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The prevalence of run-off-road crashes, particularly in rural areas, underscores the significance of roadside characteristics in safety analysis. This paper proposes a novel approach for automated roadside safety assessment using deep convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs) for data augmentation. The CNN models evaluate roadside features through two-dimensional (2D) image analysis, whereas GANs expand the data set by generating additional diverse samples. The proposed framework aligns with the standard rating system of the Federal Highway Administration (FHWA) and encompasses four distinct models for guardrail detection, clear zone width assessment, rigid obstacle detection, and sideslope estimation. The performance of each model is compared against non-GAN augmented models to assess the efficacy of using GANs for data augmentation. The results show that the proposed approach outperforms existing methods in terms of accuracy, which is measured with 96% in detecting guardrails, 88% in detecting clear zones, 80% in detecting rigid obstacles, and 84% in detecting roadside slopes. Compared with manual approaches, the proposed method offers advantages such as cost-effectiveness, ease of implementation, and the ability to rapidly rank state roads. The developed framework can assist departments of transportation (DOTs) in efficiently identifying problematic road segments and prioritizing safety improvement projects based on FHWA standard rating system. This research focuses on the development of computer vision models for roadside safety assessment. The models successfully detect and classify important features such as guardrails, clear zones, rigid obstacles, and roadside slopes in images. The practical applications of this research are significant for transportation authorities, engineers, and practitioners involved in roadway safety. By utilizing these computer vision models, they can efficiently analyze large amounts of visual data and identify potential safety hazards along roadways. These models can aid in identifying areas with inadequate clear zones, which are crucial for preventing roadside crashes. They can also detect the presence of guardrails, which play a vital role in redirecting vehicles and minimizing the severity of accidents. Moreover, the models provide insights into the presence and distance of rigid obstacles, such as trees, rocks, and mountains, which are essential for assessing the overall safety of roadside environments. Last, the models assess roadside slopes, allowing practitioners to identify areas with steep inclines that may pose a higher risk to motorists. Overall, the practical applications of these computer vision models enable stakeholders to prioritize safety interventions, implement targeted improvements, and enhance roadway safety for both drivers and pedestrians. [ABSTRACT FROM AUTHOR]

Automatically converting three-dimensional (3D) point clouds into building information modeling (BIM) has been an active research area over the past few years. However, existing solutions in the literature have been suffering the limitations of covering all different design scenarios (prior knowledge-based approach) or collecting sufficient point clouds as training data sets (3D deep learning–based approach). To tackle this issue, we propose a fused system to automatically develop as-built BIMs from photogrammetric point clouds. A series of images is captured to generate a high-quality point cloud, which is then preprocessed by removing noise and downsizing points. Meanwhile, a two-dimensional (2D) deep-learning method, DeepLab, is utilized to semantically segment elements (e.g., walls, slabs, and columns) from the collected images. Subsequently, an inverse photogrammetric pipeline is employed to recognize element categories in the point cloud by projecting the isolated 3D planes into 2D images and assigning the identified elements to the 3D planes. Finally, the industry foundation classes are devised to create as-built BIMs based on the segmented point clouds. In order to evaluate the performance of the proposed system, we selected six cases with various elements as the testbed. The prospective results reveal that (1) our system can provide a highly automated solution to develop as-built BIMs; and (2) 39 out of 45 elements in six different cases are successfully recognized in point clouds. [ABSTRACT FROM AUTHOR]

Automated three-dimensional (3D) as-built modeling of built infrastructure with embedded rebar is an ongoing area of research. The current practice for such a process is either highly manual, due to the need for localizing rebar during the scanning process, or not compatible with popular and widely used building information modeling (BIM) software. In order to fill the existing gap, this paper proposes a highly integrated approach that utilizes ground-penetrating radar (GPR) to detect rebar from in-service buildings and automatically translate the extracted rebar from GPR data into existing BIM. Rebar is localized through GPR data using the shearlet transform and the inverse correlation between the maximum intensity and depth parameters. To map the localized rebar into corresponding building elements in BIM, a classification system is developed based on a deep-learning algorithm trained by several on-site data sets. To evaluate the performance of the presented system, two buildings and their various concrete components are selected as the testbed. The obtained results are promising and reveal that (1) the proposed GPR2BIM system is highly automated and accurately integrates localized rebar into existing BIM; (2) the shearlet transform precisely recognizes rebar through GPR data; (3) the lowest absolute error for the depth estimation algorithm is 5.12%; and (4) rebar from GPR is automatically classified and assigned to corresponding building elements with an accuracy of 100%. The findings demonstrate that the highly integrated system developed in this research is capable of automatically building a complete BIM with both surface elements and rebar for in-service building. [ABSTRACT FROM AUTHOR]

In the construction industry, especially for civil infrastructure projects, a large portion of overall project expenses are allocated towards various costs associated with heavy equipment. As a result, continuous tracking and monitoring of tasks performed by construction heavy equipment is vital for project managers and jobsite personnel. The current approaches for automated construction equipment monitoring include both location and action tracking methods. Current construction equipment action recognition and tracking methods can be divided into two major categories: 1) using active sensors such as accelerometers and gyroscopes and 2) implementing computer vision algorithms to extract information by processing images and videos. While both categories have their own advantages, the limitations of each mean that the industry still suffers from the lack of an efficient and automatic solution for the construction equipment activity analysis problem. In this paper we propose an innovative audio-based system for activity analysis (and tracking) of construction heavy equipment. Such equipment usually generates distinct sound patterns while performing certain tasks, and hence audio signal processing could be an alternative solution for solving the activity analysis problem within construction jobsites. The proposed system consists of multiple steps including filtering the audio signals, converting them into time-frequency representations, classifying these representations using machine learning techniques (e.g., a support vector machine ), and window filtering the output of the classifier to differentiating between different patterns of activities. The proposed audio-based system has been implemented and evaluated using multiple case studies from several construction jobsites and the results demonstrate the potential capabilities of the system in accurately recognizing various actions of construction heavy equipment. [ABSTRACT FROM AUTHOR]

The global scale of point cloud data (PCD) generated through monocular photography and videogrammetry is unknown and can be calculated using at least one known dimension of the scene. Measuring one or more dimensions for this purpose induces a manual step in the three-dimensional reconstruction process; this increases the effort and reduces the speed of reconstructing scenes, and induces substantial human error in the process due to the high level of measurement accuracy needed. Other ways of measuring such dimensions are based on acquiring additional information by either using extra sensors or specific classes of objects existing in the scene; it was found that these solutions are not simple, cost effective, or general enough to be considered practical for reconstructing both indoor and outdoor built infrastructure scenes. To address the issue, this paper proposes a novel method for automatically calculating the absolute scale of built infrastructure PCD. A premeasured cube for outdoor scenes and a sheet of paper for indoor environments are used as the calibration patterns. Assuming that the dimensions of these objects are known, the proposed method extracts the objects' corner points in two-dimensional video frames using a novel algorithm. The extracted corner points are then matched between the consecutive frames. Finally, the corresponding corner points are reconstructed along with other features of the scenes to determine the real-world scale. To evaluate the performance of the method, 10 indoor and 10 outdoor cases were selected and the absolute-scale PCD for each case was computed. Results illustrated the proposed algorithm is able to reconstruct the predefined objects with a high success rate, while the generated absolute-scale PCD is sufficiently accurate. [ABSTRACT FROM AUTHOR]

Automatically recognizing activities of heavy construction equipment using sound data has recently received considerable attention as a promising research area in construction. Although existing methods are effective, they only focus on tracking the activities of one single piece of equipment. On construction job sites, multiple equipment sound signals are mixed in the environment; Thus, there is a need for a robust method to recognize these activities that are taking place simultaneously. To address this shortcoming, we proposed a multi-label multi-level sound classification method based on Short-Time Fourier Transform (STFT) and Convolutional Neural Network (CNN) that only requires a single-channel off-the-shelf microphone. In addition, we developed a data augmentation method to simulate real-world equipment sound mixtures. We tested the proposed method on both synthetic and real-world equipment sound mixtures. The results of our study showed that this method was effective in identifying activities of multiple pieces of equipment on real construction job sites without the need for separating sound signals in advance. Future studies can focus on other potential applications of sound signal processing in the construction domain, including analyzing engine abnormalities and monitoring environmental performance of the equipment. • This study introduces a novel method for multiple construction equipment activity recognition using off-the-shelf single-channel microphone. • This method utilizes sound-based image signatures to extract specific features for equipment activities. • The method identifies the activities directly without the need for separating sound signals. • Also, a novel data augmentation method is introduced to simulate the real-world sound mixtures when multiple machines performing activities simultaneously. • This method is tested on both synthetic and real-world mixed data and the results are promising. [ABSTRACT FROM AUTHOR]

A quantitative structure-property relationship (QSPR) study based on partial least squares (PLS) and artificial neural network (ANN) was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP), ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. After optimization and training of the network according to back-propagation algorithm, a 5-5-1 neural network was generated for prediction of ferric iron precipitation. The root mean square error for the neural network calculated ferric iron precipitation for training, prediction and validation set were 32.860, 40.739 and 35.890, respectively, which were smaller than those obtained by the PLS model (180.972, 165.047 and 149.950, respectively). The obtained results reveal the reliability and good predictivity of the neural network model for the prediction of ferric iron precipitation in bioleaching process. [ABSTRACT FROM AUTHOR]

Abstract: Videogrammetry is an inexpensive and easy-to-use technology for spatial 3D scene recovery. When applied to large scale civil infrastructure scenes, only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames requires careful consideration. Videotaping a built infrastructure scene results in large video files filled with blurry, noisy, or redundant frames. This is due to frame rate to camera speed ratios that are often higher than necessary; camera and lens imperfections and limitations that result in imaging noise; and occasional jerky motions of the camera that result in motion blur; all of which can significantly affect the performance of the videogrammetric pipeline. To tackle these issues, this paper proposes a novel method for automating the selection of an optimized number of informative, high quality frames. According to this method, as the first step, blurred frames are removed using the thresholds determined based on a minimum level of frame quality required to obtain robust results. Then, an optimum number of key frames are selected from the remaining frames using the selection criteria devised by the authors. Experimental results show that the proposed method outperforms existing methods in terms of improved 3D reconstruction results, while maintaining the optimum number of extracted frames needed to generate high quality 3D point clouds. [Copyright &y& Elsevier]

Selecting a suitable project manager for construction projects is one of the most important decisions made by construction firms. In the traditional approach, interviews are conducted by senior managers of the company, who consider the project requirements and the candidates’ capabilities. However, interviewing candidates is usually time consuming and there is the risk of impaired judgment, leading to human error. Decision-making support systems are therefore very useful. In previous work, the authors proposed a fuzzy system to address the issues described above (Rashidi et al. 2011). In this paper, a simpler robust model is presented. The advantages of this new model lie in its simplicity and the fact that it is not necessary to consider many criteria in the selection procedure when using this model. This model only requires 15 fields of candidate information. In the development of this model, the first step is construction of an initial fuzzy model based on all the criteria that may be considered when selecting a project manager. The significance of the coefficients of the criteria are then determined. In the next step, the model is optimized by changing the number of fuzzy rules and reducing the number of criteria. Finally, the most appropriate model is chosen on the basis of the least number of criteria required to obtain accurate results. To show the model's capability, it is used in real interviews. The obtained results indicate the high accuracy of the model in predicting the output, that is, the best candidate in the interviews. [ABSTRACT FROM AUTHOR]

As-built spatial data are useful in many construction-related applications, such as quality control and progress monitoring. These data can be collected using a number of imaging and time-of-flight-based (e.g., laser scanning) sensor methods. Each application will demand a particular level of data accuracy and quality, yet little information is available to help engineers choose the most cost-effective approach. This paper presents an analytical and quantitative comparison of photogrammetric, videogrammetric, and time-of-flight-based methods. This comparison is done with respect to accuracy, quality, time efficiency, and cost. To this end, representative image-based three-dimensional reconstruction software and commercially available hardware (two cameras and a time-of-flight-based laser scanner) are evaluated. Spatial data of typical infrastructure (two bridges and a building) are collected under different settings. The experimental parameters include camera type, resolution, and shooting distance for the imaging sensors. By comparing these data with the ground truth collected by a total station, it is revealed that video/photogrammetry can produce results of moderate accuracy and quality but at a much lower cost as compared to laser scanning. The obtained information is useful to help engineers make cost-effective decisions and help researchers better understand the performance impact of these settings for the sensor technologies. [ABSTRACT FROM AUTHOR]

Precise documentation of as-is status of transportation infrastructures is an essential task for several public transportation agencies. To tackle this task, such agencies have adopted various advanced technologies, such as light detection and ranging (LiDAR), for three-dimensional (3D) reconstruction and as-built documentation purposes. In this research, we study the feasibility of an alternative 3D reconstruction method, photogrammetry, which has the advantage of being inexpensive and easy to operate compared to LiDAR. To assess the proposed alternative method in transportation asset-inventory collection, this paper evaluates the feasibility of using photogrammetry in providing an acceptable 3D point cloud model of two important categories of transportation assets: roadway assets and pedestrian access ramps. The analysis of the data quality and associated cost attests to the feasibility of using close-range photogrammetry in pedestrian access ramps inventory, while using this technology as a complementary tool with LiDAR in the mobile setting holds the promise for a feasible roadway asset data collection. [ABSTRACT FROM AUTHOR]

As-built spatial data are useful in many construction-related applications, such as quality control and progress monitoring. These data can be collected using a number of imaging and time-of-flight-based (e.g., laser scanning) sensor methods. Each application will demand a particular level of data accuracy and quality, yet little information is available to help engineers choose the most cost-effective approach. This paper presents an analytical and quantitative comparison of photogrammetric, videogrammetric, and time-of-flight-based methods. This comparison is done with respect to accuracy, quality, time efficiency, and cost. To this end, representative image-based three-dimensional reconstruction software and commercially available hardware (two cameras and a time-of-flight-based laser scanner) are evaluated. Spatial data of typical infrastructure (two bridges and a building) are collected under different settings. The experimental parameters include camera type, resolution, and shooting distance for the imaging sensors. By comparing these data with the ground truth collected by a total station, it is revealed that video/photogrammetry can produce results of moderate accuracy and quality but at a much lower cost as compared to laser scanning. The obtained information is useful to help engineers make cost-effective decisions and help researchers better understand the performance impact of these settings for the sensor technologies. [ABSTRACT FROM AUTHOR]

Selecting a project manager is a major decision for every construction company. Traditionally, a project manager is selected by interviewing applicants and evaluating their capabilities by considering the special requirements of the project. The interviews are usually conducted by senior managers, and the selection of the best candidate depends on their opinions. Thus, the results may not be completely reliable. Moreover, conducting interviews for a large group of candidates is time-consuming. Thus, there is a need for computational models that can be used to select the most suitable applicant, given the project specifications and the applicants' details. In this paper, a case study is performed in which a Fuzzy Multiple Criteria Decision Making (FMCDM) model is used to select the best candidate for the post of project manager in a large construction firm. First, with the opinions of the senior managers, all the criteria and sub-criteria required for the selection are gathered, and the criteria priorities are qualitatively specified. Then, the applicants are ranked using the Analytic Hierarchy Process (AHP), approximate weights of the criteria, and fuzzy technique for order performance by similarity to ideal solution (TOPSIS). The results of the case study are shown to be satisfactory. [ABSTRACT FROM AUTHOR]

Choosing a project manager for a construction project-particularly, large projects-is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates' information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system's output error and propagating this error within the system. After specifying the system parameters, the membership function parameters-which by means of clustering and projection were approximated-were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions. [ABSTRACT FROM AUTHOR]

Automatically recognizing and tracking construction equipment activities is the first step towards performance monitoring of a job site. Recognizing equipment activities helps construction managers to detect the equipment downtime/idle time in a real-time framework, estimate the productivity rate of each equipment based on its progress, and efficiently evaluate the cycle time of each activity. Thus, it leads to project cost reduction and time schedule improvement. Previous studies on this topic have been based on single sources of data (e.g., kinematic, audio, video signals) for automated activity-detection purposes. However, relying on only one source of data is not appropriate, as the selected data source may not be applicable under certain conditions and fails to provide accurate results. To tackle this issue, the authors propose a hybrid system for recognizing multiple activities of construction equipment. The system integrates two major sources of data—audio and kinematic—through implementing a robust data fusion procedure. The presented system includes recording audio and kinematic signals, preprocessing data, extracting several features, as well as dimension reduction, feature fusion, equipment activity classification using Support Vector Machines (SVM), and smoothing labels. The proposed system was implemented in several case studies (i.e., ten different types and equipment models operating at various construction job sites) and the results indicate that a hybrid system is capable of providing up to 20% more accurate results, compared to cases using individual sources of data. [ABSTRACT FROM AUTHOR]

According to energy policies, it is necessary to replace electrical energy or fossil fuels by renewable energy sources. Solar energy systems are recognized as one of these sources. In such systems, high-performance is one of the essential needs. Porous materials have been introduced as one of the most efficient and affordable techniques to improve the heat transfer and energy efficiency in solar energy systems. In this review, the applications of porous materials and their advantages and limitations on different types of solar energy systems are summarized. Moreover, the structures of each system with their applications are briefly introduced. Solar energy systems covered in this paper contains both energy conversion and energy storage systems. [ABSTRACT FROM AUTHOR]

Increased heat stress and air pollution are two major causes of health issues and productivity loss in the construction industry. This will be exacerbated by global warming and an increased possibility of extreme weather conditions in the future. Previous studies revealed that each 1°C rise in temperature reduces construction workers' productivity by up to 2%. Also, it is found that a 10 μg/m3 particulate matter sized 2.5 μm (PM2.5) fluctuation over 25 days lowers workers' daily production by 1%. As an effective dust control and cooling strategy, water spraying is used by practitioners to mitigate the effect of air pollution and heat-related problems on construction workers. Considering low costs and high mobility, unmanned aerial vehicles (UAVs) could be considered as a potential alternative for ground-based, stationary water spraying systems. To this end, a case study approach is adopted in this project to investigate the feasibility of using nebulizer-retrofitted UAVs in controlling air pollution and reducing heat stress at construction job sites. The case study consists of two different residential job sites in the state of Utah: one in Salt Lake City and the other one in St. George. The main contribution of this study is to determine the effect of aerial water spraying on air temperature and pollution at job sites. The outcomes of this study show that the average wet bulb globe temperature (WBGT) decreases 1.7° during the flight phase compared to average values for preflight and postflight phases. The aerial water spraying technique yields better and more water-efficient results in decreasing temperature at job sites compared to the existing approaches, such as stationary fans. Moreover, the results of PM variations illustrate that the mean value of particulate change was significantly different between flight and preflight (p=0.005), and flight and postflight (p<0.001) modes. Future studies should include the deployment of multiple drones flying simultaneously at job sites to cover larger areas. [ABSTRACT FROM AUTHOR]

Abstract: A number of methods are commonly used today to collect infrastructure''s spatial data (time-of-flight, visual triangulation, etc.). However, current practice lacks a solution that is accurate, automatic, and cost-efficient at the same time. This paper presents a videogrammetric framework for acquiring spatial data of infrastructure which holds the promise to address this limitation. It uses a calibrated set of low-cost high resolution video cameras that is progressively traversed around the scene and aims to produce a dense 3D point cloud which is updated in each frame. It allows for progressive reconstruction as opposed to point-and-shoot followed by point cloud stitching. The feasibility of the framework is studied in this paper. Required steps through this process are presented and the unique challenges of each step are identified. Results specific to each step are also presented. [Copyright &y& Elsevier]

Automatically recognizing rebar by using ground penetrating radar (GPR) is an ongoing research area that attracts much attention from researchers and practitioners. Existing studies in the literature have presented various methods to recognize rebar from GPR data, but these methods are mainly focused on straight-forward cases and do not systematically address the signal interference challenges. In this manuscript, the authors propose a novel method to address these challenges based on designing robust cascaded frequency filters. The frequency filters are pertinently designed to decompose GPR data into different components according to the directional transformation of the GPR image, which are then reconstructed as a refined hyperbolic pattern based on a similarity comparison between right and left tail images. Finally, the proposed method is validated using both synthetic and actual on-site datasets and the obtained results are compared with the results of two other state-of-the-art methods existing in the literature. • Challenges within recognizing rebar in GPR data with complex signal interference are discussed and addressed. • Cascaded spatial frequency filters are proposed to automatically recognize rebar in such challenging scenarios. • A similarity score is proposed and computed to reconstruct rebar and remove GPR image noise. • Synthetic and on-site datasets are used to verify the performance of the proposed method and the results are promising. [ABSTRACT FROM AUTHOR]

As the construction industry experiences a high rate of casualties and significant economic loss associated with accidents, safety has always been a primary concern. In response, several studies have attempted to develop new approaches and state-of-the-art technology for conducting autonomous safety surveillance of construction work zones such as vision-based monitoring. The current and proposed methods including human inspection, however, are limited to consistent and real-time monitoring and rapid event recognition of construction safety issues. In addition, the health and safety risks inherent in construction projects make it challenging for construction workers to be aware of possible safety risks and hazards according to daily planned work activities. To address the urgent demand of the industry to improve worker safety, this study involves the development of an audio-based event detection system to provide daily safety issues to laborers and through the rapid identification of construction accidents. As an evidence-driven approach, the proposed framework incorporates the occupational injury and illness manual data, consisting of historical construction accident data classified by types of sources and events, into an audio-based safety event detection framework. This evidence-driven framework integrated with a daily project schedule can automatically provide construction workers with prenotifications regarding safety hazards at a pertinent work zone as well as consistently contribute to enhanced construction safety monitoring by audio-based event detection. By using a machine learning algorithm, the framework can clearly categorize the narrowed-down sound training data according to a daily project schedule and dynamically restrict sound classification types in advance. The proposed framework is expected to contribute to an emerging knowledge base for integrating an automated safety surveillance system into occupational accident data, significantly improving the accuracy of audio-based event detection. • This study aims to develop a new audio-based event detection system for automated construction safety surveillance. • The evidence-driven approach is based on historical safety databases, project schedule, and detected work sound data. • Audio data of construction work activities are analyzed by a machine learning-based sound classification technique. • This system provides a knowledge base for integrating an automated safety surveillance system in occupational accident data. • The proposed system provides possible daily safety issues to laborers and help their rapid identification. [ABSTRACT FROM AUTHOR]

No Feed and Single Withdrawal (NFSW) cascades are of non-conventional gas centrifuge cascades. The present study has been conducted to investigate the effect of holdup of pipes and stages as well as the shape of cascade in separating the multi-component isotopes in NFSW cascades. These cascades are optimized by the Particle Swarm Optimization (PSO) algorithm with an advanced objective function to evaluate the effect of the cascade shape. Moreover, we take into account the holdup term in the equations to study the effect of the holdup of pipes and stages. By investigating the effect of the Holdup of pipes and stages, we found that as the holdup increases, the amount of flow concentration changes. We also compare the optimal tapered structure with the square shape of the NFSW cascade. The results showed that the optimal tapered shape is superior over the square shape in separating the middle component of Xenon. • The holdup of pipes and stages is added to the mass conservation equations. • The results showed that the holdups of pipes and stages affect the separation of middle components in NFSW cascades. • The Particle Swarm Optimization (PSO) algorithm is introduced for optimization of NFSW cascades. • The results revealed the superiority of the taper arrangement of gas centrifuges over square arrangement in NFSW cascades. [ABSTRACT FROM AUTHOR]

Despite significant advances in the treatment of diabetic foot ulcers and below-the-knee critical ischemia, there are ongoing efforts to achieve a method with low complication, high success rate and persistence of long-term effects. The aim of the study was to examine the outcome of angioplasty in patients with below-the-knee critical ischemia referred to Hospital. Material and methods. This semi-experimental study conducted on diabetics patients treated with PTA (Percutaneous transluminal angioplasty) with critical ischemia of lower limbs referred to Sina Hospital. After discharge, the patients were followed weekly for the first month and then monthly up to 12 months. The procedure short-term effects were examined through evaluation of wound healing as well as patients' recovery and pain relief, after one month. Given the distribution type, parametric and non-parametric test were used to compare the results before and after treatment. Pearson's correlation coefficient was used to determine the correlation between variables. Results. Twenty four patients participated in this study. The mean ankle-brachial index (ABI) at baseline was 0.55±0.17. A month after angioplasty, the index increased statistically significant to 0.93±0.16. The mean health score expressed by the patients at baseline was 5.48±1.39. A month after angioplasty, it was significantly increased (6.32±1.24). The mean pain score before enrollment was 6.68±2.52 (according to VAS scale). There was a significant decrease over time (3.45±1.13). The overall mean score of all patients at Rutherford Classification was 3.88±0.63 at baseline. During the 1st month and 6th month follow-up, it was changed to Class 0 that was statistically significant in the first month. Conclusions. This study represents the mid-term outcomes of PTA. Although PTA treatment was associated with improved pain scores, satisfaction with health, classification of limb ischemia and diabetic foot ulcers, the effects only remain short-term and mid-term. However, long-term efficacy of PTA needs to be investigated further. [ABSTRACT FROM AUTHOR]

Abstract: In this investigation, bioleaching of vanadium from uranium ore residue of the leaching stage was studied by Aspergillus niger in a two-step process at 30°C and 150rpm. The first step was initiated by growth of fungi in the absence of mine waste. Using response surface methodology, three factors were surveyed for fungal growth: initial pH, sucrose concentration and spore population. Also concentrations of oxalic, citric, and gluconic acids were measured as response in this step. During 30days, maximum productions of these acids were 3265, 11578, and 7988mg/l, respectively. Initial pH and sucrose concentration were significant factors for oxalic and citric acid production; however, for gluconic acid production sucrose concentration and spore population were significant. Then, the content of each flask was filtered and mine waste was added to liquor with pulp density of 3%. During 3days, in the second step, vanadium recovered about 44.8% in the liquor. [Copyright &y& Elsevier]