Metallurgical and Materials Engineering (E-Journal)
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A Survey On The Form And Color In Persian Figurative Single-Sheet Miniatures Comparative Study Of The Works Of Mohammad Yousef And Mohammad Qasem
Get PDFHuman figure has always been considered as the main element in Persian miniature especially in Isfahan painting school. This feature in the works of artists of that period, is completely obvious like MohammadyousefHoseini and MohammadqasemTabrizi, especially in their single-sheet miniatures. Considering the political, military and commercial changes of the Safavid period, we can see miniatures that indicate the influence of western art on these artists; however, the attempts of the miniaturists in Iranizing the figures is perceptible. Accurate details, in form and color, are applied to present the reality in their works too. The purpose of the present research is identifying the features of the figures created by these two artists and also the influence of the western art on them, in this way, we are looking for answers to the influence of these artists from European art penetration in Iran and similarities and differences in the iconography of these two miniatures. The method of the data collection in this research, is descriptive-analytic and the data is first gathered through library sources and then the differences and similarities of figures are shown in some tables. The results show the political, economic and social situations' influence on Persian paintings of Isfahan art school. This influence is studied in the miniatures of Mohammadyousef and Mohammadqasem as two contemporary artists. Here we see miniatures with European signs that are completely Persian in the space, their originality and the way they are executed
Porous Dielectric Hydrogel In MEMS Capacitive Sensors: Static And Dynamic Nonlinear Analysis
Get PDFThis study investigates the static and non-linear behavior of a circular microplate resting on a porous polymeric/elastomeric dielectric hydrogel foam, which fills the gap of the capacitive accelerometer sensor and is simultaneously subjected to transverse harmonic base acceleration excitation and static bias excitation. Based on the von-Karman relations and the Hamilton’s principle and introducing the Airy stress function, the governing nonlinear coupled partial differential equations of the problem under fully clamped edge boundary conditions are derived and using the Galerkin’s procedure are reduced to a set of nonlinear ODEs with time. Then, the validity of the formulation for analyzing the pull-in behavior of the problem achieved by comparing the obtained results with those of the literature. The static pull-in behavior of an electrostatically actuated microplate is investigated with respect to variations of the hydrogel layer parameters by solving the governed equations of multi-degree of freedom equilibrium states. In a following using the perturbation method and considering the nonlinear approximate solution of third order, the primary resonance of the problem is analyzed under the base excitation by transverse acceleration. This is done by deriving the modulation equations for the frequency response and the basic acceleration response under steady-state conditions. The existence and stability conditions of Mult-coexisting non-trivial solutions for nonlinear responses are discussed and the bifurcation points of the related characteristic curves are derived and it is shown that changes in different parameters can lead to the jump phenomenon. Performing 2-Dim and 3_Dim bifurcation diagrams and through a comprehensive analysis, the influences of the problem parameters such as bias voltage, acceleration amplitude, acceleration frequency, damping and initial volume fraction of porosity, dielectric coefficient and initial Young's modulus of the hydrogel on the nonlinear behavior of the sensor are studied and it is shown that the porosity of the hydrogel has a significant influence on the resonance amplitude
AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology
Get PDFThe hospitality technology industry has advanced infrastructure with integrated information systems. Well-designed AI modules—within an established Data Ecosystem and Development Framework—can support various organizational roles and enhance efficiency, speed, and reliability. Managerial decisions often require the help of data- and/or model-driven Decision Support Systems (DSS), such as reporting dashboards, staff scheduling tools, inventory management systems, and revenue management solutions. Demand forecasting, operational-level staffing, inventory management, dynamic pricing, and service-level optimization are primary decision areas. However, several limits impede DSS effectiveness: lack of user trust, excessive cognitive load, inadequate validation and monitoring, and poorly executed data-driven embeddings. Well-designed AI DSS addresses the challenges.
The proposed DSS concepts combine Decision Science fundamentals with specific Hospitality Technology requirements. Five central dimensions apply: key phases in substantial decision-making processes; different Decision Support levels in the operational-executive domain; the roles of decision-makers—receivers, planners, and validators; Human-in-the-loop methodology; and theoretical pillars, including Robustness, Interpretability, and Explainability. With a suitable architecture, Data Ecosystem, specific methods, and Human-AI collaboration, an AI DSS can assist operational optimization
The Effect of Liquidus Aging on The Performance of Phase-Stabilized Wax with Solid Nano Additives
Get PDFThe present work assesses phase stabilized HW (hydrocarbon wax) with nano additives through liquidus aging treatment. The aging treatment is performed by storing the sample in the liquid phase at 130 °C for 250 hours. The sample performance is assessed according to the heating and cooling rate change before and after aging treatment. The finding indicates a severe decrement in the heating rate of thermal conductivity enrichment (TCE)-HW up to 24.4% and 7.5% for the discharge rate. The phase-stabilized HW performs better according to its heating rate, which only decreases by around 10.9% and the discharge rate by only 1.2%. The heating profile for HW shows a distinctive phenomenon, indicated by a two-step temperature spike of 6.8 °C and 11.8 °C at the solid-solid and solid-liquid transition. Contrary to that, the SHW presents a suitable profile where the temperature increases steadily until 86.3 °C with the average heating rate around 2.97 °C/min. The surface observation shows that the phase-stabilized polyethylene (HDPE) decreases the potential of void formation. As a result, the SHW maintains suitably the distribution of nano additives after aging treatment. Thus, phase stabilization is critical to ensure stable operation of HW with nano additives for the TES system
The Prevalence of Dental Fear and Its Relationship to Dental Caries and Gingival Diseases Among School Children
Get PDFBackground: Dental fear is a significant public health concern that impacts oral health, particularly in children. This study investigates the prevalence of dental fear among school children aged 7–13 years and its relationship to dental caries and gingival diseases.
Methods: A cross-sectional study was conducted involving 250 children (125 boys and 125 girls) from rural schools. Data collection included a modified Dental Fear Survey questionnaire and clinical oral health examinations assessing dental caries using the DMFT/dmft index and gingival health using the Gingival Index (GI). Statistical analyses evaluated correlations between dental fear and oral health indicators.
Results: Approximately 30.8% of children exhibited high dental fear, with girls reporting higher fear levels than boys. Children with high dental fear had significantly worse oral health outcomes, as indicated by higher mean DMFT/dmft scores (3.8 ± 1.2) and GI scores (2.2 ± 0.8) compared to those with low fear (2.1 ± 0.9 and 1.3 ± 0.6, respectively; p < 0.01).
Conclusion: Dental fear is prevalent among school children and correlates with poorer oral health, particularly higher rates of dental caries and gingival diseases. Targeted interventions, including positive reinforcement and preventive dental care, are essential to mitigate dental anxiety and improve oral health outcomes in children
The Relationship of Melatonin to the Physiological Parameters of Pregnant Women During Period Pregnancy
Get PDFIn recent times, there has been an explosive expansion in the comprehension of the hormone melatonin, particularly regarding its role in physiology, regulation, and therapeutic applications within various realms of clinical medicine. Melatonin serves as a vital biological agent capable of modulating mitochondrial performance, showcasing anti-inflammatory,antioxidant, and neuroprotective properties, promoting restful slumber, and bolstering the immune system. Moreover, it boasts bioavailability and minimal toxicity, positioning it as a promising candidate for the safe and effective treatment of an array of ailments while safeguarding human well-being. Within this manuscript, we endeavored to explore the significance of melatonin during the nascent stages of human existence, encompassing pregnancy, fetal development, and the newborn phase, through a thorough evaluation of contemporary literature
Neuro-Fuzzy-Based Vertical Handoff Algorithm For Always Best Connectivity
Get PDFIn the era of Internet of Everything (IOE), prime concern is to provide flawless connectivity among all connected devices and machines. Internet of Everything (IOE) encompasses not only machine-to-machine communication (M2M) but also people to machine (P2M) and people to people (P2P) communication through technology. But ensuring ‘Always Best Connectivity’ in diverse environments is a challenge. Providing seamless connectivity to provide a strong foundation to IOE is the requirement. Hence, in this paper, a smart handoff decision mechanism is proposed which works in two phases. This paper presents a Neuro-Fuzzy-based vertical handoff mechanism that ensures seamless connectivity by optimizing the network selection process. The proposed approach utilizes Neuro-fuzzy with Multi- Attribute Decision Making (MADM) techniques, specifically VIKOR and Fuzzy VIKOR, to initialize handoff and rank network alternatives. Three network types (WiFi-N1, LTE-N2, and WiFi-N3) are evaluated based on beneficial and non-beneficial parameters for different traffic classes: voice, video, and browsing. The simulation results demonstrate the effectiveness of the proposed approach by comparing handoff blocking probability, ping- pong effect probability, and corner effect probability with existing methods. The results validate the superiority of the Neuro-Fuzzy model in enhancing handoff accuracy and reducing inefficiencies
AI-Driven Predictive Modeling for COVID-19 Case Trends: An Ensemble Learning Approach
Get PDFPandemics and epidemics present major challenges to global health and economies, as underscored by the COVID-19 pandemic, which has highlighted the critical need for advanced predictive analytics to support informed decision-making and efficient resource allocation. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools for forecasting pandemic trends. This study investigates various ML regression techniques to predict COVID-19 case trends using real-world data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), covering the period from January 22, 2020, to September 3, 2023. The regression models evaluated include Linear Regression, Support Vector Regression (SVR), Random Forest, Gradient Boosting, AdaBoost, and a Stacking Model, with performance assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² Score. Results reveal that the Stacking Model achieved the highest accuracy, with the lowest MAE (49,182,280), lowest MSE (3.32e+15), and best R² Score (-2.669640), outperforming all other models. Random Forest and Gradient Boosting also performed well with R² Scores of -4.857282 and -4.863941, respectively, while SVR proved unsuitable with an R² Score of -265.084156. These findings underscore the superior performance of ensemble learning techniques, particularly Stacking, Random Forest, and Gradient Boosting, in predicting COVID-19 trends and emphasize the importance of selecting appropriate regression models to enhance the reliability of epidemiological forecasting
Awareness and Risk Factors of Diabetic Ketoacidosis among General Population in KSA: A Cross-Sectional Study
Get PDFObjective: This research aims to determine Awareness and Risk Factors of Diabetic Ketoacidosis among General Population in KSA.
Methods: his study will employ a cross-sectional design to assess the awareness and risk factors of diabetic ketoacidosis (DKA) among the general population in the Kingdom of Saudi Arabia (KSA). The cross-sectional design is chosen to provide a snapshot of the current levels of awareness and the prevalence of risk factors associated with DKA within the population.
Results: The study included 350 participants. The study included 350 participants. The most frequent weight among them was 86-95 kg (n= 110, 31.4%), followed by 76-85 kg (n= 84, 24%), then 66-75 kg (n=54, 15.4%). The most frequent height among study participants was 171-180 cm (n= 117, 33.4%), followed by 161-170 cm (n= 105, 30%), then 150-160 cm (n=36, 10.3%). The most frequent gender among study participants was female (n= 183, 52.2%) and male (n= 167, 47.7%%). The nationality of the study participants most of them was Saudi (n= 315, 90%) and non-Saudi (n= 35, 10%). The employment of the study participants most of them were employed (n= 118, 33.7%), followed by unemployed (n= 94, 26.8%), then self-employed (n=71, 20.2%), and students (n=67, 19.1%). Participants were asked if they smoking. The most of them was smoke (n= 210, 60%) and non-smoke (n=140, 40%). The participants were asked about general awareness. Diabetic ketoacidosis. The results were an emergency event occurs a complication of diabetes and requires an urgent intervention (n=187, 53.4%), followed by normal physiological changes in response to diabetes (n=79, 22.6%), then a chronic complication of diabetes, which occurs over a long time and doesn’t require an urgent (n=68, 19.4%), and I don’t know (n=16, 4.6%).
Conclusion: The study highlights a high level of awareness and knowledge about colorectal cancer among medical staff in Saudi Arabia. Most participants recommended colorectal cancer screening for their families and friends, emphasizing the importance of early detection and preventive measures in reducing cancer mortality rates. However, there are still barriers such as limited resources and misconceptions that need to be addressed to further improve screening participation rates
Examine the Influence of AI Tools on the Workflow of Dermatology Practice, Focusing on Diagnostic Efficiency, Patient Management, and Clinician Decision-Making
Get PDFIntroduction: Skin conditions are an important health issue. On average, individuals experience 1.6 skin conditions each year, with skin-related doctor appointments making up 20% of all primary care visits, of which approximately 35% are directed to a dermatologist. Machine learning (ML) models have the potential to assist primary care professionals by analyzing and enhancing intricate datasets. Furthermore, ML models are being more commonly used in dermatology for aiding in diagnosis through image analysis, particularly for detecting and categorizing skin cancer.
Aim: This research seeks to validate a machine learning image analysis model prospectively as a diagnostic aid for diagnosing dermatological conditions.
Method: In this upcoming study, 100 successive patients seeking care for a skin issue from a participating general practitioner (GP) in central Catalonia were selected. The planned duration for data collection was set at 7 months. Anonymized images of skin conditions were captured and fed into the ML model interface (able to detect 44 different skin conditions), which provided the top 5 diagnoses with the highest probability. The identical picture was also transmitted for a teledermatology consultation in accordance with the established workflow. The GP, ML model, and dermatologist's evaluations will be compared to determine the precision, sensitivity, specificity, and accuracy of the ML model. Each type of skin disease class will have its results displayed globally and individually through a confusion matrix and the one-versus-all approach. The amount of time needed to conduct the diagnosis will also be factored in.
Results: Patient enrollment started in June 2021 and continued for a duration of 5 months. At present, all participants have been enrolled and the images have been presented to the GPs and dermatologists. The examination of the findings has already commenced.
Conclusion: This research will offer insights into the efficacy and constraints of ML models. External testing is necessary for controlling these diagnostic systems for the implementation of ML models in a primary care environment