Shiraz University

This review analyzes the literature of bone grafts and introduces tissue engineering as a strategy in this field of orthopedic surgery. We evaluated articles concerning bone grafts; analyzed characteristics, advantages, and limitations of the grafts; and provided explanations about bone-tissue engineering technologies. Many bone grafting materials are available to enhance bone healing and regeneration, from bone autografts to graft substitutes; they can be used alone or in combination. Autografts are the gold standard for this purpose, since they provide osteogenic cells, osteoinductive growth factors, and an osteoconductive scaffold, all essential for new bone growth. Autografts carry the limitations of morbidity at the harvesting site and limited availability. Allografts and xenografts carry the risk of disease transmission and rejection. Tissue engineering is a new and developing option that had been introduced to reduce limitations of bone grafts and improve the healing processes of the bone fractures and defects. The combined use of scaffolds, healing promoting factors, together with gene therapy, and, more recently, three-dimensional printing of tissue-engineered constructs may open new insights in the near future.

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

In this paper, we study the performance of multiple-input multiple-output channel estimation methods using training sequences. We consider the popular linear least squares (LS) and minimum mean-square-error (MMSE) approaches and propose new scaled LS (SLS) and relaxed MMSE techniques which require less knowledge of the channel second-order statistics and/or have better performance than the conventional LS and MMSE channel estimators. The optimal choice of training signals is investigated for the aforementioned techniques. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE) scheme for their linear combining is developed and studied.

The incentive for use of renewable resources to replace fossil sources is motivating extensive research on new and alternative fuels derived from biomass. Bio-oils derived from cellulosic biomass offer the prospect of becoming a major feedstock for production of fuels and chemicals, and lignin is a plentiful, underutilized component of cellulosic biomass. Lignin conversion requires depolymerization and removal of oxygen. Likely processes for lignin conversion involve depolymerization (e.g., by pyrolysis) and catalytic upgrading of the resultant bio-oils. A major goal of the upgrading is catalytic hydrodeoxygenation (HDO), which involves reactions with hydrogen that produce hydrocarbons and water. The aim of this review is to present a critical introduction to HDO chemistry focused on compounds derived from lignin, including a summary of HDO reactions and those that accompany them, with a comparison of catalysts addressing their activities, selectivities, and stabilities. The reactions are evaluated in terms of reaction pathways of compounds representative of lignin-derived bio-oils, including anisole, guaiacol, and phenol. The review includes recommendations for further research and an attempt to place HDO in a context of options for renewable fuels and chemicals, but it does not provide an economic assessment.

Plants are constantly challenged by various abiotic stresses that negatively affect growth and productivity worldwide. During the course of their evolution, plants have developed sophisticated mechanisms to recognize external signals allowing them to respond appropriately to environmental conditions, although the degree of adjustability or tolerance to specific stresses differs from species to species. Overproduction of reactive oxygen species (ROS; hydrogen peroxide, H2O2; superoxide, [Formula: see text]; hydroxyl radical, OH(⋅) and singlet oxygen, (1)O2) is enhanced under abiotic and/or biotic stresses, which can cause oxidative damage to plant macromolecules and cell structures, leading to inhibition of plant growth and development, or to death. Among the various ROS, freely diffusible and relatively long-lived H2O2 acts as a central player in stress signal transduction pathways. These pathways can then activate multiple acclamatory responses that reinforce resistance to various abiotic and biotic stressors. To utilize H2O2 as a signaling molecule, non-toxic levels must be maintained in a delicate balancing act between H2O2 production and scavenging. Several recent studies have demonstrated that the H2O2-priming can enhance abiotic stress tolerance by modulating ROS detoxification and by regulating multiple stress-responsive pathways and gene expression. Despite the importance of the H2O2-priming, little is known about how this process improves the tolerance of plants to stress. Understanding the mechanisms of H2O2-priming-induced abiotic stress tolerance will be valuable for identifying biotechnological strategies to improve abiotic stress tolerance in crop plants. This review is an overview of our current knowledge of the possible mechanisms associated with H2O2-induced abiotic oxidative stress tolerance in plants, with special reference to antioxidant metabolism.

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Noncommunicable diseases (NCDs) account for 76% of deaths in Iran, and this number is on the rise, in parallel with global rates. Many risk factors associated with NCDs are preventable; however, it is first necessary to conduct observational studies to identify relevant risk factors and the most appropriate approach to controlling them. Iran is a multiethnic country; therefore, in 2014 the Ministry of Health and Medical Education launched a nationwide cohort study-Prospective Epidemiological Research Studies in Iran (PERSIAN)-in order to identify the most prevalent NCDs among Iran's ethnic groups and to investigate effective methods of prevention. The PERSIAN study consists of 4 population-based cohorts; the adult component (the PERSIAN Cohort Study), described in this article, is a prospective cohort study including 180,000 persons aged 35-70 years from 18 distinct areas of Iran. Upon joining the cohort, participants respond to interviewer-administered questionnaires. Blood, urine, hair, and nail samples are collected and stored. To ensure consistency, centrally purchased equipment is sent to all sites, and the same team trains all personnel. Routine visits and quality assurance/control measures are taken to ensure protocol adherence. Participants are followed for 15 years postenrollment. The PERSIAN study is currently in the enrollment phase; cohort profiles will soon emerge.

Abstract Horizon-scale images of black holes (BHs) and their shadows have opened an unprecedented window onto tests of gravity and fundamental physics in the strong-field regime. We consider a wide range of well-motivated deviations from classical general relativity (GR) BH solutions, and constrain them using the Event Horizon Telescope (EHT) observations of Sagittarius A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> (Sgr A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi> </mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> ), connecting the size of the bright ring of emission to that of the underlying BH shadow and exploiting high-precision measurements of Sgr A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi> </mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> ’s mass-to-distance ratio. The scenarios we consider, and whose fundamental parameters we constrain, include various regular BHs, string-inspired space-times, violations of the no-hair theorem driven by additional fields, alternative theories of gravity, novel fundamental physics frameworks, and BH mimickers including well-motivated wormhole and naked singularity space-times. We demonstrate that the EHT image of Sgr A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> places particularly stringent constraints on models predicting a shadow size larger than that of a Schwarzschild BH of a given mass, with the resulting limits in some cases surpassing cosmological ones. Our results are among the first tests of fundamental physics from the shadow of Sgr A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi/> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> and, while the latter appears to be in excellent agreement with the predictions of GR, we have shown that a number of well-motivated alternative scenarios, including BH mimickers, are far from being ruled out at present.

The bactericidal efficiency of various positively and negatively charged silver nanoparticles has been extensively evaluated in literature, but there is no report on efficacy of neutrally charged silver nanoparticles. The goal of this study is to evaluate the role of electrical charge at the surface of silver nanoparticles on antibacterial activity against a panel of microorganisms. Three different silver nanoparticles were synthesized by different methods, providing three different electrical surface charges (positive, neutral, and negative). The antibacterial activity of these nanoparticles was tested against gram‐positive (i.e., Staphylococcus aureus , Streptococcus mutans , and Streptococcus pyogenes ) and gram‐negative (i.e., Escherichia coli and Proteus vulgaris ) bacteria. Well diffusion and micro‐dilution tests were used to evaluate the bactericidal activity of the nanoparticles. According to the obtained results, the positively‐charged silver nanoparticles showed the highest bactericidal activity against all microorganisms tested. The negatively charged silver nanoparticles had the least and the neutral nanoparticles had intermediate antibacterial activity. The most resistant bacteria were Proteus vulgaris . We found that the surface charge of the silver nanoparticles was a significant factor affecting bactericidal activity on these surfaces. Although the positively charged nanoparticles showed the highest level of effectiveness against the organisms tested, the neutrally charged particles were also potent against most bacterial species.

Flood is one of the most devastating natural disasters with socio-economic and environmental consequences. Thus, comprehensive flood management is essential to reduce the flood effects on human lives and livelihoods. The main goal of this study was to investigate the application of the frequency ratio (FR) and weights-of-evidence (WofE) models for flood susceptibility mapping in the Golestan Province, Iran. At first, a flood inventory map was prepared using Iranian Water Resources Department and extensive field surveys. In total, 144 flood locations were identified in the study area. Of these, 101 (70%) floods were randomly selected as training data and the remaining 43 (30%) cases were used for the validation purposes. In the next step, flood conditioning factors such as lithology, land-use, distance from rivers, soil texture, slope angle, slope aspect, plan curvature, topographic wetness index (TWI) and altitude were prepared from the spatial database. Subsequently, the receiver operating characteristic (ROC) curves were drawn for produced flood susceptibility maps and the area under the curves (AUCs) was computed. The final results indicated that the FR (AUC = 76.47%) and WofE (AUC = 74.74%) models have almost similar and reasonable results. Therefore, these flood susceptibility maps can be useful for researchers and planner in flood mitigation strategies.

Hybrid AC/DC microgrids have been planned for the better interconnection of different distributed generation systems (DG) to the power grid, and exploiting the prominent features of both ac and dc microgrids. Connecting these microgrids requires an interlinking AC/DC converter (IC) with a proper power management and control strategy. During the islanding operation of the hybrid AC/DC microgrid, the IC is intended to take the role of supplier to one microgrid and at the same time acts as a load to the other microgrid and the power management system should be able to share the power demand between the existing AC and dc sources in both microgrids. This paper considers the power flow control and management issues amongst multiple sources dispersed throughout both ac and dc microgrids. The paper proposes a decentralized power sharing method in order to eliminate the need for any communication between DGs or microgrids. This hybrid microgrid architecture allows different ac or dc loads and sources to be flexibly located in order to decrease the required power conversions stages and hence the system cost and efficiency. The performance of the proposed power control strategy is validated for different operating conditions, using simulation studies in the PSCAD/EMTDC software environment.

Ionic liquid, n-octylpyridinum hexafluorophosphate (OPFP) has been used to fabricate a new carbon composite electrode with very attractive electrochemical behavior. This type of carbon electrode has been constructed using graphite mixed with OPFP as the binder. The electrode has combined advantages of edge plane characteristics of carbon nanotubes and edge plane pyrolytic graphite electrodes together with the low cost of carbon paste electrodes and robustness of metallic electrodes. It provides a remarkable increase in the rate of electron transfer of different organic and inorganic electroactive compounds and offers a marked decrease in the overvoltage for biomolecules such as NADH, dopamine, and ascorbic acid. It also circumvents NADH surface fouling effects as well as furnishing higher current density for a wide range of compounds tested. Depending on the choice of the electrolyte, the electrode can have the ion-exchange property and adsorptive characteristics of clay-modified electrodes. The proposed electrode thus allows sensitive, low-potential, simple, low-cost, and stable electrochemical sensing of biomolecules and other electroactive compounds. Scanning electron microscopy images indicate significant improvement in the microstructure of the proposed electrode compared to carbon paste electrodes. Such abilities promote new opportunities for a wide range of electrochemical and biosensing applications.

Abstract Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.

BACKGROUND: Nursing student's experiences of their clinical practice provide greater insight to develop an effective clinical teaching strategy in nursing education. The main objective of this study was to investigate student nurses' experience about their clinical practice. METHODS: Focus groups were used to obtain students' opinion and experiences about their clinical practice. 90 baccalaureate nursing students at Shiraz University of Medical Sciences (Faculty of Nursing and Midwifery) were selected randomly from two hundred students and were arranged in 9 groups of ten students. To analyze the data the method used to code and categories focus group data were adapted from approaches to qualitative data analysis. RESULTS: Four themes emerged from the focus group data. From the students' point of view," initial clinical anxiety", "theory-practice gap"," clinical supervision", professional role", were considered as important factors in clinical experience. CONCLUSION: The result of this study showed that nursing students were not satisfied with the clinical component of their education. They experienced anxiety as a result of feeling incompetent and lack of professional nursing skills and knowledge to take care of various patients in the clinical setting.

Introduction: Diabetes is a serious metabolic disorder and plenty of medical plants are used in traditional medicines to treat diabetes. These plants have no side effects and many existing medicines are derived from the plants. The purpose of this systematic review is to study diabetes and to summarize the available treatments for this disease, focusing especially on herbal medicine. Methods: Required papers about diabetes and effective plants were searched from the databases, including Science direct, PubMed, Wiley, Scopus, and Springer. Keywords in this study are "medicinal plants", "diabetes", "symptom", "herbal", and "treatment". Out of the 490 collected articles (published in the period between 1995 and 2015), 450 were excluded due to non-relevance or lack of access to the original article. Results: Diabetes is mainly due to oxidative stress and an increase in reactive oxygen species that can have major effects. Many plants contain different natural antioxidants, in particular tannins, flavonoids, C and E vitamins that have the ability to maintain -cells performance and decrease glucose levels in the blood. Conclusion: According to published results, it can be said that medical plants are more affordable and have less side effects compared synthetic drugs, and are more effective in treatment of diabetes mellitus.

Abstract The use of fossil fuels has been strongly related to critical problems currently affecting society, such as: global warming, global greenhouse effects and pollution. These problems have affected the homeostasis of living organisms worldwide at an alarming rate. Due to this, it is imperative to look for alternatives to the use of fossil fuels and one of the relevant substitutes are biofuels. There are different types of biofuels (categories and generations) that have been previously explored, but recently, the use of microalgae has been strongly considered for the production of biofuels since they present a series of advantages over other biofuel production sources: (a) they don’t need arable land to grow and therefore do not compete with food crops (like biofuels produced from corn, sugar cane and other plants) and; (b) they exhibit rapid biomass production containing high oil contents, at least 15 to 20 times higher than land based oleaginous crops. Hence, these unicellular photosynthetic microorganisms have received great attention from researches to use them in the large-scale production of biofuels. However, one disadvantage of using microalgae is the high economic cost due to the low-yields of lipid content in the microalgae biomass. Thus, development of different methods to enhance microalgae biomass, as well as lipid content in the microalgae cells, would lead to the development of a sustainable low-cost process to produce biofuels. Within the last 10 years, many studies have reported different methods and strategies to induce lipid production to obtain higher lipid accumulation in the biomass of microalgae cells; however, there is not a comprehensive review in the literature that highlights, compares and discusses these strategies. Here, we review these strategies which include modulating light intensity in cultures, controlling and varying CO 2 levels and temperature, inducing nutrient starvation in the culture, the implementation of stress by incorporating heavy metal or inducing a high salinity condition, and the use of metabolic and genetic engineering techniques coupled with nanotechnology.

Background: The world is affected by the Corona Virus Disease 2019 (COVID-19). Because of their direct contact with patients, health workers, especially nurses, play critical roles in the prevention of the COVID-19 outbreak through proper care and preventive procedures. Objectives: This study aimed to measure the awareness level of nurses in Shiraz, Iran, during the current COVID-19 outbreak. Methods: A self-administered questionnaire containing knowledge questions was distributed to 85 participants to complete. Results: More than half of the nurses (56.5%) had good knowledge about sources, transmission, symptoms, signs, prognosis, treatment, and mortality rate of COVID-19. The sources of information for the nurses were the World Health Organization and the Ministry of Health (55.29%), social applications (48.23%), and media (42.35%). Conclusions: Nurses had almost good knowledge of COVID-19. However, the WHO and the Ministry of Health still must provide more information for the medical staff for better control of the infectious disease.

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Naive Bayes (NB), for landslide susceptibility modeling and comparison of their performances. Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue. This study was carried out using GIS and R open source software at Abha Basin, Asir Region, Saudi Arabia. First, a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources. All the landslide areas were randomly separated into two groups with a ratio of 70% for training and 30% for validating purposes. Twelve landslide-variables were generated for landslide susceptibility modeling, which include altitude, lithology, distance to faults, normalized difference vegetation index (NDVI), landuse/landcover (LULC), distance to roads, slope angle, distance to streams, profile curvature, plan curvature, slope length (LS), and slope-aspect. The area under curve (AUC-ROC) approach has been applied to evaluate, validate, and compare the MLTs performance. The results indicated that AUC values for seven MLTs range from 89.0% for QDA to 95.1% for RF. Our findings showed that the RF (AUC ​= ​95.1%) and LDA (AUC ​= ​941.7%) have produced the best performances in comparison to other MLTs. The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.

DC microgrids encounter the challenges of constant power loads (CPLs) and pulsed power loads (PPLs), which impose the requirements of fast dynamics, large stability margin, high robustness that cannot be easily addressed by conventional linear control methods. This necessitates the implementation of advanced control technologies in order to significantly improve the robustness, dynamic performance, stability and flexibility of the system. This article presents an overview of advanced control technologies for bidirectional dc/dc converters in dc microgrids. First, the stability issue caused by CPLs and the power balance issue caused by PPLs are discussed, which motivate the utilization of advanced control technologies for addressing these issues. Next, typical advanced control technologies including model predictive control, backstepping control, sliding-mode control, passivity-based control, disturbance estimation techniques, intelligent control, and nonlinear modeling approaches are reviewed. Then the applications of advanced control technologies in bidirectional dc/dc converters are presented for the stabilization of CPLs and accommodation of PPLs. Finally, advanced control techniques are explored in other high-gain nonisolated (e.g., interleaved, multilevel, cascaded) and isolated converters (e.g., dual active bridge) for high-power applications.

The field of electrocardiography has been in existence for over a century, yet despite significant advances in adult clinical electrocardiography, signal processing techniques and fast digital processors, the analysis of fetal ECGs is still in its infancy. This is, partly due to a lack of availability of gold standard databases, partly due to the relatively low signal-to-noise ratio of the fetal ECG compared to the maternal ECG (caused by the various media between the fetal heart and the measuring electrodes, and the fact that the fetal heart is simply smaller), and in part, due to the less complete clinical knowledge concerning fetal cardiac function and development. In this paper we review a range of promising recording and signal processing techniques for fetal ECG analysis that have been developed over the last forty years, and discuss both their shortcomings and advantages. Before doing so, however, we review fetal cardiac development, and the etiology of the fetal ECG. A selection of relevant models for the fetal/maternal ECG mixture is also discussed. In light of current understanding of the fetal ECG, we then attempt to justify recommendations for promising future directions in signal processing, and database creation.