SMART GREENHOUSE PLATFORM: AN INFRASTRUCTURE FOR AGRICULTURAL APPLICATION AS CLOUD COMPUTING SERVICE

In today’s world some technology companies are using sophisticated IT systems to integrate urban services and infrastructures. The smart city is made up of different parts that make the Internet of Things one of the most important infrastructural necessities for optimizing process automation functions. With the increase in world population and the need for food supply on one hand and the lack of water, energy and agricultural land on the other hand, traditional agriculture no longer meets the food needs of the world population, so smart agriculture has received more attention in recent years. The Internet of Things (IoT) is a novel technique which is able to provide many solutions for agricultural modernization. The agricultural process is affected using wireless sensor network and is anticipated to utilized from the IoT. The problem that we are trying to solve is to minimize the energy consumption in smart green houses. Each sensor should communicate with each other and their own headers with efficient energy consumption. The main contribution of this thesis is to study the IoT applications in a smart city, Investigate the use of IoT in smart greenhouses, explore the infrastructure required to use the Internet of Things and provide solutions for efficiency in smart greenhouse performance by improving IoT energy consumption

Evaluation of the Use and Reasons for Not Using a Helmet by Motorcyclists Admitted to the Emergency Ward of Shahid Bahonar Hospital in Kerman

Background: Motorcycle crashes are the cause of severe morbidity and mortality especially because of head injuries. It seems that wearing a helmet has an effective role in protection against head injuries. Nevertheless, motorcyclists usually have no tendency to wear a helmet when driving in cities and have several reasons for this behavior. Objectives: This study aimed to evaluate the use and reasons for not using a helmet by motorcyclists admitted to an emergency ward of a trauma hospital due to accident in Kerman, Iran. Patients and Methods: This study was carried out by recoding the opinions of motorcyclists who had been transferred to the emergency ward of Shahid Bahonar Hospital (Kerman/Iran). Since no data was available on the frequency of the use of helmets, a pilot study was carried out and a sample size of 377 was determined for the main study. Then a researcher-made questionnaire was used to investigate the motorcyclists’ reasons for not using a helmet. Results: Only 21.5% of the motorcyclists had been wearing helmets at the time of the accident. The most frequent reasons for not using a helmet were the heavy weight of the helmet (77%), feeling of heat (71.4%), pain in the neck (69.4%), feeling of suffocation (67.7%), limitation of head and neck movements (59.6%) and all together, physical discomfort was the main cause of not wearing a helmet during motorcycle rides. Conclusions: In general, it appears that it is possible to increase the use of helmets by eliminating its physical problems, and increasing the knowledge of community members in relation to the advantages of helmet use, which will result in a significant decrease in traumas resulting from motorcycle accidents

Job profile and infection control of dental assistants in Tehran, Iran

Objectives: This study aimed to investigate background and job characteristics of dental assistants in Iran and to assess their knowledge and practice about infection control. Methods: This cross-sectional study conducted on a sample of dental assistants in Tehran. The participants answered an online questionnaire including demographic (age, gender, marital status, and the highest educational attainment) and job related (years in practice, income, working hours, job related educational course and certificate, knowledge and practice regarding infection control) questions. Objectives This study aimed to investigate the background characteristics and job profile of dental assistants in Iran and to assess their knowledge and practice regarding infection control.   Methods This cross-sectional study was conducted on a sample of dental assistants in Tehran, Iran. The participants completed an online questionnaire, including demographic (age, sex, marital status, and the highest level of educational attainment) and job-related (years of practice, income, working hours, job-related educational course and certificate, and knowledge and practice related to infection control) information. Results A total of 386 dental assistants completed the questionnaires. The mean age of the assistants was 29.27±6.8 years, and 96% of them were female. Overall, 56-68% of the respondents reported <5 years of working experience, worked 30-80 hours per week with a monthly salary of nearly $80-100, and completed an educational course on dental assistance. Nearly one-third of the participants reported high satisfaction with their job, and less than 10% of them were satisfied with their salary. The assistants had adequate knowledge about most aspects of infection control. Nearly 40% of them reported no education on infection control, and 35-45% declared that they usually sterilized dental rotary instruments using disinfectants, but not autoclaving. Almost 60% of the assistants reported complete vaccination before entering their job. Conclusion Dental assistants in Iran are rather young, highly educated women with a relatively high workload, but low job satisfaction. Their professional knowledge and performance were deficient in some aspects, reflecting inadequacies in their education. Delivery of efficient dental services with high standards requires qualified dental personnel with formal academic education based on an efficient credentialing system

LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application

The development of Adaptive Cruise Control (ACC) systems aims to enhance the safety and comfort of vehicles by automatically regulating the speed of the vehicle to ensure a safe gap from the preceding vehicle. However, conventional ACC systems are unable to adapt themselves to changing driving conditions and drivers' behavior. To address this limitation, we propose a Long Short-Term Memory (LSTM) based ACC system that can learn from past driving experiences and adapt and predict new situations in real time. The model is constructed based on the real-world highD dataset, acquired from German highways with the assistance of camera-equipped drones. We evaluated the ACC system under aggressive lane changes when the side lane preceding vehicle cut off, forcing the targeted driver to reduce speed. To this end, the proposed system was assessed on a simulated driving environment and compared with a feedforward Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model. The results show that the LSTM-based system is 19.25% more accurate than the ANN model and 5.9% more accurate than the MPC model in terms of predicting future values of subject vehicle acceleration. The simulation is done in Matlab/Simulink environment

Developing a Bi-Level Structure for Evaluation of Regional Bank Branch Managers Focusing on their Consumption

Regional bank branch management is the most important elements of a bank’s structure. Each regional bank branch manager (RBBM) manages a large group of branches. In this paper, we develop a bi-level structure for the evaluation of RBBMs. In the developed bi-level structure, RBBMs are positioned at the upper level, and each RBBM has a group of branches located at the lower level. Generally, each RBBM, including their branches, tries to use inputs and produce outputs efficiently. However, each branch performs according to its goals and limited resources. The evaluation is a data envelopment analysis (DEA)-based model that focuses on the bank’s consumption perspective. We apply the suggested model to a real-world case study to evaluate five RBBMs, who altogether manage 110 branches in one of the expert banking systems

Movement Optimization of Robotic Arms for Energy and Time Reduction using Evolutionary Algorithms

Trajectory optimization of a robot manipulator consists of both optimization of the robot movement as well as optimization of the robot end-effector path. This paper aims to find optimum movement parameters including movement type, speed, and acceleration to minimize robot energy. Trajectory optimization by minimizing the energy would increase the longevity of robotic manipulators. We utilized the particle swarm optimization method to find the movement parameters leading to minimum energy consumption. The effectiveness of the proposed method is demonstrated on different trajectories. Experimental results show that 49% efficiency was obtained using a UR5 robotic arm

The measurement of relative efficiency using data envelopment analysis with assurance regions that link inputs and outputs

The most popular weight restrictions are assurance regions (ARs), which impose ratios between weights to be within certain ranges. ARs can be categorized into two types: ARs type I (ARI) and ARs type II (ARII). ARI specify bounds on ratios between input weights or between output weights, whilst ARII specify bounds on ratios that link input to output weights. DEA models with ARI successfully maximize relative efficiency, but in the presence of ARII the DEA models may under-estimate relative efficiency or may become infeasible. In this paper we discuss the problems that can occur in the presence of ARII and propose a new nonlinear model that overcomes the limitations discussed. Also, the dual model is described, which enables the assessment of relative efficiency when trade-offs between inputs and outputs are specified. The application of the model developed is illustrated in the efficiency assessment of Portuguese secondary schools.info:eu-repo/semantics/publishedVersio

Mobile mass in the aortic arch: A case report

BACKGROUND: The finding of a floating mass in the aortic arch is rare and the management remains controversial. CASE REPORT: We describe a 42-year-old woman with an embolic infarction in whom transesophageal echocardiography revealed a mobile mass in the aortic arch that was characterized as atherothrombi with an evidence of embolic infarction in the territory of the middle cerebral artery. Treatment with antiplatelet and anticoagulants failed to resolve the mass and is surgically resected. CONCLUSION: In conclusion, the presence of mobile aortic mass seems to carry a high embolic risk. The optimal treatment for mobile aortic arch atherothrombi remains to be elucidated

A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems

Accurate lane change prediction can reduce potential accidents and contribute to higher road safety. Adaptive cruise control (ACC), lane departure avoidance (LDA), and lane keeping assistance (LKA) are some conventional modules in advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle communication (V2V), vehicles can share traffic information with surrounding vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies on the vehicle's sensors to obtain the position and velocity of the leading vehicle, CACC also has access to the acceleration of multiple vehicles through V2V communication. This paper compares the type of information (position, velocity, acceleration) and the number of surrounding vehicles for driver lane change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD dataset to predict lane change intention. Results indicate a significant improvement in accuracy with an increase in the number of surrounding vehicles and the information received from them. Specifically, the proposed model can predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and CACC scenarios, respectively

Optimized Implementation of Memristor-Based Full Adder by Material Implication Logic

Recently memristor-based applications and circuits are receiving an increased attention. Furthermore, memristors are also applied in logic circuit design. Material implication logic is one of the main areas with memristors. In this paper an optimized memristor-based full adder design by material implication logic is presented. This design needs 27 memristors and less area in comparison with typical CMOS-based 8-bit full adders. Also the presented full adder needs only 184 computational steps which enhance former full adder design speed by 20 percent.Comment: International Conference on Electronics Circuits and Systems (ICECS), 201