The present study evaluates the density and specific heat capacity of a CuO–GO hybrid nanofluid dispersed in a water–ethylene glycol (W–EG) mixture with a 60:40 volume ratio. The CuO and GO nanoparticles were incorporated at a 50:50 ratio by volume. CuO nanoparticles with a density of 6300 kg m⁻³ and graphene oxide (GO) nanoparticles with a density of 1800 kg m⁻³ were considered. The total hybrid nanoparticle concentrations were 0.05, 0.10, 0.25 and 0.50 vol.%, and calculations were performed over a temperature range of 25–55 °C. The density of the hybrid nanofluid was determined using a mixture-based relation, while specific heat capacity was calculated using a density-weighted thermal-equilibrium relation. For a 50:50-by-volume CuO–GO mixture, the effective density was calculated as 4050 kg m⁻³, while the effective specific heat capacity was 0.5686 kJ kg⁻¹ K⁻¹ based on the constituent properties used in the present analysis. The predicted density decreased with increasing temperature and increased with increasing nanoparticle concentration. Conversely, specific heat capacity increased with temperature and decreased with nanoparticle concentration. At 0.50 vol.% and 55 °C, the predicted density was 1041.88 kg m⁻³, corresponding to an increase of approximately 1.47% relative to the base fluid, whereas the predicted specific heat capacity was 3.3979 kJ kg⁻¹ K⁻¹, corresponding to a reduction of approximately 1.62%. The results provide model-based information regarding the influence of temperature and hybrid nanoparticle concentration on the density and energy-storage characteristics of CuO–GO/W–EG hybrid nanofluids.
Enterprise services are becoming more reliant on the Domain Name System (DNS) infrastructure, and with that increased reliance comes an increased need for proactive monitoring solutions that can detect operational anomalies that can affect service availability. Traditional DNS monitoring methods are largely dependent on availability checks and manual log analysis, which can hinder timely insights into infrastructure health. This paper proposes a pattern-based architecture for real-time monitoring of the DNS infrastructure in enterprise grid networks, based on syslog analysis. The proposed framework starts by collecting the DNS operational logs using Python, parses the logs using structured log events, stores them in SQL Server, visualizes them using Grafana, and automatically alerts using the information gathered. A systematic event classification model is created to classify DNS events based on their operational significance, to monitor zone transfers, replication status, notification events, and configuration changes. The framework aims to be scalable for large enterprise deployments, improve observability of infrastructure, accelerate incident detection, and improve operational decision-making. The proposed approach guarantees efficient and cost-effective solutions to enhance DNS operational resilience through the use of structured log mining and real-time visualization. The insights have been compiled into a practical monitoring architecture that can enable the modern enterprise DDI environment and provide a starting point for future infrastructure monitoring with AI.
As enterprise information technology (IT) environments grow more complex, there is a greater need for efficient Domain Name System (DNS) domain management to facilitate digital services, cloud adoption, and cross-functional collaboration. For many large organizations, domain-specific questions continue to be handled traditionally through centralized IT Service Desks, which causes high ticket volumes, long resolution times, and poor use of technical staff time. The present invention provides a self-service DNS domain management portal having central intelligence of domain information to simplify domain discovery and metadata retrieval and to decrease operational overhead. The solution being proposed involves consolidating all the internal DNS records, organisational metadata, and information from external domain registries into a central repository, allowing authenticated users to conduct real-time searches of the domains with a secure web-based front-end. Built on ASP. The portal improves accessibility, improves governance, and reduces manual support processes and reliance on NET Core, as well as increasing governance and support using Microsoft SQL Server with Windows Authentication. The proposed architecture illustrates the benefits of centralising domain intelligence for more efficient service delivery, enterprise-wide decision-making, and optimised IT services. The study provides a scalable and practical approach to modern enterprise DNS management, which is aligned with the platform engineering, self-service IT, and intelligent network management trends.
The increasing development of data centers has raised concerns about their effects on electrical grid performance, particularly in relation to electricity demand, network capacity, power reliability, and environmental sustainability. This study examines how data center operations affect electrical grids and evaluates the opportunities for improving their integration into modern power systems. A qualitative literature review approach was adopted, drawing on 13 academic and technical publications addressing data center energy consumption, grid infrastructure, workload management, smart grid integration, and renewable energy. The findings indicate that data centers contribute to increased electricity demand and concentrated grid loading, potentially requiring additional network capacity and infrastructure reinforcement. However, their effects on grid stability and reliability depend on existing network conditions, operational demand characteristics, and effective power system management. The review further identifies workload scheduling, demand response, and energy storage as potential mechanisms through which data centers can support load balancing and grid flexibility. Renewable energy integration offers opportunities to reduce environmental impacts but introduces challenges associated with generation variability, storage costs, and grid dependency. The study concludes that data centers are not merely substantial electricity consumers but can also contribute to improved grid performance when supported by coordinated infrastructure planning, flexible energy management, and sustainable power supply strategies.
The Urban Heat Island (UHIs) is an important environmental problem of rapidly developing cities, contributing to the exposure to heat, aggravating air quality, and enhancing the rate of health risks caused by climate changes. Since the process of urbanization all over the world has been growing evenly during the past decade, urban areas have gotten hotter in comparison to the surrounding land areas, which has resulted in the need to consume more energy, overburdening the infrastructure, and making the process of thermoregulation unpleasant. The most recent advancement of machine learning (ML) introduced the application of powerful analytical tools that can identify the presence of UHI trends, extreme heat events, and simplify the climate-resilient infrastructure policy (Zhou et al., 2019). The paper will also determine the application of ML-based models in the reduction of UHIs through the use of predictive intelligence, dynamically allocating resources, and planning cities based on data.To predict the urban temperature data, the satellite-based land surfaces, and infrastructure vulnerability indicators, this article uses empirical data (2016 2020) that is back-dated to run the empirical data. Another hybrid algorithm- Urban Heat Island Neural Network ( UHINet ) is introduced and offered to educate spatial-temporal change of the temperature and propose certain mitigating actions, such as planting sites, reflective surfaces, and the best construction of buildings. The complementary geospatial models also are incorporated like Geo-Heat Mapping System (GHMS) and the Adaptive Environmental Heat Forecasting Model (AEHF) to supplement pattern recognition and complementary interpretability. It was discovered that UHINet has the potential to perform much better than classical algorithms and enhance the accuracy of prediction by 14.6 percent and decrease the mean temperature forecasting error by 22 percent in all datasets.It is also stated in the research that ML can effectively measure effectiveness of mitigation measures with the help of statistical tools and formulas of heat-intensity. It has shown that green-infrastructure interventions delivered an average of 1.8 o C of urban cooling and 2.3 o C maximum surface temperature of local surfaces of high-albedo surface treatments (Li and Bou-Zeid, 2018). Such findings justify the radical application of machine learning in fostering the resilience of cities. With climate science, data analytics, and smart optimization models, the research will indicate a scalable solution, whereby cities can be in a position to adapt to the change in the heat stress and come up with climate-resilient infrastructure by the next few decades.
