Search

This article presents a mathematical model developed using the M5P tree to predict the shear strength of steel-fiber-reinforced concrete (SFRC) for slender beams using soft computing techniques. This method is becoming increasingly popular for addressing complex technical problems. Other approaches, such as semi-empirical equations, can show known inaccuracies, and some soft computing methods may not produce predictive equations. The model was trained and tested using 332 samples from an experimental database found in the previous literature, and it takes into account independent variables such as the effective depth d, beam width bw, longitudinal reinforcement ratio ρ, concrete compressive strength fc, shear span to effective depth ratio a/d, and steel fiber factor Fsf. The predictive performance of the proposed M5P-based model was also compared with the one of existing models proposed in the previous literature. The evaluation revealed that the M5P-based model provided a more consistent and accurate prediction of the actual strength compared to the existing models, achieving an R2 value of 0.969 and an RMSE value of 37.307 for the testing dataset. It was found to be a reliable and also straightforward model. The proposed model is likely to be highly helpful in assessing the shear capacity of SFRC beams during the pre-planning and pre-design stages and could also be useful to help for future revisions of design standards.

Polyvinyl alcohol (PVA) defines as a self-curing agent when it forms films prevent evaporation water required to hydration. In this study was found the effect of self-curing factor on some mechanical properties is studied by adding of PVA solution to cement ratio (P/C) with varying conditions of curing. The PVA dissolved in water from 1% by weight of cement. It was seen that PVA could service by self- curing through providing increase strength compare with traditional curing. The results showed increased percentages of flexural, tensile and compressive strength of cement mortars with PVA as self-curing agent under dry condition for 28 days were 62%, 101%and 1.5% respectably to compare with self-curing for cement mortars composite modified with PVA as curing agent under wet curing conditions as soon as cement mortars composite modified without PVA were 110%, 5%and14.5% respectably in dry condition also 48 %, 55%and 41% in wet curing conditions respectably. The use of Fourier Transforms Infrared (FTIR) spectra and techniques like scanning electron microscope (SEM) is very significant to show the impacts of the existence of PVA as water-soluble polymer in the fine structure of modified cement mortar by polymers.

The present research studies the effect of adding sugar factory waste sugarcane molasses as an alternative to chemical inhibitory additives manufactured on concrete cast during hot weather in the summer of Iraq. The current study includes a study of the setting time for cement paste and workability for fresh concrete and some mechanical properties of hardened concrete with sugarcane molasses using percentage 0–0.3% of cement weight. The study also included studying the value of pH of concrete to investigate the effect of sugarcane molasses on reinforced concrete. The results showed that the sugarcane molasses can be used by about 0.1% of cement weight for medium projects and between 0.1 and 0.2% for large projects. It was also found that the compression strength increased by about 11.5% and the indirect tensile strength increased by about 7.5 and 7.4% for splitting and flexural test, respectively for concrete mixtures containing 0.2% of sugarcane molasses. [ABSTRACT FROM AUTHOR]

This study targeted to investigate a type of excavation slurry by local available material in Najaf region. On the other hand, chief practises is supported the deep sides of foundation in particular pile foundation and, drilling oil wells. The results of slurries are checking, for seven properties, which includes the viscosity (apparent plastic and funnel Marsh), yield stress, density, acidity, and gel power (10–0 min). Which are 11.7367, 8.8733, 38.1667, 11.5467 mPa·s, 1.1045 kg m−3, 10.9067, and 11.2683 mPa·s, respectively. On the basis of the results tests for the above properties, that is possible to produce a product that conforms to the specifications of American Petroleum Institute specifications, and according to the requirements of the standard ACI 336.3R-93:2006 without additives. The rheological behavior improvement is achieved through the breakdown of the bundles of palygorskite fibers that normally make up the texture of these mudstones by increasing the time and speed of the shear force as soon as increasing the concentration of clay powder. [ABSTRACT FROM AUTHOR]

Abstract Background Lifestyle modification can have beneficial effects on improving symptoms of ovary syndrome and anthropometric changes, particularly in obese individuals… However, it is not clear whether these affects in obese adolescents with PCOS are the same as non-PCOS adolescents. We had a study question” Can lifestyle promotion programs, which focus on changing behavioral habits, have an effect on anthropometric parameters and the manifestation of polycystic ovary syndrome (PCOS) in adolescents?” Methods This was a cluster randomized trial (CRT) that started from January 2021 and follow-up ended in March 2022. 128 participants included adolescent girls (from 14 to 18 years old). The status of PCOS was determined for the participants, following which both the PCOS-afflicted and non-PCOS cohorts were subjected to randomization to either partake in a lifestyle promotion program or to proceed without it. This program included eight sessions that were designed to be implemented for two months. This intervention provides recommendations for a balanced diet and regular exercise, as well as advice on behavior change for adolescents, including those with PCOS, regardless of their weight. All participants were followed up for 12 months and were evaluated at three time points: baseline, 6 and 12 months. Outcomes included changes in the anthropometric indices (weight, hip and waist circumstance), regularity of menstrual cycle, hirsutism score by the modified-Ferriman-Gallwey scale, acne score by the Investigator’s Global Assessment Scale, hair loss scores by the Sinclair Graphic Instrument and then physical activity by the Caspian tool and dietary intake status by the Food Frequency Questionnaire. Data was analyzed using the non-parametric Mann-Whitney test for variables with two-time point assessments and generalized estimation equations (GEE) for variables with three time point assessments. Results In the study, the intervention group of girls with PCOS exhibited a significant reduction in weight and waist circumference, with an average decrease of 3.14 kg and 4.68 cm, respectively (P

Abstract Background The COVID-19 pandemic has led to the broad acceptance of distance education (DE), with university professors and students conducting the teaching–learning process remotely from their homes. The propose of this study to investigate the prevalence of musculoskeletal disorders (MSDs) before and during the COVID-19 pandemic and identify risk factors associated with DE that may contribute to an increased incidence of these disorders among university professors. Methods This cross-sectional analytical study took a comparative approach and involved 310 university professors in Iran. Data were gathered using an online questionnaire. Initially, demographic and occupational information of the professors, hours of physical activity, and hours spent using electronic devices were recorded. Participants were then asked to report MSDs in various body areas throughout the previous year and the previous seven days. Finally, MSDs risk factors such as workstation ergonomics during computer, laptop, smartphone, and tablet use, as well as working postures during online teaching or offline content development during the COVID-19 pandemic, were examined. Results The majority of the participants were male (66.13%), with a PhD (46.77%) and a faculty member position (74.2%). On average, the use of computers and laptops increased by 2.67 h and 2.72 h, respectively, during the pandemic compared to before the pandemic. This increase was statistically significant (P

An economic model generally describes the mechanisms used to generate revenue from a business. If it fails to generate income, its failure is inevitable. Therefore, decisions regarding missions are not based solely on technical specifications; economic profitability is also a crucial factor in decision-making and is a primary consideration for commercial investments. This consideration becomes particularly important in high-cost, long-duration projects, such as space missions, where the return on investment and economic justification are more complex. Lunar mining projects are no exception and require a comprehensive financial and technical feasibility assessment approach.To analyze the economic feasibility of any project, it is essential to evaluate its economic model. This paper conducts an economic valuation of lunar mining, focusing on materials available on the Moon, using fuzzy analysis and sensitivity analysis to identify suitable materials for exploitation. The materials are categorized into oxides, metals, and fuels. Using an appropriate economic model for space mining, the study evaluates the economic potential of selling these materials on Earth.Based on the economic feasibility analysis, each group identifies SiO₂, Mg, and helium-3 (3He) as high-priority materials. SiO₂ and Mg are expected to be profitable in conservative scenarios with increased mission duration and frequency. In optimistic scenarios, all three materials could be profitable. Ultimately, SiO₂ is selected as the target material for lunar mining.

The near-surface boundary layer over patchy snow is highly heterogeneous and dynamic. Layers of opposing stability coexist within only a few horizontal meters. Conventional experimental methods to investigate this layer suffer from limitations related to the fixed positions of eddy covariance sensors. To overcome these difficulties, we set up a centimeter-resolution large eddy simulation of flow across an idealised transition from bare ground to snow. We force the simulation with high-frequency eddy covariance data recorded during a field campaign. We show that the model can represent the real flow by comparing it to independent eddy covariance data. However, the simulation underestimates vertical wind speed fluctuations, especially at high frequencies. Sensitivity analyses show that this is influenced by grid resolution and surface roughness representation but not much by subgrid-scale parameterization. Nevertheless, the model can reproduce the experimentally observed plumes of warm air intermittently detaching from bare ground and being advected over snow. This process is highly dynamic, with time scales of only a few seconds. We can show that the growth of a stable internal boundary layer adjacent to the snow surface can be approximated by a power law. With low wind speeds, deeper stable layers develop, while strong wind speeds limit the growth. Even close to the surface, the buoyancy fluxes are heterogeneous and driven by terrain variations, which also induce the frequent decoupling of a thin layer adjacent to the snow surface. Our simulations point the path towards generalizing point-based and aerial measurements to three dimensions.

Abstract The network security analyzers use intrusion detection systems (IDSes) to distinguish malicious traffic from benign ones. The deep learning-based (DL-based) IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction process. However, this new generation of IDSes still needs to overcome a number of challenges to be employed in practical environments. One of the main issues of an applicable IDS is facing traffic concept drift, which manifests itself as new (i.e. , zero-day) attacks, in addition to the changing behavior of benign users/applications. Furthermore, a practical DL-based IDS needs to be conformed to a distributed (i.e. , multi-sensor) architecture in order to yield more accurate detections, create a collective attack knowledge based on the observations of different sensors, and also handle big data challenges for supporting high throughput networks. This paper proposes a novel multi-agent network intrusion detection framework to address the above shortcomings, considering a more practical scenario (i.e., online adaptable IDSes). This framework employs continual deep anomaly detectors for adapting each agent to the changing attack/benign patterns in its local traffic. In addition, a federated learning approach is proposed for sharing and exchanging local knowledge between different agents. Furthermore, the proposed framework implements sequential packet labeling for each flow, which provides an attack probability score for the flow by gradually observing each flow packet and updating its estimation. We evaluate the proposed framework by employing different deep models (including CNN-based and LSTM-based) over the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. Through extensive evaluations and experiments, we show that the proposed distributed framework is well adapted to the traffic concept drift. More precisely, our results indicate that the CNN-based models are well suited for continually adapting to the traffic concept drift (i.e. , achieving an average detection rate of above 95% while needing just 128 new flows for the updating phase), and the LSTM-based models are a good candidate for sequential packet labeling in practical online IDSes (i.e. , detecting intrusions by just observing their first 15 packets).

Introduction: Today, among synthetic materials, its production in various types and applications is increasing day by day. Plastic and plastic compounds. These plastics have been used in the production of various industrial products, and these materials have created many environmental challenges as waste resistant to microbial decomposition. So that the use of industrial polymers causes problems such as solid waste and global warming. And the materials that are currently used in the packaging of food, beverages, medical and pharmaceutical products, as well as in industrial uses, are non-degradable. For this reason, researchers are looking for the production of biodegradable plastics to solve this challenge.Materials and Methods: The main goal of the current research is to investigate the barriers to the production of biodegradable disposable containers under conditions of uncertainty with the help of the fuzzy Delphi technique. The statistical population of the research includes the specialists and experts in the production of disposable biodegradable containers in the factories of Behzit Tehran, Zoebbehd Pars, Zarin Shimi, MG Plast, Kimia Samane Sabz, Navad Tabriz, Pars Plastic Khuzestan, Shiraz Sabz, Mehrparsa, Adine Plast. Sampling of experts was done until reaching theoretical saturation, and the sample size of 10 people was selected from experts of the factories.Results: The results of the present research show that among the economic challenges, lack of investment in production with a factor of 0.72, the cost of raw materials for production with a factor of 0.71, lack of providing suitable facilities for production with a factor of 0.77 and the cost of providing machinery for production Biodegradable plastics are effective with a coefficient of 0.78. Among the technological challenges, poor skill and expertise for product packaging and production with a factor of 0.81, lack of access to global markets to update new production technologies with a factor of 0.72 and lack of access to the technology required to implement the production of biodegradable disposable containers They are effective with a factor of 0.7. Finally, among the legal challenges, the non-compliance of container production with environmental rules and regulations with a coefficient of 0.75 and cumbersome administrative policies and rules for the production of biodegradable plastics with a coefficient of 0.71 are effective. Discussion: Based on the results obtained in the current research, it has been determined that there are important challenges in the production of biodegradable disposable containers in Iran from all three dimensions, economic, technological and legal, of which technological challenges seem to be the most important challenges. and then there are economic challenges and obstacles related to the current laws.

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Melt pool geometry is a deterministic factor affecting the characteristics of metal Additive Manufacturing (AM) components. The wide array of physical and thermal phenomena involved during the formation of the AM melt pool, along with the great variety of alloy compositions and AM methods, coupled with the clear influence of multiple process parameters, make it difficult to predict the melt pool geometry under a given set of conditions. Therefore, using Artificial Intelligence (AI) approaches such as Machine Learning (ML) is necessary for accurate predictions. Using a physics-informed feature selection strategy along with the application of atomic features for the first time, this work aims to offer accurately trained models relying on existing high-fidelity data for most common alloys in AM academia and industry, i.e., 316 L stainless steel, Ti6Al4V, and AlSi10Mg. Multiple ML algorithms were trained, and the results revealed that the average R2 and RMSE obtained by the K-fold cross-validation (K = 5) were significantly enhanced when laser and material properties, inspired by the analytical models for AM melt pool geometry, were used as the model features. Removing the excess features and applying atomic features further enhanced the accuracy of the models. As a result, R2 for the XGBoost, CatBoost, and GPR models were 0.907, 0.889, and 0.882, respectively, while the hold-out cross-validation led to 0.978, 0.976, and 0.945, respectively. Furthermore, the results showed that the XGBoost model outperforms the Rosenthal equation. This approach provides a pathway to more accurately predict the properties of metal AM components.

AbstractThe purpose of this research was to design and validate a model for improving attention deficit and hyperactivity in primary school students of Ardabil City. The research was applied in terms of purpose, mixed in terms of the nature of data, and exploratory in terms of execution method. In the qualitative part, the semi-structured interview and database theory have been used. The statistical population of the qualitative section was university professors and experts in the field of school counseling and educational sciences, 15 of whom were selected based on the principle of opinion saturation. The statistical population in the quantitative section was all the primary school teachers of Ardabil City in the academic year of 2023-2024 in the number of 2600 people, according to Morgan's table, 335 of them were selected based on stratified random sampling. The data collection tool in this research was a researcher-made questionnaire resulting from the refined concepts obtained from the primary codes. The data sets obtained after the continuous process of open, central, and selective coding were organized in the form of 156 open codes, 146 concepts, 12 categories and 6 classes based on a systematic approach. The findings of the research show the improvement pattern of attention deficit and hyperactivity in elementary school students of Ardabil city under the categories of personal and psychological characteristics, motor skills, social skills, attention deficit disorder, educational strategies at the individual level, educational strategies at the interpersonal and communication level, pharmaceutical interventions and nutrition, physiological and behavioral interventions, psychological control, strengthening motor and physical abilities, psychological growth and social participation were categorized and fit in terms of validation (p

Background: Due to the increasing consumption of bottled water in recent years, to ensure the safety, we aimed to evaluate the concentration of 6 toxic heavy metals, nitrate and nitrite components in the commonly sold bottled water in Mashhad, Iran.Methods: The 11 best-selling bottled water brand in Mashhad were identified. Eight bottles from each brand were randomly collected and delivered at the same day to toxicology laboratory of Imam Reza Hospital and refrigerated at 4-6 °C. Spectrophotometry and atomic absorption spectrometry and were used to measure the nitrate, nitrite and heavy metals, respectively. The results were analyzed by SPSS version 16 and compared with the WHO and Australian guidelines. Also, the discrepancy between measured components and the depicted labels’ values were compared.Results: The mean and SD of concentrations of the heavy metals in 11 brand were as below: lead 1.62±0.86 [µg/L], chromium 1.03±0.84 [µg/L], cadmium 0.17±0.07 [µg/L], mercury 3.86±1.57 [µg/L], arsenic 0.89±0.46 [µg/L], aluminum 6.56±4.54 [µg/L]. The mean and SD measured quantities of nitrate, nitrite and pH were 9.96±5.95 [mg/L], 0.01±0.03 [mg/L] and 7.92±5.95, respectively. There was a significant difference between the label values and the quantitative levels except for 3 brands, which was observed with a p value of 0.518 and 0.642 for nitrate level in N4 and N11 brands, as well as 0.681 for pH level in N7 brand. The measured values of heavy metals, nitrate and nitrite in all samples were within domestic, WHO and Australian limits, except for mercury in 9 samples which exceeded the Australian standard [less than 1 µg/L].Conclusion: The heavy metals, nitrate and nitrite concentrations in all samples were within the domestic, WHO and Australian ranges, except for mercury in 9 samples that exceeded the Australian standard. There was a discrepancy between entries of the bottled labels and the measured quantities.

Dry leaf of trees, their powder and essential oil are used as flavoring agents in the food industry, and due to their antimicrobial and antioxidant properties, they increases the shelf life of food. The aim of this research is to investigate the chemical and antimicrobial properties of bay leaf essential oil. Extraction of essential oils was done by clevenger method. The results showed that the amount of carbohydrate, total fat and protein in the leaves were 2.93, 6.51 and 6.33 grams per 100 grams of dry matter, respectively, and antioxidant activity, total phenol and flavonoid content of the prepared essential oil, respectively, with IC50 of the number is 0.91 micrograms per milliliter, 0.65 m/g of gallic acid and 0.07 m/g of quercetin. The results of GC-MS showed that the main components of the essential oil include cineole (33.63 %), camphene (19.76 %), eugenol (11.73 %), terpineol (5.30 %), alphapinene (45.45 %.), Sabnin (3.42%), alpha-terpineol (0.45%) and methyl eugenol (0.33%). The evaluation of the antimicrobial activity of laurus nobilis essential oil indicates that the produced essential oil had antimicrobial activity against Staphylococcus aureus with a halo diameter of 14.60 ± 0.47 mm, while it was effective against Escherichia coli and Salmonella bacteria, respectively. It had less effect with halo diameter of 10.60 ± 0.47 and 9.60 ± 0.47 mm.

This article reviews some of the smart antennas used in Eyewear, which can receive frequencies of 700-960 MHz and 2.7-1.7 GHz, as well as Wi-Fi 5G (5.18-5.85 GHz) and Wi-Fi 6e (5.925 -7.125 GHz) to be used in wireless Eyewear glasses. Since the antennas of Eyewear devices that work in 4G, 5G cellular communication standard and Wi-Fi 6 internal wireless communication is a new category, first a general study for antenna bandwidth in different locations is done. Given that, these antennas are designed as mutual elements (coupled) and are mostly placed on FR4 or polycarbonate substrate. Then the MIMO antennas used in these glasses are investigated and gain, efficiency and SAR studies are performed along with different modes. In order to obtain a good adaptation to the use cases, the human head is always considered in the simulations. After that, antenna designs with suitable dielectric cover and frame are suggested.

In recent years, image annotation is one of the active research topics. In this article, a semi-supervised cooperative clustering technique is proposed for image annotation. Clustering methods are very popular because they do not require annotations. In order to achieve the highest efficiency, the clustering results of six systems with different color space and similarity criteria are cooperatively combined with the majority vote. When the number of votes for an image is low, relevant feedback is used to annotate it. One of the most important parts of the image retrieval system and clustering algorithm is determining the appropriate similarity criteria between images. Nowadays, the linear similarity criterion is mostly used to determine the similarity between images, but the nonlinear models can have much better performance due to their proximity to the human vision system, for this purpose, the KMRBF nonlinear similarity criterion is used to simulate vision. Humans and improvement of recovery results are suggested. Experiments on the Corel image database and satellite images show that the proposed method has good performance. According to the results obtained in the satellite image database, the YIQ color space has a higher accuracy (82.5%). Also, the three color spaces CIELab, HSV and YIQ have higher efficiency, because in these color spaces, luminance is separated from chrominance and these color spaces are closer to the human vision system.

In this paper, for the classification of images, the observed cooperative classification method is proposed with the aim of reducing the semantic vacuum. Because most classification methods are sensitive to cluster centers at initialization, the algorithm converges optimally locally if the quantification is not done correctly. It is also very difficult to combine the results of the classification due to the fact that the labels of the work centers are not clear. Supervised semi-classification is used to solve these problems. To achieve the highest performance, the system classification results are combined with different color space and similarity criteria with multiple features as a semi-supervised cooperative. When the number of features is effective, the relevant feedback is used for its semi-regulatory classification. One of the most important parts of an image retrieval system and a classification algorithm is to determine the appropriate similarity between images. In this study, two methods of classification of cases have been used, which include k-NN and PNN classification, which according to the results in all proposed methods, a better response from k-NN classification than PNN has been observed. The proposed algorithm is very suitable for classifying large image databases due to the reduction of time complexity. Also, in image retrieval in different color spaces and using different similarity criteria, different classifications are obtained. Better results can be achieved if the classification results are combined.

In this paper, a beam selection method is presented for a switched beam antenna (SBA) specifically designed and built for long-range wireless communications. The proposed SBA operates with eight beams for automatic direction finding (DF) and 360-degree beam activation at 900-1400 MHz frequency range. Remarkably, this method employs a simple switch board and one sampler without synchronization requirement, avoiding the need for complex circuitry typically required for separate beam sampling. Instead, a convolutional neural network (CNN) processes the sampled signals to accurately determine the signal direction. The proposed CNN is trained by using a two-stage method with both artificial and real data to achieve fine-tuned accuracy of 99.79% with artificially generated data and 95% with real data for signal-to-noise ratios (SNR) above −10 dB. Additionally, the CNN is optimized to run on low-end hardware. A significant 16% accuracy enhancement compared to the direction estimation method based on conventional power measurement (PM) under identical conditions is demonstrated through real-time beam selection using a commercially available Raspberry Pi board. This proposed low-cost, wideband, high-accuracy, and highly efficient SBA is an excellent candidate for practical applications, showcasing the effectiveness and accuracy of data-driven methods in real world, especially in low SNR scenarios ( $\lt -10$ dB).

Fungal diseases have emerged as a significant global health threat, with the potential to cause widespread outbreaks and significant morbidity and mortality. Anticipating future pandemic fungal diseases is essential for effective preparedness and response strategies. This comprehensive literature review aims to provide a comprehensive analysis of the existing research on this topic. Through an extensive examination of scholarly articles, this review identifies potential fungal pathogens that have the potential to become pandemics in the future. It explores the factors contributing to the emergence and spread of these fungal diseases, including climate change, globalization, and antimicrobial resistance. The review also discusses the challenges in diagnosing and treating these diseases, including limited access to diagnostic tools and antifungal therapies. Furthermore, it examines the strategies and interventions that can be employed to mitigate the impact of future pandemic fungal diseases, such as improved surveillance systems, public health education, and research advancements. The findings of this literature review contribute to our understanding of the potential risks posed by fungal diseases and provide valuable insights for public health professionals and policymakers in effectively preparing for and responding to future pandemic outbreaks. Overall, this review emphasizes the importance of proactive measures and collaborative efforts to anticipate and mitigate the impact of future pandemic fungal diseases.

Abstract The present study was developed to comprehensively analyze experts' views and content of documents focusing on goals and criteria of sustainable waste management system in Tehran, Iran. To this end, the suitable goals for sustainable waste management system in Tehran city were adopted from domestic, national and international documents. For this purpose, 27 national and international documents and 2 domestic documents related to waste management were selected and analyzed by using content analysis according to Gall, 1994. Further, in order to formulate goals in case of bottlenecks and challenges of waste management in Tehran, the focus group technique was used based on Stewart and Shamdasani, 2014. At this stage, 24 key experts in the field of waste management were interviewed in the form of 4 focus groups. Data collection were performed via audio recording and word-for-word implementation of conversations, taking notes and writing field notes. The data collection continued until reaching theoretical saturation. Next, content analysis and coding methods were used to analyze the data. Finally, the goals of waste management were divided into five general categories including: institutional (with emphasis on the integration and inclusion of the key elements of the urban waste management system), technical and infrastructural (with emphasis on the optimization of existing processes in the use of urban waste management technologies), environmental (with emphasis on minimizing the adverse health and environmental effects of the urban waste management system, economic (with emphasis on the economic and financial sustainability of the urban waste management system), and cultural-social (with emphasis on attracting the maximum participation of citizens and service recipients). The results clearly showed that sustainable waste management measures in Tehran should follow these five components in order to reduce the problems caused by unrealistic waste management and make sustainable use of basic, natural, financial and human resources.

Risk assessment is utilized to prioritize preventive measures based on the probability of dispersal success of pests. A main part of the risk assessment procedure is to determine the effects of environmental variables on the current and potential geographical distributions. In the present study, the spatial distribution of the Mediterranean pine engraver, Orthotomicus erosus (Wollaston), was mapped and predicted using MaxEnt. Presence records of O. erosus (north, northeast, west and centre of Iran), environmental and topographic variables, with the lowest correlations among themselves and the highest effects on the pest distribution were used. A total of 76 presence records of O. erosus were collected. The results of the distribution prediction modelling revealed that the northern part of Iran and the areas along the Zagros are the most suitable habitats for this species. Examining environmental variable importance on the distribution of O. erosus showed that the variables related to temperature and precipitation had more contribution in the MaxEnt model, respectively than the altitude. Furthermore, the high accuracy of the model (0.928) indicated that the MaxEnt had an acceptable performance for the prediction of O. erosus distribution. These findings would provide primary and critical information about the potential distribution of O. erosus in Iran, which could be effective for the stable population regulation of this destructive pest.

Background and Objective: Occupational safety is an important and significant issue that helps maintain the health and safety of employees and prevent occupational accidents and injuries. Therefore, the present study was conducted with the aim of reviewing and analyzing books related to occupational safety. Materials and Methods: The current research employed a descriptive and bibliometric methodology. The research population included all the books registered on the National Library website from the beginning to the end of March 2023 using keywords such as safety, occupational accidents, and fire safety. The data were analyzed using Excel (version 2016) and Minitab (version 19.2020.1). Descriptive statistics and frequency distribution tables were used for data analysis. Results: The results indicated that a total of 2,617 book titles were published by the end of March 2023. Furthermore, the trend of book publishing has been generally increasing over the last five decades. The majority of the published books were in the subject categories of general safety (17.7%), fire (11.9%), as well as crisis management and emergency response (7.94%). Conclusion: Overall, it can be concluded that safety is a crucial concern in the industry and workplace, and Iranian authors have devoted significant attention to this field by writing various books. To enhance the quality of published books, authors are advised to conduct regular research, consult with experts in the field of occupational safety, assess industry needs through communication, engage with book audiences and gather feedback, and plan for updates to their published works.

Abstract Self‐reflection practice in coaching can help with time management by promoting self‐awareness. Through this process, a coach can identify habits, tendencies and behaviours that may be causing distraction or make them less productive. This insight can be used to make changes in behaviour and establish new habits that promote effective use of time. This can also help the coach to prioritise goals and create a clear roadmap. An AI powered system has been proposed that maps the conversion onto topics and relations that could help the coach with note‐taking and progress identification throughout the session. This system enables the coach to actively self‐reflect on time management and make sure the conversation follows the target framework. This will help the coach to better understand the goal setting, breakthrough moment, and client accountability. The proposed end‐to‐end system is capable of identifying coaching segments (Goal, Option, Reality, and Way forward) across a session with 85% accuracy. Experimental evaluation has also been conducted on the coaching dataset which includes over 1k one‐to‐one English coaching sessions. In regards to the novelty, there are no datasets of such nor study of this kind to enable self‐reflection actively and evaluate in‐session performance of the coach.