Osteoarthritis (OA) is the most common degenerative joint disease that causes chronic pain and disability. Different innate immune components, including macrophages, T cells, and neutrophils, participate in osteoarthritis pathophysiology. Neutrophils are the most abundant circulating leukocytes with multiple specialized functions contributing to innate and adaptive immune functions. Although neutrophils produce proinflammatory cytokines and chemokines, reactive oxygen species (ROS), matrix-degrading enzymes, and neutrophil extracellular traps (NET) that promote joint degradation as the first recruit cells in an inflamed joint, these cells also play an important role in joint repair by regulating the immune response, releasing anti-inflammatory factors, and activating some protective genes. In this review, various aspects of neutrophil biology, their role in inflammation and its association with osteoarthritis, and possible therapeutic approaches to target neutrophils for the treatment of osteoarthritis are described. Understanding neutrophil heterogeneity and their mechanisms of action in joint inflammation, provides a potential strategy for OA management.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

Background: Iranian traditional medicine is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified terminology has been proposed for the concepts of this medicine up to the present time. Considering the extensive use of concepts in the numerous textbooks written by the scholars over centuries, comprehending the totality of the terminology is obviously a very challenging task. To resolve this issue and overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity.Methods: Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of Mizaj. The steps followed to accomplish this task included (1) compiling the list of classes for Mizaj; (2) arranging the classes in taxonomy; (3) determining object properties and their cardinalities; (4) specifying annotation properties including codes, labels, synonyms, and definitions for each concept; (5) reviewing the fields pertaining to Mizaj of all monographs in Makhzan al-Advieh. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry. Results: Mizaj ontology was constructed with a final inclusion of 105 classes, three object properties, and 1078 axioms in the Iranian Traditional Medicine General Ontology database, IrGO, freely available at http://jafarilab.com/irgo/. An indented tree view and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in the UNaProd database to create a knowledge-base of Mizaj. Conclusion: We constructed an ontology-based knowledge base of ITM concepts of Mizaj in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. Extending IrGO will bridge the gap between traditional and conventional schools of medicine and help guide future research on new treatment options.

An axial wind turbine's performance is analyzed numerically using geometrical and operational parameters. The performance of an axial wind turbine is simulated using three-dimensional computational fluid dynamics (CFD) and compared with experimental data, which confirms the validity of the assumptions and method used. A study of the effects of air in clean and sand particlesy conditions is then conducted. A study was also conducted on mechanical power and the impact of the blade tip on the extension of the vortex. It has been found that different sand particle sizes have a significant impact on wind performance numerically from 0.1 to 0.9, and the results indicate that mechanical power and thrust increase as wind velocity increases, but larger sand particle sizes reduce power, thrust, and torque as wind velocity increases. The turbulence intensity causes the pressure distribution on the walls to be irregular, thereby increasing mechanical power and propulsion force. Moreover, as the wind velocity increases, the tip speed ratio increases and the vortexes are subsequently extended.

This scoping review examines global strategies and enterprises for sustainable solid waste management, with a focus on alternative landfilling approaches. The study collected and analyzed a significant number of documents from different regions, revealing Asia as the major contributor (for the collected documents) (48.7%), followed by North America (24.3%) and Europe (15.8%). Recycling emerged as the most effective alternative waste treatment method, representing 52.3% of the documented approaches, with industrial recycling (22.6%) and residential/non-residential recycling (20.2%) as prominent categories. Food waste was a significant concern across regions, constituting 21.4% of the collected documents. Composting was widely adopted (15.4%) due to its simplicity and benefits for gardening and soil improvement. Other methods like biogas extraction, reusing, raising awareness, incinerating, redistributing, reducing, and fermentation accounted for 13.1% cumulatively. The study highlights the need for tailored waste management solutions based on regional challenges and successful practices. Promoting recycling infrastructure, composting, and waste reduction approaches are crucial to achieving sustainable waste management aligned with SDGs. Collaboration and knowledge-sharing between regions are essential to improve inefficient waste management mechanisms. Integrating the findings into policymaking and industry practices can lead to a more sustainable future with reduced environmental impact.

Renewable energy research becomes increasingly necessary as it grows in importance. Wind turbines are an excellent method of harnessing wind energy, which is one of the most important renewable energy sources. In light of the importance of studying wind turbine performance, this study describes the background and principles of wind turbine operation. Computational fluid dynamics (CFD) was used to evaluate the hydrodynamic performance of two types of offshore and onshore wind turbines. Mechanical power and thrust force are calculated using the CFD model for both types of turbines in a steady state based on the number of blades and rotors. The results of three-dimensional simulations are compared with those of experimental testing. Based on the method and assumptions used, the offshore turbine's mechanical torque showed an average deviation of about 4%. Increased wind speed increased mechanical power and thrust, while turbulence caused irregular pressure distributions on surfaces as a result of turbulence intensity. Furthermore, as the free wind speed increases, the blade tip and hub speed will also increase, expanding the vortices created by that flow.

It is imperative to examine the performance of wind turbines both in steady and unsteady states due to their significant role in generating renewable energy by utilizing wind power. The computational fluid dynamics method (CFD) is one of the most efficient tools in this field, and the unsteady state has a great deal of importance when predicting turbine performance. Using the CFD method, the hydrodynamic performance of two types of offshore and onshore horizontal-axis wind turbines in the unsteady state was examined. Offshore wind turbines were modeled using the SST turbulence model and onshore wind turbines were modeled using the large eddy simulation (LES). Three-dimensional results of the simulation were compared and validated with experimental results, and good agreement between the two results indicates that the assumptions and method were accurate. Vortices were also studied in relation to the blade tip and their formation and development. Increasing the number of blades results in a reduction in torque, as well as a rise in vortices caused by the tips of the rotor blades. Due to the intensity of turbulence, the distribution of pressure on the surfaces will become irregular as the wind speed increases.

In this study, an experimentally validated computational model was developed to investigate the hydrodynamics in a rotor-stator vortex RVR agglomeration reactor having a rotating disc at the centre with two shrouded outer plates. A numerical simulation was performed using a simplified form of the reactor geometry to compute the 3D flow field in batch mode operations. Thereafter, the model was validated using data from a 2D Particle Image Velocimetry (PIV) flow analysis performed during the design of the reactor. Using different operating speeds—70, 90, 110 and 130 rpm, the flow fields were computed numerically followed by a comprehensive data analysis. The simulation results showed separated boundary layers on the rotating disc and the stator. The flow field within the reactor is characterized by a rotational plane circular forced vortex flow in which the streamlines are concentric circles with a rotational vortex. Overall, the results of the numerical simulation demonstrate a fairly good agreement between the CFD model and the experimental data as well as the available theoretical predictions. The swirl ratio β was found to be approximately 0.4044, 0.4038, 0.4044 and 0.4043 for operating speeds of N=70, 90, 110 and 130 rpm respectively. In terms of the spatial distribution, the turbulence intensity and kinetic energy are concentrated on the outer region of the reactor while the axial velocity showed a decreasing intensity towards the shroud. However, a comparison of the CFD and experimental predictions of the tangential velocity and the vorticity amplitude profiles shows that these parameters were under-predicted by the experimental analysis which could be attributed to some of the experimental limitations rather than the robustness of the CFD model or numerical code.

An investigation of compressible flow over two-dimensional obstacles is presented in this paper. When shock waves are formed on surfaces due to compressibility and these shocks interfere with the boundary layer, undesirable effects such as friction, separation, and flow instability occur. To weaken the shock and reduce its effects, various methods have been employed in the past, including surface suction, surface blowing, and vortex generators. Using numerical solutions, the present work attempts to reduce the negative effects of the shock generated by the obstacle by creating porosity on its surface. Numerical methods such as the finite volume method and the Navier-Stokes equations as well as the Spalart-Allmaras model are used. By creating suction and blowing and increasing the cross-sectional area of the flow, porous surfaces reduce the effects of shock. Achieving around 50% porosity on the obstacle weakened the shock and reduced the Mach number after it, according to this paper's results. Also, the area under the skin friction diagram has decreased by about 38% as a result of less interference between the shock and the boundary layer and blowing caused by the porous surface.

Xenorhabdus nematophila and Photorhabdus luminescens are entomopathogenic symbionts that produce several toxic proteins that can interfere with the immune system of insects. Here, we showed that outer membrane proteins (OMPs) could be involved as virulence factors during bacterial symbiont pathogenesis. Purified OMPs from bacterial culture were injected fifth instar larvae of Spodoptera exigua Hübner. Larvae were surveyed for fluctuations in total haemocyte counts (THC), granulocyte percentage (cellular defence), protease, phospholipase A2 (PLA₂), and phenoloxidase (PO) activities (humoral defence) at specific time intervals. Changes in the expression of the three antimicrobial peptides (AMPs), cecropin, attacin, and spodoptericin, were also measured. Larvae treated with both types of OMPs had more haemocytes than did the negative controls. OMPs of X. nematophila caused more haemocyte destruction than did the OMPs of P. luminescens. The OMPs of both bacterial species initially activated insect defensive enzymes post-injection, their activating fluctuated in different ways. Attacin, cecropin and spodoptericin were up-regulated by OMP injections more than in normal larvae. The expression of these three AMPs was maximal at four hpi with P. luminescens OMPs treatment. Expression of the three AMPs in X. nematophila treatment was irregular and lower than in the P. luminescens OMPs treatment. These findings provide insights into the role of OMPs of entomopathogenic nematode bacterial symbionts in countering the physiological defenses of insects.

Background: Neurological disorders are the leading cause disability in Australia and the world. Combating the perceived difficulty of neurology or “neurophobia” and improving physician education is a key component in addressing this problem. We aim to conduct the first study to identify whether neurophobia exists in medical students and junior doctors in an Australian population and try to identify factors that may contribute to this in this population. Methods: A 24 question online validated survey was distributed via email broadcast to all medical students and junior doctors at a metropolitan tertiary care centre in Australia. Responses were collected over 6 weeks with weekly reminder emails for 4 weeks after the initial invitation email. Results: 114 medical students and junior doctors participated in the study. Participants perceived neurology as the most difficult medical speciality compared to 10 other medical specialties (p=0.001). The top three factors contributing to this perceived difficulty were: a lack of understanding of neuroanatomy, lack of diagnostic certainty and lack of clinical exposure. 65% of the participants stated that they had too little planned teaching in neurology with only 36% of the participants having performed a neurology rotation during medical school. Conclusion: The prevalence of neurophobia in this Australian cohort of medical students and junior doctors is consistent with previous findings from around the world. This concerning finding requires further examination into the contributing factors in order to created trials of targeted interventions in order to resolve this.

The effect of divergence of chute sidewalls with three different baffle block geometries namely USBR, trihedral and semicircular blocks, as well as the depth and dimensions of the scour hole downstream of the chute were studied using a physical model. For this purpose, 9 models of baffled chutes were designed and constructed with divergence ratios of 1.45, 1.75, and 2.45 and without divergence (with a divergence ratio of 1). Comparing the results on the effect of block geometry at different divergence ratios revealed that the use of blocks proposed in this study instead of standard USBR blocks reduced the mean and maximum scour hole by 50%. For a given block geometry, the mean depth, maximum depth, and length of scour hole were reduced by 75%, 58%, and 50%, respectively.

Introduction: The relation between physical well-being and chronic pain is complex and involves several subjective and objective covariates. We aimed to assess the role of media-tor, confounder, or interactor played by covariates, including sleep quality, physical activity, perceived stress, smoking, and alcohol drinking in the relation between physical well-being and chronic pain. Method: We used Poisson regression to obtain incidence rate ratios (IRR) of the associa-tion between physical well-being and chronic pain in a cohort study carried out among uni-versity students. We applied General Structural Equation Modeling (GSEM) to assess me-diation, and stratum-specific analysis to distinguish confounding from interaction. We computed Relative Excess Risks due to Interaction (RERI), Attributable Proportion (AP), and Synergy index (S) to measure additive interaction. Results: High physical well-being is related to a large decrease in the risk of chronic pain (IRRTotal Effect= 0.58; 95%CI 0.50-0.81). Perceived stress mediates 12.5% of the total effect of physical well-being on chronic pain. The stratum-specific IRRs of current smokers and non-current smokers were different from each other and were larger than the crude IRR (IRR=1.49; 95% CI: 1.24-1.80), which indicates that smoking could be both confounder and interactor. Interaction analyses showed that physical activity could act as a potential interactor (RERI=0.25; 95% CI: 0.13, 0.60). Conclusion: Perceived stress is an important mediator of the relation between physical well-being and chronic pain, while smoking is both a confounder and an interactor. Our findings may prove useful in distinguishing high-risk from low-risk groups, in the interven-tions aimed at reducing chronic pain.

This study will lead to the development of a numerical data set that will assist in the design of Savonius wind turbines. A major objective of this study is to develop a better design for Savonius turbine blades in order to increase their torque coefficients, rotational speeds, and pressure coefficients. As part of the experimental design methodology, a full dataset was generated by simulating three-dimensional scale models utilizing computational fluid dynamics (CFD) simulations that were validated by wind tunnel data. This process is multi-objective optimization for optimizing turbine performance. The twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. The group method of data handling (GMDH) algorithm was utilized to model objective functions based on input-output data. The Pareto fronts were plotted using polynomial models obtained from the evolutionary Pareto-based optimization approach (modified NSGA-II), and the optimal commercial points were determined using TOPSIS. A comparison of three- and two-objective optimization data revealed that two-objective optimization data lie within the boundaries of a three-objective optimization problem. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively, using multi-objective optimization. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

: Background: The thymus gland is the first lymphoid organ formed to regulate a newborn's immunity. It reaches its maximum size during puberty, after which it undergoes an atrophic procedure called involution, but its ability to grow again in response to some stresses, such as infections, neoplasia, surgeries, chemotherapy, and radiotherapy is maintained. There is not any comprehensive study on computed tomographic features of thymus gland in dogs. So, the goal of the present study is to achieve some information about thymus gland using computed tomography as a non-invasive method. Methods: One hundred and fifty dogs classified in 5 age groups and 2 breed groups were entered to this study and the thymus gland was evaluated using 2-slice computed tomography machine. The inclusion criteria for the present study were having normal complete blood count, plain and post-contrast CT scan examination of thoracic region and no history of neoplasia, chemotherapy or radiotherapy. The visibility, density, enhancement, grade, size, volume, shape, borders and lateralization of the thymus were evaluated and statistical analysis were performed. The effect of obesity on thymic grade and volume were also investigated. Results: The visibility, density, dorsal length, volume and grade decreased with increasing age. The thymic shape and lateralization were mostly wedge-shaped and left sided respectively. The borders became concave with aging and increasing body fat content caused to increase fatty degeneration of thymus. Conclusions: Declining of thymic density, grade, size and volume with aging was related to thymic involution and fatty degeneration was accelerated by increasing body fat content. Females and Males were different only in thymic shape and small and large breeds were different only in thymic volume. The thymus was visible in some geriatric dogs with no underlying disease. We hope that the present work can be used by radiologists in reading thoracic computed tomography but investigation of thymic characteristics in dogs with neoplasia and history of chemotherapy, radiotherapy and thoracic surgeries can complete this study.

Background: Sudden changes in clinical practice and the altered ability to care for patients due to the COVID-19 pandemic have been associated with moral distress and mental health concerns in healthcare workers internationally. This study aimed to investigate the severity, prevalence, and predictors of moral distress experienced by Australian healthcare workers during the COVID-19 pandemic. Methods: A nationwide, voluntary, anonymous, single time-point, online survey of self-identified frontline healthcare workers was conducted between 27th August and 23rd October 2020. Participants were recruited through health organisations, professional associations or colleges, universities, government contacts, and national media. Results: 7846 complete responses were received from nurses (39.4%), doctors (31.1%), allied health staff (16.7%) or other roles (6.7%). Many participants reported moral distress related to resource scarcity (58.3%), wearing PPE (31.7%) limiting their ability to care for patients, exclusion of family going against their values (60.2%), and fear of letting co-workers down if they were infected (55.0%). Many personal and workplace predictors of moral distress were identified, with those working in certain frontline areas, metropolitan locations, and with prior mental health diagnoses at particular risk of distress. Moral distress was associated with an increased risk of adverse mental health outcomes. Feeling appreciated by the community mitigated this risk in healthcare workers. Conclusions: Safeguarding healthcare workforces during crises is important for both patient safety and workforce longevity. Targeted interventions are required to prevent or minimise moral distress and associated mental health concerns in healthcare workers during COVID-19 and other crises.

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño-Southern Oscillation (ENSO) is considered a significant signal of global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, which month the low-resolution sea surface temperature data is from. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

Migraine is one of the leading causes of disability worldwide [1,2]and patients with acute migraine frequently present to emergency departments (ED)[3]. The current literature suggests that ED treatment of migraine headache varies across institutions [4-7]. Considering this, we conducted a scoping review to summarize trends in medication prescribing patterns for acute migraine treatment in the ED setting. Trends were evaluated for factors influencing treatment choices, with particular attention placed on opioids and migraine specific therapy. This scoping review was based on the Arksey and O'Malley methodological framework[8]and included studies published between 1 January 2000 until 31 May 2020. 14 publications met the inclusion criteria. The most common classes of medication prescribed were often anti-emetics or Non-steroidal anti-inflammatory drugs (NSAID), but rates varied between studies. There was a concerning trend towards an underutilization of triptans and overutilization of opiates. The use of specific clinical goals of treatment (e.g. two-hour pain free freedom response) was also not evident. Additionally, 88% (n=8) of the 9 studies commenting on adherence to hospital or evidence-based guidelines stated that practices were non-adherent. Overall, the reviewed literature reveals treatment practices for acute migraine in the ED are heterogeneous and deviate from established international recommendations.

This systematic literature review explores the impact of social media use (SMU) and practices (SMPs) on team feedback (TF) and performance (TP) in construction project management. The study delves into the intricate dynamics between SMU and SMPs and their influence on communication, collaboration, and stakeholder engagement within construction projects. It investigates how these digital practices reshape the traditional methods of team interaction, feedback mechanisms, and overall team performance in the complex and multifaceted environment of construction sites. The review aims to provide a comprehensive understanding of the role of social media in modern construction project management, highlighting its significance in enhancing team communication, coordination, and performance. This research is crucial for understanding the evolving landscape of construction management, where digital tools and platforms are increasingly integral to successful project outcomes. The findings are expected to offer valuable insights for professionals in the construction industry, helping them leverage social media effectively for improved project management and team collaboration.

Abstract: The Tropical Rainfall Measuring Mission (TRMM) and then Global Precipitation Mission (GPM) are the most important and widely used data sources in the forecasting of drought, flood, and water resources management. However, since this sensor’s data is primarily used for tropical regions, it is necessary to evaluate the accuracy for optimal use of the data across varying climatic and physiographic conditions. In this study, the accuracy of the satellite data for a span of 17 years (2000-2017) for three climatic zones has been explored using synoptic ground station data. The climates include a) arid and low rainfall, b) semi-arid and low rainfall, and c) humid and high rainfall. We evaluated satellite data accuracy in drought and wet conditions based on the Standard Precipitation Index (S.P.I.) and different seasons. For available ground control stations, 13 stations were used in the humid, seven stations in a semi-arid climate, and 12 stations in the dry climate. The results show that the monthly precipitation product of GPM (IMERG product) and TRMM (TMPA/3B43 product) overestimate the rainfall. In the arid climate, the precipitation is estimated 43%, in the semi-arid environment 50%, and in the humid weather 11% more than the ground-based data on average. Therefore, to use satellite data in different climates, it is necessary to make corrections to obtain precise results. Based on 32 ground stations, the correction coefficient has a positive relationship with average precipitation and altitude and an inverse relationship with the latitude. Further in-depth investigations showed that the accuracy of satellite data in wet conditions is higher than the accuracy of normal circumstances, and the accuracy of normal conditions is more accurate than drought conditions. Besides, the accuracy of satellite data in wet or dry conditions increases with increasing time scales. The highest accuracy was obtained for a 12-month time scale and the lowest accuracy for the 3-month time scale of drought conditions in the arid climate.

Inertial microfluidics offers high throughput, label-free, easy to design, and cost-effective solutions and is a promising technique based on hydrodynamic forces (passive techniques) instead of external ones, which can be employed in lab-on-a-chip and micro-total-analysis-systems for focusing, manipulation, and separation of microparticles in chemical and biomedical applications. The current work, studies the focusing behavior of microparticles in an asymmetric curvilinear microchannel. For this purpose, focusing behavior, including position and band width, of microparticles of diameters of 10, 15 and 20 µm, which served as representatives of different cells, in an asymmetric curvilinear microchannel with curvature angle of 280° was experimentally studied at flow rates from 400 to 2700 µL/min (corresponding to Reynolds numbers between 30 and 205). The results revealed that the largest distance between focusing bands of 20 µm and 10 µm microparticles as well as between focusing bands of 15 µm and 10 µm was obtained at Reynolds number of 121. For the case of microparticles of diameters 20 µm and 15 µm, the largest distance was seen at Reynolds number of 144. The focusing band width became smaller in the asymmetric microchannel so that focusing could be more clearly observed in this configuration.

This paper proposes a game-theoretic framework for quantifying effects of strategic bidding behavior of load aggregators (LAs) on wholesale electricity market equilibrium. The independent system operator (ISO) employs double-sided auction design to settle transactions in the wholesale electricity market. Generating companies (GENCOs) and LAs submit their bids to the ISO in the economic model of supply function equilibrium. Under the smart grid paradigm, a LA participates in the electricity market on behalf of end-consumers to minimize their total payment for purchasing electricity. In this paper, bi-level programming (BLP) method is employed to determine optimal bidding strategy of self-interested market participants. In the upper level, the profit of intended market participant is maximized and in the lower level, the ISO clears the market via a bid-based security constrained economic dispatch (SCED). Also, competition of GENCOs and LAs is modeled as a non-cooperative game. To examine the effects of strategic bidding behavior of LAs on market equilibrium, the proposed model is applied to the IEEE 9-bus and the IEEE 30-bus test systems. According to numerical results, to allow the LAs to behave strategically along with the GENCOs makes the former better off and the latter worse off, while the net effect of this on total social welfare turns out to be case-contingent.

The aim of current study is adjusted and synthesized liposomal compound of N. sativa and evaluation its antifungal properties against C. parapsilosis isolates. Fifteen clinical isolates of C. parapsilosis complex isolates were obtained from hospitalized patients affected by candidemia in Mashhad city, Iran, along with a reference strain of C. parapsilosis (ATCC 22019) were assessed by flight mass spectrometry (MALDI-TOF) method, as described previously. N. sativa is encapsulated in liposomal Nanocariers by using thin film hydration technique. At the beginning liposomal nanoparticles was characterized and confirmed with the dynamic light scattering technique (DLS) and Transmission electron microscopy. Then minimum inhibitory concentration of liposomal N. sativa oil was conducted with the CLSI M27 A3 protocol and finally Cytotoxicity function of N. sativa oil liposomal nanocarriers on PBMCs was investigated and confirmed with MTT assay by the results of this research N. sativa oil-Lip-NP didn’t show any toxic effect on PBMCs and The minimum inhibitory concentration (MIC) range of free N. sativa oil and liposomal formulation with inhibitory effects on candida isolates was between 128 - 8, 250 - 31.25 µg ml also MIC50 and MIC90 were 125,187 and 32,96, µg ml respectively. Due to the hydrophobicity and hydrophilicity, biocompatibility, particle size, non-toxic effect, and higher cell viability of N. sativa oil -Lip-NP, it could be considered a more effective approach to treating fungal infections.

The great importance of estimating dissolved oxygen (DO) dictates utilizing proper evaluative models. In this work, a multi-layer perceptron (MLP) network is trained by three capable metaheuristic algorithms, namely multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE) for predicting the DO using the data of the Klamath River Station, Oregon, US. The records (DO, water temperature, pH, and specific conductance) belonging to the water years 2015 - 2018 (USGS) are used for pattern analysis. The results of this process showed that all three hybrid models could properly infer the DO behavior. However, the BHA and SCE accomplished this task by simpler configurations. Next, the generalization ability of the developed patterns is tested using the data of the 2019 water year. Referring to the calculated mean absolute errors of 1.0161, 1.1997, and 1.0122, as well as Pearson correlation coefficients of 0.8741, 0.8453, and 0.8775, the MLPs trained by the MVO and SCE perform better than the BHA. Therefore, these two hybrids (i.e., the MLP-MVO and MLP-SCE) can be satisfactorily used for future applications.

Background: Migraine is the commonest complex neurological disorder affecting over a billion people worldwide. Neuroinflammation has long been considered to play an important role in the pathophysiology of migraine. The main aim of this single-centre, proof of concept, retrospective study was to determine the possible clinical utility of systemic immune-inflammatory response in migraine with and without aura in a hospitalized cohort. We measured the role of universally available serial white blood cell counts to calculate the serial systemic immune-inflammatory indices (SSIIi) and the potential association between aura and SSIIi changes in a cohort of migraine patients admitted to tertiary care center in Melbourne Australia.Main body: We retrospectively assessed patients' medical records presenting to Western Health with migraine over an 18 month period. Patients were classified as either having migraine with aura (MA) or without aura (MO) based on ICHD-3 criteria. Baseline demographics and brain imaging findings in each group were evaluated. Patients with two sets of white blood cell counts during the admission were included in the analysis. SSIIi were calculated as, neutrophil counts x platelet counts / lymphocyte counts. Correlation between SSIIi and MA and MO were assessed using SPSS27.Conclusion: SSIIi were elevated in MA and MO followed by a downward trend in both groups, with MA being statistically significant. This proof-of-concept study suggests the potential role of systemic inflammation in pathobiology of migraine. SSIIi appear to show clinical utility. Further comprehensive ,controlled , multicenter studies are required to clarify the exact role of systemic inflammatory response and the clinical utility of SSIIi in a subset of migraine in the Emergency department setting.

Nowadays, one of the biggest concern of human being is greenhouse gas emission, especially carbon dioxide emission in developed and under-developing countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO₂ emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay. The studied region is modelled based on the available input data in terms of Million tons including oil (Million tons), gas (Million tons oil equivalent), coal (Million tons oil equivalent), $R_{ew}$ (Million tons oil equivalent), and Gross Domestic Product (GDP) in terms of billion US dollars.} Moreover, the available patents in the fields of climate change mitigation in six Latin American countries namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay has been reviewed and analyzed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies which account for 60% and 72% respectively.

Stroke represents one of the most important causes of morbidity ( eighty million patients with disabling of ongoing effects of stroke at a given time, globally) and mortality (the second leading cause of death) worldwide. Innovative systems biology-based approach is likely to increase the understanding of the underpinning of acute stroke promise to enhance stroke prevention, acute treatment, and neurorehabilitation. Recent growing body of evidence with shared pathobiology with COVID-19 and the critically important role of inflammation in the context of stroke points to far-reaching consequences of acute stroke, just as in the case of COVID-19 ( post-acute event issues as well as long term issues ) . So far, stroke typically defined by late-appearing disease manifestation by the range of stroke subtypes as defined by the WHO or American Stroke Association. This definition neglects the underlying pathobiological mechanisms such as low-grade chronic inflammation and already compromised vascular system. Diseases such as stroke is hardly a simple result of a single problem, but rather a complex cascade of pathobiological processes and interactions in a complex biochemical environment. The evidence of changes in innate immunity and adaptive immunity during the index event of acute stroke and recovery over next 3-12 months can be easily elicited with simple bedside blood tests such as neutrophil-lymphocyte ratio (NLR) with well over 300 published papers including several systematic reviews and meta-analyses confirming this. Global standard operating procedures (SOP) of stroke care dictated by the national and international stroke guidelines at present. It is imperative to explore the evidence of systems biology approach in current stroke guidelines. This is likely to be a key turning point in managing stroke across the continuum (prevention, management of acute event and rehabilitation).

The electrical and optical properties of sol-gel derived aluminum-doped0 zinc oxide thin films containing 2 at.% Al were investigated considering the modifying effects of 1) increasing the sol H₂O content; and 2) thermal treatment procedure with high-temperature approach followed by an additional heat-treatment step under a reducing atmosphere. According to the results obtained via the TG-DTA analysis, FT-IR spectroscopy, X-ray diffraction technique and four-point probe resistivity measurement, it is argued that the sol hydrolysis, decomposition of the deposited gel and crystallization of grains result in grains of larger crystallite size and stronger c-axis preferred orientation with slightly less microstrain in the modified sample. The consequent morphology and grain-boundary characteristics turn out as improved conductivity, implying higher values of concentration and mobility of charge carriers. A detailed investigation on samples optical properties, in terms of analyzing their absorption and dispersion behaviors through the UV-Vis-NIR spectroscopy, support our reasoning for the increase of the mobility, and to a lesser extent, the concentration of charge carriers, while causing only a slight degradation of optical transmission. Hence, an enhanced performance as a transparent conducting film is claimed for the modified sample by comparing the figure-of-merit values.

Regarding the high efficiency of metaheuristic techniques in energy performance analysis, this paper scrutinizes and compares five novel optimizers, namely biogeography-based optimization (BBO), invasive weed optimization (IWO), social spider algorithm (SOSA), shuffled frog leaping algorithm (SFLA), and harmony search algorithm (HSA) for the early prediction of cooling load in residential buildings. The algorithms are coupled with a multi-layer perceptron (MLP) to adjust the neural parameters that connect the CL with the influential factors. The complexity of the models is optimized by means of a trial-and-error effort, and it was shown that the BBO and IWO need more crowded spaces for fulfilling the optimization. The results revealed that the internal parameters (i.e., biases and weights) suggested by the BBO generate the most reliable MLP for both analyzing and generalizing the CL pattern (with nearly 93 and 92% correlations, respectively). Followed by this, the IWO emerged as the second powerful optimizer with mean absolute errors of 1.8632 and 1.9110 in the training and testing phases. Therefore, the BBO-MLP and IWO-MLP can be reliably used for accurate analysis of the CL in future projects.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Memory is probably one of the most complex cognitive functions of the human, and in many years, thousands of studies have helped us to better recognize this brain function. One of the reference textbooks in neuroscience, which has also elaborated on the memory function, is written by Prof. Kandel and his colleagues. In this book, I encountered a number of ambiguities when it was explaining the memory system. Here, I am sharing those points, either to find an answer for them, or to let them be a suggestion for our future works. Prof. Kandel has spent most of his meritorious lifetime on studying the memory system; however, the brain is extremely complex, and as a result, we still have many years to comprehensively understand the neural mechanisms of brain functions.

Stroke is one of the leading cause of adult disability and the second leading cause of death worldwide. The immune system actively participates in the pathobiological process of AIS, during the index event and during the repair process despite the limited attention drawn to this aspect in the existing stroke guidelines globally. The similar clinical course and similar circulating innate and adaptive immune cell counts in AIS and COVID-19 has created a renewed interest in these easily available biomarkers innate and adaptive immunological changes in AIS with potential diagnostic, prognostic, and therapeutic implications. The current scoping review aimed to assess the significance of circulating neutrophil and lymphocyte counts and their ratio (NLR) in AIS and explore their association with post-stroke recovery trajectory. The Arksey and O'Malley methodological framework was employed to review the published papers on the neutrophil-lymphocyte ratio (NLR) and AIS in late November 2020. Only studies published in English from 2000-2020 were included in this scoping review. Fifty-three published papers were reviewed. This review's key finding is that a canonical inflammatory response occurs in AIS just as in the case of COVID-19 and neurological involvements well described in the recent literature. An excessive circulating innate immune cells (neutrophils) and reduced circulating adaptive immune cells (lymphocytes ) are associated with poorer outcomes during the acute interventions ( reperfusion therapies) as well as the recovery trajectory. Main representatives of innate and adaptive immunity follow a canonical course in AIS and COVID-19. Exaggerated circulating innate ( elevated neutrophils and elevated NLR) and reduced adaptive immune response (lymphopenia) correlate with the worse outcome in AIS and COVID-19. This scoping review's findings make the strongest case for a systems biology-based approach to the standard operating procedures in stroke care urgently.

The fundamental challenge for the structural design of complicated shapes and lightweight structures such as Tensile Membrane Structures (TMS) is a reliable estimation of lateral loading such as wind load. Wind pressure coefficients can’t be correctly calculated by standards for a large range of intricate forms of structures. Computational Fluid Dynamics (CFD), a strong computational technique for evaluating wind pressure distribution on complicated geometry, can be used to determine wind-related parameters. The average wind related-distributions for the tensile membrane surface are validated with a few contributions from prior studies, which are important to validate with benchmarks in experimental wind tunnel experiments. For the double-curved surface, the results show good agreement between experimental tests and CFD simulations. In this study, CFD simulation was used to calculate the mean surface pressure coefficient (Cp) for generic shapes and tensile membrane forms using the steady Reynolds-Averaged Navier-Stokes (RANS) technique. Wind force coefficients and the effect of wind directions are also explored across a wide range of classical and tensile forms. The purpose of the current research is to look at the effect of wind assault angle on the Cp distribution for tensile membrane structures and general shapes.

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961-2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into 1961-1995 (normal lake level) and 1996-2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.

As a holistic medical school, Iranian traditional medicine (ITM) considers the human body as a dynamic and intricate network of interconnecting processes. Currently, systems biology and more precisely systems medicine and pharmacology can be an aid in providing rationalizations for many traditional medications and treatments and elucidating a great deal of knowledge they can offer to guide future research in medicine. Therefore, re-organization and standardization of traditional medicine data are requested more than ever before. To address this issue, we have constructed UNaProd, a Universal Natural Product database for materia medica of ITM. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, this database was created using both text mining methods and manual editing. UNaProd is currently hosting to 2696 monographs from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. In the current version, UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features respectively and it is freely available at http://jafarilab.com/unaprod/.

Available water resources in the Middle East, as one of the most water-scarce regions of the world, have undergone extra pressure due to climatic change, population growth, and economic development during the past decades. The objective of this study is to detect the trends and quantify the changes in aridity with respect to precipitation and potential evapotranspiration in 20 countries of the Middle East and the adjacent area. A Pixel-wised trend analysis was conducted on precipitation, potential evapotranspiration, and aridity index for 71 years from 1948 to 2018. A nonparametric Mann-Kendall test was used over 14106 points in the study area to detect the trends at monthly and annual time scales. Results showed statistically significant (|Z| >1.96) upward trends in aridity (a downward trend in aridity index) up to 96 percent from December through September in most parts of the region. Aridity in October and November had a downward tendency in most parts of the study area. At the annual time scale, 62.5 percent of the statistically significant trends in aridity were found to be upward (up to 96 percent increase in aridity) due to the combined effects of the decrease in precipitation and the increase in potential evapotranspiration and 37.5 percent of the detected trends were downward (up to 61 percent decrease in aridity). The highest and the lowest trends in aridity were found in the north of Sudan (96 percent increase in aridity) and Eastern Arabia (61 percent decrease in aridity), respectively.

A Savonius wind turbine is one of the simplest vertical-axis turbines. Several researchers have been attracted to it because of its simple structure, low noise, appropriate torque, and independence from wind direction. SR3345 and SR5050 blade profiles will be investigated for their effects on the performance of Savonius wind turbines. First, the fabricated wind turbine is placed in front of an open circuit wind tunnel with a velocity of 6 m/s. Furthermore, governing relationships are used to calculate power and torque coefficients as well as tip speed. In this study, the power and torque coefficients of the turbine are compared with the two profiles considered based on overlap ratios between 0 and 0.3. Despite its increased stability, the central shaft reduced the power coefficient of the turbine. For blade SR3345, the best performance occurs at tip speed ratios of less than 1, while for blade SR5050, the best performance occurs at higher tip speed ratios. Both blades SR3345 and SR5050 achieve the highest power coefficients at overlap ratios of 0 and 0.18 when a central shaft is present.

Background: The SARS-CoV-2 virus is a new highly pathogen virus that can suppress cell immune response (Poly ADPR process, and interferon releasing in infected cell) by viral macrodomain and create fatal pneumonia. Cytokine storm phenomenon which can create consequence of SARS COV-2 by increase of the performance of human TRPM2, is a dangerous condition for human and its body organ such as kidney, heart, and liver. it seems like prevention of beginning of these events is necessary. Material and methods: in this study drug and drug like database was used for inhibition of the viral macrodomain and human TRPM2 that can initiate of the viral cell cycle infection and human cytokine storm respectively. Ligand base drug designed, pharmacophore modeling, docking and molecular dynamic simulation was performed Results: among up to billion compounds, 20 compounds were screened by infinisee program. Then 7 compounds were selected by pharmacophore modeling. between 7 compounds, 5 compound was removed because of high similarity to ADPR and possibility of toxic effect for human RNA and DNA polymerase. One compound was losartan and selected for docking and molecular dynamic simulation. Conclusion: losartan earned a proper dock score and binding affinity to create the complexes with TRPM2 and macrodomain. Molecular dynamic simulation has shown losartan had an adequate binding free energy for human trpm2 and viral macrodomain. The inhibitory effect of losartan on these protein has shown and it could interfere in several points (PARP, PARG- macrodomain and TRPM2) and decreases oxidative stress, apoptosis, cytokine storm in COVID-19.

Manufacturing environments, characterized by rapid, unpredictable changes, uncertainties, risks, and uncontrollable fluctuations, pose significant challenges to minimizing disruptions in processes. This study introduces an innovative approach that prioritizes curbing risk propagation among processes to enhance robustness. It emphasizes the integration of engineering management principles and modular design within manufacturing. Adopting a system engineering perspective, all manufacturing process activities are viewed as interrelated components within a unified system. By employing Axiomatic Design (AD) theory and the Design Structure Matrix (DSM) method, manufacturing process architecture is modularized, yielding heightened robustness. The proposed mathematical model equips engineering and manufacturing managers with a potent tool for designing robust processes while adeptly managing system complexity. The study's outcomes underscore a substantial enhancement in modularization, leading to elevated overall robustness in manufacturing processes. To validate the methodology, the architectural design of manufacturing processes is examined in a real-case scenario, specifically the Barez Industrial Group in Iran. This verification substantiates the 'manufacturing processes' of the case, presenting an optimally modularized architecture. The results affirm the proposed approach's efficacy, demonstrating improved modularization that contributes to bolstered robustness in manufacturing processes.

A population-based seroepidemiological and molecular survey for detection of earlier and re-cent SARS-CoV-2 infection was done in children aged 14 years or less in Tehran between 19 September 2020 and 21 June 2021. Demographic data, COVID-19 symptoms and infection status were recorded and IgG antibodies and RNA of SARS-CoV-2 were detected in sera and nasopha-ryngeal swab samples, respectively. Out of 1517 participants, cardinal symptoms of COVID-19 (fever >38 oC and/or cough and/or diarrhea) were detected in 18% and serological history of SARS-CoV-2 infection and PCR positivity were confirmed in 33.2% and 10.7% of the weighted-population, respectively. SARS-CoV-2 infection was significantly higher among 10–14-year-old children. Active infection was significantly higher in symptomatic children and during autumn 2020 and spring 2021. The RT-qPCR positivity was related to contact with in-fected persons. RT-qPCR positivity was significantly higher among families with a lower socio-economic status, while no association between RT-qPCR- or seropositivity was determined with household size, underlying diseases, or gender. In conclusion, high SARS-CoV-2 infection prev-alence and seroprevalence was detected in children in Tehran in different seasons. The infection was significantly higher in older age children, and those with a positive-history of close contact with infected cases and/or lower socioeconomic status.

The purpose of this paper is to explore which financial performance indicators (FPIs) evaluate the level of supply chain capability (SCC) that explicitly touches all of the business functions and processes within and beyond the company. The authors investigated nine FPIs that were selected from the financial statements of 155 companies within nine industries. The authors find that suitable FPIs to measure SCC for shareholders’ value are return-on-assets (ROA), days-sales-outstanding (DSO), and current ratio (CR). This means that higher ROA, shortened DSO, and an appropriate level of CR could reach a sustainable supply chain. These results will help the industry to avert a major disruption in supply chain processes and activities using suitable financial performance indicators.

A new approach for predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called group method of data handling (GMDH) which is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in literature. The model was then evaluated using statistical indices as well as compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate a high accuracy of the proposed model with values of 0.9719 and 0.9513 for training and testing for the dimensionless of the distance from the nozzle to the return point xr/D, and 0.9454 and 0.9565 for training and testing for the dimensionless of the terminal rise height yt/D . Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of estimate (SEE) was applied to demonstrate which model performed the best in terms of getting closer to the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and was the best model with approximately 10% error, which was the lowest value of error among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

Reliable prediction of sustainable energy consumption is the key to designing environmental friend buildings. In this study, two novel hybrid intelligent methods, namely grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO) is employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in heating ventilation and air conditioning (HVAC) systems. Concerning the optimization of applied hybrid algorithms, a series of swarm-based iteration is performed, and the best structure for the abovementioned methods are proposed. Besides, through sensitivity analyzing the relationship between the HLs and CLs and influential factors are highlighted. In other words, the GOA, WDO, and BBO algorithm are mixed with a class of feedforward artificial neural networks (ANN), which called MLP (multi-layer perceptron) to predict the HLs and CLs. According to the provided sensitivity analysis, the WDO with swarm size = 500 proposes the most proper-fitted terms after it has been combined with optimized MLP. The proposed WDO-MLP (training (R2 correlation=0.977 and RMSE error=0.183) and testing (R2 correlation=0.973 and RMSE error=0.190)) provided accurate prediction in the heating load and (training (R2 correlation=0.99 and RMSE error=0.147) and testing (R2 correlation=0.99 and RMSE error=0.148)) presents the most-fit prediction in the cooling load.

Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., N_Pop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Image fusion is a useful tool for producing a high-resolution multispectral image to be used for land use and land cover mapping. In this study, we use nine pansharpening algorithms namely Color Normalized (CN), Gram-Schmidt (GS), Hyperspherical Color Space (HCS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Principal Component Analysis (PCA), Resolution Merge (RM), Stationary Wavelet Transform (SWT), and Wavelet Resolution Merge (WRM) to fusion Worldview-3 multispectral Bands and panchromatic band. In spectral and spatial fidelity, several image quality metrics are used to evaluate the performance of pansharpening algorithms. The SWT and PCA algorithms showed better results compared to other pansharpening algorithms while GS and CN algorithms showed the worst results for the original image fusion. The effect of fusion on each band was separately investigated and according to the calculations, we found that the CoastalBlue band and the Blue band showed the best result and the NIR-1 band and NIR-2 band show the worst result for the original image fusion. In the end, we conclude that the choice of fusion method depends on the requirement of remote sensing application.

Most large fibers in central nervous system of vertebrates are decussated. The cause or the evolutionary force of this phenomenon is not convincingly described. Here we show a pattern for this nervous system property that simultaneously is found to a more basic rule in evolution.The idea here is the "function to anatomy" theorem. We claim that functions of physical, biochemical, enzymatic, etc., in lower scale of living organisms "emerge" to anatomy in larger scales. And reversely, the anatomy we see in multicelular organisms are/were functions in microscopic scales.We propose descriptions, predictions, and a probable road map to what this can implicate in biological and medical issues.

Recent advances in single-atom catalysis resulted in readily accessible materials whose application in most catalytic reactions mediated by conventional nanoparticle-based catalysts often results in higher activity and selectivity. Can we expect catalysis by atomically dispersed atoms to find practical applications? Which are the hurdles to be overcome prior to widespread uptake of atomically dispersed metals in industrial synthetic processes and in hydrogen fuel cells?

The operational measure (OM) of the Second-Generation Intact Stability Criteria (SGISC) is the initial step toward the design of a performance-based dynamic stability assessment of the ship by considering both the vessel’s operation, loading condition and weather parameters. The SGISC recommends the standard wave scatter table (WST) for the environmental data, an indefinite requirement for a simplified assessment pathway, which provides the probability of wave occurrence. The existing standard WST was developed based on the North Atlantic Ocean. This study aims to identify the discrepancy in the probability of wave occurrence in the IMO-recommended WST when compared with developed hindcast WST for smaller regions of the North Atlantic Ocean for the application of SGISC. The significant difference in the existing standard WST is identified when compared with hindcast data, especially across different seasons. A case study of OM on the C11 class post-Panamax container ship for excessive acceleration is provided to better represent the study. The identified limitation limits the use of standard WST in ship stability assessment. The study recommends using hindcast-based WST for SGISC applications that are region and season based. This recommendation is beneficial in improving the safety assessment by OM, given the data is reliable and available for the season and region-specific, and hence the accuracy of the ship stability can be improved while using for the SGISC OM assessment. Further, it makes the WST adhere to the actual framework of the SGISC, i.e., using existing environmental data for design assessment and improving the simplified stability analysis.

The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study evaluates the impact of two categories of PDMP and Pill Mill regulations on the supply of opioid prescriptions at the level of dispensers and distributors (excluding manufacturers) using ARCOS data. The study uses a difference-in-difference method with a two-way fixed design to analyze the data. The study finds that both of the regulations are associated with reductions in the volume of opioid distribution. However, the study reveals that these regulations may have unintended consequences, such as shifting the distribution of controlled substances to neighboring states. For example, in Tennessee, the implementation of Operational PDMP regulations reduces in-state distribution of opioid drugs by 3.36% (95% CI, 2.37 to 4.3), while the out-of-state distribution to Georgia, which did not have effective PDMP regulations in place, increases by 16.93% (95% CI, 16.42 to 17.44). Our studies emphasize that policymakers should consider the potential for unintended distribution shifts of opioid drugs to neighboring states with laxer regulations as well as varying impacts on different dispenser types.

Introduction Color vision deficiency (CVD) constitutes one of the frequently observed eye disorders in all human populations. Color is a prominent sign utilized in the medical profession to study and identify histopathological specimens, lab instruments, and patient examination. Color deficiency affects the medical skills of students resulting in poor clinical examination and color appreciation. There is no effective screening of CVD at any level of the medical profession. Hence, this study was aimed to determine the prevalence of CVD among medical students. Materials and methods This was a cross-sectional study conducted from September 2019 to February 2020 over a period of six months in Karachi, Pakistan. All medical students aged 18-21 years of either gender enrolled in the first and second years of medical college were included in this study. The examination was performed during daylight. Ishihara plates were placed at a distance of 75 cm from the subject and tilted so that the plane of the paper lies perpendicular to the line of vision. Students were given five seconds to read the plate and one examiner was instructed to mark the checklist. A score of less than 12 out of 14 red/green test plates (not including the demonstration plate) was considered as a CVD. All statistical analysis was performed using Statistical Package for Social Sciences version 20.0 (Armonk, NY: IBM Corp). Results The mean age of the medical students was 19.61± 1.22 years. There were (n=123) 53.0% females and (n=111) 47.0% males. Most of the medical students (n=131, 56.0%) belonged to the upper-middle-class socioeconomic group. CVD was observed in (n=13) 6.0%of medical students. Age (p=0.001) and socioeconomic status (p=0.001) were the only demographic factors significantly associated with color deficiency. Conclusions Color deficiency, although an unnoticed concern, is fairly common among medical students. Medical students must be screened for CVD as this will enable them to be aware of their limitations in their future observational skills as a doctor and devise ways of overcoming them in clinical practice.

In this study, we have first studied the trend in meteorological data from the Harmaleh, Vanai and Farsesh stations in the 50-year period in the Dez catchment area. The meteorological data will be then forecasted using SWAT and Mann-Kendall. Forecasting the results in the Mann-Kendall and SWAT model has been done using the code written in MATLAB software and RCP (4.5, 8.5) scenarios, respectively. Studying the results of the trend in the data of meteorological stations in this catchment area indicated that these two parametric and non-parametric methods have been used to determine trends in meteorological data. The results of the parametric method are positive in all meteorological parameters. Non-parametric method over a period of 50 years shows the presence of trends in the data. The comparison on the forecasting results at maximum temperature suggested that during summer, we will see an increase in temperature compared to the ground state in all three forecasts. The results of the minimum temperature forecast show a decrease in the minimum increase during the winter and the precipitation forecast indicates that at the end of autumn (Nov) precipitation decreased by 20 mm in the Mann-Kendall and 4.5 RCP while RCP8.5 suggests the increase in precipitation compared to the ground state. Studying the runoff forecast results using SWOT show that at the end of winter (Feb) and almost all spring (Mar, Apr) a decrease of about 40%, 15% and 14% will be seen, respectively

The Internet of Things (IoT) signifies a revolutionary technological advancement, enhancing various applications through device interconnectivity while introducing significant challenges due to these devices' limited hardware and communication capabilities. To navigate these complexities, the Internet Engineering Task Force (IETF) has tailored the Routing Protocol for Low-Power and Lossy Networks (RPL) to meet the unique demands of IoT environments. However, RPL struggles with traffic congestion and load distribution issues, negatively impacting network performance and reliability. This paper presents a novel enhancement to RPL by integrating learning automata designed to optimize network traffic distribution. This enhanced protocol, the Learning Automata-based Load-Aware RPL (LALARPL), dynamically adjusts routing decisions based on real-time network conditions, achieving more effective load balancing and significantly reducing network congestion. Extensive simulations reveal that this approach outperforms existing methodologies, leading to notable improvements in packet delivery rates, end-to-end delay, and energy efficiency. The findings highlight the potential of our approach to enhance IoT network operations and extend the lifespan of network components. The effectiveness of learning automata in refining routing processes within RPL offers valuable insights that may drive future advancements in IoT networking, aiming for more robust, efficient, and sustainable network architectures.

CoFe2O4 is a promissing catalytic material for many chemical reactions. We have used ab initio molecular dynamic simulations to study the structure and reactivity of the A- and B-terminations of the low index CoFe2O4 (001) surfaces to water adsorption at room temperature. Upon adsorption, water partly dissociates on both termination with a higher dissociation degree on the A- termination (30% versus 19%). The 2-fold coordinated Fe3+(tet) in the tetrahedral voids and the 5-fold coordinated Fe3+(oct) in the octahedral voids are the main active sites for water dissociation on the A- and B-termination, respectively. Molecular water, hydroxydes and surface OH resulting from proton transfer to surface oxygens are present on the surfaces. Both water free sur- face terminations undergo reconstruction. The outermost Fe3+(tet) on the A-termination and B-termination move towards the nearby unoccupied octahedral voids. In the presence of a thin film of 32 water molecules, the reconstructions are partially and completely lifted on the A- and B-termination, respectively.

Porter's diamond model was used because it is an index in the field of competitiveness and increasing the index of competition in the international arena. In order to measure actions in the field of commerce and marketing trends and its compliance with Porter's diamond model, an organization named Isfahan Chamber of Commerce, Industries, Mines and Agriculture was selected from among the existing non-governmental organizations in Iran. This non-governmental organization, having different departments has members and subgroups related and influential in the field of commerce and marketing. In order to answer the question raised in this research, that is, to determine the position of the Isfahan Chamber of Commerce, Industries, Mines and Agriculture in Porter's diamond model, by referring to this organization in person, the available documents, information and data available in a descriptive-survey manner. It was reviewed until October 2022. All key activities and works performed in this organization in the period from March 2019 to March 2022 were identified and separated according to the internal factors of Porter's Diamond model. Finally, the credibility and effectiveness of this non-governmental organization as a link between the private sector and the government was measured in the Porter's diamond model.

This work attempted to fabricate superhydrophobic fabric via simple immersion technique. Textile fabrics were coated with silica nanoparticles prepared from tetraethoxysilane (TEOS) to obtain sufficient roughness with hydrophobic surface chemistry. Then the coated fabrics were treated with polydimethylsiloxane (PDMS) and aminopropyltriethoxysilane (APTES) to reduce the surface energy. The effects of PDMS concentration on the surface morphology and superhydrophobicity of as-prepared fabric were investigated. The morphology and the composition of superhydrophobic fabric was characterized by scanning electron microscopy (SEM), energy dispersive X-ray (EDS) and Fourier transform infrared (FTIR) spectroscopy. The results revealed the formation of spherical silica nanoparticles with average particle size of 250 nm throughout the fabric surface. The possible interactions between silica nanoparticles and APTES, as well as the fabrics were elucidated. Investigating the hydrophobicity of fabrics via water contact angle (WCA) measurement showed that the treated fabric exhibits excellent water repellency with a water contact angle as high as 151° and a very low water sliding angle. It also found that the treated fabric maintained most of its hydrophobicity against repeated washing. The comfort properties of the obtained superhydrophobic fabrics in term of air permeability and bending length did not reveal any significant changes.

We used ab initio molecular dynamics simulations to compare the activation of 2-propanol on the low index Co3O4 (111), (110) and (001) surfaces in dry conditions. The thermal and surface assisted decomposition of a film of 2-propanol to 2-propoxide on the B-termination of each surface was monitored and analyzed. The investigations suggest an activity order of Co3O4 (111) > (110) > (001). On the B-terminated (111) surface, full dissociation of all 2-propanol molecules at the interface is observed, accompanied by a Mars-van Krevelen-type mechanism upon hydroxylation of the surface. On the (110) surface, 2-propanol dissociation is driven by temperature, which activates the two-fold coordinatively unsaturated surface oxygens. The (001) surface on which almost no dissociation occurs is the least active. No formation of acetone is observed in the simulations conditions.

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

Extracellular vesicles (EVs) are enclosed by a lipid-bilayer membrane and secreted by all types of the cells under various physiological conditions. EVs play important roles in intercellular communication and crosstalk between tissues in the body. They are classified into three groups, such as apoptotic bodies, microvesicles, and exosomes. Exosomes were isolated from biofluids including blood, urine, milk, and cell culture media. Exosomes have significant potential for drug delivery and diagnosis. However, the method of isolation affects the physical and biological properties of exosomes. Several methods based on different principles have been developed for exosome isolation. These include ultrafiltration, precipitation, ultracentrifugation, size-exclusion chromatography, and microfluidics. In this study, we applied three common methods, such as ultrafiltration, precipitation, and ultracentrifugation, to isolate exosomes from the cell culture medium and investigated the effects of these different isolation methods on the size distribution and quality of the isolated exosomes. Field emission scanning electron microscopy (FESEM) images, size distribution, total protein content, and the effect of exosomes on the viability of hypoxic cells were analyzed in this study. The analysis revealed that compared to other methods, the ultracentrifugation method can isolate exosomes with smaller diameter (ranging from 20 to 80 nm), lower total protein content (50 µg/ml), and causing the increased viability of the hypoxic cells. The precipitation method does not require special equipment and is inexpensive, if the quality and purity of this method are solved, and it can be used as the best method for exosome isolation. This study can serve as a guide for choosing the best exosome isolation method for applications in medicine according to the needs, time, cost, and equipment.

In this study, the elastic properties of composite materials are investigated, considering the effects of separation of fiber-matrix joint boundary and matrix failure. In this method, by assuming periodic microstructure and using a linear approximation of the displacement field by applying continuity and equilibrium conditions, the composite fiber composite relation is determined. The effect of separation is assumed by introducing tangential and normal scalar parameters in the equations by assuming the displacement field jump at the common boundary. In order to express the effect of matrix micro-cracks, the fracture mechanics framework of continuous environments was used and the micro-cracks parallel to the fibers, perpendicular to the fibers and in the thickness direction with scalar parameters were expressed. At the end of the effect of these parameters the results are presented in graphs. The results show that the presence of defective joint at the joint boundary and the matrix micro-components reduce the hardness of the composite and thus it’s bearing load, which is more significant at the defective joint state.

Background: Upper extremities’ function impairments (UEFI) and gait impairment are frequently reported even in the early stages of Multiple Sclerosis (MS) disease. These motor deficits can negatively affect the quality of life (QOL) in MS patient. Exercise has been repeatedly introduced as a beneficial adjuvant therapy for MS, attenuating a wide range of physical and psychological symptoms without any side effects or increased risks of relapse or exacerbation in disease symptoms.Aim: This paper investigates the effect of multi-function swing suspension training (MFSST) program on upper and lower extremities’ function (ULEF), and QOL in MS women according to expanded disability status scale (EDSS) score.Design: Experimental method with a pre-test and post-test design with control group. Setting: The department of Sports Injuries and Corrective Exercise at the Shahid Bahonar University of Kerman.Population: Forty-seven MS women.Methods: Eligible subjects who matched the selection criteria were randomly assigned into three groups as follow: i. Intervention group A (EDSS of 2 – 4), ii. Intervention group B (EDSS of 4.5 – 6.5), and iii. Control group (EDSS of 2 – 6.5). The intervention groups have been trained by the SET program for a duration of 8 weeks. At baseline and after the intervention, the upper extremities’ function (UEF) and the lower extremities’ function (LEF) were assessed by 9-hole peg test (9HPT) and the timed 25-foot walk (T25FW) test, respectively. Moreover, participants’ QOL was measured using the Short Form questionnaire (SF-36).Results: A significant difference was found between the pre-test and post-test variables of walking speed and upper extremity endurance in intervention groups, and between the intervention groups and control group (P < 0.05). Compared to control group, both intervention groups showed improvements in all subscales of QOL, except emotional role limitation score.Conclusions: In summary, these results show that the present protocol of MFSST could be effective in improving ULEF and QOL in MS patients, showing the improvements after 4^th week. Moreover, this protocol was more useful for patients with an EDSS score of more than 4.5. Therefore, it seems that exercise therapy should be considered as a beneficial clinical intervention approach to improve ULEF and QOL in MS patients.

Fiber laser technology has been demonstrated as a versatile and reliable approach for laser source manufacturing with a wide range of applicability in various fields ranging from science to industry. The power/energy scaling of single fiber laser systems has faced several fundamental limitations. To overcome them and to boost the power/energy level even further, combining the output powers of multiple lasers has become the primary approach. Among various combining techniques, the coherent beam combining of fiber amplification channels is the most promising approach, instrumenting ultra-high power/energy lasers with near-diffraction-limited beam quality. This paper provides a comprehensive review of the progress of coherent beam combining for both continuous-wave and ultrafast fiber lasers. The concept of coherent beam combining from basic notions to specific details of methods, requirements, and challenges are discussed, along with reporting some practical architectures for both continuous and ultrafast fiber lasers.

A micromechanical model is implemented to indicate the progressive fatigue problem of a laminated composite with a central circular hole under fatigue loading based on a finite element model. The mechanical properties of the composite lamina are represented based on the characteristics of the fiber and the matrix through a micromechanics model. An appropriate algorithm is then adopted to simulate fatigue damage development in the composite lamina. According to this algorithm, the stress field of the composite subjected to fatigue load is initially obtained using the finite element method. Finally, the predicted results of the stresses in the constituents i.e. fiber and matrix are determined according to the micromechanical bridging model. Finally applying proper damage driving relations leads to damage degree in each element. The proposed model is proven to be successful in the observation of the fatigue behavior with stiffness degradation in each element of the composite in each cycle. Results are reported and validated using those micromechanical models and experimental data available in the literature.

The development and optimization of an effective non-viral gene delivery method for genetic manipulation of primary human T cells is a major challenge in clinical immunotherapy researches. According to the low transfection efficiency of conventional methods in human primary T cells, there is an effort in order to increase the transfection rate in these cells. Protamine is an FDA-approved compound with a documented safety profile that enhances DNA condensation for gene delivery. In this study, the effect of protamine on the transfection efficiency of standard transfection reagents, TurboFect, and Lipofectamine 2000 was evaluated in order to transfect primary human T cells. Results demonstrated that protamine condenses DNA and increases the positive charge of DNA/Cargo complex efficiently without any cytotoxic effect on the primary human T cells. The results also revealed that the DNA/Protamine/Cargo complexes effectively transfect human primary T cells.

The emerging of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is associated with high morbidity and mortality rates globally. One of the most prominent characteristics of coronavirus disease-19 (COVID-19) is lymphopenia which is in contrast to other viral infections. This controversy might be explained by the evaluation of impaired innate and adaptive immune responses during the SARS-CoV-2 infection. During the innate immune response, poly-ADP-ribose polymerase (PARP) hyperactivated due to virus entry and extensive DNA damage sequentially leading to NAD+ depletion, ATP depletion and finally cell death. In contrast to the immune response against viral infections, cytotoxic T lymphocytes decline sharply in SARS-CoV-2 infection which might be due to infiltration and trapping in the lower respiratory tract. In addition, there are more factors proposed to involve in lymphopenia in COVID-19 infection like the role of CD38 which functions as NADase and intensifies NAD depletion which in turn affects NAD+ dependent Sirtuin proteins, as the regulators of cell death and viability. Lung tissue sequestration following cytokine storm supposed to be another reason for lymphopenia in COVID-19 patients. Protein 7a as one of the virus-encoded proteins induces apoptosis in various organ-derived cell lines. These mechanisms proposed to induce lymphopenia, although there are still more studies needed to clarify the underlying mechanisms for lymphopenia in COVID-19 patients.

This study introduces a new discrete distribution which is a weighted version of Poisson-Lindley distribution. The weighted distribution is obtained using the negative binomial weight function and can be fitted to count data with over-dispersion. The p.m.f., p.g.f. and simulation procedure of the new weighted distribution, namely weighted negative binomial Poisson-Lindley (WNBPL), are provided. The maximum likelihood method for parameter estimation is also presented. The WNBPL distribution is fitted to several insurance datasets, and is compared to the Poisson and negative binomial distributions in terms of several statistical tests.

It has been shown that Human papillomavirus (HPV) infection is associated with several forms of cancer. Additionally, although there are more than 100 types of HPV, more than ten types of it, including types 16 and 18, are considered high-risk. The two proteins that make up the viral capsid of HPV 16 are L1, the major capsid protein, and L2, the minor capsid protein. FDA (The Food and Drug Administration) approved HPV vaccines mostly target L1 and L2 capsid proteins, which facilitate intracellular entry during infection. In this study, it was set out to analyze the creation of phylogenetic relations of L1 nucleic acid sequences of viral isolates. This study also analyzed sequences of three key elements within the amino acid sequences of HPV 16 L1: loops critical for structural stability, nuclear localization signal sequences, and residues implicated in viral attachment. As highlighted by the literature, these elements represent pivotal aspects of viral function. Furthermore, the report highlighted the particular inter-sequence variability within the HPV 16 L1 protein sequences. Overall analysis revealed clustering of sequences primarily due to geographical and characterized by more phylogenetic relatedness within location-specific clusters.

In the presence of aquatic plants in streams, a variety of flow structures have been reported. Re-sults of earlier investigations indicated that the hydrodynamic features of flows in rivers, and streams are influenced by a wide range of submerged and non-submerged vegetation. In the pre-sent study, eight experiment runs were conducted in two artificial pools built in a laboratory flume to assess the flow structures across vegetation cover. The slopes for entrance and exit sec-tions of these two pools are of 5 and 10 degrees, respectively. A patch of artificial grass covered the entire pool area over the gravel bed. The effect of stream flow velocity was investigated by considering two aspect ratios of 2 and 2.7. Also, distributions of flow velocity, Reynolds normal and shear stresses, turbulence intensities, turbulent kinetic energy (TKE), and higher-order mo-ments of velocity fluctuations have been investigated. By means of quadrant analysis, it is found that the skewness coefficients of velocity profiles are significant parameters to evaluate. Based on skewness values for the bursting processes, along the pool entrance section with a smaller slope, the contribution of sweep motion occurs in a more restricted zone above the canopy, and this im-plies that the ejection motion occurs in a pool entrance section with a higher slope. Immediately above the vegetation canopy, the dominant process is the sweep motion of bursting events. Whereas, at the upstream boundary of the vegetation patch, an outward motion with slightly pos-itive value occurs; and around the downstream boundary of the vegetation patch, the isotropic turbulence appears at the boundary of the vegetation and gravel bed. Over the vegetation canopy in the pool, less mixing eddies result in lower Reynolds shear stress values, and thus the turbulent flow becomes weak with lower turbulent kinetic energy (TKE). Because of the shallow flow in an artificial channel with an aspect ratio less than five and the availability of vertical non-zero ve-locity components, secondary currents become stronger in the channel. Consequently, there is a divergence from a linear dispersion of Reynolds shear stress.

The agricultural sector faces challenges due to climate change and the growing global population. Alkaline stress could adversely impact plant growth and crop production. This stressor diminishes water quality essential for crop cultivation, consequently impairing plant growth and overall productivity. Gamma-aminobutyric acid (GABA), a non-protein amino acid, may exhibit multifaceted roles in plant responses to adverse environmental conditions. Optimization of crop production in controlled environmental agriculture under artificial light attracted much attention. In the present study, we investigated the effects of different light spectra and GABA concentrations on Lollo Rosso lettuce plants under alkaline stress. Seedlings were placed under different light spectra [red (R), blue (B), white (W), 3R:1B, and 2R:1B:1far-red (FR)] and treated by GABA (0 and 50 µmol). Alkaline conditions were instituted by applying NaHCO3 at a concentration of 40 mM. The experiment incorporated a control treatment without NaHCO3. In general, R light exposure caused the highest growth performance of lettuce plants. GABA augmented biomass production in control and alkaline-treated plants across all light spectra. Additionally, it increased the concentrations of photosynthetic and protective pigments under alkaline conditions. GABA also improved the photosynthetic performance under all light spectra. Non-photochemical quenching and pigmentation were enhanced by exposure to B light, especially in plants treated with GABA. Alkaline stress induced an increase in soluble carbohydrate content in the lettuce leaves. These findings highlight the support of GABA application in facilitating coping plants to alkaline stress.

Generating early warning signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such early warning signal systems are still limited by the lack of data and uncertainties associated with predicting complex systems such as ecosystems. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land cover. To identify early warning signals, indicators such as autocorrelation (acf(1)), standard deviation (SD), and skewness (SK) are applied. The findings indicate a signal of reduced resilience by a significant increase in NDVI statistical metrics (acf(1): 0.50, SD: 0.9). Although MNDWI showed significant early warning signals in Qeshm Island (acf(1); 0.86, SD: 0.90), it provided a false alarm in the reference study site. MVWR failed to generate early warning signals of reduced resilience (acf(1); -0.100, SD: -0.07, SK: -0.21). Land-use land-cover change may explain reduced resilience in the forests. This study not only emphasizes the potential of remote sensing in monitoring the state of mangrove forests but also contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts aimed at safeguarding mangroves. This is the first report of the successful application of remote sensing in generating early warning signals of reduced resilience within mangrove forests in the Middle East.

The rapid increase in the number of mobile phone base stations (MPBS) has raised global concerns about the potential adverse health effects of exposure to radiofrequency electromagnetic fields (RF-EMF). The application of machine learning techniques can enable healthcare professionals and policymakers to proactively address concerns surrounding RF-EMF exposure near MPBS. In this study, we investigated the potential of machine learning models to predict health symptoms associated with RF-EMF exposure in individuals residing near MPBS.We utilized support vector machine (SVM) and random forest (RF) algorithms, incorporating 11 predictors related to participants' living conditions. A total of 699 adults participated in the study, and model performance was assessed using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). The SVM-based model demonstrated strong performance, achieving accuracies of 85.3%, 82%, 84%, 82.4%, and 65.1% for headache, sleep disturbance, dizziness, vertigo, and fatigue, respectively. The corresponding AUC values were 0.99, 0.98, 0.920, 0.89, and 0.81. Compared to the RF model and a previously developed model, the SVM-based model exhibited higher sensitivity, particularly for fatigue, with sensitivities of 70.0%, 83.4%, 85.3%, 73.0%, and 69.0% for these five health symptoms. Particularly for predicting fatigue, sensitivity and AUC were significantly improved (70% vs 8% and 11.1% for SVM, MLPNN, and RF, respectively, and 0.81 vs 0.62 and 0.64, for SVM, MLPNN, and RF, respectively). These findings suggest that machine learning methods, specifically SVM, hold promise in effectively managing health symptoms in individuals residing near or planning to settle in the vicinity of MPBS.

In this paper, a fuzzy logic-based energy management system and multi-port quasi-z-source converter are proposed that utilizes hybrid renewable energy sources. The system ensures that each energy source module can be used individually by employing fuzzy logic to define power modes. This approach also helps prevent switching losses resulting from extra switching of the source modules. Also, the proposed energy management does not have a mathematical model, so applicability is simple and it is suitable for different multiple input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system because of the linguistic rules. Moreover, by adopting the quasi-z-source network to the end of the multi-port converter, the switching losses of the multi-port modules have been significantly reduced. The simulation results are obtained using MATLAB Simulink, and the experimental results are obtained by connecting the circuit to MATLAB Simulink via an Arduino Due.

Electrical microgrids are deemed to be the future of modern power systems. Microgrids are sophisticated, decentralized, and self-sufficient small-scale power systems consisting of various resources ranging from wind turbines, solar photovoltaics, electric vehicles, smart energy storage, and complex communication infrastructure. However, renewable energy sources such as solar photovoltaics and wind-based generators are highly intermittent, and if not appropriately planned, they can compromise the stability of the grid. Formal methods can define and analyze the functionality and behavior of any system and show if the system design is correct before the actual system is implemented. Although formal methods have been around for many years, it is surprising that little to none are utilized in the design of safety-critical electrical power systems. Currently, in modeling microgrids, few to no attempts of formalization are being used to improve the design reliability and reduce system operating costs and time. This work demonstrates how complex systems such as microgrids can be modeled elegantly using a formal specification method. In this work, the Z state-based formal specification language (Z-Method) is used to model and verify microgrid designs. In this work, 3-interconnected microgrid systems with a high penetration level of solar and wind-based renewable energy sources with plugin hybrid electric vehicles (PHEV) as battery energy storage systems (BESS) are modeled using the Z-method. To the best of authors, the knowledge presented formal method is one of the first reported attempts in modeling microgrid communities using Software Engineering formalism.

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the COVID-19 spread. This study presents a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It is found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad citizens and tourists could not visit it and take photos to post on their Instagram accounts.

Background: Low-level physical activity (PA) among Chronic Low Back Pain (cLBP) is associated with various biopsychosocial factors. This research aimed to study the predictors of PA behavior among cLBP patients. Methods: In the present study 300 eligible patients with cLBP who referred to comprehensive health service centers in the Shahid Beheshti University of Medical Sciences (SBUMS) in Tehran, Iran were random selected.To diagnose the predictors of PA behavior, all the Theory of Reasoned Action (TRA) constructs were examined as risk factors to see if they influence on the probability of PA behavior occurrence and were interpreted through odds ratio (OR). SPSS version 19 was used to analyze the data. Results: Totally 280 cLBP patients with mean age of 57.07 ±13.09 years old participated in the study. This study showed that motivation to comply significant predictor the cLBP patients for subjective norm OR (%95CI): 2.095(0.116-2.792), p-value<0.001), intention was significant predictor for perform the PA behavior OR (%95CI): 1.431(0.138-1.538), p-value <0.001), behavior beliefs could predictor for attitude OR (%95CI): 1.276(0.106-1.355), p-value= 0.002). attitude, normative beliefs, subjective norm ,and evaluation outcome behavior could predictors the cLBP patients for intention to perform the PA behavior OR (%95CI): 1.188(0.032-1.312), p-value<0.001)., OR (%95CI): 1.158(0.076-2.208), p-value=0.003) ., OR (%95CI): 1.104(0.076-1.128), p-value<0.001) ., OR (%95CI): 0.814(0.301-1.440), p-value=0.007). Conclusions: This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm).

Thyroid cancer is the most prevalent malignancy of the endocrine system and the ninth most common cancer globally. Despite the advances in the management of thyroid cancer, there are critical issues with the diagnosis and treatment of thyroid cancer that result in the poor overall survival of undifferentiated and metastatic thyroid cancer patients. Recent studies have revealed the role of different non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) that are dysregulated during thyroid cancer development or the acquisition of resistance to therapeutics, and may play key roles in treatment failure and poor prognosis of the thyroid cancer patients. Here, we systematically review the emerging roles and molecular mechanisms of ncRNAs that regulate thyroid tumorigenesis and drug response. We then propose the potential clinical implications of ncRNAs as novel diagnostic and prognostic biomarkers for thyroid cancer.

We employ the solution concept of ex post Nash equilibrium to predict the interaction of a finite number of agents competing in a finite number of basic games simultaneously. The competition is called a multi-game. For each agent, a specific weight, considered as private information, is allocated to each basic game representing its investment in that game and the utility of each agent for any strategy profile is the weighted sum, i.e., convex combination, of its utilities in the basic games. Multi-games can model decision making in multi-environments in a variety of circumstances, including decision making in multi-markets and decision making when there are both material and social utilities for agents as, we propose, in the Prisoner's Dilemma and the Trust Game. Given a set of pure Nash equilibria, one for each basic game in a multi-game, we construct a pure Bayesian Nash equilibrium for the multi-game. We then focus on the class of so-called uniform multi-games in which each agent is constrained to play in all games the same strategy from an action set consisting of a best response per game. Uniform multi-games are equivalent to multi-dimensional Bayesian games where the type of each agent is a finite dimensional vector with non-negative components. A notion of pure type-regularity for uniform multi-games is developed and it is shown that a multi-game that is pure type-regular on the boundary of its type space has a pure ex post Nash equilibrium which is computed in constant time with respect to the number of the types and is independent of prior probability distributions. We then develop an algorithm, linear in the number of types of the agents, which tests if a multi-game is pure type-regular on the boundary of its type space in which case it returns a pure ex post Nash equilibrium for the multi-game.

Marfan syndrome is an autosomal dominant genetic connective tissue disorder that results from mutations in the fibrillin-1 gene located on chromosome band 15q 15–21. Fibrillin, a glycoprotein, is widely expressed throughout the body and contribute to elasticity and force-bearing capacity of connective tissue. In the eye, fibrillin is a key constituent of the ciliary zonules, which suspend the crystalline lens in place. The zonular defect leads to ectopia lentis that is hallmark of Marfan ocular abnormalities and occurs in 60 to 80% of cases. Other less common ocular features of Marfan syndrome are increased axial length, astigmatism, and flat cornea. Visual function in Marfan syndrome could be affected in several ways: ectopia lentis, refractive error, amblyopia, retinal detachment, cataract, and glaucoma. Management of subluxated lens starts with correction of refractive error with eyeglasses in mild cases. In more severe cases, especially when lens bisects the pupil, complete correction of refractive error is impossible without removing subluxated lens. The best method for visual rehabilitation after lens extraction is still debated. Aphakic Artisan implantation at the time of subluxated lens removal results in good visual outcomes with acceptable safety profile. Studies with longer term follow up and larger sample populations are needed to evaluate the safety of this procedure in patients with Marfan syndrome.

Let G be a finite group. The prime graph Γ(G) of G is defined as follows: The set of vertices of Γ(G) is the set of prime divisors of |G| and two distinct vertices p and p' are connected in Γ(G), whenever G has an element of order pp'. A non-abelian simple group P is called recognizable by prime graph if for any finite group G with Γ(G)=Γ(P), G has a composition factor isomorphic to P. In [4] proved finite simple groups ²D_n(q), where n ≠ 4k are quasirecognizable by prime graph. Now in this paper we discuss the quasirecognizability by prime graph of the simple groups ²D_2k(q), where k ≥ 9 and q is a prime power less than 10⁵.

A transport process was studied from an aqueous solution containing oxalic acid and 100 mg/L Ge using a flat sheet supported liquid membrane (FSSLM) system. Cyanex 923 immobilized in a polytetrafluoroethylene membrane was employed as a carrier. The solution chemistry and related diagrams were applied to study the transport of germanium. The effectual parameters such as oxalic acid, carrier concentration, and strip reagent composition were evaluated in this study. Based on the results, the oxalic acid concentration of 0.075 mol/L and the carrier concentration of 20 %v/v were the condition in which the efficient germanium transport occurred. Among strip reagents, NaOH (0.04-0.1 mol/L) had the best efficiency to transport germanium through the SLM system. Furthermore, the permeation model was obtained to calculate the mass transfer resistances of the membrane (Δ_m) and feed (Δ_f) phases. According to the results, the values of 1 and 1345 s/cm were evaluated for Δ_m and Δ_f, respectively.

This study aimed to identify and categorize the influencing factors on the purchase of dairy products among customers of dairy companies in Mashhad city. This study is a mixed research (qualitative and quantitative), conducted with the aim of identifying and categorizing the influencing factors on consumers’ purchase. Therefore, the theoretical foundations were examined in the initial step, after which the customers and their needs and demands were addressed in terms of the company and its products. In the qualitative part, interviews were conducted in the focus group (23 people in 5 groups) to identify and extract these factors using the theme analysis technique. The reliability of the interviews was confirmed by retesting and the reliability between two encoders. Sixty themes (factors) that affected the purchase of dairy products were collected. In quantitative part, 517 questionnaires were collected from 13 regions of Mashhad city to perform statistic tests. A questionnaire was designed using the extracted themes in the previous (qualitative) stage to assess customers’ feedback. The validity of the questionnaire was confirmed by content and structure validity (exploratory and confirmatory factor analysis), and its reliability was checked by Cronbach’s alpha. Fourteen components were obtained by exploratory and confirmatory factor analyses, after which the variables were labeled as intrinsic, psychological, and personality factors, the identity of the company, production power of the company, competitive power of the company, competitive prices, social awareness, and store capabilities. The construct validity was confirmed using confirmatory factors.

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: the dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM).

We introduce a new, and quite general variational model for opinion dynamics based on pairwise interaction potentials and a range of opinion evolution protocols ranging from random interactions to global synchronous flows in the opinion state space. The model supports the concept of topic “coupling”, allowing opinions held by individuals to be changed via indirect interaction with others on different subjects. Interaction topology is governed by a graph that determines interactions. Our model, which is really a family of variational models, has, as special cases, many of the previously established models for the opinion dynamics. After introducing the model, we study the dynamics of the special case in which the potential is either a tent function or a constructed bell-like curve. We find that even in these relatively simple potential function examples there emerges interesting behavior. We also present results of preliminary numerical explorations of the behavior of the model to motivate questions that can be explored analytically

Conservation Agriculture (CA) alters soil properties and microbial processes compared to conventional agriculture. These changes can affect soil-atmosphere greenhouse gas (GHG) fluxes. In this overview, we summarized the results of global literature and the gaps in measuring and understanding of GHG fluxes in CA systems and conventional agriculture. Some studies compared soil carbon sequestration and soil respiration in conservation agriculture and no-tillage system with conventional agriculture and the results were not consistent in all experiments. Interactions between CA pillars and soil factors such as soil moisture, temperature, texture can determine the rate of respiration rate and soil-atmosphere CO₂ fluxes. The majority of studies reported larger N₂O emissions in no-tillage treatment compared with conventional tillage while some other studies reported no difference between no-tillage and conventional tillage systems. In the majority of CA studies, there is lack of required information which is necessary to understand the mechanisms and processes that affect soil GHG fluxes. Determining factors like climate, amount of plant residues, soil type, crop types included in crop rotation and cover crops and duration of the study are not considered. Static chamber method was used for measuring soil-atmosphere GHG fluxes in the majority of studies. Spatial and temporal changes in GHG flux rates are high and missing part of highly episodic events by using static chamber method may result over- or under-estimation in flux balance calculation. Applying standard techniques for measuring continuous fluxes can help to calculating accurate GHG balance.

Today, due to recent developments in technology, children devote plenty of time for screen viewing. However, its harmful effects are not yet clear. The purpose of present study was to examine the associations among screen viewing and sleep duration, and body mass index (BMI) in under-five years old children. This cross-sectional study was conducted with 322 under-five healthy children that were selected using multistage stratified cluster sampling method in 2017. The data that were gathered by time-use diary method were analyzed using Kolmogorov-Smirnov test, Spearman correlation tests, multiple linear regression analysis, one-way ANCOVA, two-way ANCOVA. There was a negative correlation between screen time and sleep duration (rs = -0.42, p = 0.00), positive correlation between screen time and BMI (rs = 0.38, p = 0.00) and sleep duration negatively correlated with BMI (rs = -0.22, p = 0.00). screen viewing was a predictive factor for both sleep duration (β = -0.26, p = 0.00) and BMI (β = -0.26, p = 0.00). screen viewing had a significant impact on sleep duration (4, 314) = 5.02, P = 0.001) and BMI (F (4, 314) = 1.16, P=0.298). Results of this study indicated that screen viewing is related to sleep duration and BMI in under-five children. furthermore, screen time has an impact on sleep duration and BMI scores of children. findings of our study suggest that sleep duration negatively is associated with BMI in under-five-year-old children.

This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand fluctuations. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its own importance, and we applied the Feature Weighting approach to analyse their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a stronger influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.

Background and Objective: Intimate relationships hold significant importance during emerging adulthood. This study aimed to investigate the critical determinants of perceived well-being in friendship relationships during emerging adulthood.Method: Using a quantitative approach, a sample of 726 undergraduate students from the Ferdowsi University of Mashhad were selected through convenience sampling. They completed the Interpersonal Behaviors Questionnaire (IBQ), the Compassionate and self-image goals scale (CSIGS), the Basic Need Satisfaction in Relationships Scale (BNS-RS), the Interpersonal Needs Questionnaire (INQ-10), Friendship Self-Regulation Questionnaire (SRQ-F), Interpersonal Behaviors Questionnaire-Self (IBQ-self), State Level Subjective Vitality Scale (SVS-SL), the Perceived Relationship Quality Component (PRQC), and the Basic Empathy Scale (BES). Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results: The results indicated that the critical determinants in predicting perceived well-being in friendship relationships were, in order, goals in friendship relationships, friendship self-regulation, perceived support of basic psychological needs by friends, satisfied basic psychological needs, frustrated psychological needs, and supporting the basic psychological needs of friends.Conclusion: The findings demonstrate that self-determination theory (SDT) provides a coherent framework for explaining perceived well-being in friendships.

Some of Iran's gas resources are known as sour gas because one of the major by-products of this gas is water. In fact, natural gas is combined with water vapor in underground reservoirs. In some areas, the amount of water vapor in natural gas is so high that it can be used as a source of drinking water. However, it should be noted that water vapor in natural gas may exacerbate corrosion in pipes and industrial equipment, and the combustion of more natural gas produces high temperatures that can lead to the production of nitrogen oxides (NOx), which They are the cause of air pollution. This is while the processed gas has a standard percentage composition in which the amount of water has the lowest position. The removal of moisture from natural gas is due to the need to prevent the reduction of the calorific value of the gas and also to purify the gas in order to achieve the necessary standards for delivering gas to petrochemicals, industries and domestic uses. The main goal of this research is to investigate the influencing parameters on zeolite TiO2 nanocomposite membranes in order to absorb moisture from gas. For this reason, with the construction of a TiZ-V membrane as a standard membrane, the result of the initial evaluation of a suitable membrane for gas dehumidification was made, and this membrane was placed as a standard for measuring the effect of the manufacturing parameters in a way that in each stage of membrane construction It was found that it was different from standard TiZ-V membrane only in terms of one parameter. This type of investigation made it possible to determine the effect of various effective parameters in membrane construction on efficiency. The findings showed that increasing the concentration of SiO2 has the greatest effect on increasing the water flux of the membrane. According to the other findings of this study, the effect of increasing the reaction time of the vapor phase carrier has been to reduce the process of paternity selectivity loss at higher pressures. Although due to the safety limitation of the equipment to measure the efficiency of the membrane at pressures higher than 7 bar, this measurement was not carried out, but charts were presented regarding the state of the water in the water, which were a measure of the superiority of the water in the water in comparison to the gas in the water, which is the higher water in the water It is an indicator of the appropriateness of the membrane's efficiency at its corresponding pressure, so that the examination of the difference between these two graphs shows the possibility of using the membrane at higher pressures

In this paper, the effect of preheating on the mechanical properties and micro structure of similar friction stir welded AA6061 aluminum alloys sheets was investigated. Aluminum alloy 6061 sheets with a thickness of 5 mm and threaded cylindrical pins were used. Rotation and traverse speeds were 1200 rpm and 75 mm/min, respectively. The results of tensile tests performed on the welded samples showed that compared to the non-preheated samples, preheating had increased the strength and elongation of the joints by 58% and 46%, respectively. In the present study, during the welding process preheating cause emerged heat with lower slope from stir zone. This phenomenon may result in Increase the deformation resistance of material and consequently decrease of grain size. This grain refinement can improve the mechanical properties of welds. Accordingly, hardness and strength of the material will be increased.

In this study, the potential of Fe3Al coating material as an environmentally friendly alternative to coatings containing critical elements for brake discs was investigated. A buffer layer of Cr-Mo steel (Ferro 55) was applied to enhance coating quality and prevent the formation of hot cracks during solidification. Microstructural analysis of the cross-section of the coating showed that the buffer layer diffused into the Fe3Al coating, forming a combination of Fe3Al, Fe, and Fe3AlC0.5 phases. The tribological properties of the Fe3Al coating were evaluated by pin-on-disc tests using two different copper-free friction materials extracted from commercial brake pads. The wear results showed a coefficient of friction comparable to that of an uncoated disc, but with a reduction in Particulate Matter (PM) emissions, an interesting aspect that is gaining increasing importance in view of the upcoming international standards..

Powered lower limb exoskeletons (PLLE) have attracted much interest due to their potential applications. They provide assistance for persons with disabilities to accomplish activities of daily living (ADL), and more importantly assist them in achieving their rehabilitation goals. However, still there are unclear evidences on the quality and benefits that PLLEs can deliver to patients. This is due to limited usability and performance of current PLLEs, insufficient clinical use of PLLEs for different patients with high diversity in their disability type and impairment, and also the large gap between the technological state of the art and clinical expectations. In this study, we review and analyse various factors that can improve the effectiveness of PLLEs at yielding better assistance and rehabilitation training for patients with motor impairments. First, we define a set of criteria that characterize the majority of expectations for the rehabilitation and assistance domains and we use them for evaluating PLLEs depending on the context. Then, we include the effects of control strategies and combined approaches which include auxiliary devices such as Functional electrical stimulation and smart crutches applied to PLLEs with regard to the criteria we defined.

Background: Methadone is a centrally-acting synthetic opioid analgesic widely used in the methadone maintenance therapy (MMT) programs throughout the world. Considering its neurotoxic effects particularly on the cerebellum, this study aims to address the behavioral and histological alterations in the cerebellar cortex associated with methadone administration. Materials and Methods: Twenty-four adult male albino rats were randomized into two groups of control and methadone treatment. Methadone was subcutaneously administered (2.5–10 mg/kg) once a day for two consecutive weeks. The functional and structural changes in the cerebellum were compared to the control group. Results: Our data revealed that treating rats with methadone not only induced cerebellar atrophy, but also prompted the actuation of microgliosis, astrogliosis, and apoptotic biomarkers. We further demonstrated that treating rats with methadone increased complexity of astrocyte processes and decreased complexity of microglia processes. Our result showed that methadone impaired motor coordination and locomotor performance and neuromuscular activity. Additionally, relative gene expression of TNF-α, caspase-3 and RIPK3 increased significantly due to methadone. Conclusions: Our findings suggest that methadone administration has a neurodegenerative effect on the cerebellar cortex via dysregulation of microgliosis, astrogliosis, apoptosis, and neuro-inflammation.

Human papillomavirus (HPV) infections, primarily transmitted through sexual contact, have been linked to various cancers, including cervical, penile, anal, oropharynx, breast, and prostate cancers. This study presents a unique case of concurrent high-risk HPV35, HPV45, and HPV59 infections in both prostate and bladder cancer tissues from a single patient, representing the first documented instance worldwide with identical HPV types detected in two adjacent organs of the same individual. Employing a multiplex-PCR approach, gel electrophoresis, and Sanger sequencing, we confirmed the presence of these high-risk HPV types. Additionally, Western blot analysis using an HPV E7 antibody demonstrated the active expression of HPV oncoproteins in both cancer types. This discovery underscores the potential for HPV intra-organ transmission and necessitates further exploration of alternative transmission routes. The implications of our results offer new insights into the complex dynamics of HPV transmission in cancer pathogenesis.

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

Evaporation from surface water plays a key role in water accounting of basins, water resources management, and irrigation systems management, so simulating evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. In the second method, the best overall relationship obtained in the first method was used as the basic relationship, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation was drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

An integrated approach to active flow control is proposed by finding both the drooping leading edge and the morphing trailing edge for flow management. This strategy aims to manage flow separation control by utilizing the synergistic effects of both control mechanisms, which we call the Combined Morphing Leading Edge and Trailing Edge (CoMpLETE) technique. This design is inspired by a bionic porpoise nose and the flap movements of the cetacean species. The motion of this mechanism achieves a continuous, wave-like, variable airfoil camber. The dynamic motion of the airfoil's upper and lower surface coordinates in response to unsteady conditions is achieved by combining the thickness-to-chord (t/c) distribution with the time-dependent camber line equation. A parameterization model was constructed to mimic the motion around the morphing airfoil at various deflection amplitudes at stall angle of attack and morphing actuation start times. The mean properties and qualitative trends of the flow phenomena are captured by the transition-SST (Shear Stress Transport) model. The effectiveness of the dynamically morphing airfoil as a flow control approach is evaluated by obtaining the flow field data, such as velocity streamlines, vorticity contours, and aerodynamic forces. Different cases are investigated for the CoMpLETE morphing airfoil, which evaluates the airfoil’s parameters, such as its morphing location, deflection amplitude, and morphing starting time. The morphing airfoil’s performance is analyzed to provide further insights into the dynamic lift and drag force variations at pre-defined deflection frequencies of 0.5 Hz, 1 Hz, and 2 Hz. The findings demonstrate that adjusting the airfoil camber reduces streamwise adverse pressure gradients, thus preventing significant flow separation. Although the trailing edge deflection and its location along the chord influence the generation and separation of the Leading-Edge Vortex (LEV), these results show that the combined effect of the morphing leading edge and trailing edge has the potential to mitigate flow separation. The morphing airfoil successfully contributes to the flow reattachment and significantly increases the maximum lift coefficient (c_(l,max))). This work also broadens its focus to investigate the aerodynamic effects of a dynamically morphing leading and trailing edge, which seamlessly transitions along the side edges. The aerodynamic performance analysis is investigated across varying morphing frequencies, amplitudes, and actuation times.