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Background Many women enjoy wearing high heels despite knowing they can be harmful to the feet. There are many uncomfortable conditions that can originate from wearing this type of shoe leading to biomechanical changes in ankle joints. Hence the study is aimed to identify the effects of massage therapy in improving muscular flexibility among women wearing high heels Methodology Forty female participants with chronic heel pain were included in the single-blinded randomized controlled trial. Participants were divided into Group-A (Stretching and deep heat) and Group-B (petrissage and deep heat) group. The treatment was performed for four weeks, 3 sessions/ week in both groups. Foot function index and ankle dorsiflexion were recorded at baseline and after 4 weeks of intervention. Results Forty female participants with a mean age of 28.23±6.24 were recruited in the study. Both groups showed significant improvement in all three variables i.e. pain, disability, and ankle dorsiflexion. However, group B showed more significant results with mean differences of 1.80±2.22 and 4.1±6.7 (p-value

Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.

With the help of machine learning, many tasks can be automated. The use of computers and mobile devices in “intelligent” buildings may make tasks such as controlling the indoor climate, monitoring security, and performing routine maintenance much easier. Intelligent buildings employ the Internet of Things to establish connections among the many components that make up the structure. As the notion of the Internet of Things (IoT) gains attraction, smart grids are being integrated into larger networks. The IoT is an integral part of smart grids since it enables beneficial services that improve the experience for everyone inside and individuals are protected because of tried-and-true life support systems. The reason for installing Internet of Things gadgets in smart structures is the primary focus of this investigation. In this context, the infrastructure behind IoT devices and their component units is of the highest concern.

Sign languages display the same linguistic characteristics as oral languages and utilize the same language services. Sign language processing solutions provide a communication link for persons with hearing impairments and healthy persons. Without these icons' ability to understand, deaf children experience several challenges in learning social norms and cannot meet adults to exchange knowledge. Parents find it challenging to express their messages to their deaf children and not hear their children. This paper focused on establishing Urdu sign language to reduce the communication barrier between ordinary folks and physically impaired people. The present study observed the Urdu Sign Language in deaf children. In this paper, the process of detecting Urdu sign language alphabets is proposed. All the 37 alphabets are identified by using KNN, ANN, and SVM classifiers. Through these alphabets, the teachers at schools and the parents at home can communicate efficiently with their deaf children. Histogram of Gradient technique is used for feature extraction. Urdu Alphabetic are identified. Maximum accuracy is obtained by using a KNN classifier that was 99, which is a significant contribution. Our proposed results are comparable to the state of the art techniques.

For the communication of information over sensor networks, power consumption, and spectrum usage play a significant role. As the number of sensors increases tremendously, it’s vital to optimize power and spectrum usage. The HSA has already been implemented for repetitive subcarrier and power allocation in smart downlink multicarrier systems to accurately implement power and subcarrier allocation. This work maximizes energy efficiency through routing optimization in a senor communication network using a harmony search algorithm. Simulation results describe the significant improvement in a network life time and residual energy of nodes.