4-week follow-up to examine the wound diameter and survival rates.The XRD-pattern related to CaONPs and MgONPs indicating the cubic phase and Rocksalt cubic structures. According the effects of nanoparticle on promastigotes the ICAccording to the results of the present research, MgONPs and CaONPs showed good in vitro and in vivo effects on L. major promastigotes and intracellular amastigotes especially MgONPs, and also it seems that MgONPs are applicable in Leishmania infection treatment due to their potential antileishmanial effects.

Coronavirus disease 2019 (COVID‐19) is the seventh member of the bat severe acute respiratory syndrome family. COVID‐19 can fuse their envelopes with the host cell membranes and deliver their genetic material. COVID‐19 attacks the respiratory system and stimulates the host inflammatory responses, enhances the recruitment of immune cells, and promotes angiotensin‐converting enzyme 2 activities. Patients with confirmed COVID‐19 may have experienced fever, dry cough, headache, dyspnea, acute kidney injury, acute respiratory distress syndrome, and acute heart injury. Several strategies such as oxygen therapy, ventilation, antibiotic or antiviral therapy, and renal replacement therapy are commonly used to decrease COVID‐19‐associated mortality. However, these approaches may not be good treatment options. Therefore, the search for an alternative‐novel therapy is urgently important to prevent the disease progression. Recently, microRNAs (miRNAs) have emerged as a promising strategy for COVID‐19. The design of oligonucleotide against the genetic material of COVID‐19 might suppress virus RNA translation. Several previous studies have shown that host miRNAs play an antiviral role and improve the treatment of patients with COVID‐19. miRNAs by binding to the 3′‐untranslated region (UTR) or 5′‐UTR of viral RNA play an important role in COVID‐19‐host interplay and viral replication. miRNAs interact with multiple pathways and reduce inflammatory biomarkers, thrombi formation, and tissue damage to accelerate the patient outcome. The information in this review provides a summary of the current clinical application of miRNAs for the treatments of patients with COVID‐19.

Manufacturing has adopted technologies such as automation, robotics, industrial Internet of Things (IoT), and big data analytics to improve productivity, efficiency, and capabilities in the production environment. Modern manufacturing workers not only need to be adept at the traditional manufacturing technologies but also ought to be trained in the advanced data-rich computer-automated technologies. This study analyzes the data science and analytics (DSA) skills gap in today's manufacturing workforce to identify the critical technical skills and domain knowledge required for data science and intelligent manufacturing-related jobs that are highly in-demand in today's manufacturing industry. The gap analysis conducted in this paper on Emsi job posting and profile data provides insights into the trends in manufacturing jobs that leverage data science, automation, cyber, and sensor technologies. These insights will be helpful for educators and industry to train the next generation manufacturing workforce. The main contribution of this paper includes (1) presenting the overall trend in manufacturing job postings in the U.S., (2) summarizing the critical skills and domain knowledge in demand in the manufacturing sector, (3) summarizing skills and domain knowledge reported by manufacturing job seekers, (4) identifying the gaps between demand and supply of skills and domain knowledge, and (5) recognize opportunities for training and upskilling workforce to address the widening skills and knowledge gap.

Removal of lead from aqueous solutions was studied using nanocomposite absorbent of bentonite/-alumina. The novel absorbent was characterized using XRD, FT-IR and SEM-EDX. Absorption process optimization using response surface methodology (RSM) and experimental design was performed with central composite design technique. The effects of Pb(II) initial concentration, adsorbent dosage, and composite percentage on Pb(II) removal percentage and adsorption capacity were examined. The adsorption capacity of 166.559 mg/g and removal % of 82.9887 with desirability equal to 0.763 were obtained for optimal initial concentration of 200 mg•l-1, adsorbent dosage of 0.5 mg•l-1, and composite percentage of 7.08 % determined using RSM design. The equilibrium adsorption data were investigated by Langmuir, Freundlich and Dubinin-Radushkevich isotherm models. It was found that Freundlich isotherm model fits better compared with other models.

The prognosis of COVID-19 cases that suffer from particular comorbidities is worse. The impact of chronic neurological disorders (CNDs) on the outcome of COVID-19 patients is not clear yet. This study aimed to assess whether CNDs can predict in-hospital mortality or severity in COVID-19 patients.Following a cross-sectional design, all consecutive hospitalized patients with PCR-confirmed COVID-19 who were hospitalized at three centers from February 20th, 2020 to March 20th, 2022, were studied. CND was defined as neurological conditions resulting in permanent disability. Data on demographic and clinical characteristics, COVID-19 severity, treatment, and laboratory findings were evaluated. A multivariate Cox-regression log-rank test was used to assess the primary outcome, which was in-hospital all-cause mortality. The relationship among CND, COVID-19 severity and abnormal laboratory findings was analyzed as a secondary endpoint.We studied 7370 cases, 43.6% female, with a mean age of 58.7 years. 1654 (22.4%) patients had one or more CNDs. Patients with CNDs had higher age, were more disabled at baseline, and had more vascular risk factors and comorbidities. The ICU admission rate in CND patients with 59.7% was more frequent than the figure among non-CND patients with 20.3% (p = 0.044). Mortality of those with CND was 43.4%, in comparison with 12.8% in other participants (p = 0.005). Based on the Cox regression analysis, CND could independently predict death (HR 1.198, 95% CI 1.023-3.298, p = 0.003).CNDs could independently predict the death and severity of COVID-19. Therefore, early diagnosis of COVID-19 should be considered in CND patients.

Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential basic quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment. This study examines the full cardiac coverage using a 3D convolutional model and then reduces the number of false predictions using an innovative salient region detection model. Salient regions are extracted from the short-axis cine CMR stacks using a three-step proposed algorithm. Combining the 3D CNN baseline model with the proposed salient region detection model provides a cascade detector that can reduce the number of false negatives of the baseline model. The results obtained on the images of over 6,200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous state-of-the-art studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracy of the baseline model in identifying the presence/absence of basal/apical slices is 96.25\% and 94.51\%, respectively, which increases to 96.88\% and 95.72\% after improving using the proposed salient region detection model. Using the salient region detection model by forcing the baseline model to focus on the most informative areas of the images can help the model correct misclassified samples' predictions. The proposed fully automated model's performance indicates that this model can be used in image quality control in population cohort datasets and also real-time post-imaging quality assessments.

Understanding the impact of wetland water area (WWA) fluctuations on air pollution in nearby cities is of great environmental importance. This study is the first effort for investigating the WWA changes in Iran and their impacts on air pollution in the surrounding cities during different seasons. Three-hourly data related to wind speed, wind direction, and horizontal visibility recorded in meteorological stations around Iranian wetlands were used to identify cities located in the direction of dusty winds blown from shrinking wetlands in Iran. Meteorological data were also used to calculate the pollution of dust storm index (PDSI) as a representative of dust pollution in the surrounding areas. Global water surface (GWS) product for a long-term period (1988 to 2018) was used to monitor the WWA in Iran. The correlation between PDSI in dusty cities and WWA in nearby wetlands were analyzed using the Pearson correlation coefficient (r). The results showed that the cities located around Hamoun, Jazmourian, Parishan, and Hourolazim wetlands were affected by dusty winds blown from the wetlands in most seasons. However, the cities around Gavkhouni International Wetland have been affected by the winds only in the warm season. In winter and spring, the strongest negative correlations between PDSI-WWA was respectively observed in Shiraz-Parishan (r = - 0.33; p-value 0.05) and Zabol-Hamoun (r = - 0.32, p-value 0.05). However, in the summer and autumn, no strong correlation was observed between the studied variables. On the annual scale, 25% and 15% of changes in dust pollution across the cities around the Hamoun and Parishan international Wetlands were due to the decrease in their water area from 1988 to 2018. On a seasonal scale, about 11% of the changes in PDSI were due to changes in the water area in these wetlands. These results can be useful for implementing air pollution reduction programs in cities affected by dusty winds blowing from the destroyed wetlands.

This study is aimed at assessing the ecological risk of heavy metals (HMs) in the International Hamoun wetland, southeastern Iran. Twenty sediment samples were collected from the wetland surface for geochemical analysis of 23 HMs. The inverse distance weighting (IDW) technique was used to map the HMs. The single and multi-element pollution indicators and PER index (PERI) were respectively used to determine the contamination intensity and PER level. The principal components analysis (PCA) was performed to identify the HM source. The mean concentration of cesium (Cs: 5.2 µg/g), selenium (Se: 0.9 µg/g), and tellurium (Te: 0.2 µg/g) was higher than their mean values in the Earth's crust. The enrichment factor (EF) showed the Hamoun was high to extremely enriched by Te, As, and Se. The geo-accumulation index (GeoI) revealed the highest level of contamination caused by As, barium (Ba), cobalt (Co), chromium (Cr), cuprum (Cu), ferrum (Fe), manganese (Mn), nickel (Ni), lead (Pb), rubidium(Rb), titanium (Ti), vanadium(V), yttrium (Y), and zinc (Zn) in most study sites. The sediment contamination factor in more than 55% of the sediment samples was between 8 and 16, indicating very high contamination intensity in the studied wetland. The PER values were between 80 and 160 in more than 60% of the sediment samples, suggesting a considerable risk in the wetland. The PCA showed both anthropogenic and crustal activities were effective in increasing the concentration of HMs in the wetland. The largest ecological risk was due to arsenic (As) and cadmium (Cd). It is recommended to pay more attention to these HMs, which could cause more environmental pollution in the International Hamoun wetland, southeastern Iran.

Background Drought is one of the most important environmental stresses that can adversely influence soil properties, plant growth, and productivity of agricultural crops including eggplant as an important vegetable crop. In recent years, the use of agricultural wastes has been reported as a beneficial and sustainable measure in soil water retention and fertility enhancement. In this study, the effect of date palm and pistachio biochar, vermicompost and a combination of biochar and vermicompost was evaluated on eggplant growth, yield and water use efficiency under deficit irrigation. Materials and methods The experiment was done in a split-split plot based on randomized complete block design with three replications and under open field conditions. The main plot was deficit irrigation in three levels of 100, 75 and 50% of plant water requirement (PWR), and the sub-plots were vermicompost in two levels of 0, 1500 g m−2, and biochar in three levels of 0, 500 g m−2 of the pistachio biochar and 500 g m−2 of the date palm biochar. Results The results showed that soil amendment with vermicompost and pistachio biochar and 100% PWR showed the best plant growth and performance. The early crop yield was highest under pistachio biochar and 50% PWR, while the total plant yield was highest under combined application of vermicompost and pistachio biochar at 100% PWR. The plant water use efficiency was maximum under mixed application of vermicompost and pistachio biochar at 50% PWR. Application of both pistachio biochar and vermicompost at 100% PWR resulted in the highest leaf concentrations of nitrogen, phosphorus, potassium, iron and manganese. The highest levels of physiologically important stress metabolites, including malondialdehyde, guaiacol peroxidase, superoxide dismutase and catalase enzymes were found in treatments without amendments or only with vermicompost and 50% PWR. Conclusion The results indicate that under normal and particularly water deficit conditions, vermicompost and biochar increased eggplant vegetative growth, yield and water use efficiency.

Summary The root-knot nematode, Meloidogyne javanica, is the most damaging species of plant-parasitic nematodes in eggplant cultivation in Iran. We studied the effect of pistachio and date palm biochars, vermicompost and a mixture of each of these biochars with vermicompost on M. javanica. To investigate the effect of these organic materials on survival, hatching and attraction of the second-stage juvenile (J2) towards the root, bioassays were performed in the laboratory using extracts of organic material. Vermicompost extract increased J2 mortality and decreased hatching and the number of J2 attracted to the eggplant roots. However, either type of biochar alone or mixed with vermicompost did not cause J2 mortality, nor did they inhibit hatching and attraction of J2 towards the root. To determine the effect of organic matter on J2 invasion and reproduction, pot experiments were performed in a completely randomised design with four replications in the glasshouse. Vermicompost reduced the number of J2 that penetrated the roots, number of egg masses and the final population of M. javanica eggs and J2. The suppressing effect of vermicompost might be attributed to the release of toxic compounds such as ammonium and improved soil nutrient and plant growth, leading to plants more tolerant to nematode damage. Pistachio and date biochars alone or combined with vermicompost had no adverse effect on the nematode reproduction compared to non-treated soil. Both types of biochar reduced the suppressing effect of vermicompost, most probably due to the absorbance of suppressive compounds in vermicompost by the high surface area of the biochar.

Nowadays, social network users are facing with a large number of images with the fast development in the social image sharing media. In this situation, finding the images which can satisfy user preferences is more important. Since users' liking behavior has not a certain manner, it can be modeled by fuzzy logic because fuzzy logic is a decision-making system based on degrees of truth and deals with vague and imprecise information. Therefore, in the present study, we proposed a fuzzy inference system based on user preferences, consisting of a set of rules according to distinguishing features for each user. Also together with fuzzy inference system, this method is proposed based on feature extraction and feature selection in order to predict the images which are liked by different users. In the proposed method, the features from user preferred images are extracted. Then, feature selection is used to determine the most discriminant features for each user, and accordingly fuzzy inference system is employed to learn user preferences. We used fuzzy clustering to generate soft clusters and fuzzify user preferences. Then, this system is used to predict images that should be recommended to the user. Experimental results on datasets from Flickr and Pinterest indicated that the proposed method can significantly improve Recall@k and Precision@k for personalized image recommendation compared to some related works.

This article deals with the problem of robust fractional-order fixed-structure controller design for commensurate and non-commensurate fractional-order interval systems using fractional Kharitonov theorem. The contribution of this study is to develop a simple control methodology to stabilize the fractional-order Kharitonov-defined vortex polynomials. Using the idea of robust stability testing function and extending it to the systems under study, the straightforward graphical and systematic procedures are proposed to investigate the robust stability of the system by searching for a non-conservative fractional-order Kharitonov region in the controller parameters plane. This region can establish all the fractional-order controllers that make the uncertain fractional-order systems stable. The relation between the fractional-order Kharitonov region and the parameters of the stabilizing controller is also found. Finally, comparison results with three relevant works are given to illustrate the feasibility of the proposed method.

Background and Objective: Congenital anomalies are one of the major causes of neonatal mortality. Congenital heart disease (CHD) is the most common birth defect world wide, and recent studies about CHD have reported congenital heart disease approximately is about one percent of all live births. This study was done to determine the frequency of congenital heart disease using ecocardiography in patients with extra-cardiac anomalies in Gorgan peadiatric center in north of Iran. Methods: In this descriptive study, 55 girls, 84 boys a total of 139 patients were in the age range of birth to 12 years of age whom have referred to Taleghani Teaching and Medical Center in Gorgan during 2012-17. 139 cases of congenital heart disorders with echocardiographic report sheet in their medical record were evaluated. Information of patients including age, sex, ethnicity, type of extra-cardiac, anomalies, cardiac anomalies (simple, complex and ductal anomalies), cardiac murmur status, and clinical symptoms were extracted from their medical records. Results: From of total of echocardiography, 139 patients (88.5%) were affected by congenital heart disease. The anomalies were included of 88 cases (56.05%), 48 cases (31.21%), 2 cases (1.27%) simple, complex anomaly and ductal dependent anomaly respectively. The other disorders accompanied with congenital heart diseases were 17 cases (12.2%) with cleft palate, 4 cases (2.9%) with congenital diaphragmatic hernia, 8 cases (5.8%) with closed anus, 52 cases (37.4%) with Down syndrome, 4 cases (2.9%) with Turner syndrome, 4 cases (2.9%) with ileum atresia, 11 cases (7.9%) with Esophageal atresia, 7 cases (5%) with Hirschsprungchr('39')s disease, 6 cases (4.3%) with urogenital anomalies and 26 cases (18.7%) with other extra-cardiac abnormalities. Conclusion: Down syndrome and cleft palate are among the highest prevalant anomalies with congenital heart diseases.

Industry 4.0 envisions adaptable and resilient manufacturing and logistics operations capable of handling dynamic changes or deviations in operations using intelligent sensing and computation technologies. Recent advances in artificial intelligence and collaborative robotics have created unprecedented opportunities to fully automate a variety of industrial tasks such as material handling, assembly, machine tending, and inspection, among other. With the rapidly growing interest in the vision of lot-size-of-one, a fundamental and challenging question remains open: How can robots leverage the knowledge of previously-learned tasks to expedite their learning on new tasks? We tackle this problem by developing and testing a novel deep reinforcement learning framework with task modularization to enhance adaptability of collaborative robots in performing a multitude of simulated tasks. The framework is built upon the actor-critic method and the notion of task modularity, and is compared against the Soft Actor-Critic (SAC) algorithm as a baseline. Numerical experiments on the Meta-World dataset prove the ability of the proposed framework in improving the adaptability and efficiency of collaborative robots to new tasks through task modularization and transfer of policies from previously-learned task modules.

Herein, we evaluated the structural and optoelectronic properties of Ti3C2Tx MXene transparent conductive electrodes (TCEs) prepared from microwave synthesized Ti3AlC2 MAX phase. A mixture of Ti, Al, and C was heated at 1300 °C for 30 min in a microwave oven. We synthesized MXene by two different etchant concentrations. A 1.6 g LiF in 9 M HCl solution brought about a higher degree of delamination in MXene with an inter-layer space of 1.35 nm Ti3C2Tx MXene prepared from microwave synthesized MAX phase exhibited a lateral size of ~80 nm with crystalline defects. We examined the effect of spinning speed on optoelectronic properties of Ti3C2Tx MXene TCEs. Increasing the spinning speed from 1000 to 4000 resulted in an increase in transparency (T550) (from 72% to around 94%) and sheet resistance (Rs) (from 2010 to 23660Ω/sq). A FoM value of 2.027 ± 0.163 was calculated from data fitting of T550 vs. Rs (R2 > 0.97). It is suggested that small sheet size and formation of defects are responsible for the relatively high Rs.

Background and objectives: BK virus (BKV) reactivation is a major challenge for renal transplant recipients. The purpose of this study was to summarize current knowledge on the status of BKV in Iranian renal transplant recipients. Methods: Specific terms, including “BKV” and “Renal Transplantation” were used to search the online databases. I2 and Cochran’s Q-value were tested for heterogeneity. The incidence rate was determined at 95% confidence interval. Publication bias was also investigated using funnel plot, the Egger’s and Begg’s statics. Results: Twelve studies were included in the study. The random modelchr('39')s overall evidence rate was 0.347 (CI 95%, 0.225-0.493, p-value=0.04). Conclusions: In Iran, the estimated prevalence of BKV among renal transplant recipients is 34.7% (~10-60%), which is higher than the rate reported from other parts of the world. Therefore, it is recommended to screen organ donors for BKV in Iran.

There is a strong need to identify simple and cost-effective biomarkers for multiple sclerosis (MS).To evaluate the serum levels of receptor for advanced glycation end products (RAGE) ligand, the high-mobility group box (HMGB) 1 and its correlation with changes in the physical and psychological indicators in MS patients.During the 12-month follow-up, the serum level of HMGB1, expanded disability status scale (EDSS) score, rate of clinical relapse, quality of life, and other psychological indicators were assessed at baseline, after 6 months, and after 12 months and compared between 60 newly diagnosed MS patients with 60 healthy controls (HCs). Data were analyzed using t-test and Mann-Whitney U test, two-way repeated measures analysis of variance (ANOVA) and Spearman's rank correlation coefficient.A significant decrease was observed in the EDSS score (P0.001) and a significant increase in the serum level of HMGB1 in all MS patients (P = 0.009). The serum level of HMGB1 was higher in MS patients, compared with HCs (baseline: 65.8%, P = 0.007; six-month follow-up: 73.9%, P = 0.004; and 12-month follow-up: 77.6%, P = 0.021). There were significant positive correlations between the serum level of HMGB1 and scores of MS impact scale-psychological subscale (MSIS-PS) (r = 0.59, P0.001), Beck depression inventory (BDI) (r = 0.491, P = 0.031), and Pittsburgh sleep quality index (PSQI) (r = 0.471, P = 0.035).The serum level of HMGB1 could predict the patients' psychiatric status better than their physical status.

Background: Variations in the viral receptor human angiotensin-converting enzyme 2 (ACE2) may specify the susceptibility of a certain population to severe acute respiratory syndrome coronavirus 2. Objective: Evaluation of the affinity of severe acute respiratory syndrome coronavirus 2 spike glycoprotein to the Iranian genetic variants of ACE2. Materials & methods: Single nucleotide polymorphisms of ACE2 among the Iranian population were collected from the Iranome database. Missense mutations in the N-terminal peptidase domain were selected for in silico analysis. Results: 17 missense single nucleotide polymorphisms were found at ACE2. Viral glycoprotein had the lowest affinity to ACE2 mutant V485L. Discussion: The V485L variant of ACE2 could be a natural resistance mutation among the Iranian population. In addition, variant S331F can increase slightly the susceptibility to infection with the virus.

Objective: To evaluate the wound healing activity of Crocus pallasii subsp. haussknechtii boiss leaves extract on infected wounds in diabetic rats. Methods: Fifty male diabetic rats were randomized into two sets of 25 animals each. Each group was sub divided into five groups of five animals, each for excisional and incisional wound models, respectively. Induction of diabetes was achieved using 60 mg/kg streptozotocin. In group I, 0.1 mL sterile saline 0.9% solution was added to the wounds with no infection. In group II, the wounds were infected with Methicillin-resistant staphylococcus aureus (MRSA) and only treated with 0.1 mL the sterile saline 0.9% solution. In group III, infected wounds were treated with application of base formulation ointment. In group IV, animals with infected wounds were treated with 0.1 mL topical application of 1 mg/mL methicillin and base formulation ointment. In group V, animals with infected wounds were treated with topical application of 0.1 mL solution of methicillin (1 mg/mL) and with 1g of powder extract of the plant material in ointment. The healing of the wound was assessed based on planimetry, hydroxyproline estimation, microbiological, biomechanical and biochemical studies Results: Microbiological examination, planimetric, histological and quantitative morphometric studies and determination of hydroxyproline levels showed that there was significant difference between animals in group V compared to other groups (p=0.001). Biomechanical indices in incisional groups showed there was significant difference between animals in group V compared to other groups (p=0.001). Conclusion: It was possible to conclude that the ointment of the extract of Crocus pallasii subsp. haussknechtii boiss. leaves have significant wound-healing activity in diabetes.

Recently, members of the semaphorin family have received major attention in various medical fields, especially autoimmunity. In this study, we selected semaphorin-3A (Sema3A), semaphorin-7A (Sema7A), and their receptors to determine the possible relationship between these molecules and multiple sclerosis (MS).We measured the gene expression of Sema3A, Sema7A, neuropilin-1 (NP-1), plexin-C1, and β1 integrin in the blood samples of relapsing-remitting multiple sclerosis (RRMS) patients, treated with high-dose interferon-β1a (IFN-β1a), low-dose IFN-β1a, IFN-β1b, and glatiramer acetate (GA) via quantitative real-time polymerase chain reaction (qRT-PCR) assay, and then, compared the results of treatment-naive patients with the healthy controls.The gene expression of Sema3A (P = 0.02), NP-1 (P 0.001), and plexin-C1 (P 0.01) significantly decreased in the treatment-naive group, compared to the healthy controls. Sema3A significantly increased in all treated patients, compared to the treatment-naive patients (P 0.001). However, expression of NP-1 (P 0.001), plexin-C1 (P 0.001), and β1 integrin (P 0.05) only increased in patients receiving high-dose IFN-β1a, IFN-β1b, and GA. Expression of Sema7A increased in only two groups of patients treated with IFN-β1b (P 0.001) and GA (P = 0.018), without any significant decrease in the treatment-naive group, compared to the healthy controls (P 0.05).Our findings confirm that the presence of Sema3A, Sema7A, and their receptors can play critical roles in the treatment of MS patients. Therefore, they can be potential target molecules for MS treatment in the future.

Understanding the effect of tidal turbines on local erosion of the estuarine bed is crucial for design and maintenance of turbines with stable foundations and assessment of their environmental impacts. This report describes the results of flume experiments on clear-water scour caused by a single cross-flow turbine in steady flow conditions. The turbine investigated is a Momentum Reversal Lift (MRL) turbine originally designed in collaboration with the University of Exeter. Results show that the turbine can cause significant bed scour, particularly when it was not spinning and in a particular orientation of blades. This is opposite to the previous findings for axial flow turbines. The bottom plate of the turbine, although increasing scour depth, was found to increase the turbine performance and reduce adverse effects on the downstream flow. The findings highlight the importance of regular monitoring and taking immediate repair actions for a tidal installation.