Internet of Things in Smart Agriculture: Enabling Technologies

In this paper, an IoT technology research and innovation roadmap for the field of precision agriculture (PA) is presented. Many recent practical trends and the challenges have been highlighted. Some important objectives for integrated technology research and education in precision agriculture are described. Effective IoT based communications and sensing approaches to mitigate challenges in the area of precision agriculture are presented

Urban Underground Infrastructure Monitoring IoT: The Path Loss Analysis

The extra quantities of wastewater entering the pipes can cause backups that result in sanitary sewer overflows. Urban underground infrastructure monitoring is important for controlling the flow of extraneous water into the pipelines. By combining the wireless underground communications and sensor solutions, the urban underground IoT applications such as real time wastewater and storm water overflow monitoring can be developed. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. It has been shown that the communication range of up to 4 kilometers can be achieved from an underground transmitter when communicating through 10cm thick asphalt layer

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A review of slicing techniques in software engineering

Program slice is the part of program that may take the program off the path of the desired output at some point of its execution. Such point is known as the slicing criterion. This point is generally identified at a location in a given program coupled with the subset of variables of program. This process in which program slices are computed is called program slicing. Weiser was the person who gave the original definition of program slice in 1979. Since its first definition, many ideas related to the program slice have been formulated along with the numerous numbers of techniques to compute program slice. Meanwhile, distinction between the static slice and dynamic slice was also made. Program slicing is now among the most useful techniques that can fetch the particular elements of a program which are related to a particular computation. Quite a large numbers of variants for the program slicing have been analyzed along with the algorithms to compute the slice. Model based slicing split the large architectures of software into smaller sub models during early stages of SDLC. Software testing is regarded as an activity to evaluate the functionality and features of a system. It verifies whether the system is meeting the requirement or not. A common practice now is to extract the sub models out of the giant models based upon the slicing criteria. Process of model based slicing is utilized to extract the desired lump out of slice diagram. This specific survey focuses on slicing techniques in the fields of numerous programing paradigms like web applications, object oriented, and components based. Owing to the efforts of various researchers, this technique has been extended to numerous other platforms that include debugging of program, program integration and analysis, testing and maintenance of software, reengineering, and reverse engineering. This survey portrays on the role of model based slicing and various techniques that are being taken on to compute the slices

Forecasting Energy Consumption Demand of Customers in Smart Grid Using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers\u27 requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers\u27 future demand. Using the proposed study, production companies can accurately have energy per their customers\u27 needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques; hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model\u27s performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model\u27s performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Smart grids and smart homes are getting people\u27s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with day as covariates remained better than the 1, 2, 3, and 4-week scenarios

Research methodologies an Islamic perspective

This book presents the most necessary aspects for conducting research including Islamic perspectives on research. It is very suitable for research beginners and as well as experienced researchers to conduct research. The content of this book start from simple research definitions, literature review and its important outcomes, research designs, methods and methodologies, scales of measures and analysis methods and presented in an easiest way to understand. The main aim of this book is to provide research students a comprehensive and complete view of research process and material to begin research with accurate and very precise understanding of the same

A new approach to seasonal energy consumption forecasting using temporal convolutional networks

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network

An Experimental Study of Bond Behavior of Micro Steel Fibers Added Self-compacting Concrete with Steel Reinforcement

The obstruction offered by the surrounding concrete to the pulling out of embedded steel bar is known as bond strength. Steel fibers addition to concrete improves its bond strength by arresting the cracks due to their bridging effect. Bond failure occurs when cracks in the surrounding concrete initiates, providing enough space for bar to be pulled-out. Micro steel fibers efficiently control the formation of micro cracks and may improve bond strength to a greater extent compared to the longer steel fibers. However, it reduces the workability of concrete which is of greater importance in case of self-compacting concrete (SCC). Reduction of workability is less pronounced when straight micro steel fibers are used due to their shorter lengths and straight geometry. Thus, different amount of straight micro steel fibers (0.25 %, 0.5 %, 0.75 %) were incorporated in to SCC to investigate their fresh and mechanical properties with major emphasis on the bond strength. Results indicate that steel fibers addition to SCC improve the splitting tensile strength and bond strength significantly with a maximum increase of 33.5 % and 54.9 % respectively with 0.75 % fibers addition. An equation is proposed for the calculation of bond strength with micro steel fibers addition to SCC with a maximum variation of 4 % to those of experimental values

Milligan Morgan Haemorrhoidectomy vs LigaSure Haemorrhoidectomy : Comparative Postoperative Outcomes

Objective: To compare the traditional Milligan Morgan haemorrhoidectomy with haemorrhoidectomy using LigaSure in terms of postoperative complications, patient satisfaction and hospital stay. Methodology: This is a randomized controlled trial carried out at the Department of Surgery Liaquat university hospital Jamshoro from July 2017 to June 2019. A total of 88 patients were admitted with the diagnoses of 3rd and 4th degree haemorrhoid were included in the study. Patients were randomly divided into two groups by lottery method. Group A underwent Milligan Morgan Haemorrhoidectomy and group B underwent Haemorrhoidectomy by Ligasure after the informed consent. Outcomes of both procedures were also compared by complications, patient satisfaction and hospital stay. Results: Out of 88 patients 35 were male (39.78%) and 53 were female (60.22%). The most common group of age involved was between 35–55 years. Third degree Haemorrhoids were present in 40 (45.45%) of patients while the remaining 48 (54.55%) had fourth degree Haemorrhoids. Group A included 44(50%) cases while Group B included 44 (50%) cases. The mean operating time of Group A was 50.5 (minutes) with a standard deviation of 11.5 while it was 35.5 ± 9.4 in B group. The mean blood loss in group A was 65.30 ml with a standard deviation of 14.58 while it was 45.45 ml ± 20.49 in group B. Conclusion: The Haemorrhoidectomy done by Ligasure is comparatively better than the Milligan Morgan Heamorrhoidectomy, in terms of operative time, less bleeding, less pain, less hospital stays and early return to work