Building Interpretable Methods For Identifying Bridge Maintenance Patterns

According to the American Road and Transportation Builder Association, approximately 47,000 or 9.1\% of bridges are structurally deficient. About 235,000 bridges or 38\% of the bridges require immediate maintenance. Bridge engineers are constantly looking for methods to extract insight from the bridge inspections records to plan bridge maintenance efficiently. Previous researchers have developed machine learning models that have identified influential factors for bridge maintenance. Despite the current understanding of significant factors that drive bridge maintenance, interactions between these influential factors that explain maintenance patterns remain incomplete. In this research study, we developed a method that adopts a decision tree model to generate a decision tree and apply associated rule mining to identify influential patterns that contribute to bridge maintenance

Analytical Guidelines for Designing Curvature-Induced Dielectrophoretic Particle Manipulation Systems.

Curvature-induced dielectrophoresis (C-iDEP) is an established method of applying electrical energy gradients across curved microchannels to obtain a label-free manipulation of particles and cells. This method offers several advantages over the other DEP-based methods, such as increased chip area utilisation, simple fabrication, reduced susceptibility to Joule heating and reduced risk of electrolysis in the active region. Although C-iDEP systems have been extensively demonstrated to achieve focusing and separation of particles, a detailed mathematical analysis of the particle dynamics has not been reported yet. This work computationally confirms a fully analytical dimensionless study of the electric field-induced particle motion inside a circular arc microchannel, the simplest design of a C-iDEP system. Specifically, the analysis reveals that the design of a circular arc microchannel geometry for manipulating particles using an applied voltage is fully determined by three dimensionless parameters. Simple equations are established and numerically confirmed to predict the mutual relationships of the parameters for a comprehensive range of their practically relevant values, while ensuring design for safety. This work aims to serve as a starting point for microfluidics engineers and researchers to have a simple calculator-based guideline to develop C-iDEP particle manipulation systems specific to their applications

PLANNING, DESIGNING AND IMPLEMENTATIONOF NETWORK AT CORPORATE LEVEL

The computer networking technology has entered almost in all aspects of day to day life. In today’s world life has become fast and human does not like to waste their precious time, many technologies have been introduced in market to fulfill human needs. As we written above network technology are entering in all aspect of day to day life. The people want almost all things faster and reliable so we are tried to apply that in our project. We tried to make our project reliable, fast and most important secured

Design and Fabrication of Submersible Pump Lifting Machine

This project report deals with study and development of mechanism for submersible pump lifting. Human society is developing with rapid momentum and achieved success for making its livelihood better. The current study mainly aimed to develop the machine which reduce the human effort and stresses on human body. It is the most important source of employment for the majority of the work force in the country. Approximately 38% of the total labor force was engaged in agriculture in 1999. Among that highest percentage was in agriculture sector. Releasing of the work force of agriculture sectors than other is important to develop the country. To release the work force of the farmer, mechanization plays a big role. To feed growing population is a huge challenge. Mechanization of submersible pump lifting machine will lead to time consuming with releasing of work force from other work. The objective of this project was to design a submersible pump lifting machine mechanism to lift the submersible pump from bore well by small scale farmers in the country

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Understanding the effects of Precipitation on Bridge Health in the US

Bridges in the US scored a C+ on the infrastructure report card in 2017. There is a need for substantial improvement in conditions of these bridges as they are structurally deficient and can become unsafe in the near future. The most recent estimate puts the nation’s backlog of bridge rehabilitation needs at $123 billion. Throughout the country, many state departments of transportation (DOT) have limited resources, leaving them with difficult decisions about where to invest and allocate their limited resources. To make cost-effective decisions, these bridge stakeholders need clean data and studies to estimate the future condition of bridges. This will allow them to have data-driven accurate life-cycle models and improved inspections intervals. Previous researchers have identified potential independent variables that may cause deterioration in bridges using a variety of deterioration models. Unfortunately, these researchers limit their data to specific regions and type of bridges. This severely limits the general applicability of their results. In this research, we approach bridge health-related decision making challenges using a novel data science perspective. This study of bridge health deterioration provides new insights into making bridge rehabilitation and reconstruction decisions. In this research, we demonstrate the use of large datasets, including records of all the bridges maintained by Federal Highway Agency, known as the National Bridge Inventory or simply NBI and precipitation data from Center of Disease Control (CDC) and Prevention to perform sound statistical analysis. We specifically contribute to this domain by 1) providing a reference implementation of a big data pipeline for bridge health-related datasets and compute scores for evaluating bridges; 2) demonstrating the feasibility of using data science to study the deterioration of the bridges Further, the curated datasets and methods developed through this research are used to analyze the statistical significance of independent variables related to bridge deterioration as identified in the literature and from subject matter experts at the Nebraska State DOT. The data used in our research spans all available inspection records in the NBI and precipitation rates from all US states

Building Interpretable Methods For Identifying Bridge Maintenance Patterns

According to the American Road and Transportation Builder Association, approximately 47,000 or 9.1\% of bridges are structurally deficient. About 235,000 bridges or 38\% of the bridges require immediate maintenance. Bridge engineers are constantly looking for methods to extract insight from the bridge inspections records to plan bridge maintenance efficiently. Previous researchers have developed machine learning models that have identified influential factors for bridge maintenance. Despite the current understanding of significant factors that drive bridge maintenance, interactions between these influential factors that explain maintenance patterns remain incomplete. In this research study, we developed a method that adopts a decision tree model to generate a decision tree and apply associated rule mining to identify influential patterns that contribute to bridge maintenance

Analytical Guidelines for Designing Curvature-Induced Dielectrophoretic Particle Manipulation Systems.

Curvature-induced dielectrophoresis (C-iDEP) is an established method of applying electrical energy gradients across curved microchannels to obtain a label-free manipulation of particles and cells. This method offers several advantages over the other DEP-based methods, such as increased chip area utilisation, simple fabrication, reduced susceptibility to Joule heating and reduced risk of electrolysis in the active region. Although C-iDEP systems have been extensively demonstrated to achieve focusing and separation of particles, a detailed mathematical analysis of the particle dynamics has not been reported yet. This work computationally confirms a fully analytical dimensionless study of the electric field-induced particle motion inside a circular arc microchannel, the simplest design of a C-iDEP system. Specifically, the analysis reveals that the design of a circular arc microchannel geometry for manipulating particles using an applied voltage is fully determined by three dimensionless parameters. Simple equations are established and numerically confirmed to predict the mutual relationships of the parameters for a comprehensive range of their practically relevant values, while ensuring design for safety. This work aims to serve as a starting point for microfluidics engineers and researchers to have a simple calculator-based guideline to develop C-iDEP particle manipulation systems specific to their applications