Bond characherization between concrete substrate and repairing materials

The purpose of this investigation was to study the effect of bonding behavior of concrete substrate and repair materials. Three different cementitious or modified-cementitious repair materials and three surface roughnesses were studied. Repair materials were ordinary mortar, modified cementitious mortar by silica fume and modified cementitious mortar by styrene butadiene rubber latex. Surface preparations were smooth surface, rough surface and epoxy resin adhesive as a bonding agent. The method used for evaluation of bond strength was pull-off test. the influence of the electrical conductivity of repairing materials was analyzed by rapid chloride permeability test. Finally, the performance of the adhesives was evaluated considering both the bond strength and electrical conductivity. Results obtained from these tests indicated that the roughness of substrate surface has a main effect on the performance of bond between adhesives and concrete. There are not great differences in bonding strength between various repairing materials but considering electrical conductivity, modified cementitious mortars are better materials for using in corrosive environments to increase service life of repaired structure

A Bispecific Antibody Based Assay Shows Potential for Detecting Tuberculosis in Resource Constrained Laboratory Settings

The re-emergence of tuberculosis (TB) as a global public health threat highlights the necessity of rapid, simple and inexpensive point-of-care detection of the disease. Early diagnosis of TB is vital not only for preventing the spread of the disease but also for timely initiation of treatment. The later in turn will reduce the possible emergence of multi-drug resistant strains of Mycobacterium tuberculosis. Lipoarabinomannan (LAM) is an important non-protein antigen of the bacterial cell wall, which is found to be present in different body fluids of infected patients including blood, urine and sputum. We have developed a bispecific monoclonal antibody with predetermined specificities towards the LAM antigen and a reporter molecule horseradish peroxidase (HRPO). The developed antibody was subsequently used to design a simple low cost immunoswab based assay to detect LAM antigen. The limit of detection for spiked synthetic LAM was found to be 5.0 ng/ml (bovine urine), 0.5 ng/ml (rabbit serum) and 0.005 ng/ml (saline) and that for bacterial LAM from M. tuberculosis H37Rv was found to be 0.5 ng/ml (rabbit serum). The assay was evaluated with 21 stored clinical serum samples (14 were positive and 7 were negative in terms of anti-LAM titer). In addition, all 14 positive samples were culture positive. The assay showed 100% specificity and 64% sensitivity (95% confidence interval). In addition to good specificity, the end point could be read visually within two hours of sample collection. The reported assay might be used as a rapid tool for detecting TB in resource constrained laboratory settings

Ebola GP-Specific Monoclonal Antibodies Protect Mice and Guinea Pigs from Lethal Ebola Virus Infection

Ebola virus (EBOV) causes acute hemorrhagic fever in humans and non-human primates with mortality rates up to 90%. So far there are no effective treatments available. This study evaluates the protective efficacy of 8 monoclonal antibodies (MAbs) against Ebola glycoprotein in mice and guinea pigs. Immunocompetent mice or guinea pigs were given MAbs i.p. in various doses individually or as pools of 3–4 MAbs to test their protection against a lethal challenge with mouse- or guinea pig-adapted EBOV. Each of the 8 MAbs (100 µg) protected mice from a lethal EBOV challenge when administered 1 day before or after challenge. Seven MAbs were effective 2 days post-infection (dpi), with 1 MAb demonstrating partial protection 3 dpi. In the guinea pigs each MAb showed partial protection at 1 dpi, however the mean time to death was significantly prolonged compared to the control group. Moreover, treatment with pools of 3–4 MAbs completely protected the majority of animals, while administration at 2–3 dpi achieved 50–100% protection. This data suggests that the MAbs generated are capable of protecting both animal species against lethal Ebola virus challenge. These results indicate that MAbs particularly when used as an oligoclonal set are a potential therapeutic for post-exposure treatment of EBOV infection

Expert system for assessing the failure of concrete slabs In the Persian Gulf region

As we can see in many bridges in the country, due to the lack of implementation of the bridge maintenance management system, the range of failures is gradually It has expanded and led to declining resistance and increasing the vulnerability of bridges. Due to the hot and humid weather conditions and the presence of salts in it The Persian Gulf region has seen signs of failure, including numerous cracks and local damage, in many of the concrete slabs in the area's structures and bridges. It is possible. Therefore, the assessment of the condition of concrete slabs prevents the progress of failure, removal from service and repair of structures.In this paper, the Expert-Slab Bridge expert system is presented to assess the failure of concrete slabs. Expert system, computer program   It is intelligence that uses the knowledge and methods of inference and inference to solve problems. This system includes an interpreter, an inference method Knowledge-based and database. Bridge deck failures due to environmental conditions and cracked concrete structures, failures Surface and instrumental failures have been evaluatedSeveral case studies and comparisons between technical inspection results and the expert system In order to gain reliability and accuracy, the results of the system have been done. System outcomes include a concrete bridge slab identification, inference Expert system with failures and their causes, status index, status description, suggested solution for repair and diagram of useful life of concrete slab Is

Evaluation of key performance indicators from the perspective of stakeholders in construction projects

In this research, according to the existing gaps and research objectives, at first, a review of the literature on the subject and research background in the field of key performance indicators has been done. According to the results of the research background, a list of frequent indicators was obtained. In the rest of the research, using Q method and related software as well as questionnaires (48 questionnaires completed by 4 groups of stakeholders including employers, contractors, consultants, and project managers) were analyzed by factor analysis. Though this research has been done in the field of residential, office, and commercial buildings, the overall research process can be implemented in all projects and identify the views of different stakeholders. By identifying the different views of stakeholders, their serious similarities and differences were identified and conflicting views analyzed. The evaluation results show that there are many similarities between the views of one of the identified factors in one stakeholder group, with another factor in another stakeholder group. For instance, the second view of the contractor and the second view of the project manager or the first view of the employer and the third view of the project management are very close. Generally, the key performance indicators including time, quality, safety, and cost and procurement of construction projects seem to be of great importance and can cover the concerns of many stakeholders. Time and quality indicators were present in the priorities of the first views of all stakeholders. Finally, the practical project management dashboard should be designed with a small number of metrics that have a significant impact on project success. The results of this research and the introduced priority indicators that were agreed upon by all stakeholders (Like time and quality) can be appropriate indicators for evaluating the performance of the project

Ranking and analyzing key motivation factors of engineers in the construction industry

There is no doubt that human resources have a significant role in running any successful business. The literature indicates that this role is almost neglected in the construction industry. One of the important factors to increase productivity in various industries is to increase the productivity of human resources. Human resource productivity is a function of various factors where motivation is one of the most important factors. Considering the effect of motivation on innovation and the necessity of innovation in construction design firms, studying motivational factors of Architects and Engineers (AE) is of great importance. In this research, the most critical factors affecting Architects’ and Engineers’ motivation were recognized through literature review. These factors were classified into five main categories including Leadership and management, service compensation system, organizational atmosphere, social status, and job nature (current tasks – immediate situation and job development). Then, the factors were ranked by the industry experts through online questionnaires. Learning opportunity, distributive, procedural & interactional justice, and job promotion opportunity were ranked as the top critical motivational factors. However, it must be noted that important factors may differ considering the demographic properties of the interviewees. As a result, factors were ranked considering demographic factors including gender, marital status, age, educations, years of experience, and profession (civil engineer and architect). Finally, it should be noted that motivation of engineers and architects depends on several factors that must be considered in the design of human resource subsystems such as job analysis, performance appraisal and service compensation, training systems, and promotion to improve performance and increase productivity. In addition, motivation is one of the most important factors affecting personnel retention. Therefore, human resource management experts are advised to provide necessary grounds to increase the motivation of construction industry employees by designing appropriate human resource systems

County-scale crop yield prediction by integrating crop simulation with machine learning models

Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield prediction complex. Building upon a previous work in which we coupled crop modeling with machine learning (ML) models to predict maize yields for three US Corn Belt states, here, we expand the concept to the entire US Corn Belt (12 states). More specifically, we built five new ML models and their ensemble models, considering the scenarios with and without crop modeling variables. Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for low or high model prediction errors. Our results indicated that the prediction accuracy increases by coupling crop modeling with machine learning. The ensemble model overperformed the individual ML models, having a relative root mean square error (RRMSE) of about 9% for the test years (2018, 2019, and 2020), which is comparable to previous studies. In addition, analysis of the sources of error revealed that counties and crop reporting districts with low cropland ratios have high RRMSE. Furthermore, we found that soil input data and extreme weather events were responsible for high errors in some regions. The proposed models can be deployed for large-scale prediction at the county level and, contingent upon data availability, can be utilized for field level prediction.This article is published as Sajid, Saiara Samira, Mohsen Shahhosseini, Isaiah Huber, Guiping Hu, and Sotirios Archontoulis. "County-scale crop yield prediction by integrating crop simulation with machine learning models." Frontiers in Plant Science 13 (2022): 4841. DOI: 10.3389/fpls.2022.1000224. Copyright 2022 Sajid, Shahhosseini, Huber, Hu and Archontoulis. Attribution 4.0 International (CC BY 4.0). Posted with permission