Deep Learning Approaches in Pavement Distress Identification: A Review

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society

Preparation of an Optimized Model of Water Transmission Lines Route Using Meta-Heuristic Algorithm

Nowadays, due to the expansion of cities as well as the increase in the urban population, the lack of drinking water of good quality is always one of the most important issues for the country's water and sewage authorities, especially the people. Delivering water for consumption is one of the important activities of water transmission lines. In this context, optimizing the route of transmission lines is important. In this research, the plan to transfer water from the balance tank near Khoshab's agricultural and industrial lands and Roza Dam in Madhim City to the cities of Tabas Messina, Asadiyeh, and Gazik, has been obtained as the optimal route in terms of technical and engineering, economic and passive defense, and environment.  In this regard, the required information maps, such as topography, land use, rivers and roads, infrastructure, faults, geology, and population centers, were prepared in the form of raster maps in the GIS environment to obtain a cost map. Then, based on the Analytical Hierarchy Process (AHP), these maps were weighted and integrated, and the cost map was obtained. Finally, from the cost map, five routes were determined as the best routes, and to optimize hydraulically, these routes were entered into the EPANET software. The hydraulic design of the routes was done in this software by linking it with the multi-objective particle swarm algorithm. Two technical and economic objective functions of Pareto curves for these routes were obtained in a MATLAB environment. By analyzing these curves, route 2 out of these 5 routes has a more appropriate drop and cost and has been obtained as the optimal route for this water transfer, and this route has a length of 112 km

Developing criteria for Cesarean Section using the RAND appropriateness method

<p>Abstract</p> <p>Background</p> <p>Cesarean section rates are increasing worldwide, and a rapid increase has been observed in Iran. Disagreement exists between clinicians about when to use cesarean section. We aimed to identify the appropriateness criteria for the use of cesarean section in Iran.</p> <p>Method</p> <p>A consensus development study using a modified version of the RAND Appropriateness Method (RAM). We generated scenarios from valid clinical guidelines and expert opinions. A panel of experts participated in consensus development: first round via mail (12 members), second round face-to-face (9 members). We followed the RAM recommendations for the development of the scenario lists, rating scales, and statistical analyses.</p> <p>Results</p> <p>294 scenarios relevant to cesarean section were identified. 191 scenarios were considered appropriate, of which 125 scenarios were agreed upon. The panel found cesarean inappropriate for 21% of scenarios, and 'equivocal' for 14% of scenarios.</p> <p>Conclusion</p> <p>RAM is useful for identifying stakeholder views in settings with limited resources. The participants' views on appropriateness of certain indications differed with available evidence. A large number of scenarios without agreement may partly explain why it has been difficult to curb the growth in cesarean section rate.</p

A note on weighted composition operator on L p spaces

Abstract. In this paper we will consider the weighted composition operators uC ϕ between two different L p (X, Σ, µ) spaces, generated by measurable and non-singular transformations ϕ from X into itself and measurable functions u on X. We characterize the functions u and transformations ϕ that induce weighted composition operators between L p -spaces by using some properties of conditional expectation operator, pair (u, ϕ) and the measure space (X, Σ, µ). Also, some other properties of these types of operators will be investigated

Weighted multiple bit-plane matching, a simple and efficient matching criterion for electronic digital image stabilizer application

In this paper we propose two new matching criteria for template matching. The performance evaluation of these two criteria is applied to electronic digital image stabilizer (EDIS) application. These two criteria are based on bit-plane matching (BPM) criterion, where four decimated bit-planes are used in our criteria. These criteria can be realized using only Boolean functions; hence they can be realized very simple in any digital systems. We compared our criteria with other known criteria by employing twenty real video sequences