Islamic Azad University, Isfahan

Scherrer Equation, L=Kλ/β.cosθ, was developed in 1918, to calculate the nano crystallite size (L) by XRD radiation of wavelength λ (nm) from measuring full width at half maximum of peaks (β) in radian located at any 2θ in the pattern. Shape factor of K can be 0.62 - 2.08 and is usually taken as about 0.89. But, if all of the peaks of a pattern are going to give a similar value of L, then β.cosθ must be identical. This means that for a typical 5nm crystallite size and λ Cukα1 = 0.15405 nm the peak at 2θ = 170° must be more than ten times wide with respect to the peak at 2θ = 10°, which is never observed. The purpose of modified Scherrer equation given in this paper is to provide a new approach to the kind of using Scherrer equation, so that a least squares technique can be applied to minimize the sources of errors. Modified Scherrer equation plots lnβ against ln(1/cosθ) and obtains the intercept of a least squares line regression, ln=Kλ/L, from which a single value of L is obtained through all of the available peaks. This novel technique is used for a natural Hydroxyapatite (HA) of bovine bone fired at 600°C, 700°C, 900°C and 1100°C from which nano crystallite sizes of 22.8, 35.5, 37.3 and 38.1 nm were respectively obtained and 900°C was selected for biomaterials purposes. These results show that modified Scherrer equation method is promising in nano materials applications and can distinguish between 37.3 and 38.1 nm by using the data from all of the available peaks.

Among the numerous attempts to integrate tissue engineering concepts into strategies to repair nearly all parts of the body, neuronal repair stands out. This is partially due to the complexity of the nervous anatomical system, its functioning and the inefficiency of conventional repair approaches, which are based on single components of either biomaterials or cells alone. Electrical stimulation has been shown to enhance the nerve regeneration process and this consequently makes the use of electrically conductive polymers very attractive for the construction of scaffolds for nerve tissue engineering. In this review, by taking into consideration the electrical properties of nerve cells and the effect of electrical stimulation on nerve cells, we discuss the most commonly utilized conductive polymers, polypyrrole (PPy) and polyaniline (PANI), along with their design and modifications, thus making them suitable scaffolds for nerve tissue engineering. Other electrospun, composite, conductive scaffolds, such as PANI/gelatin and PPy/poly(ε-caprolactone), with or without electrical stimulation, are also discussed. Different procedures of electrical stimulation which have been used in tissue engineering, with examples on their specific applications in tissue engineering, are also discussed.

The preliminary and inevitable interest in the use of partial replacements or by – products as complementary pozzolanic materials was mostly induced by enforcement of air pollution control resulted from cement production industry. Rise husk is by- product taken from rice mill process, with approximately the ratio of 200 kg per one ton of rice, even in high temperature it reduces to 40 kg. This paper presents benefits resulted from various ratios of rice husk ash(RHA) on concrete indicators through 5 mixture plans with proportions of 5, 10, 15, 20 and 25% RHA by weight of cement in addition to 10% micro- silica (MS) to be compared with a reference mixture with 100% Portland cement. Tests results indicated the positive relationship between 15% replacement of RHA with increase in compressive strengths by about 20%. The optimum level of strength and durability properties generally gain with addition up to 20%, beyond that is associated with slight decrease in strength parameters by about 4.5%. The same results obtained for water absorption ratios likely to be unfavourable. Chloride ions penetration increased with increase in cement replacement by about 25% relative to the initial values (about less than one fifth).

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

This paper describes the development a novel 8,9-dihydroxy-7-methyl-12H-benzothiazolo[2,3-b]quinazolin-12-one-ZnO/CNTs modified carbon paste electrode (DMBQ/ ZnO/CNTs/CPE)for the electrocatalytic determination of hydroxylamine (HX) in the presence of phenol (PL) and sulfite (ST) in water and waste water samples. We describe synthesis and characterization of ZnO/CNTs nanocomposite with different methods such as Scanning electron microscopy (SEM); Energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD). Result shows for the mixture containing HX, PL and ST, the peaks potential well separated from each other. Their square wave voltammetrics (SWV) peaks current increased linearly with their concentration at the ranges of 0.09– 350, 0.5–500 and 0.7–400 μM, respectively with the detection limits of 0.04, 0.1 and 0.3 μM, respectively. The modified electrode was successfully used for the determination of the analytes in real samples with satisfactory result.

Purpose The purpose of this paper is to provide an integrated approach that prioritizes organizational key performance indicators (KPIs) in terms of the criteria of SMART (Specific, Measurable, Attainable, Realistic, Time‐sensitive) goal setting. Design/methodology/approach The research was carried out using the analytical hierarchy process (AHP) technique as the basis for pairwise comparisons of SMART criteria, considering each KPI. Findings A new approach is outlined, encompassing step‐by‐step guidelines for decision makers to conduct the prioritization process of SMART KPIs. The results of the case study highlight the applicability of the proposed approach and the calculation process for prioritizing KPIs. Research limitations/implications The rating scales used in the AHP analysis are conceptual; although it identifies which dimensions require improvement, the proposed approach does not provide guidance on an appropriate action plan to address deficiencies; another limitation is that the framework adopted only the SERVQUAL service dimensions. Originality/value This paper gives a novel approach for prioritization of KPIs. The proposed approach has a holistic mechanism; it could empower decision‐making teams; it is capable of enhancing advanced quality engineering approaches; and provides great opportunities for future research.

BACKGROUND: The Edinburgh Postnatal Depression Scale (EPDS) is a widely used instrument to measure postnatal depression. This study aimed to translate and to test the reliability and validity of the EPDS in Iran. METHODS: The English language version of the EPDS was translated into Persian (Iranian language) and was used in this study. The questionnaire was administered to a consecutive sample of 100 women with normal (n = 50) and caesarean section (n = 50) deliveries at two points in time: 6 to 8 weeks and 12 to 14 weeks after delivery. Statistical analysis was performed to test the reliability and validity of the EPDS. RESULTS: Overall 22% of women at time 1 and 18% at time 2 reported experiencing postpartum depression. In general, the Iranian version of the EPDS was found to be acceptable to almost all women. Cronbach's alpha coefficient (to test reliability) was found to be 0.77 at time 1 and 0.86 at time 2. In addition, test-rest reliability was performed and the intraclass correlation coefficient was found to be 0.80. Validity as performed using known groups comparison showed satisfactory results. The questionnaire discriminated well between sub-groups of women differing in mode of delivery in the expected direction. The factor analysis indicated a three-factor structure that jointly accounted for 58% of the variance. CONCLUSION: This preliminary validation study of the Iranian version of the EPDS proved that it is an acceptable, reliable and valid measure of postnatal depression. It seems that the EPDS not only measures postpartum depression but also may be measuring something more.

With the outbreak of the COVID-19 worldwide pandemic, many countries have imposed lockdowns which have caused an increase in Internet use. As large-scale disasters may have an impact on addictions, a review on Internet-based addictive behaviors seems necessary. The goals of this review are to find whether Internet-based addictive behaviors have increased during the pandemic and to define the main reasons for this increase. The systematic search was conducted in Google Scholar, Science Direct, PsycINFO, and PubMed in October of 2020, to determine the current evidence and observations concerning the Internet-based addictive behaviors amid COVID-19. Studies were included if they considered the Internet-based addictive behaviors during the current pandemic. We used all the names of the coronavirus 2 (SARS-CoV2 previously 2019 nCoV), the name of the coronavirus disease 2019 (COVID-19), and common Internet-based addictive behaviors, namely Internet addiction, online gaming disorder, online gambling disorder, pornography use, and smartphone use disorder. The study design is PEOs, finding if individuals' exposure to the COVID-19 pandemic has caused an increase in Internet-based addictive behaviors. The quality of the studies was assessed independently by two authors using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The articles found in this review proved an increase in Internet-based addictive behaviors during the COVID-19 pandemic mostly due to financial hardships, isolation, problematic substance use, and mental health issues such as depression, anxiety, and stress. Effective interventions should be scaled up to prevent and reduce online addictive behaviors, as well as accessible guidelines, particularly for adolescents.

Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information. Indeed, early diagnosis may increase the chances of being lifesaving. However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time-consuming, and formidable task. Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners. The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (i.e., 19 layered Visual Geometric Group) model. Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training. The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments. The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

BACKGROUND: Urinary tract infections (UTIs) are one of the most common bacterial infections with global expansion. These infections are predominantly caused by uropathogenic Escherichia coli (UPEC). METHODS: Totally, 123 strains of Escherichia coli isolated from UTIs patients, using bacterial culture method were subjected to polymerase chain reactions for detection of various O- serogroups, some urovirulence factors, antibiotic resistance genes and resistance to 13 different antibiotics. RESULTS: According to data, the distribution of O1, O2, O6, O7 and O16 serogroups were 2.43%, besides O22, O75 and O83 serogroups were 1.62%. Furthermore, the distribution of O4, O8, O15, O21 and O25 serogroups were 5.69%, 3.25%, 21.13%, 4.06% and 26.01%, respectively. Overall, the fim virulence gene had the highest (86.17%) while the usp virulence gene had the lowest distributions of virulence genes in UPEC strains isolated from UTIs patients. The vat and sen virulence genes were not detected in any UPEC strains. Totally, aadA1 (52.84%), and qnr (46.34%) were the most prevalent antibiotic resistance genes while the distribution of cat1 (15.44%), cmlA (15.44%) and dfrA1 (21.95%) were the least. Resistance to penicillin (100%) and tetracycline (73.98%) had the highest while resistance to nitrofurantoin (5.69%) and trimethoprim (16.26%) had the lowest frequencies. CONCLUSIONS: This study indicated that the UPEC strains which harbored the high numbers of virulence and antibiotic resistance genes had the high ability to cause diseases that are resistant to most antibiotics. In the current situation, it seems that the administration of penicillin and tetracycline for the treatment of UTIs is vain.

This study was carried out to investigate the effect of economic globalization on economic growth in OIC countries. Furthermore, the study examined the effect of complementary policies on the growth effect of globalization. It also investigated whether the growth effect of globalization depends on the income level of countries. Utilizing the generalized method of moments (GMM) estimator within the framework of a dynamic panel data approach, we provide evidence which suggests that economic globalization has statistically significant impact on economic growth in OIC countries. The results indicate that this positive effect is increased in the countries with better-educated workers and well-developed financial systems. Our finding shows that the effect of economic globalization also depends on the country's level of income. High and middle-income countries benefit from globalization whereas low-income countries do not gain from it. In fact, the countries should receive the appropriate income level to be benefited from globalization. Economic globalization not only directly promotes growth but also indirectly does so via complementary reforms.

Purpose In line with the general purpose mentioned, this paper aims to determine the impact of the Islamic work ethic (IWE) on job satisfaction and organizational commitment among the employees of Bank Maskan by examining the mediating role of intrinsic motivation. Design/methodology/approach Analysis of data obtained from 220 questionnaires related to research variables with AMOS software shows a positive and significant relationship between IWE and job satisfaction and organizational commitment with the mediating role of intrinsic motivation. Findings The findings revealed a direct effect of IWE on job satisfaction, but there was no direct significant relationship between this variable and organizational commitment. Also, intrinsic motivation plays a partial and completely mediatory role in the relationship between IWE and job satisfaction and between IWE and organizational commitment. Research limitations/implications The impact of participation in strategic planning on managers’ creation of budgetary slack: The mediating role of autonomous motivation and affective organizational commitment. Originality/value As the nature of bank employees’ work is such that it confronts them with numerous ethical choices, the adherence to ethical standards, particularly IWE, can greatly affect their enthusiasm and, as a result, their satisfaction and organizational commitment.

The excellent conductivity and versatile surface chemistry of MXenes render these nanomaterials attractive for sensor applications. This mini-review puts recent advances in MXene-based sensors into perspective and provides prospects for the area. It describes the attractive properties and the working principles of MXene-based sensors fabricated from a MXene/polymer nanocomposite or a pristine MXene. The importance of surface modification of MXenes to improve their affinity for polymers and to develop self-healing and durable sensors is delineated. Several novel sensor fabrication methods and their challenges are discussed. Emerging applications of MXene-based sensors including moisture, motion, gas, and humidity detection as well as pressure distribution mapping are critically reviewed. Potential applications of MXene-based sensors in the food industry to monitor food materials and production plants are highlighted.

The high-penetration of distributed generation technologies in the form of MicroGrids (MGs), in recent years, has increased the risk of frequency instability since their energy is supplied by renewable energy resources (RESs) with uncertain nature. Under such circumstances, providing an MG model with an efficient load frequency control (LFC) has a fundamental role in restoring the stability of the unstructured power system. In this study, a hybrid power system with the application of the Tidal Power Unit (TPU) and Vehicle-to-Grid (V2G) is effectively planed as an isolated MG. A new fractional gradient descent (FGD) based on a single-input interval type-2 fuzzy logic controller (SIT2-FLC) is suggested as the main LFC controller, where the footprint of uncertainty (FOU) coefficient of the SIT2-FLC is specifically adjusted to enhance the LFC performance. Additionally, a deep deterministic policy gradient (DDPG) with the actor-critic framework is considered to generate the supplementary control action, which is useful for the frequency stabilization by adapting to the randomness of load disturbances and RESs. Lastly, a model-in-the-loop (MiL) simulation is conducted to appraise the feasibility and applicability of the suggested design method from a systemic perspective.

The nonlinearities and unmodeled dynamics inevitably degrade the quality and reliability of power conversion, and as a result, pose big challenges on higher-performance voltage stabilization of dc-dc buck converters. The stability of such power electronic equipment is further threatened when feeding the nonideal constant power loads (CPLs) because of the induced negative impedance specifications. In response to these challenges, the advanced regulatory and technological mechanisms associated with the converters require to be developed to efficiently implement these interface systems in the microgrid configuration. This article addresses an intelligent proportional-integral based on sliding mode (SM) observer to mitigate the destructive impedance instabilities of nonideal CPLs with time-varying nature in the ultralocal model sense. In particular, in the current article, an auxiliary deep deterministic policy gradient (DDPG) controller is adaptively developed to decrease the observer estimation error and further ameliorate the dynamic characteristics of dc-dc buck converters. The design of the DDPG is realized in two parts: (i) an actor-network which generates the policy commands, while (ii) a critic-network evaluates the quality of the policy command generated by the actor. The suggested strategy establishes the DDPG-based control to handle for what the iPI-based SM observer is unable to compensate. In this application, the weight coefficients of the actor and critic networks are trained based on the reward feedback of the voltage error, by using the gradient descent scheme. Finally, to investigate the merits and implementation feasibility of the suggested method, some experimental results on a laboratory prototype of the dc-dc buck converter, which feeds a time-varying CPL, are presented.

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

Critical infrastructures play a significant role in countries because of the essentiality of nation security, public safety, socioeconomic security, and way of life. According to the importance of infrastructures, it is a necessity to analyze the potential risks to do not allow these risks be converted into events. The main purpose of this paper is to provide a developed framework with the aim to overcome limitations of the classical approach to build a more secure, safer, and more resilient critical infrastructures in order to develop, implement, control. The proposed framework extends conventional RAMCAP (Risk Analysis and Management for Critical Asset Protection) through introducing new parameters the effects on risk value. According to the complexity of problem and the inherent uncertainty, this research adopts the fuzzy COPRAS (COPRAS-F) as a fuzzy multi criteria decision making technique to determine the weights of each criterion and the importance of alternatives with respect to criteria. Case analysis is implemented to illustrate the capability and effectiveness of the model for ranking the risk of critical infrastructures. The proposed model demonstrates a significant improvement in comparison with conventional RAMCAP.

Light-emitting diodes (LEDs) are a promising technology with a potential to improve the irradiance efficiency, light quality, and the light spectrum for increasing plant yield and quality. In this experiment, we investigated the impacts of various LED light qualities, including 100% red, 100% blue, 70% red + 30% blue, and 100% white, on the growth and photosynthesis, phytochemical contents, and mineral element concentrations in lettuce (Lactuca sativa L. cv. 'Grizzly') in comparison to normal greenhouse conditions. Photon flux of 300 µmol m-2 s-1 was provided for 14 h by 120 LEDs set on a 60 cm × 60 cm sheet of aluminum platform in the growth chambers, where plants were grown for 60 d. Fresh mass per plant was significantly higher when grown under 100% blue and 70% red + 30% blue LEDs compared to the other environments including greenhouse conditions. Phytochemical concentrations and a nutritive value of lettuce were also significantly affected by the light treatments. Chlorophyll and carotenoid concentrations increased in the plants grown under 70% red + 30% blue LEDs compared to those grown in the greenhouse. Vitamin C content was 2.25-fold higher in the plants grown under 100% blue LEDs compared to those grown in the greenhouse. Higher photosynthesis and maximal quantum yield of PSII photochemistry were also observed in the plants treated with LED lights. The application of LED light led to the elevated concentrations of macro-and micronutrients in lettuce possibly because of the direct effect of LED light and lower stress conditions in the growth chambers compared to the greenhouse. Although the mechanism of the changes in lettuce grown under LED is not well understood, the results of this study demonstrated that LED light could be used to enhance the growth and nutritional value of lettuce in indoor plant production facilities.

The main purpose of this research is to study the relationship between enablers as independent variable and knowledge management as dependent variable. The main hypothesis in this study is that the enablers are significantly related to knowledge management processes and improving the condition of enablers in organization leads to efficiency of knowledge management processes. In this research, Lawson’s model for measuring knowledge management processes, and Lee and Choi’s model for measuring the enablers are used. The findings of this study in employee’s population accept the main and secondary hypotheses and show that enablers were significantly related to knowledge management processes. Technology and culture variables significantly were related to knowledge management processes and structure variable was not significantly related to knowledge management processes. Among the three enablers, technology and culture have the most effect on the knowledge management processes respectively.