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Abstract Groundwater resources are essential for ensuring a consistent water supply in many regions. Groundwater potential maps (GPMs) can be utilized in many ways to estimate the quantity, quality, and distribution of subsurface water, supporting the decision-making processes of numerous stakeholders. This study contributes to improving the accuracy of GPMs, focusing on implementing Geospatial Artificial Intelligence (GeoAI) models. For this purpose, the accuracy performance of the Extreme Gradient Boosting (XGBoost) algorithm is improved in this study. To do this, two such popular metaheuristic algorithms, i.e., invasive weed optimization (IWO) and biogeography-based optimization (BBO), are integrated into the XGBoost algorithm for modeling and spatial prediction of the areas prone to groundwater. Three models—XGBoost, XGBoost-IWO, and XGBoost-BBO—are implemented within the Python programming environments to execute spatial modeling and generate predictive maps. The evaluation of results unfolds in two stages: model validation and GPM validation. For the training data, the root mean square error (RMSE) and mean absolute error (MAE) indices were 0.165 and 0.121 for XGBoost, 0.13 and 0.087 for XGBoost-IWO, and 0.114 and 0.082 for XGBoost-BBO, respectively. The test data showed similar trends, with XGBoost yielding RMSE and MAE values of 0.424 and 0.295, XGBoost-IWO at 0.416 and 0.287, and XGBoost-BBO at 0.39 and 0.28. XGBoost-BBO, XGBoost-IWO, and XGBoost had a prediction accuracy higher than other models. The respective area under the curve (AUC) of GMPs using receiver operating characteristic (ROC) curves for XGBoost, XGBoost-IWO, and XGBoost-BBO were 81.8 %, 83.1 %, and 83.7 %. Using bio-inspired metaheuristic algorithms, the GPM accuracy rate has improved further. The study of groundwater resources demonstrated how geological feature extraction by GeoAI may help employ advanced techniques.

The aim of the current study was to estimate the genetic and environmental parameters for weekly egg weights using a random regression model in wild and white Japanese quails. The base population included 300 wild and white Japanese quails, with a mating ratio of 1:2 (one male with two females). Four mating groups including pure and cross-breeding methods, were considered to produce the next generation. Eggs were collected and numbered based on their sire and dam. At the fifth week of age, 508 female quails were transferred to the laying cages. The data on egg weight was recorded from the seventh to fifteenth week of age. Nine random regression models were analyzed to determine the best orders of Legendre polynomials. The model with first and second-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, indicated the lowest AIC value and was chosen as the appropriate model. The heritability estimates and the ratios of the permanent environment to the phenotypic variance of weekly egg weights ranged from 0.09 to 0.35 and from 0.08 to 0.51, respectively. The effect of permanent environmental factors on the average weight of laid eggs decreased with increasing laying weeks, followed by an increase in the additive genetic effect on the occurrence of this trait. The estimates of genetic and phenotypic correlations between weekly egg weights varied from 0.59 to 0.98 and from 0.21 to 0.51, respectively. As a result, because of higher heritability estimates for later ages, selection to improve weekly egg weight is better performed based on at least the fifth week of the laying period onward. In conclusion, due to high positive genetic correlations among weekly egg weights, the selection basis of each weekly part record can lead to improving the consecutive weekly egg weights.

ABSTRACTThis study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R2) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R2 of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R2 of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R2 of 0.842, and AUC of 0.924.

This study addresses the critical need for comprehensive multi-pollution modeling in urban environments by employing Geospatial Artificial Intelligence (GeoAI) techniques. Specifically, it aims to elucidate the variability of air and noise pollution levels in Tehran, Iran, and develop precise multi-pollution susceptibility maps. A spatial database was compiled, encompassing annual average data (2019–2022) of the Air Quality Index (AQI) and Equivalent Continuous Sound Level (Leq), along with 19 influential factors related to both pollutants. The study utilized the CatBoost machine learning algorithm, enhanced with Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO) algorithms, to model and predict pollution patterns. Key quantitative achievements include a remarkable 15% reduction in Root Mean Square Error (RMSE) for air pollution and a 12% reduction in noise pollution when using the CatBoost-HHO configuration compared to the standalone CatBoost algorithm. Validation of the multi-pollution maps utilizing the Area Under the Curve (AUC) analysis showed high accuracy, with the CatBoost-HHO achieving AUC values of 0.943 for air pollution and 0.936 for noise pollution, outperforming CatBoost-GWO and standalone CatBoost models. These results underscore the efficacy of our approach in accurately identifying and mapping multi-pollution hotspots, contributing valuable insights for urban environmental management and public health preservation.

Background: Avian trichomoniasis is an important disease that causes bird mortality, both wild and captive, around the world. This study evaluated the in vitro cytotoxicity, antioxidant, and antiparasitic activity of citral (3.7-Dimetil-2.6-octadienal) and geraniol (trans-3.7-Dimetil-2.6-octadien-1-ol) against Trichomonas gallinae trophozoites. Methods: In vitro assays were conducted at the Laboratory of Protozoology and Entomology (LAPEN) at the Federal University of Pelotas (UFPel), Brazil in 2019 using tests with 106 parasites and citral and geraniol at concentrations ranging from 10 to 100 μM and four controls: NC (culture medium and trophozoites), MTZ (trophozoites plus 100 μM of metronidazole), and TW (trophozoites plus vehicles used for solubilizing derivatives (0.01% Tween). Results: The citral (60 μM) and geraniol (50 μM) concentrations reduced the trophozoitesʼs viability by 100%. The molecular docking experiment demonstrated that citral and geraniol might inhibit a hydrogen enzyme for T. gallinae survival. Conclusion: The major compounds of lemongrass have potential antitrichomonal activity against T. gallinae in vitro.

In this research, the effect of protease enzyme type (pepsin and pancreatin) and hydrolysis time (40-200 minutes) on the degree of hydrolysis and antioxidant properties (DPPH radical scavenging activity, Fe chelating activity, Fe reducing power and total antioxidant capacity) of flaxseed meal protein hydrolysates was investigated. The results showed that increasing the hydrolysis time increased the degree of hydrolysis, and the samples obtained from pancreatin had a higher degree of hydrolysis than pepsin. The highest activity of Fe2+ chelating (53.71 ± 0.45%) and Fe3+ reduction (1.32 ± 0.02, absorbance at 700 nm) was achieved by pancreatin after 200 minutes of hydrolysis. Pancreatin samples were more capable of inhibiting DPPH free radicals than pepsin, and their activity increased with increasing time up to 160 minutes. The highest total antioxidant capacity (1.36 ± 0.08 absorbance at 695 nm) among the samples was obtained after 160 minutes of hydrolysis with pancreatin. The antioxidant capacity of flax seed protein hydrolysates in inhibiting DPPH radical, Fe chelating activity, and total antioxidant capacity was lower than the antioxidant capacity of vitamin C at a concentration of 50 (mg/ml), but it had more Fe reducing power than vitamin C. Therefore, it can be concluded that compared to pepsin, pancreatin had a greater ability to produce flaxseed protein hydrolysates with significant antioxidant properties. According to the results, flaxseed protein hydrolysates from pancreatin enzyme and a hydrolysis time of 160 minutes have the ability to be used in food formulations to produce functional products.

IntroductionBeans are one of the cheap and important sources of high-quality vegetable protein, which is present in the diet of many people in developing countries and completes the cereal protein. Bean seeds have almost as much energy per unit weight as cereal seeds, and their protein content varies from 20-25% (about twice that of cereal). With 8-14% protein content, bean straw is very suitable fodder for livestock. The production of vegetable protein of grains has several advantages over animal protein in terms of cost and consumer health. Among other features of this plant, we can point to the ability to coexist with air nitrogen-fixing bacteria and their role in productivity, as well as strengthening and improving the physical properties of the soil. Through the ability of nitrogen fixation in these plants, placing them in rotation helps the stability of agricultural systems. At present, the largest area of bean cultivation is in the form of traditional cultivation and bean irrigation is also in the form of flooding. In this production method, more than 15,000 cubic meters of water is consumed. This amount of water consumption, along with the lack of proper productivity of other factors in production, has caused beans to be one of the least efficient products in terms of water consumption efficiency. Materials and MethodsIn order to investigate the effect of four irrigation methods on the damage of weeds, two-spotted spider mite (Tetranychus urticae Koch) and bacterial blight disease in chitti bean (Phaseolus vulgaris L.), a research study was conducted in 2022 and 2023 at the Bean Research and Education Center in Khomein, Iran. Despite employing a field with uniform conditions, the study was designed as a multi-location (regional) experiment using a randomized complete block design with three replications. The irrigation methods were categorized into four treatment levels: classical sprinkler irrigation, New-fit sprinkler irrigation, rain flat irrigation, and drip tape irrigation. Different types of chitti bean cultivars with three growth habits were randomly sown in these locations. Results and DiscussionThe experimental results showed that the irrigation method had a significant effect on seed germination percentage and bean plant height. The highest and lowest seed germination percentage and plant height were associated with drip tape irrigation and classical sprinkler irrigation, respectively. Also, the type of irrigation significantly impacted weed density, total biomass of weeds, the intensity of two spotted spider mite infestation, and bacterial blight disease. The highest weed density and biomass were observed in the New-fit and classical sprinkler irrigation methods, and the classical sprinkler irrigation exhibited the highest incidence of bacterial blight disease, too. Conversely, the lowest values for these parameters were obtained with the drip tape irrigation method. However, the highest damage caused by two spotted spider mite was observed in the drip tape irrigation method, while the lowest damage by two spotted spider mite was present in the classical sprinkler irrigation method. Furthermore, both irrigation method and bean cultivar significantly affected bean grain yield. The highest grain yield (293.6 g.m-2) was associated with drip tape irrigation, while the lowest yield (225.4 g.m-2) was observed in the classical sprinkler irrigation treatment. Among the different bean cultivars, the highest grain yield (274.1 g.m-2) was obtained from the Kousha cultivar. One of the important solutions in achieving the goals of reducing water consumption and increasing the efficiency of water consumption in bean cultivation is the implementation of pressurized irrigation instead of conventional cultivation. Pressure irrigation methods in planting beans reduce water consumption to a great extent and increase the efficiency of water consumption. The two main methods of irrigation under pressure in the country are the drip tape method and the fixed classical sprinkler with a movable sprinkler, each of which has its own advantages and disadvantages. In addition to the positive aspects of increasing yield, drip tape irrigation method in bean planting will help to further reduce water consumption. ConclusionsIn general, the experiment results demonstrated that drip tape irrigation not only increased seed germination percentage and plant height in different bean cultivars, but also led to higher grain yield compared to other irrigation methods. Additionally, the presence of weeds and bacterial blight disease were reduced in the drip tape irrigation method.

Abstract Urban gas pipelines pose significant risks to public safety and infrastructure integrity, necessitating thorough risk assessment methodologies to mitigate potential hazards. This study investigates the dynamics of population distribution, demographic characteristics, and building structures to assess the risk associated with gas pipelines. Using geospatial analysis techniques, we analyze population distribution patterns during both day and night periods. Additionally, we conduct an in-depth vulnerability assessment considering multiple criteria maps, highlighting areas of heightened vulnerability in proximity to gas pipelines and older buildings. This study incorporated the concept of individual risk and the intrinsic parameters of gas pipelines to develop a hazard map. Hazard analysis identifies areas with elevated risks, particularly around main pipeline intersections and high-pressure zones. Integrating hazard and vulnerability assessments, we generate risk maps for both day and night periods, providing valuable insights into spatial risk distribution dynamics. The findings underscore the importance of considering temporal variations in risk assessment and integrating demographic and structural factors into hazard analysis for informed decision-making in pipeline management and safety measures.

Background Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases. Objective This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants. Methods The proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets. Results The proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches. Conclusions The proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.

The larval stages of Echinococcus multilocularis (E. multilocularis) are what cause the zoonotic disease known as alveolar echinococcosis (AE). Identifying the antigens that trigger immune responses during infection is extremely important for the development of vaccines against Echinococcus infections. Several studies conducted in recent decades have described the specific traits of the protective antigens found in E. multilocularis and their role in immunizing different animal hosts. The objective of the current systematic review was to summarize the findings of relevant literature on this topic and unravel the most effective vaccine candidate antigens for future research. A comprehensive search was conducted across five databases, including ProQuest, PubMed, Scopus, ScienceDirect, and Web of Science, until March 1, 2023. Two reviewers autonomously conducted the screening and evaluation of data extraction and quality assessment. In the present study, a total of 41 papers matched the criteria for inclusion. The study findings indicate that the combination of Em14-3-3 and BCG is widely considered the most often employed antigens for E. multilocularis immunization. In addition, the study describes antigen delivery, measurement of immune responses, adjuvants, animal models, as well as routes and doses of vaccination. The research indicated that recombinant vaccines containing EMY162, EM95, and EmII/3-Em14-3-3 antigens and crude or purified antigens containing ribotan-formulated excretory/secretory antigens exhibited the most favorable outcomes and elicited protective immune responses.

Background: Current study was designed to provide a better insight into the circulating genotypes, genetic diversity, and population structure of Echinococcus spp. between southeast of Iran and Pakistan. Methods: From Jun 2020 to Dec 2020, 46 hydatid cysts were taken from human (n: 6), camel (n: 10), goat (n: 10), cattle (n: 10) and sheep (n: 10) in various cities of Sistan and Baluchestan Province of Iran, located at the neighborhood of Pakistan. DNA samples were extracted, amplified, and subjected to sequence analysis of cox1 and nad1 genes. Results: The phylogeny inferred by the Maximum Likelihood algorithm indicated that G1 genotype (n: 19), G3 genotype (n: 14) and G6 genotype (n: 13) assigned into their specific clades. The diversity indices showed a moderate (nad1: Hd: 0.485) to high hap- lotype diversity (cox1: Hd: 0.867) of E. granulosus s.s. (G1/G3) and low nucleotide diver- sity. The negative value of Tajima’s D and Fu’s Fs test displayed deviation from neutrali- ty indicating a recent population expansion. A parsimonious network of the haplotypes of cox1 displayed star-like features in the overall population containing IR9/PAK1/G1, IR2/PAK2/G3 and IR18/G6 as the most common haplotypes. A pairwise fixation index (Fst) indicated that E. granulosus s.s. populations are genetically moderate differen- tiated between southeast of Iran and Pakistan. The extension of haplotypes PAK18/G1 (sheep) and PAK26/G1 (cattle) toward Iranian haplogroup revealed that there is dawn of Echinococcus flow due to a transfer of alleles between mentioned populations through transport of livestock or their domestication. Conclusion: The current findings strengthen our knowledge concerning the evolution- ary paradigms of E. granulosus in southeastern borders of Iran and is effective in control- ling of hydatidosis.

The importance of knowledge organization and information retrieval techniques has been evident throughout human history, becoming even more crucial in the digital age. While computer systems and the web have facilitated information retrieval, challenges arose with the increasing volume of data. The introduction of Semantic Web technologies aimed to enhance precision and accuracy by converting the web into a structured data format. The Cultural Heritage (CH) community has been at the forefront of adopting Semantic Web practices to promote interoperability and shared understanding. In this study, we present a comprehensive conceptual framework that spans cultural heritage, information modeling, and information retrieval. Our model addresses early solutions in knowledge organization systems, highlighting the evolution from classification systems and controlled vocabularies to the significance of metadata schemas. We delve into the limitations of traditional knowledge organization systems and the necessity of formal ontologies, particularly in the cultural heritage domain. The comparative analysis of CRM vs. EDM, ontology-based metadata interoperability, and ontology technologies elucidate our contributions to the field. This paper outlines the process from the initial steps of adopting Semantic Web technologies in the CH domain to the latest developments in CH information retrieval. In this paper, we also reviewed intelligent applications and services developed in the CH domain after establishing semantic data models and Knowledge Organization Systems. Finally, challenges and possible future research directions are discussed. The findings revealed that GLAMs (Galleries, Libraries, Archives, and Museums) are excellent and comprehensive sources of CH information. The CH community has put in a lot of time and effort to develop data models and knowledge organization tools; now it’s time to use this valuable resource to construct smart applications that are still in their early phases. This could benefit the CH industry even more.

The purpose of this research is to identify the dimensions and explain the technology valuation strategies of Iranian startups. The research method was applied in terms of purpose and based on the method of data collection, with a qualitative research approach. The statistical population of the research included experts, policymakers, consultants and CEOs active in Iran's startup ecosystem, 10 people were selected using the purposeful sampling and available method, and it was also analyzed using the interview content analysis method. Based on the findings of the study, 7 subcategories were identified in the main category of strategies. These sub-categories include the development and promotion of the technology valuation discourse, the transformation of existing knowledge in the field of technology into desirable and valuable knowledge, leadership and idea management, comprehensive technology evaluation system, culture building, regulation and regulation in the technology market, and the localization of technology valuation. Therefore, these strategies, while paying attention to Iran's environmental and cultural issues, can help the development and progress of Iranian startups in the technology valuation process and play an important role in the country's economic development. Extended Abstract Introduction Startups are created as small, new, and independent businesses by individuals for self-employment (Birley & Westhead, 1994). Granlund et al., (2005) provide a definition of a start-up considering fast growth or fast growing companies operating in ICT businesses and the biotechnology (life sciences) industry (Granlund & Taipaleenmäki, 2005). In general, new businesses that have started from scratch are referred to as startups (Audretsch, Belitski, & Caiazza, 2021). However, the valuation of startups is a vital issue that must be addressed in order to attract capital and provide the necessary financial resources for their growth and development. The importance of correct startup valuation is directly related to the importance of this type of company in today's economy (Akkaya, 2020). Also, in recent years, the economic value of startups has been increasing (Stangler, 2019). In addition, startups are the main actors promoting technological innovation (Montani, Gervasio, & Pulcini, 2020). Valuing technology in startups is one of the main challenges in the development and sustainability of these companies. The correct valuation of technology as one of the most fundamental components of decisions related to investment, development strategies, and attracting financial resources can help startups to be more productive of their resources and experience more sustainable growth. Therefore, considering the expansion and importance of startups in the economy, it is important to understand the correct methods to measure their value (Sander & Kõomägi, 2007). Over time, it became clear that traditional valuation methods (cost approach, income approach, and market approach) are not enough to assess the value of a startup and other things should be considered. In addition, start-up companies typically have almost no track record, meaning that venture capital must "rely on a subjective evaluation method, based on the business plan provided by the investment management." Therefore, it is natural to assume that management is interested in providing a high equity value in order to prepare well for its exit (Hoffmann, 2023). Considering the importance of technology valuation of Iranian startup companies and also the mentioned cases, the present study introduces and examines technology valuation strategies in Iranian startups. So the main issue here is how to choose and implement appropriate strategies for technology valuation in these companies. In this regard, the main question of the study is raised as follows: what are the effective strategies in valuing technology in Iranian startups? Literature Valuing technologies and innovations is difficult due to high uncertainty in commercialization (Kim, Ahn, Kwon, & Lee, 2019). Valuing technology is an illogical goal, and this means reversing the relationship between the means and the goal. This inverted valuation is harmful to the cultivation of mental culture (Gunderson, 2017). Access to information improves efficiency in valuation. The role of the Internet and Internet searches is important in disambiguating or moderating valuation bias (Chang & Kwon, 2018). Technology evaluation based on price adjustment by the transferor is a model for technology valuation that has three stages: first, the technology price is set in negotiations, and in the second stage, the price range is gradually limited, and in the last stage, the time interval for bargaining is determined to ensure a scientific and reasonable price (Ma, 2015). Business team, opportunity size, marketing, sales channels, partners, competitive environment, product strength, intellectual characteristics, idea implementation time, investment periods, and rules and regulations have the most impact on the value of oil startups in Iran (Ghanbari, Hoseini, & Moradi Esfanjani, 2019). To calculate the value of technology, the three components of the costs incurred for it in different stages (laboratory, prototyping, semi-industrial, etc.), analysis of the future revenues of the technology and the impact of its special features can be used (Fakour, 2017). At the strategic level of ecosystem innovation, performance is considered in the form of environment and infrastructure preparation (Javidaneh, Jafari, & Vazidedoost, 2021). The possible distribution of the project value and the percentage of the obtained profit are the most important for making decisions in investing in venture projects (Hernandez-Garcia, Guemes-Castorena, & Jaramillo, 2017). Research methodology The current research is applicable in nature, and has been implemented in the framework of qualitative method. The statistical population of the research is made up of 10 policy makers, and managers of startups, knowledge centers, accelerators and activists in the country's start-up businesses. The method of data collection was done by conducting face-to-face, in-depth interviews with open questions between 30 and 60 minutes. To ensure the validity of the research, the central coding stage was reviewed by the participants and their views were applied in the central coding stage. Also 3 management professors reviewed the findings and commented on the central coding stage. At the end, the participants helped the analysis and interpretation of the data at the same time. Findings According to the conducted interviews and analyzes done to extract the components and dimensions of the model, the main and subcategories or indices were identified. Based on this, 7 subcategories have been identified for the main category of strategies. Based on this, the results of the present study showed that in order to design and implement the most successful technology valuation model in Iranian startups, adopting strategies such as 1. Developing and promoting the technology valuation discourse, 2. Converting existing knowledge in the field of technology into desirable and valuable knowledge, 3. Idea leadership and management, 4. Comprehensive technology evaluation system, 5. Cultivation, 6. regulation in the technology market, and 7. Localization of technology valuation can be the way forward. Conclusion The functioning of the results of the present study is influenced by the economic-infrastructural, cultural-social, and legal-political background conditions of Iran. These strategies, while paying attention to Iran's environmental and cultural issues, can help the development and progress of Iranian startups in the technology valuation process and play an important role in the country's economic development. Also, interfering factors such as conflicts of interest and technology brokers can also influence the aforementioned strategies and technology valuation in Iranian startups. If the strategies mentioned in the findings of the study are implemented correctly, it can be hoped that in the future, start-up businesses and the entire innovation ecosystem in the country will benefit from the profits of calculating the real value of startup technology and the profits of the technology valuation process itself, regardless of the outcome of this process. In this regard, Fakour (2017) presented a framework for valuing technologies in the early stages of development, which included three components: costs incurred for technology in different stages (laboratory, prototyping, and semi-industrial), analysis of future revenues, and the impact of specific technology features. In this study, the role of factors such as the type of business, technology brokers, the bubble of startups claiming technology and innovation, the necessary requirements for valuation, culture making in this field, etc., are not mentioned. khatami firoozabadi et al. (2017) also presented a model for valuing electronic services (information technology) in large non-profit organizations. In this study, the researchers presented strategies for electronic services in their own model. Based on the results of the study, the researchers state that the opinion of customers is one of the factors that determine the value of electronic services. Also, the costs of providing systems hardware is one of the variables that determine the value of electronic services in the public sector, but in non-profit institutions due to the lack of legal requirements, extract of system hardware costs from financial books is not easy, and due to the multiple use of hardware to provide different electronic services, it is not easy to measure the cost share of each software or system. Menville & Kraemer (2004) developed a resource-based model for IT business valuation that includes IT resources and complementary resources that affect business processes, which in turn affect organizational performance. Also, Chow et al. (2007) argue that the value of information technology is the value provided as a result of the use of information technology. Researchers propose a three-dimensional classification that includes four main levels of user satisfaction, individual impact, organizational impact and social impact (Chau, Kuan, & Liang, 2007). Kohli and Grover (2008) also developed a framework for defining intermediate value (e.g., business process improvement), output value (e.g., better customer service), and competitive value (e.g., first to market) of business information technology (Kohli & Grover, 2008). According to the results of the present study and the positive effect of the strategies proposed in the design and implementation of the technology valuation model on Iranian startups, it is suggested that organizations and startups in Iran take advantage of these strategies. Therefore, it is suggested that the officials and policy makers in the field of entrepreneurship and productivity take the necessary measures regarding the importance of valuing technology in the media and social networks. In this regard, it is important to hold conferences and seminars with the participation of elites and experts in the field of technology to create a valuation discourse. Also, encouraging cooperation between universities and industry in order to exchange knowledge and commercialize university technologies, create mechanisms and standard criteria to evaluate and measure the value of technology in startups and industry, amend laws and regulations related to technology in order to facilitate the process of valuing and commercializing technology, and adapting strategies and Valuation models with Iran's local and cultural conditions can be helpful in this field.

Abstract Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, and performance, make it difficult to apply and modify widely. The use of automated WCE to collect data and perform the analysis is essential for finding anomalies. Medical specialists need a significant amount of time and expertise to examine the data generated by WCE imaging of the patient’s digestive tract. To address these challenges, several computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level of accuracy, and more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19 + CNN, ResNet152V2, Gated Recurrent Unit (GRU) + ResNet152V2, and ResNet152V2 + Bidirectional GRU (Bi-GRU) and applied it on different publicly available databases for diagnosing ulcerative colitis, polyps, and dyed-lifted polyps using WCE images. To our knowledge, this is the only study that uses a single DL model for the classification of three different GI diseases. We compared the classification performance of the proposed DL classifiers in terms of many parameters such as accuracy, loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive predictive value (PPV), and F1-score. The results revealed that the Vgg-19 + CNN outperforms the three other proposed DL models in classifying GI diseases using WCE images. The Vgg-19 + CNN model achieved an accuracy of 99.45%. The results of four proposed DL classifiers are also compared with recent state-of-the-art classifiers and the proposed Vgg-19 + CNN model has performed better in terms of improved accuracy.

Abstract The NorA efflux pump of Staphylococcus aureus is known to play a major role in the development of resistance against quinolone drugs by reducing their concentration inside target pathogens. The objective of this study was to evaluate the ability of tannic acid to inhibit the gene expression of the NorA efflux pump in Staphylococcus aureus and to evaluate the in silico effect on the pump. Efflux pump inhibition was evaluated by fluorimetry. The checkerboard method evaluates the effect of the test substance in combination with an antimicrobial at different concentrations. To gene expression evaluation NorA the assay was performed using: a sub-inhibitory concentration preparation (MIC/4) of the antibiotic; a sub-inhibitory concentration preparation (MIC/4) of the antibiotic associated with tannic acid at a sub-inhibitory concentration (MIC/4). In this study, docking simulations were performed by the SWISSDOCK webserver. The ability of tannic acid to inhibit the NorA efflux pump can be related to both the ability to inhibit the gene expression of this protein, acting on signaling pathways involving the ArlRS membrane sensor. As well as acting directly through direct interaction with the NorA protein, as seen in the approach and in silico and in vitro per checkerboard method and fluorimetry of bromide accumulated in the cell.

Abstract In the context of Industry 4.0, which prioritizes intelligent and efficient solutions for industrial systems, this paper introduces an innovative methodology for fault detection and fault-tolerant control of DC motors. Leveraging the capabilities of machine learning and reinforcement learning, our approach aims to achieve optimal performance while maintaining a low computational burden. At the heart of our strategy lies a reinforcement learning-enhanced proportional-integral controller meticulously designed for precise positioning of DC motors. Through extensive comparative analysis, we establish the superiority of this controller in terms of precision, efficiency, and user accessibility when compared to traditional techniques. To ensure robust fault detection, we synergize a model-based observer with Mahalanobis distance-based outlier analysis, creating a swift and accurate diagnostic method for sensor faults. In cases of sensor malfunctions, an internal model-based control strategy comes into play, enabling the system to uphold its effectiveness despite disruptions. The effectiveness of our proposed methods is vividly demonstrated through simulations in the MATLAB environment, utilizing a DC motor subjected to sensor failures. The results unequivocally highlight the advantages of our approach, showcasing improved precision, faster operation, cost-effectiveness, and streamlined simplicity. As such, our approach finds suitability for industrial applications. In our quest to strike a delicate balance between performance and complexity, our techniques are purposefully crafted to provide intelligent yet pragmatic solutions that promote reliability, safety, and sustainability. This paper contributes to the evolving landscape of intelligent industrial solutions by offering a comprehensive framework that optimizes performance while minimizing complexity and costs. In doing so, we lay the foundation for a more efficient and resilient industrial ecosystem.

Background Propolis is a natural resinous product from a variety of plants and combines it with beeswax and salivary enzymes to create bee glue. Its potentials have been employed in the treatment of many diseases and conditions for centuries. This study, therefore, aims to determine the anti-Acanthamoeba properties of the propolis extract, eye drops coupled to some mechanisms such as inhibition of encystation and excystation. Methods The minimum inhibitory concentrations (MICs) of the most active propolis extract were assessed in trophozoites and cysts of Acanthamoeba triangularis (A. triangularis) at 0.256 and 1 mg/mL, respectively. Results Active eye drops inhibited trophozoites and cysts with a MIC value of 100%. At concentrations lower than their MICs values (1/2–1/16 MIC), propolis and eye drops revealed inhibition of encystation. In addition, at 1/2 MIC, both the propolis extract and eye drops showed potential inhibition of excystation. In combined sets of propolis extract and eye drops, they showed slightly increasing inhibition of encystation and excystation. Surprisingly, the MIC of chlorhexidine decreased when combined with the propolis and eye drops. SEM and TEM images displayed morphological changes in A. triangularis treated with combined sets of propolis extract and eye drops. Conclusion From this study, a new combined eye drop solution with propolis extract was found to be helpful in reducing encystation and excystation in A. triangularis. Therefore, this combination is an alternative for the treatment of eye diseases in early stages along with reducing the entry into the cyst stage of Acanthamoeba. The results of this study revealed new anti-Acanthamoeba inhibitors with promising combined alternative therapeutic potential for the treatment of Acanthamoeba keratitis.

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

ABSTRACTMost countries and territories worldwide are affected by coronavirus disease 2019 (COVID-19), and some cities have become known as epicenters owing to high outbreaks. Because of the changeable and unknown nature of the virus, managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different methods. It would be even more beneficial if they analyzed the experiences of similar cities. The similarity of such cities could be examined within a geographic information system based on various criteria. This study investigated the similarities among eight cities – Wuhan, Tehran, Bergamo, Madrid, Paris, Daegu, New York, and Berlin – in terms of the COVID-19 situation (target) in these locations based on proximity factors, weather, and demographic criteria. First, the factor and target layers were prepared, and then similar cities were identified using a similarity model and different distance metrics. The results were aggregated using the Copeland method because of the different outcomes for each metric. The most similar city was identified for each selected city, and its similarity level was determined based on these criteria. The results suggested the following pairs of similar cities: Wuhan–Berlin, Tehran–Berlin, Daegu–Wuhan, Bergamo–Madrid, Paris–Madrid, and New York–Paris based on COVID-19 related data up to 15 April 2020 (target T1), and Daegu–Wuhan, Tehran–Madrid, Bergamo–Paris, Berlin–Paris, and New York–Madrid up to 8 December 2021 (target T2) with a minimum and maximum similarity rate of 82.85% and 92.36%, respectively. For similar cities, the most similar factors among the proximity criteria are the distance from bus and metro stations; among weather, the criteria are humidity and pressure; and among demographics, the criteria are male and female population ratios, literacy ratio, and death ratio from asthma and cancer, with a minimum and maximum difference of 0% and 64.94%, respectively. In addition, according to the random forests ranking results (with root mean squared error = 0.23), temperature, distance from the bank, and gender were the most important criteria for the eight studied cities. Identifying these important factors helps to determine hotspots or places of future outbreaks to choose control strategies according to the cultural and ecological conditions of each city.

With the worldwide awareness of sustainability, biomass-derived carbon electrode materials for supercapacitors have attracted growing attention. In this research, for the first time, we explored the feasibility of making use of the carbon byproduct from hydrothermal liquefaction (HTL) of microalgae, termed herein as algae-derived carbon (ADC), to prepare sustainable carbon electrode materials for high-performance supercapacitor development. Specifically, we investigated carbon activation with a variety of activating reagents as well as N- and Fe-doping of the obtained ADC with the intention to enhance its electrochemical performance. We characterized the structure of the activated and doped ADCs using scanning electron microscope (SEM), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and BET surface area and pore analysis, and correlated the ADCs’ structure with their electrochemical performance as evaluated using cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), impedance, and cycle stability through an assembled symmetric two-electrode cell with 1 M H2SO4 as electrolyte. It was found that the ADC that is activated using KOH (KOH-ADC) showed the best electrochemical performance, and its specific capacitance was 14.1-fold larger with respect to that of the raw ADC and reached 234.5 F/g in the GCD test at a current density of 0.5 A/g. The KOH-ADC also demonstrated excellent capacitance retention (97% after 10,000 cycles at a high current density of 10 A/g) for stable long-term operations. This research pointed out a promising direction to develop sustainable electrode materials for supercapacitors from the carbon byproduct produced after HTL processing of algae.

This research aimed to predict soil’s physical and chemical properties with a state-of-the-art hybrid model based on deep learning algorithms and optical satellite images in a region in the north of Iran. As dependent data, 317 soil samples (0–30 cm) were collected in field surveying and analyzed by the soil and water research institute for their physical (clay, silt, and sand) and chemical [electrical conductivity (EC), organic carbon (OC), phosphorus (P), soil reaction (pH), and potassium (K)] properties. Based on independent data, 23 remote sensing (RS) parameters (extracted from Landsat 8 optical images), 17 topographical parameters [extracted from the digital elevation model (DEM)], and four climatic parameters (derived from the meteorological organization). Spatial prediction of physical and chemical properties was implemented using a convolutional neural network (CNN), recurrent neural network (RNN), and hybrid CNN-RNN models. The evaluation results indicated that the hybrid CNN-RNN model had higher accuracy in all soil properties, followed by the RNN and CNN models. In the hybrid CNN-RNN model, pH (0.0206), EC (0.0958 dS/m), silt (0.0996%), P (0.1078 ppm), K (0.1185 ppm), sand (0.1360%), OC (0.1361%), and clay (0.1419%) had higher prediction accuracy, as determined by the root mean-squared error (RMSE) index. The hybrid CNN-RNN model proved to be the most effective for soil property prediction in this region. This finding underscores the potential of deep learning techniques in harnessing RS data for precise soil property mapping, with implications for land management and agricultural practices.

The majority of the world's nations have faced the second-highest cancer mortality rate. The main causes of cancer include an unbalanced diet, genetic factors, and a few specific environmental substances. Recently, a variety of substances have been used to treat cancer, and some are still being studied. It has long been known that the mid of the twentieth century that plant and marine species create a wide range of chemically and physiologically diverse metabolites with a variety of biological effects, including anticancer, anti-inflammatory, antioxidant, antibacterial, antifouling and so on. The focus of this study is on newly found compounds from plant and marine sources that have potent anticancer effects.

Technology valuation is needed by companies in today's knowledge-based economy. The intangible nature of technology, influence of environmental factors and conditions on it, have made calculating its value complicated. At present, a large part of the country's economic activities in the technological ecosystem is carried out by companies and startups, whose growth and development are possible with the influx of capital. Therefore, the aim of the upcoming research was to identify dimensions and explain the technology valuation model in Iranian startups. The approach of the research is qualitative, and in the research process, the Grounded Theory has been used. The 10 participants including active experts, policymakers, consultants, and CEOs in Iran's startup ecosystem, were selected by using the purposeful sampling method and analyzed by using the content analysis method. Participants identified factors such as lack of technology valuation requirements, bubble of startups claiming technology, innovation as causal factors, technology brokers and conflict of interests as interfering factors in the strategies of the desired phenomenon, development and promotion of technology valuation discourse, transformation of existing knowledge in the field of technology to valuable knowledge, leadership and idea management and localization of technology valuation as solution-oriented strategies, benefits of calculating the real value of technology and valuation process itself regardless of the result were expressed as the consequences of expanding the technology valuation model in Iranian startups. In this way, the dimensions of the desired model were extracted and explained.

Background: Trichomonas vaginalis, a parasitic flagellated protozoan, is one of the main non-viral sexually transmitted diseases worldwide. Treatment options for trichomoniasis are limited to nitroimidazole compounds. However, resistance to these drugs has been reported, which requires the development of new anti-Trichomonas agents that confer suitable efficacy and less toxicity. Methods: In the present work, we assessed the effectiveness of the liposomal system containing essential oils of Bunium persicum and Trachyspermum ammi against T. vaginalis in vitro. The chemical composition of B. persicum and T. ammi were analyzed using gas chromatography-mass spectrometry (GC–MS). Liposomal vesicles were prepared with phosphatidylcholine) 70%) and cholesterol)30%) using the thin-film method. The essential oils of B. persicum and T. ammi were loaded into the liposomes using the inactive loading method. Liposomal vesicles were made for two plants separately. Their physicochemical features were tested using Zeta-Sizer, AFM and SEM. The anti-Trichomonas activity was determined after 12 and 24 h of parasite cultures in TYI-S-33 medium. Results: After 12 and 24 h of administration, the IC50 of the B. persicum essential oil nano-liposomes induced 14.41 μg/mL and 45.19 μg/mL, respectively. The IC50 of T. ammi essential oil nano-liposomes induced 8.08 μg/mL and 25.81 μg/mL, respectively. Conclusions: These data suggested that nano-liposomes of the essential oils of B. persicum and T. ammi may be a promising alternative to current treatments for Trichomonas infection.

The advent of human emotion data availability signifies a pivotal advancement, propelling intelligent systems to elevate human-technology interaction. The fusion of emotions with pioneering technologies such as artificial intelligence (AI), geospatial information systems (GIS), and extended reality (XR) heralds a groundbreaking era of emotion-driven intelligent systems, poised to revolutionize urban applications. In this context, our research carves a distinctive niche by engaging in a comprehensive review of pertinent literature, culminating in the formulation of an incisive 3-layer framework. Encompassing the bedrock of emotion analysis, seamless technology integration, intricate privacy considerations, and pragmatic implementation, this framework serves as a beacon for future endeavors. Most notably, we introduce the trailblazing concept of emotion-intelligent systems, an outcome of the harmonious blend of emotion data and pioneering technologies. Our research navigates the intricate terrain of cultivating emotionally sentient intelligent systems, deftly addressing complexities and surmounting challenges to forge a path towards a more emotionally attuned generation. With transformative implications, these avant-garde systems stand poised to reshape human-technology interfaces, thereby beckoning novel avenues for future exploration.

Marburg virus (MARV) has been a major concern since 1967, with two major outbreaks occurring in 1998 and 2004. Infection from MARV results in severe hemorrhagic fever, causing organ dysfunction and death. Exposure to fruit bats in caves and mines, and human-to-human transmission had major roles in the amplification of MARV outbreaks in African countries. The high fatality rate of up to 90% demands the broad study of MARV diseases (MVD) that correspond with MARV infection. Since large outbreaks are rare for MARV, clinical investigations are often inadequate for providing the substantial data necessary to determine the treatment of MARV disease. Therefore, an overall review may contribute to minimizing the limitations associated with future medical research and improve the clinical management of MVD. In this review, we sought to analyze and amalgamate significant information regarding MARV disease epidemics, pathophysiology, and management approaches to provide a better understanding of this deadly virus and the associated infection.

Background and aims: Urinary tract infections (UTIs) are intense public health problems and are precipitated by a variety of pathogens. This study was performed to determine the frequency of bacterial agents of UTIs and the antibiotic resistance pattern of Escherichia coli in urinary culture samples of patients at Shahid Ashrafi Esfahani hospital in Tehran, Iran. Methods: A cross-sectional study was conducted on all urinary tract cultures from January 2021 to January 2022 at Shahid Ashrafi Esfahani hospital in Tehran, Iran. Urine culture and antimicrobial susceptibility tests were performed according to Clinical and Laboratory Standards Institute (CLSI) guidelines. Statistical analyses were performed by appropriate descriptive and inferential tests such as the chi-square test using Stata version 17, and the level of significance was set at 0.05. Results: The mean age of patients with E. coli was 50.2 ± 1.8 (confidence interval = 46.6-53.8) years, and their age range was between 6-87 years. The highest prevalence of infection occurred in men over 60 years of age and in women aged 40-60 years. Further, the highest resistance and the highest sensitivity were related to ceftazidime (CAZ) and cefotaxime (CTX), respectively. Conclusion: In this study, the highest resistance and sensitivity belonged to CAZ and CTX, respectively. In addition, CTX is the primary antibiotic prescribed to deal with UTIs in medical practice.

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that have heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve flood susceptibility modeling by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging optimization (BFO), cuckoo search (CS), and artificial bee colony (ABC) algorithms). Initially, an integration of optical (Landsat-8) and radar (Sentinel-1) satellite images were used to monitor the flooded areas during the July 2022 monsoon in the Kazerun region, Iran. A total of 1358 flood occurrence points were considered from the monitored flood areas; 70% (952 points) and 30% (406 points) were used for modeling and evaluating the models, respectively. Based on flood points and thirteen spatial criteria influencing floods, four flood models ((XGBoost, XGBoost-ABC, XGBoost-BFO, and XGBoost-CS)) were used to generate a flood susceptibility map (FSM). According to the results, the XGBoost-CS (area under the curve (AUC) = 0.96), XGBoost-BFO (AUC = 0.953), XGBoost-ABC (AUC = 0.941), and XGBoost (AUC = 0.939) models have greater accuracy in flood susceptibility modeling, respectively. The results indicated that the models coupled with the three metaheuristic algorithms (XGBoost-ABC, XGBoost-BFO, and XGBoost-CS) exhibited higher flood susceptibility modeling accuracy than the standalone XGBoost model.

Background and Objectives: Cutaneous leishmaniasis is one of the most important health-threatening diseases in Iran and other countries. Glucantime is currently used to treat this disease, but due to its side effects and high resistance, alternative therapies such as the use of nanoparticles have been considered by researchers. This study aims to investigate the anti-leishmania activity of silver nanoparticles on Leishmania major in vitro. Methods: This is an experimental study on the anti-leishmania activities of silver nanoparticles at different concentrations of 0.75-0.96 µg/ml after 24, 48 and 72 hours of exposure to 106 live Leishmania major promastigotes. The numbers of live parasites were counted by Trypan Blue on a neobar slide using optical microscope (Hemocytometer method). Glucantime and distilled water were considered as positive and negative controls, respectively. The half-maximal inhibitory concentration (IC50) was calculated by SigmaPlot™ software, v‌ersion 13. All reactions were done three times and their average was considered as final result. Results: All concentrations of silver nanoparticles had anti-leishmania activity, where the concentration of 96 µg/ml had the highest effect (100%) 72 hours after exposure. The IC50 was obtained 36.67, 27.2 and 21.08 µg/ml after 24, 48 and 72 hours of exposure, respectively. Conclusion: Silver nanoparticles have an inhibitory effect on the growth of Leishmania major in different concentrations. However, further in-vivo studies are needed to determine the effictivness of silver nanoparticles.

Background and Aim: Lepidium meyenii Walp (Maca) is an herbaceous plant that grows in the Peruvian Andes and it has been widely used as a nutritional supplement and fertility enhancer and has been used in the treatment of a variety of diseases, such as rheumatism, respiratory disorders, and anemia. The most notable feature of Maca is its potent antioxidant capacity, which helps in the scavenging of free radicals and protection of cells from oxidative stress. This study aimed to evaluate the in vitro effect of Maca extract on thawed sperm cells from bulls. Materials and Methods: Three dilutions of 1, 10, and 100 μg/mL of Maca extract were incubated with frozen–thawed bovine semen and analyzed at 1, 3, and 24 h of exposure time, evaluating the activity of the extract on the DNA, motility, morphology, viability, integrity of the membrane and acrosome of spermatozoa. Results: The Maca extract improved the studied sperm parameters of motility, acrosome integrity, vitality, and DNA integrity of sperm cells at a concentration of 10 –g/mL, and at 1 –g/mL, an improvement was observed in the morphology and integrity of the membrane. However, the best activity of the Maca extract was observed on the DNA integrity of the sperm, which was effective at the three concentrations evaluated after 24 h of incubation. Conclusion: The results indicate that L. meyenii can help in maintaining spermatozoa cellular integrity after the frozen–thaw process, especially in the protection against DNA fragmentation. Therefore, Maca would be a feasible supplementation to protect sperm to maintain their fertile ability after thawing.

Measures taken by governments during armed conflict to safeguard their essential interests sometimes cause damages to foreign investors. The investors thus affected seek remedies in the arbitration tribunals invoking breach of host government obligations to protect investments. The host government also usually attempts to assert as defense non-precluded measures to prove its irresponsibility, or if it proves responsible, justify it by resorting to circumstances precluding wrongfulness in the customary international law. But since different courts do not consider the same requirements to invoke these rules, there is no certainty that the parties to the lawsuit will be able to invoke them and, as a result, the scope of government's obligations to protect the foreign investor during the armed conflict is obscure. To clarify the scope of the host government's obligations to protect the foreign investor during armed conflicts and balance the interests of the investor and the host government during the investment disputes arising from the armed conflict, this article explores the possibility and requirements of invoking circumstances precluding wrongfulness and non-precluded measures and the relationship between them.

A supercapacitor is an electrical energy storage system with high power output. With worldwide awareness of sustainable development, developing cost-effective, environmentally friendly, and high-performance supercapacitors is an important research direction. The use of sustainable components like wood biochar in the electrode materials for supercapacitor uses holds great promise for sustainable supercapacitor development. In this study, we demonstrated a facile and powerful approach to prepare meso-microporous carbon electrode materials for sustainable and high-performance supercapacitor development by electrospinning polyacrylonitrile (PAN) with F-treated biochar and subsequent aerogel construction followed by stabilization, carbonization, and carbon activation. The resultant carbon nanofibrous aerogel electrode material (ENFA-FBa) exhibited exceptional specific capacitance, attributing to enormously increased micropore and mesopore volumes, much more activated sites to charge storage, and significantly greater electrochemical interaction with electrolyte. This electrode material achieved a specific capacitance of 407 F/g at current density of 0.5 A/g in 1 M H2SO4 electrolyte, which outperformed the state-of-the-art specific capacitance of biochar-containing electrospun carbon nanofibrous aerogel electrode materials (

Abstract Cultural heritage (CH) reflects on the history of a society and its traditions and it is treated as the nation’s memory and identity. Digitizing and web, beside its benefits, brought some challenges in disseminating and retrieving CH information, which has heterogeneous content varying widely in type and properties yet encompassing rich semantic links. Semantic web technologies, especially ontologies, provide a common understanding inside a domain that helps sharing knowledge and interoperability. They can be very helpful in data modeling for a better information retrieval compared to relational databases as they take into account the semantics of information, guarantee reusability, and make information machine-readable that can offer more flexibility to intelligent services and applications. CH community is one of the first domains to make use semantic web technologies to deal with this issue. CIDOC CRM is the most used and famous ontology in CH domain, which is an ISO standard since 2006. Heritage sites are composed of many points of interest that attract visitors to find out about them. However, information about a particular POI is complex and interconnected with other people, events, and objects. In this paper, we aim to develop a POI-based data model for heritage sites in Iran using concepts from CIDOC CRM integrated with GeoSPARQL, the standard ontology in geospatial field, to incorporate spatial semantics with heritage information. This way the user can freely explore their preferred information about the places they desire. This can make it possible to use the data model for location-based services and applications in heritage sites.

In a recent study, our research group demonstrated that the essential oil of Ocotea odorifera (EOOO) and its major compound safrole potentiated the action fluoroquinolones, modulating bacterial resistance possibly due to direct inhibition of efflux pumps. Thus, in the present study, we investigated whether these treatments could enhance the activity of gentamicin and erythromycin against multidrug-resistant (MDR) bacteria. The EOOO was extracted by hydrodistillation, and the phytochemical analysis was performed by gas chromatography coupled to mass spectrometry (GC–MS). The antibiotic-enhancing effect of the EOOO and safrole against MDR strains of Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa was analyzed by the broth microdilution method. The chemical analysis confirmed the presence of safrole as a major component among the 16 compounds identified in the EOOO. Both the essential oil and the isolated compound showed clinically relevant antibacterial activities against S. aureus. Regarding the modulation of antibiotic resistance, the EOOO was found to enhance the activity of erythromycin against the strains of P. aeruginosa and S. aureus, as well as improving the action of gentamicin against S. aureus. On the other hand, safrole potentiated the activity of gentamicin against the S. aureus strain alone. It is concluded, therefore, that the EOOO and safrole can enhance the activity of macrolides and aminoglycosides, and as such are useful in the development of therapeutic tools to combat bacterial resistance against these classes of antibiotics.

Background: Hydatidosis is known as one of the most prevalent zoonotic diseases across the world. This complication is also endemic in Iran, followed by a higher risk of infection in rural areas. To our knowledge, there has been no study on the seroprevalence of hydatidosis in Sistan and Baluchistan Province, Southeast of Iran. The main objective of the current study was to examine the seroprevalence of hydatidosis and its risk factors in high-risk individuals (farmers and ranchers) living in Sistan and Baluchistan Province. Materials and Methods: This study included 500 serum samples, and the participants were requested to complete a researcher-made questionnaire. Subsequently, counter-current immunoelectrophoresis (CCIEP) and enzyme-linked immunosorbent assay (ELISA) methods were employed to analyze the anti-Echinococcus granulosus antibody. The analysis of the obtained data was conducted by logistic regression in SPSS software, version 22. Results: According to the results, four (0.8%) cases were found positive for anti-E. granulosus antibody by both CCIEP and ELISA tests. Seroprevalence of hydatidosis was more in rural people, compared to those in urban areas. It was also higher in illiterate people than in educated people. Nevertheless, seropositivity showed no significant differences with age, gender, occupational status, education level, place of residence, and contact with dogs (P>0.05). Conclusion: The prevalence rate of hydatidosis in Sistan and Baluchistan Province was similar to that in neighboring provinces. According to the findings, high-risk individuals offer remarkable information about the epidemiology of hydatidosis in Sistan and Baluchistan province in southeastern Iran. This could help to manage and prevent this infection.

Background: Propolis is a natural resinous mixture produced by bees. It provides beneficial effects on human health in the treatment/management of many diseases. The present study was performed to demonstrate the anti-Acanthamoeba activity of ethanolic extracts of Propolis samples from Iran. The interactions of the compounds and essential proteins of Acanthamoeba were also visualized through docking simulation. Methods: The minimal inhibitory concentrations (MICs) of Propolis extract against Acanthamoeba trophozoites and cysts was determined in vitro. In addition, two-fold dilutions of each of agents were tested for encystment, excystment and adhesion inhibitions. Three major compounds of Propolis extract such as chrysin, tectochrysin and pinocembrin have been selected in molecular docking approach to predict the compounds that might be responsible for encystment, excystment and adhesion inhibitions of A. castellanii. Furthermore, to confirm the docking results, molecular dynamics (MD) simulations were also carried out for the most promising two ligand-pocket complexes from docking studies. Results: The minimal inhibitory concentrations (MICs) 62.5 and 125 µg/mL of the most active Propolis extract were assessed in trophozoites stage of Acanthamoeba castellanii ATCC30010 and ATCC50739, respectively. At concentrations lower than their MICs values (1/16 MIC), Propolis extract revealed inhibition of encystation. However, at 1/2 MIC, it showed a potential inhibition of excystation and anti-adhesion. The molecular docking and dynamic simulation revealed the potential capability of Pinocembrin to form hydrogen bonds with A. castellanii Sir2 family protein (AcSir2), an encystation protein of high relevance for this process in Acanthamoeba. Conclusions: The results provided a candidate for the development of therapeutic drugs against Acanthamoeba infection. In vivo experiments and clinical trials are necessary to support this claim.

Background: Propolis is a natural resinous mixture produced by bees. It provides beneficial effects on human health in the treatment/management of many diseases. The present study was performed to demonstrate the anti-Acanthamoeba activity of ethanolic extracts of Propolis samples from Iran. The interactions of the compounds and essential proteins of Acanthamoeba were also visualized through docking simulation. Methods: The minimal inhibitory concentrations (MICs) of Propolis extract against Acanthamoeba trophozoites and cysts was determined in vitro. In addition, two-fold dilutions of each of agents were tested for encystment, excystment and adhesion inhibitions. Three major compounds of Propolis extract such as chrysin, tectochrysin and pinocembrin have been selected in molecular docking approach to predict the compounds that might be responsible for encystment, excystment and adhesion inhibitions of A. castellanii. Furthermore, to confirm the docking results, molecular dynamics (MD) simulations were also carried out for the most promising two ligand-pocket complexes from docking studies. Results: The minimal inhibitory concentrations (MICs) 62.5 and 125 µg/mL of the most active Propolis extract were assessed in trophozoites stage of Acanthamoeba castellanii ATCC30010 and ATCC50739, respectively. At concentrations lower than their MICs values (1/16 MIC), Propolis extract revealed inhibition of encystation. However, at 1/2 MIC, it showed a potential inhibition of excystation and anti-adhesion. The molecular docking and dynamic simulation revealed the potential capability of Pinocembrin to form hydrogen bonds with A. castellanii Sir2 family protein (AcSir2), an encystation protein of high relevance for this process in Acanthamoeba. Conclusions: The results provided a candidate for the development of therapeutic drugs against Acanthamoeba infection. In vivo experiments and clinical trials are necessary to support this claim.

Education is a vital part of the development of society and it is changing over time in terms of methods, content, concepts, and models. Recently, it has been increasingly prevalent to benefit from the potentialities of Artificial Intelligence (AI) in addressing educational issues. In this research, the current state of the art of the integration of AI in K-12 education was provided. Specifically, different parts of education in which AI was employed along with the related AI categories were discussed according to different K-12 grades and courses. Additionally, technologies and environments that contributed to employing AI in education were discussed. To this end, a systematic literature review was conducted on articles and conference papers published between 2011 and 2021 in the Web of Science and Scopus databases. As the result of the initial search, 2075 documents were extracted and based on inclusive criteria and 210 documents were identified for further investigation. AI applications were categorized into Student performance, Teaching, Selection, and Behavior tasks, and Other. Machine Learning (ML) and Intelligent Tutoring System (ITS) were the most common approaches among AI categories. Furthermore, high school-related applications were more frequent and STEM courses were substantially targeted by AI. In conclusion, the remarkable impact of AI on education was concluded. The current study reveals information about the potentialities offered by AI in K-12 education which aids researchers in implementing AI-based education systems. As for future works, other databases such as ACM library and Google Scholar can be investigated as well. Furthermore, exploring the 95 papers that were excluded due to inaccessibility to their full texts can be taken into account. Finally, the papers can be also investigated in terms of pedagogical approaches or development tools.

The aims of this study to assess the efficiency of AGL against acetaminophen (APAP)-induced hepatic toxicity that was generated by mitochondrial oxidative stress and glutathione depletion. Free radical scavenging potentiality was analyzed by using 2, 2-diphenyl-1-picrylhydrazyl (DPPH), hydrogen peroxide, nitric oxide, and hydroxyl radical scavenging assays. APAP-induced liver toxicity was formed at a dose level of 640 mg/kg mg/kg BW each, p.o. for 14 days for all experimental rats except the vehicle control group. AGL (5 and 10 mg/kg) were treated orally with negative control and negative control silymarin (50 mg/kg) group. To assess the protective effect, we looked at the levels of serum biochemical markers, liver histoarchitecture, and hepatic antioxidant enzyme activity. AGL showed in vitro anti-oxidant potentialities by scavenging radicals in the respective assays. As evidenced by serum biochemical indicators and relative liver weight, AGL co-administration substantially reduced toxicant-induced hepatic damage. APAP-intoxication increased the malondialdehyde (MDA) level and declined in cellular endogenous antioxidant enzymes such as reduced catalase, superoxide dismutase, and glutathione, where, AGL treatment amended their level. In the same way, histopathological evaluation further verified that AGL protected the hepatocyte from APAP-induced damage. As AGL scavenges toxic free radicals, thereby protects mitochondria and other organelles from reactive oxygen and nitrogen species-mediated stress and its eventual consequence necrosis. Therefore, we propose the hepatoprotective activity of AGL through its antioxidant mechanism.

In today’s world, technology has assumed an indispensable role in everyday life. However, though developments in the Internet of Things (IoT) have laid a foundation for achieving different educational purposes, IoT is not being employed to its full potential. A separate issue is that the topic of geometry has been neglected or received only limited attention in schools, particularly in the early years. Moreover, as geometry is of paramount importance, the idea of teaching geometry in early childhood should be investigated. This paper proposes an IoT-based approach for teaching basic geometric shapes to children aged 5 to 6. Specifically, five nodes were designed to act as the vertices of shapes. In addition, educational videos comprising three sections —identification, recognition, and recollection — were provided. Using a sample of 35 boys the proposed solution was assessed in terms of its effect on learning outcomes and the learning process using pre-test, simultaneous test, and post-tests. The findings revealed a noticeable improvement in learning outcomes. Furthermore, the proposed approach made learning enjoyable, which, compared to the traditional approach toward learning geometric shapes, is considered an improvement.

Abstract Praziquantel is the only available drug to treat schistosomiasis, a parasitic disease that currently infects more than 240 million people globally. Due to increasing concerns about resistance and inadequate efficacy there is a need for new therapeutics. In this study, a series of 17 pyrazolines (15–31) and three pyrazoles (32–34) were synthesized and evaluated for their antiparasitic properties against ex vivo adult Schistosoma mansoni worms. Of the 20 compounds tested, six had a 50% effective concentration (EC50) below 30 μM. Our best hit, pyrazoline 22, showed promising activity against adult schistosomes, with an EC50

Background and Objectives: Cutaneous leishmaniasis is one of the common diseases between humans and animals that is considered a health problem in Iran and many countries around the world. Currently, glucantime is used to treat cutaneous leishmaniasis. Due to its high side effects and resistance, the use of alternative therapies, especially plants and natural compounds, has been highly recommended by researchers. The aim of this study is to investigate the effect of the methanolic extract of dandelion root (Taraxacum officinale) on Leishmania major in vitro techniques. Methods: In this experimental study, the extract was prepared by the Soxhlet method and to evaluate the effect of methanolic extract of dandelion root on L. major parasite at concentrations of 300-1200 mg/ml. Amphotericin B and distilled water were considered as positive and negative controls, respectively. Then, 106 live parasites were added to all tubes, and all groups were kept at 25 ±1 °C. At 24, 48, and 72 hours, the number of live parasites was counted by Trypan Blue using a neobar slide and light microscope (Hemocytometer method). Then Half-maximal inhibitory concentration (IC50) value for the above extract was calculated using SigmaPlot™ 13 software. All steps of the experiment were done in triplicate and the results were considered as average. Results: The IC50 content of methanolic extract of dandelion roots after 24, 48, and 72 hours on L. major were calculated to be 1.04, 0.9, and 0.68 µg/ml. The highest growth inhibition (100%) was observed at a concentration of 1200 μg/ml after 72 hours of exposure. There was a significant difference between the IC50 extract and amphotericin B drug after 24, 48 and 72 hours (P

Water contamination by nitrogen and phosphorus ions has a direct consequence of eutrophication to the ecosystem. The objective of this study is to investigate the production of hydrochars by acetic acid or sodium hydroxide assisted hydrothermal carbonization (HTC), various activation methods, and the potential of hydrochar as an adsorbent to remove NH4+-N and PO43−-P from wastewater. The results showed that acetic acid or sodium hydroxide assisted HTC and activation with magnesium chloride or air could improve the surface properties of hydrochar. Acetic acid modification generated extensive oxygenated functional groups, while sodium hydroxide modification produced hydrochar with a high N/C ratio and surface aromaticity. Treating hydrochar with magnesium chloride could impregnate nano-magnesium particles on the hydrochar, thereby improving the ability to remove N and P. Air activation of hydrochar resulted in more oxygen containing functional groups. The NH4+-N and PO43−-P adsorption capacities of these hydrochars ranged from 92.6 to 122.4mg/g and 1.6 to 15.8mg/g, respectively. The adsorption capacity of hydrochars in swine wastewater is similar to the results of artificial wastewater. The results suggested that Mg-nanoparticle dispersion and oxygen-containing functional groups played a major role in adsorption than ion exchange and physisorption. HIGHLIGHTS Hydrochars were produced by acetic acid and sodium hydroxide assisted HTL.; Magnesium chloride or air activation could improve the surface properties of hydrochar.; Hydrochar activated by using MgCl2 and air substantially increased the adsorption capacity.; Mg-nanoparticle dispersion and oxygen containing functional groups played a major role in adsorption.;

Cutaneous leishmaniasis is a complex infection that is caused by different species of Leishmania and affects more than 2 million people in 88 countries. Identifying the environmental factors affecting the occurrence of cutaneous leishmaniasis and preparing a risk map is one of the basic tools to control and manage this disease. The aim of this study was a spatial prediction of cutaneous leishmaniasis in Isfahan province, Iran using three machine learning algorithms (decision tree (DT), support vector regression (SVR), and linear regression (LR)). The spatial database was created using data collected on the number of diseases in Isfahan province from 2011 to 2018, as well as ten environmental parameters (temperature, humidity, rainfall, altitude, slope, wind speed, normalized difference vegetation index (NDVI), number of sunny days, number of frosty days, and distance to stream) that affect the incidence of leishmaniasis. Furthermore, the fuzzy method was employed in this study to reduce uncertainty and evaluate the effect of environmental factors on disease prevalence. Using the holdout method and 70:30 ratios, the data were used to model and prepare a leishmaniasis prediction map and evaluate the results, respectively. The accuracy of the maps satisfied with the DT, SVR, and LR algorithms was 0.951, 0.934, and 0.914, respectively, according to the receiver operating characteristic (ROC) curve and area under the curve (AUC). Furthermore, the eastern and southern parts of the province have the lowest risk of leishmaniasis. The result of this issue is the identification of high-risk areas of the disease and increase life and peace for people in the community.

Background and objectives: Soil contamination with Toxocara spp. eggs is the main source of human toxocariasis. The aim of this study was to determine rate of soil contamination with Toxocara spp. eggs in Yazd, central Iran. Methods: During September 2014 to February 2015, a total of 220 soil samples were collected from public parks, primary schools, green areas, kindergartens, suburban areas, streets, residential complexes, backyards and a bus terminal in the city of Yazd, Yazd Province. Soil samples were investigated for the presences of Toxocara eggs by flotation technique using sucrose solution (1.2 g/cm3) and using light microscopy. Data analysis was carried out by SPSS (version 23) software package. Results: A total of 220 soil samples from nine different places were examined. Of these samples, 51 (23.2%) were contaminated with Toxocara spp. eggs. The contamination rate was 26.3% in public parks, 19.2% in primary schools, 28.6% in green areas, 22.2% in kindergartens, 24% in suburban areas, 8.3% in streets, 18.8% in residential complexes, 19% in backyards and 28.6% in the bus terminal. There was no significant difference in the soil contamination rate with Toxocara spp. eggs between different sampling places (χ2 = 3.096, d.f = 8 and P= 0.92) and sampling areas (χ2= 2.289, d.f = 2 and P= 0.318). Conclusion: The high rate of soil contamination with Toxocara spp. eggs in Yazd emphasize on the need for preventive measures, such as improving sanitation, providing public health education and controlling stray dogs and cats near children play areas.

A sequential modular hydrodynamic model integrated with detailed reaction kinetics (SMHM-RK) was developed and validated to predict tar and syngas components produced by the steam gasification of biomass in a fluidized bed gasifier. The simulations showed that the prediction accuracy is sensitive to both models for hydrodynamics and reaction kinetics. The simulations showed that the tar composition predicted by the SMHM-RK was more close to the measured values than those predicted by the well-mixed hydrodynamic model integrated with the same reaction kinetics (WMHM-RK). The predictions showed that the total tar decreased, but the polycyclic aromatic tar compounds increased with the increase in gasification temperature. There was an optimum steam-to-biomass ratio (SBR) for minimizing tar formation. The simulations found that the contents of total tar and heavy tar compounds decreased by increasing the SBR from 0.3 to 0.9, and then increased by further increasing the SBR. The injection of a small amount of oxygen in steam gasification cannot reduce tar formation. The injection of oxygen in steam gasification changed the reaction pathways of naphthalene to produce more naphthalene in the syngas. The de-volatilization rate affects pyrolytic volatile compositions and subsequent tar formation. Therefore, biomass devolatilization and homogeneous gas reactions should be solved simultaneously to accurately predict the syngas and tar composition.

Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features.

Abstract Acanthamoeba spp. can cause amoebic keratitis (AK). Chlorhexidine is effective for AK treatment as monotherapy, but with a relative failure on drug bioavailability in the deep corneal stroma. The combination of chlorhexidine and propamidine isethionate is recommended in the current AK treatment. However, the effectiveness of treatment depends on the parasite and virulence strains. This study aims to determine the potential of Garcinia mangostana pericarp extract and α-mangostin against Acanthamoeba triangularis, as well as the combination with chlorhexidine in the treatment of Acanthamoeba infection. The minimal inhibitory concentrations (MICs) of the extract and α-mangostin were assessed in trophozoites with 0.25 and 0.5 mg/mL, for cysts with 4 and 1 mg/mL, respectively. The MIC of the extract and α-mangostin inhibited the growth of A. triangularis trophozoites and cysts for up to 72 h. The extract and α-mangostin combined with chlorhexidine demonstrated good synergism, resulting in a reduction of 1/4–1/16 of the MIC. The SEM results showed that Acanthamoeba cells treated with a single drug and its combination caused damage to the cell membrane and irregular cell shapes. A good combination displayed by the extract or α-mangostin and chlorhexidine, described for the first time. Therefore, this approach is promising as an alternative method for the management of Acanthamoeba infection in the future.