Energy consumption

The purpose of this research is to found the impact of built area and occupant number on winter gas consumption in the residential building sector of Iran, Shiraz.Sothe annually and monthly gas consumption data through 19 houses were analyzed to achieve the heating energy consumption and operation month.The average operation months of gas heating consumption were from first of November up to end of April and the average maximum monthly gas consumption of sample houses was in February. On the other hand, the case study houses have different built area from 80 t0 200 m2andoccupant number from 3 to 7 persons.It was concluded that the biggest and busiest houses did not consume the maximum annual gas consumption and so the smallest and low occupant ones about minimum consumption. Therefore, the samples heating consumption was not so related to the built area and occupant number. The occupant culture and behavior was more supposed to be related with energy consumption in residential sector of the city.

In recent years, the issue of energy consumption modeling techniques in the building sector has been widely considered by researchers and managers. Researches indicate that energy consumption in residential sector is one of the main parts of the total consumption. Moreover, the urban residence is supposed as a significant consumer of energy in every country and therefore a focus for energy consumption efforts. For over increasing building energy consumption there can be some causes including : climate change, increase in household electricity load, fast-growing household electrical appliances, huge energy consumption of the buildings, and the lack of strict government supervision. Recently the issue of assigning different prices to different time-periods of energy consumption has been taken into consideration for energy prices of consumers’ bills. Therefore, in this paper a model for multi-prices and multi-periods single-type energy consumption in urban residential sector is presented; also an efficient heuristic method is provided for solving the mentioned model.

In this research multiple-regression analysis with stepwise selection method was employed for investigating the effect of vertical building envelopes solar radiation (Evr) on cooling energy consumption (E cooling) in residential sector. The high capacity of solar energy in semi-arid climate (Shiraz) can provide a part of buildings required energy. Depends on house orientations in two directions of SE and SW and by using statistical data, E cooling in urban residences was analyzed. The autocorrelation in the residuals, checked by Durbin- Watson test, was not existed. By investigating the relations between average Evr and E Cooling in each group, it can be proved that climatic orientated houses can achieve lower E Cooling, owing to SE desirable orientation. The percentage of Evr in SW houses is 28.89 % and in SE it is 15.72 %, so choosing the building orientation is important to reduce energy consumption. Finally, the concluding remarks were indicated.

The epidemic situation generated as a result of COVID -19 crossways the sphere observed the practices of various emerging technology like Internet of Thing (IoT) along with norm of dynamic fields. The wireless communication  based on networks such as wireless mesh networks (WMN) and Mobile Ad-hoc networks (MANETS) proven to be very successful for monitoring of patients remotely. The MANET protocols that are simulated in this study are  Ad-hoc On Demand Vector (AODV), Secure AODV (SAODV) and Hybrid Wireless Mesh Protocol (HWMP). In this investigation work, most appropriate routing protocols to knob DDoS attacks are simulated using NS-2 and assessed in terms of average energy consumption in the state of changing speed connections among devices called mesh nodes. Further ANOVA test is utilized for further accessing for the best suited routing protocol for handling the data packets, which is HWMP , considerable less susceptible for DDoS assaults dominant in healthcare field.

The transportation sector, which is affected by travel patterns, has a significant stake in energy use; these patterns depend on a variety of factors including social and economic factors. The purpose of this paper is to examine the effects of social factors and economic factors on energy consumption, with an emphasis on family travel patterns; for this purpose, the Malek-Shahr and Amir-Arab neighborhoods in Isfahan, Iran, were selected as the research cases. A mixed-paradigm methodology (the qualitative research methodology paradigm and non-experimental research methodology from the quantitative research methodology paradigm) was used. The required information needed to examine the measures and the related factors was obtained by documentation and field study i.e. observation and questionnaire; In addition, the Mann-Whitney U test was used to examine the significant differences between the neighborhoods considering social, economic and travel pattern variables. Also, factor analysis and regression were applied in order to carry out path analysis and explain how the factors and variables were related to each other. The results indicate that the Malek-Shahr neighborhood has a significantly better social and economic status and more arbitrary travel patterns in comparison with the Amir-Arab neighborhood. It was also found that in both neighborhoods, there was no significant relationship between the social factor and the travel patterns while the economic factor played a major role in this respect; this was more pronounced in the Amir-Arab neighborhood that is afflicted by a lower economic status.

Livestock and poultry production and supply is one of the significant food sectors in which more production can lead to a decrease in dependence on exports and earning foreign exchange. Poultry farming is a vital industry for sustainable food supply in all countries. In this research, intelligent applications and solutions in the poultry industry are identified and prioritized using the simultaneous evaluation of criteria and alternatives (SECA) methodbased on criteria representing the sustainable development. Analysis showed that eighteenprincipalfieldsof intelligent solutions are identified in the poultry industry. The weights obtained for sustainable development criteria based on the SECA method are economic (0.351), social (0.3383), and environmental (0.3065) in order of value. Economic sustainability should be most importantinimplementing smart solutions-based projects in the poultry industry. One of the main challenges of the agricultural sector, especially the poultry industry, is traditional production utilization which leads to the overuse of land capacity. Also, the globalization trends, climate changes, moving from a fossil fuel-based economy to an environment-based economy, competition for land, freshwater, and labor shortage have led to more complications in supplyingnutrition. Considering the potential of smart solutions in realizing sustainable development objectives, it is suggested to focus more on the environmental aspects of poultry industry projects.

Wireless Sensor Networks (WSNs) have been employed in various real-time applications and addressed fundamental issues, such as limited power resources and network life. Several sensor nodes in a WSN monitor the actual world and relay discovered data to base stations. The biggest issue with WSN is that the sensors have a limited lifetime and use much electricity to relay data to the base station. This paper proposes an improved PSO-based Enhanced Distributed Energy Efficient Clustering (EDEEC) algorithm to extend the network's life and reduce power consumption. Clustering is the process of forming groups of sensor nodes. The cluster aims to improve the network's scalability, energy efficiency, and other characteristics. The particle swarm optimization algorithm is modified to obtain energy-efficient WSNs. The assessment is based on the essential WSN characteristics, including network lifetime and energy efficiency (power consumption). Compared to LEACH, HEED, and DEEC, our proposed IPSO-EDEEC uses less energy.

Cloud computing has emerged as a pivotal technology for managing and processing data, with a primary objective to offer efficient resource access while minimizing expenses. The allocation of resources is a critical aspect that can significantly reduce costs. This process necessitates the continuous assessment of the current status of each resource to design algorithms that optimize allocation and enhance overall system performance. Numerous algorithms have been developed to address the challenge of resource allocation, yet many fail to satisfy requirements of time efficiency and load balancing in cloud computing environments. This paper introduces a novel approach that classifies tasks according to their resource demands, employs a modified particle swarm optimization (PSO) algorithm, and incorporates load balancing strategies. The proposed method initially clusters tasks based on their resource requirements, subsequently utilizes the PSO algorithm to determine the best task-to-resource assignments, and finally implements a load balancing algorithm to reduce costs through balanced load distribution. The validity of the proposed method is tested and simulated using the Cloudsim tool. The simulation results indicate that the proposed method achieves lower average response time, waiting times, and energy consumption than existing baseline methods.