The nonlinear Korteweg–de Vries (KdV) equation is one of the most significant nonlinear partial differential equations in mathematical physics and applied mathematics. It describes the propagation of nonlinear dispersive waves in several physical systems, including shallow water waves, plasma environments, nonlinear optical fibers, and fluid dynamics. Since its development by Diederik Korteweg and Gustav de Vries in 1895, the KdV equation has become fundamental in the study of nonlinear wave propagation and soliton theory. Because of its nonlinear and dispersive structure, finding analytical and numerical solutions to the KdV equation remains an important and challenging problem in modern computational mathematics.This paper presents a comprehensive investigation of analytical and numerical methods used for solving nonlinear KdV equations and their generalized forms. Analytical techniques such as the Inverse Scattering Transform (IST), A domian Decomposition Method (ADM), Homotopy Perturbation Method (HPM), Differential Transform Method (DTM), and tanh-function method are studied in detail. Numerical techniques including Finite Difference Methods (FDM), Crank–Nicolson schemes, and Runge–Kutta methods are also analyzed with respect to accuracy, convergence, stability, and computational complexity. The paper further discusses the existence and importance of soliton solutions, which are stable nonlinear traveling waves preserving their shapes during propagation and interaction. In addition, generalized and fractional KdV equations are reviewed to demonstrate recent developments in nonlinear science and computational analysis. Comparative analysis reveals that both analytical and numerical methods are essential for understanding nonlinear evolution equations. The study concludes that continuous developments in numerical computing, artificial intelligence, and high-performance algorithms will significantly enhance future research on nonlinear KdV-type equations.
This paper presents a Python-only framework using federated Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for smart grid optimization from Earth to Low Earth Orbit (LEO). Built entirely in Google Colab using PyTorch, PyTorch-Geometric, and Flower, this modular solution enables learning across ground grids, UAVs, and satellite nodes using synthetic data. Six Jupyter notebooks simulate a real-time, multilayer smart energy network, achieving over 93% forecasting accuracy, fast recovery, and scalable control—all without proprietary software.
This project proposes redesigning a multipurpose children's hall at The Village, Seeb, Muscat, to provide kids aged 3 to 12 with a lively, engaging, and screen-free environment. The project highlights the significance of natural play, hands-on exploration, and social connection in child development in recognition of the growing concerns regarding children's reliance on digital devices. Through functional zoning that incorporates areas for open play, education, creativity, relaxation, events, and community involvement, the renovated area aims to provide a diverse range of experiences. The setting promotes independence, creativity, and cooperative play while maintaining safety and inclusiveness using natural materials, adaptable furniture, and textures that are rich in sensory experiences. Case study analysis, site inspection, and literature review are all integral to the technique, which informs a child-centered design based on the principles of experiential learning and developmental psychology. The suggested layout encourages movement, creativity, and emotional health and is influenced by modern sustainable design concepts and the Montessori approach. Additionally, the project supports education, sustainability, and cultural identity, all of which are goals of Oman Vision 2040. As a model for future community-oriented child spaces in Oman and elsewhere, this research ultimately aids in the creation of meaningful, tech-free environments that promote early childhood cognitive, physical, and social development.
The role of soil classification in agriculture has become increasingly vital with technological progress. Traditional soil classification methods typically involve laborious tasks like manual sampling, visual assessments, and basic lab tests. These approaches can be time-intensive, expensive, and occasionally less precise, making them less ideal for contemporary agricultural demands, particularly with the need for enhanced productivity and precision. Technological advancements have introduced new methods such as remote sensing, machine learning, and artificial intelligence, revolutionizing soil classification. These modern techniques enable rapid analysis of extensive land areas, enhancing both the accuracy and efficiency of soil classification. Accurate soil classification is crucial for selecting appropriate crops, determining fertilizer requirements, and forecasting water usage, all of which contribute to improved yields and resource management. Technologies like remote sensing, soil sensors, and GIS (Geographic Information Systems) facilitate efficient data collection and analysis, making precision agriculture more attainable. This data-driven strategy represents a significant advancement towards more sustainable and productive farming systems.
This paper discusses Zero Trust Architecture in software networks and hardware architecture. It highlights the importance of implementing zero trust principles at both the hardware and Network layer architecture. It analyzes the various components of ZTA and how Zero-Trust is handled in Hardware level and Software Level.
Urban interior design practices examine the dynamic relationship between interiority and the urban landscape, focusing on how effective interior design can enhance public spaces amid increasing urban density. This paper explores the evolving role of interior design in urban contexts, emphasizing its impact on physical spaces and the temporal, social, and emotional dimensions that shape our interactions with these environments. As cities face the challenges of rapid urbanization, there is a pressing need to foster connectivity among diverse communities while ensuring flexibility in design to adapt to changing urban dynamics. This study highlights the pivotal role of urban interior design in cultivating a sense of belonging and resilience within communities. The paper illustrates how interior environments can be strategically developed to promote engagement and inclusivity by incorporating methodologies such as design research and scenario-based propositions. It argues for a holistic approach to design that prioritizes adaptability, allowing spaces to evolve in response to shifting societal needs and contexts. Ultimately, this exploration seeks to position urban interior design as a vital practice in contemporary citymaking, capable of transforming underutilized urban areas into vibrant, meaningful spaces that resonate with residents' needs and aspirations. By embracing connectivity and temporal adaptability, designers can contribute to the emergence of urban environments that reflect the complexities of modern life and foster social interaction and community well-being.
Communicationskills are a matter that needs to be emphasized especially among students toincreaseselfconfidenceinadditiontobeingabletobeaqualityperson.Inthis regard, it was found that students are still less skilled in communicating when carrying out PracticalAesthetic Consultation assignment.Thisisduetoteachingandlearningtechniquesthat have less impact on students and cause students to be less interested in communicating more confidently. Therefore,thisstudywasconductedtoimprovethelevelofcommunicationskillsamongCosmetology students to carry out the practical task of aestehtic consultation in continuous assessment. Next, identify students'perceptions for theAesthetic Service Consultation (ASC) Boardgame that is used as an activity in the teaching and learning of theAesthetic Services Consultation (ASC) course.The design ofthis studyisaction research.Thisstudywasconductedon semester3 cosmetologystudents who were made as a target group at a college in the district ofKuala Langat, Selangor which was determinedthrough apurposive samplingtechnique.The initialsurveydatawascollectedbymaking initialobservationsusinganassessmentapproachtothepre-testofthePracticalAestheticConsultation assignmentwhilethecollectionofimprovementactiondatawasaquestionnaireonstudentperception oftheASCBoard,thenacomparisonoftheassessmentscoresbetweenthepre-testandpost-test will carried out to identify whether there was a difference or not.
The present study carried out with the aim of understanding the groundwater quality and its suitability for domestic and irrigation purpose. The quality of water is vital concern for mankind since it is directly linked with human health. Groundwater is highly valued because it constitutes the major drinking and irrigation water source in most of the parts of India. Water quality index for underground drinking water at Amroha for fifty-four different sites among 6 blocks has been calculated with the help of estimated values of water quality physico-chemical parameters and W.H.O. water quality standards. Underground drinking water at eight sites is found to be severely polluted. Only Mohammadpur, Khedka, and Bachhraon have drinking water with a Water Quality Index (WQI) range between 26 and 50 while remaining underground drinking water is poor or very poor for human consumption. Individuals reliant on this water are likely confronting health risks associated with contaminated drinking water, necessitating immediate water quality management in the study's catchment area
Recent advancements in space exploration platforms, such as NASA’s Artemis lunar base program and the Canadian Space Agency’s Gateway power systems, demand resilient, autonomous, and intelligent energy control solutions. These systems operate in dynamic, resource-constrained, and fault-prone environments where traditional SCADA or PLC-based controls lack adaptability and predictive capability. This paper presents HIRACLE—Hybrid Intelligent Resilient Adaptive Control and Learning Engine—a novel parallel AI framework specifically designed for microgrid systems in extraterrestrial habitats and highaltitude UAV missions. HIRACLE features a modular, edge-deployable architecture combining transformer-based forecasting, deep reinforcement learning, spiking neural fault detection, and graph-based rerouting, all supported by meta-learning for continuous mission adaptation. The software implementation utilizes containerized deep learning models (TensorFlow/PyTorch) optimized for edge inference using platforms such as NVIDIA Jetson AGX Orin and Xilinx Versal AI Edge SoCs. These models are deployed as distributed agents capable of parallel operation via high-speed buses (CAN-FD, SpaceWire), ensuring real-time coordination across subsystems. Fault classification, ripple anticipation, load optimization, and health-aware scheduling are executed concurrently without centralized computation. On the hardware front, HIRACLE integrates reconfigurable logic (FPGAs), neuromorphic processors (Intel Loihi 2), SiC-based power conditioning units, and secure telemetry interfaces into a ruggedized control environment. A new chip-level proposal—HIRACLE-IC—is introduced, consolidating all AI, logic, sensing, and secure communication into a single embedded platform ready for deployment in lunar, Martian, or stratospheric UAV energy systems. This approach not only surpasses existing state-of-the-art autonomous energy controls but also positions HIRACLE as a foundational control paradigm for future NASA and CSA missions requiring scalable, intelligent, and mission-adaptive microgrid autonomy.
Municipal Solid Waste (MSW) disposal is among the most crucial environmental issues for both developed and developing nations. Landfilling is the most universally applied methods of waste disposal because it's inexpensive and relatively easy. As MSW degrades anaerobically in landfills, it generates a combination of gases called landfill gas (LFG), which is mostly methane (45–60%) and carbon dioxide (40–60%), along with traces of VOCs, oxygen, nitrogen, and hydrogen sulfide. Methane, with more than 25 times the greenhouse warming potential of CO₂ over 100 years, is a serious environmental and public health hazard if it is released to the atmosphere without treatment. To avoid these hazards and to realize the potential of LFG as a substitute energy source, the installation of gas collection and utilization systems (GCUS) has become a critical part of landfill operations today. The landfill gases are either converted to various forms of renewable energy through conversion technologies or flared to reduce methane to less hazardous carbon dioxide. Energy recovery systems include direct use for industrial boilers, internal combustion engine or gas turbine power generation, and various stages of upgrading to pipeline quality through advanced purification processes. LFG is also sometimes converted to CNG or LNG for industrial use or automotive fuel. The new generation research is focused on fuel cell utilization and the integration of LFG with anaerobic digestion or hydrogen-producing processes for improved energy efficiency and lower emissions. This paper provides a thorough examination of the principles, design factors, technologies, advantages, and drawbacks involved in gas collection and use systems in MSW landfills. LFG generation is determined by a number of influencing factors such as waste composition, moisture content, landfill design, and operating practices. Since landfill gas recovery systems may well be at the heart of waste-to-energy objectives and their low-carbon societies counterpart, the very emphasis of the world is moving more and more toward sustainable urbanisation and circular economy principles.
This research proposes the development and implementation of an IoT-based real-time weather monitoring and forecasting system using the ESP32 microcontroller. The primary goal of this system is to enable continuous environmental data acquisition and short-term forecasting to support applications in smart agriculture, climate research, and remote environmental monitoring. The system integrates a DHT22 sensor for temperature and humidity measurement, a BH1750 lux meter for solar irradiance detection, and a digital rain sensor. These sensors are interfaced with the ESP32, which reads the sensor data, displays it on an I2C LCD, and transmits it to the ThingSpeak IoT cloud platform via Wi-Fi.The uploaded data is stored and visualized on the cloud, where a machine learning-based time series forecasting model is implemented to predict environmental conditions for the next six hours. Data preprocessing techniques such as outlier filtering and interpolation are used to improve data quality. Forecasting is performed using polynomial regression, and visual results are generated in MATLAB to show actual vs. predicted trends
Maha Kumbh Mela Haridwar 2021 (the festival of the sacred Pitcher) is the largest peaceful congregation of pilgrims, held in India. During this festival, participants bathe in a sacred river (UNESCO 2017). A Geographic information system (GIS) is a conceptualized framework that provides the ability to capture and analyze spatial and geographic data. GIS applications (or GIS apps) are computer-based tools that allow the user to create interactive queries (user-created searches), store and edit spatial and non-spatial data, analyze spatial information output, and visually share the results of these operations by presenting them as maps (Wikipedia). In the 2010 Maha Kumbh Mela, around 8 crore devotees thronged Haridwar. It is estimated that around 15 crore pilgrims from India and abroad are expected to visit the four-month-long Mela in 2021. The main purpose of devotee coming to Maha Kumbh Mela 2021 is for the scared bath (http://tourismuttarakhandtourism.gov.in/) The first approach talks about enhancing the accessibility of Ghat areas, hospital, police, parking, changing room, camping sites etc. help of GIS. Ghat areas, situated along the sacred Ganga River, are the most important as these places experience the maximum footfall during the Maha Kumbh Mela event. The second approach discusses how geospatial approaches can be utilized to provide safety to pilgrims by police, which must be considered in future development planning, as the event is prone to stampedes, and how to decrees the number of visitors in Ghat. Approaches thus proposed in this study may be adopted by other host cities of Maha Kumbh Mela which will ultimately help conserve heritage aspects of the event. The thirdly approach creation of GIS database for the geo portal for any plan for disaster, crowd, stamples, any incident and preparation of GIS maps and how to query along database. The number of people visiting places of unique cultural and historical significance has been on the rise in the past decade (Timothy and Nyaupane 2009; Jimura 2019). The GIS geoportal includes visitation to unique built cultural environments (e.g. temple, holy place, historic public buildings and homes,) and to experience intangible elements of culture (festivals and events), UNESCO has listed Maha Kumbh Mela on its representative list of Intangible Cultural Heritage of Humanity in 2017.
This project focuses on designing a night lamp that automatically turns on in the absence of light and gets turned off in the presence of light using a light-dependent resistor (LDR). The LDR is a sensor whose resistance decreases with increasing light intensity, enabling it to detect ambient light levels. When the surrounding light falls below a certain threshold, the circuit activates the lamp using a transistor as a switch. The design is simple, cost-effective, and energy-efficient, making it ideal for applications in homes, streets, and gardens.
Wood has been a necessity of society as the source of energy production, buildings, and furniture since the inception of human civilizations. To enhance the performance of furniture and building components for application under humid environments, wood has been subjected to modification through reinforcing the polymeric fillers over the decades. The present investigation deals with modification in mechanical and thermal properties of pine wood (PW). Modification of the properties of WPC over untreated wood was evaluated in terms of FTIR and simultaneous differential thermogravimetry-thermogravimetric-differential thermal analysis (DTGTG-DTA) in air. The study concludes that incorporating nanomaterials into pine wood significantly enhances its mechanical strength and thermal stability, making it more durable and suitable for demanding environments. FTIR analysis confirms adequate bonding between nanomaterials and wood, while TGA results demonstrate improved resistance to thermal degradation, with slower decomposition and reduced mass loss at high temperatures. Additionally, AIBN nanofluid enables complete thermal degradation, leaving minimal residue and making the modified wood suitable for applications requiring full combustion or breakdown. These findings highlight the potential of nanomaterial-treated wood as a high-performance material for construction and other applications in humid or thermally intense environments.
Skin diseases present significant challenges in clinical diagnosis due to their diverse presentations and overlapping symptoms. This study explores innovative approaches for the detection of skin diseases using advanced imaging techniques and machine learning algorithms. By analyzing dermoscopic images and clinical data, we developed a model that enhances diagnostic accuracy and reduces reliance on expert evaluation. Our methodology incorporates preprocessing steps, feature extraction, and classification, demonstrating promising results in identifying conditions such as melanoma, eczema, and psoriasis. The findings suggest that automated skin disease detection can improve early diagnosis, facilitate timely treatment, and ultimately enhance patient outcomes. This research underscores the potential of technology in dermatology, paving the way for future applications in telemedicine and public health. This study investigates the use of advanced imaging techniques and machine learning for the detection of skin diseases. By analyzing dermoscopic images, we developed a model that improves diagnostic accuracy for conditions like melanoma, melanocytic nevus, and ringworm. Our results indicate that automated detection can enhance early diagnosis and treatment, highlighting the potential of technology to transform dermatological care and improve patient outcome
The attempt to achieve sustainability concept of the power generation sector has been the driver for researching energy efficient solutions to supply power. An attractive option is to develop innovative energy systems including renewable sources since this paper clarify the advantage of renewable power plant in terms of greenhouse gases emission. Also, this study investigates the wind power plant in two different location Shagaya and Mutla in Kuwait, these two locations were selected based on wind speed and availability of wind throughout the year which is approximately 5.3 m/s. The aim of the presented work is to develop a simulation model to assess the capacity factor, predict the levelized cost of energy produced from wind turbine, and greenhouse gases emission reduction at the two selected locations mentioned before and keep in mind Kuwait location is considered as an ideal area for wind availability. The techno-economic analysis is conducted through a simulation model by using Ret-screen Expert program which allow us to select specific wind turbine model and capacity. As a result, each run will provide massive result data, nonetheless, the three parameters mentioned above will be the main emphasis of this study. By merging final values of the three factors, the best location and most suitable wind turbine could be defined. For each site, wind turbines Power plant Comprises of 50 wind turbines each one capacity 2MW with height equal 80 m with wind speed of 7.1m/s. Detailed analysis was conducted using the simulation model to calculate the capacity factor which is 31.3% for AlShagaya and AlMutla sites with 274,374 MWh electricity exported to the Kuwait grid . moreover, gross annual GHG emissions reduction approximately 93% for both locations. The estimated annual saving and revenue for Alshagaya is 175,560,502$ but for Almutla city is 166,855,573$. Additionally, the energy production cost (LCOE) for AlShagaya wind plant is 0.088 $/KWh, but for AlMutla city is 0.097 $/KWh. Finally, according to the simulation of RET screen program the best location based on the analysis obtained is AlShagaya due to significant impact of economic factors.
The thermal spraying technique has been developed for over a century. During this time, the technique has been enhanced, and it has gained a great deal of interest in society due to its many applications in the industrial field: textile, aerospace, chemical industries, automotive, and others. Recently, many applications related to corrosion protection with thermal spray coating have been improved using different techniques such as flame spraying (FS), atmospheric/vacuum plasma spraying (APS/VPS), arc spraying (ARC), detonation gun spraying (DGS), cold spraying (CS), high-velocity air fuel spraying (HVAF) and high-velocity oxy-fuel spraying (HVOF). In this paper, a review of corrosion protection by thermal spray coatings has been done along with its latest innovations.
The project involves the design and development of a power bank capable of charging two devices simultaneously. The power bank will incorporate dual USB output ports with varying voltage and storage capacity specifications to cater to a wide range of devices. It will be equipped with a high capacity lithium- ion battery ensuring efficient energy storage and delivery. The power bank management circuit will feature overcharge,over-current,andshort-circuitprotectiontoensuresafetyduring operation.Thepowerbankwillalso be equipped with a DC-DC converter and fast-charging protocols to minimize charging time. Additionally, itwill be equipped with LED indicators to display the battery status, charging status, and output status. Thisproject addresses the growing demand for efficient and portable energy solutions, making it ideal for daily use and travel
This paper presents a comprehensive analysis of spiral water flow dynamics and its implications for energy efficiency in hydropower dam designs. Applying principles of fluid mechanics, conservation of energy, and rotational dynamics, Mathematical models are derived that characterize the energy transformations within the system. The paper presents theoretical derivations and simulations that characterize the energy changes within the system and compare the spiral flow dam's performance with traditional designs. The results show that the novel strategy can greatly increase hydrodynamic efficiency, providing a workable way to raise the output of renewable energy. This research is supported by experiments performed in the direction of increasing energy efficiency by the spiral flow of water for spiral pumps and turbines.
