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Human activities in riverine water bodies can cause loading of nutrients and other pollutants in receiving systems, affecting species interactions and population dynamics. The present study aimed to investigate the influence of anthropogenic alteration on an artificial water body, i.e., the Shahid Rajaee reservoir, in Iran that is experiencing a serious water crisis. The reservoir is subject to high anthropogenic nutrient loads from the Shirinroud and Sefidroud rivers, the effect of which needs to be evaluated considering the strategic importance of the reservoir as a source of drinking water. A field sampling campaign was carried out at seven stations (one in each of the two river mouths and five in the reservoir itself) each month from April to July 2016. The study entailed sampling of benthic macroinvertebrate fauna at the river mouths and plankton communities at the reservoir stations. In addition to macroinvertebrate and plankton, abiotic and nutrient variables were measured at all stations. Substantial loading of organic matter, mainly from the Shirinroud River, was detected throughout the sampling period, with a detrimental effect on both benthic and plankton communities. The decline in plankton diversity was particularly noticeable at stations close to the river mouths during June and July, with a bloom of Dinoflagellata and Cyanobacteria and strong dominance of larger zooplankton taxa, e.g., Copepoda. Our results suggest that river water quality, affected by human activities, alters the planktonic communities in Rajaee reservoir. Our assessment indicates that measures on both the Shirinroud River and the reservoir are required to mitigate and prevent even stronger eutrophication of the reservoir ecosystem. [ABSTRACT FROM AUTHOR]

As the popularity of Location-based Social Networks increases, designing accurate models for Pointof-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user’s main activity location and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation. However, previous research does not consider the impact of the combination of these different factors. In this article, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this article are as follows: (i) providing an extensive survey of context-aware location recommendation; (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, which can incorporate all the major contextual information into a single recommendation model; and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy. [ABSTRACT FROM AUTHOR]

The detection of structure centerlines from imaging data plays a crucial role in the understanding, application and further analysis of many diverse problems, such as road mapping, crack detection, medical imaging and biometric identification. In each of these cases, pixel-wise segmentation is not sufficient to understand and quantify overall graph structure and connectivity without further processing that can lead to compound error. We thus require a method for automatic extraction of graph representations of patterning. In this paper, we propose a novel Deep Orientated Distance-transform Network (DODN), which predicts the centerline map and an orientated distance map, comprising orientation and distance in relation to the centerline and allowing exploitation of its geometric properties. This is refined by jointly modeling the relationship between neighboring pixels and connectivity to further enhance the estimated centerline and produce a graph of the structure. The proposed approach is evaluated on a diverse range of problems, including crack detection, road mapping and superficial vein centerline detection from infrared/ color images, improving over the state-of-the-art by 2.1%, 10.9% and 17.3%/ 4.6% respectively in terms of quality, demonstrating its generalizability and performance in a wide range of mapping problems. • We present a novel multitask learning architecture for centerline detection. • We incorporate task uncertainty to simultaneously learn multiple losses. • A graph-cut based connectivity refinement approach is proposed. • Our proposed approach outperforms state-of-the-art methods on three datasets. [ABSTRACT FROM AUTHOR]

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs' anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups. [ABSTRACT FROM AUTHOR]

In the present work, the effect of impactor nose shape and fiber stacking sequences on the low‐velocity impact properties of hybrid Kevlar/basalt fiber reinforced epoxy composites was investigated. The composite specimens were prepared using hand lay‐up method with six types of fiber stacking sequences including sandwich hybrid, intercalated hybrid and not‐hybrid. The impact tests were conducted using three types of impactor nose shapes (hemispherical, conical, and flat) at two impact energies of 40 and 60 J. Some characteristics of the composites like maximum force, absorbed energy, and damage area were evaluated. Also, the impact behavior of the composites was simulated in Abacus finite element software and the obtained results were compared with the experimental values. The results indicated the hybridization of Kevlar by basalt fibers (intercalated hybrid) considerably improved the impact properties of the specimen. Besides, when the conical impactor was employed, the highest absorbed energy and damaged area were obtained. [ABSTRACT FROM AUTHOR]

Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset. [ABSTRACT FROM AUTHOR]

The implementation of extrusion-based 3D concrete printing (3DCP) in large-scale constructions is currently limited by concerns regarding rheology control and the sustainability of this process. To address these issues, this study presents an approach to develop limestone-calcined clay-based cementitious (LC3) materials accelerated by Ca(NO 3) 2 solution in an inline static mixer-based 3DCP setup. Using this approach, a printable mixture containing only about 275 kg/m3 of Portland cement was formulated that can exhibit a good buildability performance and a 28-day compressive strength of over 30 MPa. Additionally, the effects of adding Ca(NO 3) 2 solution on the initial setting time, structural build-up, inline buildability, early-age hydration, and compressive strength of LC3 materials were investigated and discussed. Results show that the addition of Ca(NO 3) 2 solution improved the buildability and accelerated initial setting as well as the structuration due to the promoted ettringite precipitation and C –S–H nucleation. Furthermore, compressive strength at 7 and 28 days was improved through increasing the Ca(NO 3) 2 dosage, which can be attributed to the formation of NO 3 -AFm and the increase in the amount of C –S–H gels. [ABSTRACT FROM AUTHOR]

Control of a Twin Rotor Multi-input Multi-output System (TRMS) is not a simple work. Because it has complex nonlinear dynamics, cross-coupling, uncertainties, and instability. This paper provides a practical method for control of a TRMS, named Quantitative Feedback Theory (QFT) as one of the robust approaches. Firstly, the TRMS set and modeling procedure are introduced. Secondly, the nonlinear and linear equations of electrical and mechanical parts in both vertical and horizontal planes are presented. Next, using the QFT method, a controller is designed for motion in each plane. Finally, the robustness of the control strategy is illustrated by simulations of vertical and horizontal motions, including controller and pre-filter in the presence of uncertainties. The results demonstrate that the proposed robust controller can guarantee the system stabilization, as well as pitch and yaw tracking of TRMS. [ABSTRACT FROM AUTHOR]

Background: Since vascular endothelial growth factor (VEGF) is a significant regulator of cancer angiogenesis, it is essential to develop a technology for its sensitive detection. Herein, we sensitized a chemiluminescence (CL) immunoassay through the combination of H2O2-sensitive TGA-CdTe quantum dot (QD) as signal transduction, dextran as a cross-linker to prepare enzyme-labeled antigen and the ultrahigh bioactivity of catalase (CAT) as reporter enzyme. Results: Under the optimized experimental conditions, the chemiluminescence enzyme-linked immunosorbent assay (CL-ELISA) method can detect VEGF in the excellent linear range of 2–35,000 pg mL−1, with a detection limit (S/N = 3) of 0.5 pg mL−1 which was approximately ten times lower than the commercial colorimetric immunoassay. This proposed method has been successfully applied to the clinical determination of VEGF in the human serum samples, and the results illustrated an excellent correlation with the conventional ELISA method (R2 = 0.997). The suitable recovery rate of the method in the serum ranged from 97 to 107%, with a relative standard deviation of 1.2% to 13.4%. Conclusions: The novel immunoassay proposes a highly sensitive, specific, and stable method for very low levels detection of VEGF that can be used in the primary diagnosis of tumors. With the well-designed sensing platform, this approach has a broad potential to be applied for quantitative analysis of numerous disease-related protein biomarkers for which antibodies are available. [ABSTRACT FROM AUTHOR]

We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing such data is prohibitively expensive. Therefore, we develop a framework for synthesizing the training data. First, we learn representative human poses from a large corpus of real motion captured human skeleton data. Next, we fit synthetic 3D humans with different body shapes to each pose and render each from 180 camera viewpoints while randomly varying the clothing textures, background and lighting. Generative Adversarial Networks are employed to minimize the gap between synthetic and real image distributions. CNN models are then learned that transfer human poses to a shared high-level invariant space. The learned CNN models are then used as invariant feature extractors from real RGB and depth frames of human action videos and the temporal variations are modelled by Fourier Temporal Pyramid. Finally, linear SVM is used for classification. Experiments on three benchmark human action datasets show that our algorithm outperforms existing methods by significant margins for RGB only and RGB-D action recognition. [ABSTRACT FROM AUTHOR]

In this study, an analytical solution is proposed for the problem of transient anisotropic conductive heat transfer in composite cylindrical shells. The composite shells are considered to have directional heat transfer properties, which is due to the existence of fibers which can be winded in any direction. The composite shells usually show high conductivity in the direction parallel to fiber direction and low conductivity in other two orthogonal directions. To solve the heat transfer partial differential equation, finite Fourier transform and separation of variables method are used. The present solution is used to find the temperature distribution in a composite cylindrical vessel for which the composite material is graphite/epoxy and the vessel is prone to an external heat flux and also ambient flow. The analytical solution is verified perfectly by the data obtained from a second-order finite difference solution. The solution is used to investigate the effects of values of fiber angle and material conductivity coefficients on temperature distribution of the composite cylindrical vessel. The results show the important role of fiber angle values on the temperature distribution of vessel. [ABSTRACT FROM AUTHOR]

To extract structured knowledge from unstructured text sources we need to understand the semantic relationships between entities. State-of-the-art relation extraction techniques take advantage of the abundance of data on the web. However, in domains with sparse data such as social networks which have limited occurrences of entities and relationship patterns, bootstrapping techniques and pattern detection methods are inefficient and inaccurate. In this paper, we introduce REDS, a Relation Extraction approach based on Distant Supervision. REDS extracts the named entities from text documents and assigns a fingerprint to each potential relationship among the named entities. Then, it queries a knowledge repository for similar matches to each fingerprint. An assessor uses the query results and the data statistics to measure the validity of the relationships corresponding to the queried fingerprints, and labels each potential relationship with the predicted type. In addition to handling the relation extraction in presence of data sparsity, REDS uses an information retrieval framework that makes it scalable and capable of dealing with noisy data. We implement and test REDS on a non-English historical archive consisting of unstructured notarial acts and structured civil registers; By means of manual evaluations REDS achieves precision of 0.90. [ABSTRACT FROM AUTHOR]

We propose an image-based technique to measure the volume, weight and the size distribution of gravel particles in a gravel-soil mixture. The proposed method uses 3D scanning and a surface reconstruction algorithm to generate a high-resolution depth image, which is then used to accurately estimate the volume and weight of each gravel particle. The proposed method is evaluated on several gravel soil samples collected from 25 farming locations. The experimental results show that the proposed technique produces an accurate estimate of gravel volumes and gravel weights. It achieves a relative root mean square error of 4% for large gravel particles and an overall correlation of 0.99 with the ground truth, for the task of gravel volume estimation. For the estimation of gravel weight distribution, the proposed method can reach a low root mean square error of 0.54%. The rapid measurement of the full spectrum of coarse fragments in soil, using this method, is an advantage compared to the manual methods. • A method to measure size distribution of gravel particles in a gravel-soil mixture. • It is an automatic image-processing and machine learning based method. • It can accurately estimate the volume and weight of each gravel particle. • The experimental results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Composite laminates have found extensive application in various industries, and numerous studies have been conducted to investigate their behavior in flat sheet configurations. The present study aims to extend this research by examining the behavior of convex composite specimens subjected to high velocity impact (HVI), and comparing their energy absorption and failure area to that of flat sheets made of glass fibers. Additionally, we seek to investigate the influence of curvature diameter on the energy absorption and fiber failure percentage of the composite specimens. Four series of composite laminates with distinct geometries, comprising a flat specimen and three convex composite specimens, were fabricated with nine layers each and diameters of 10, 15, and 20 cm. All specimens were fully clamped and subjected to empirical gas gun impact tests at three different velocities of 200, 250, and 300 m/s. The velocity values before and after the impact were measured using velocity sensors, and the energy absorption of the laminates was computed. Of the tested composite specimens, those with smaller diameters and greater curvature exhibited reduced fiber damage percentage and increased energy absorption. Compared to the flat specimens made of glass fibers, the convex specimens with a diameter of 10 cm exhibited an energy absorption increase of approximately 47%, while the specimens with diameters of 15 and 20 cm showed approximately energy absorption increases of 42% and 7%, respectively. The results were confirmed through simulation using finite element software ABAQUS/Explicit. Highlights: Curved E‐glass laminates studied under high velocity impact which is done experimentally and numerically.Convexity increases energy absorption capacity of E‐glass reinforced epoxy composites.The results show that convexity can absorb more energy compared to flat panels.The damaged area of curved laminates are smaller than the flat one.The findings suggest that convexity can be a useful design feature for improving impact resistance in various applications. [ABSTRACT FROM AUTHOR]

Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a set of non-linear transformations that connects the views. The R-NKTM is learned from 2D projections of dense trajectories of synthetic 3D human models fitted to real motion capture data and generalizes to real videos of human actions. The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to incorporate new action classes. R-NKTM is learned with dummy labels and does not require knowledge of the camera viewpoint at any stage. Experiments on three benchmark cross-view human action datasets show that our method outperforms existing state-of-the-art. [ABSTRACT FROM AUTHOR]

Flame-retardant halogen-free epoxy resin, containing phosphorus and nitrogen atoms in the main chain, was synthesized through the curing of tris(3-(bis(oxiran-2-ylmethyl)amino)phenyl)phosphine oxide (HGE, hexaglycidyl epoxy monomer), starting from tris(3-aminophenyl) phosphine oxide (TAPO) and epichlorohydrin. The molecular structure of HGE with molecular weight 660 was confirmed using Fourier transform infrared, nuclear magnetic resonance, and liquid chromatography–mass spectrometry techniques. Epoxy equivalent weight determined by titration method was 120. The thermal curing behavior of the HGE/TAPO was investigated by differential scanning calorimetry. An intense exotherm due to curing reaction was observed in the temperature range from 123°C to 215°C. The HGE cured with TAPO, 4,4′-diaminodiphenylsulfone (DDS), and 1,5-diaminonaphthalene (DAN) and the thermal behaviors were studied by thermogravimetric analysis. The flame retardancy properties of the HGE/TAPO, DDS, and DAN were evaluated by vertical burning test (UL-94 V). The high performance cured epoxy resins showed high thermal stability and UL-94 V-0 flame retardancy rating. [ABSTRACT FROM AUTHOR]

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the histogram of oriented principal components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of spatio-temporal keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The experimental results show that our techniques provide significant improvement over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Point-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. Previous studies have shown that incorporating contextual information such as geographical, temporal, social, and categorical substantially improves the quality of POI recommendations. However, fewer works have studied in-depth the multi-aspect benefits of context fusion on POI recommendation, in particular on beyond-accuracy , fairness , and interpretability of recommendations. In this work, we propose a linear regression-based fusion of POI contexts that effectively finds the best combination of contexts for each (i) user, or (ii) group of users from their historical interactions. The results of large-scale experiments on two popular datasets Gowalla and Yelp reveal several interesting findings. First, the proposed approach does not present significant loss in accuracy and unfairness of popularity bias as with classical collaborative baselines, and yet improves the beyond-accuracy of recommendation compared with existing context-aware (CA) approaches using heuristic context fusions; for instance, the proposed approach improves the accuracy and beyond-accuracy compare to best baseline model by 25% and 30%, respectively. Second, our proposed approach is interpretable, allowing to explain to the user why she has been recommended specific POIs, based on the learned context weights from user past check-ins; for example, if you are in Rome and our method recommends you a historical place like 'Colosseum', it can also provide an explanation why this item is recommended to you based on your personal preference on context (e.g., you were recommended to visit 'Colosseum' because in the past your visited historical places). Third, by analyzing the fairness of recommendation with respect to users (based on their activity levels) and items (based on the popularity of items), we found that a model which is recommend fairly on one dataset can recommend unfair on another dataset. Overall, our study suggests that appropriate context fusion is an essential element of an accurate, fair, and transparent POI recommendation system. We highlight that while we have tested the efficacy of our context-fusion methods on two popular CA recommendation models in the POI domain, namely GeoSoCa and LORE, our system can be flexibly utilized to extend the capability of other CA algorithms. • The paper investigates the role of context fusion in POI recommendations. • Evaluations of the accuracy, beyond-accuracy, and fairness of POI recommendations. • We provide better recommendations in terms of beyond accuracy and accuracy metrics. • The study pushes the research forward by choosing several evaluation objectives. • The results of fairness show that contextual models provide better fairness results. [ABSTRACT FROM AUTHOR]

Entity Resolution (ER) is the process of identifying references referring to the same entity from one or more data sources. In the ER process, most existing approaches exploit the content information of references, categorized as content-based ER, or additionally consider linkage information among references, categorized as context-based ER. However, in new applications of ER, such as in the genealogical domain, the very limited linkage information among references results in a disjoint graph in which the existing content-/context-based ER techniques have very limited applicability. Therefore, in this paper we propose first, to use the homophily principle for augmentation of the original input graph by connecting the potential similar references, and second, to use a Random Walk based approach to consider contextual information available for each reference in the augmented graph. We evaluate the proposed method by applying it to a large genealogical dataset and we succeed to predict 420,000 reference matches with precision 92% and discover six novel and informative patterns among them which can not be detected in the original disjoint graph. [ABSTRACT FROM AUTHOR]

We propose a Locality-constrained Linear Coding (LLC) based algorithm that captures discriminative information of human actions in spatio-temporal subsequences of videos. The input video is divided into equally spaced overlapping spatio-temporal subsequences. Each subsequence is further divided into blocks and then cells. The spatio-temporal information in each cell is represented by a Histogram of Oriented 3D Gradients (HOG3D). LLC is then used to encode each block. We show that LLC gives more stable and repetitive codes compared to the standard Sparse Coding. The final representation of a video sequence is obtained using logistic regression with ℓ 2 regularization and classification is performed by a linear SVM. The proposed algorithm is applicable to conventional and depth videos. Experimental comparison with ten state-of-the-art methods on three depth video and two conventional video databases shows that the proposed method consistently achieves the best performance. [ABSTRACT FROM AUTHOR]

In this study, a solution for anisotropic conductive heat transfer in a composite cylindrical shell is computed by an exact analytical method. The fibers are considered to be winded around the cylinder at any arbitrary angle. A general linear boundary condition is applied on both circular bases of the cylindrical shell to account for various combinations of thermal conditions. The solution considers the effects of convection of the ambient flow motion and various external radiative heat fluxes. The analytical solution describes the temperature distribution in the axial and circumferential directions. In principle, the heat conduction equation should involve a dual second-order derivation, which precludes solving the equation by the direct application of common exact methods. Therefore, an appropriate canonical mapping is selected as a solution to cancel the dual derivation of temperature in the mapped equations. The separation of variables method is then applied to the mapped equations, which are conducted to obtain an exact form of the solution. Finally, an analytical technique based on the Fourier series concept is proposed to determine unknown coefficients in the final solution. The obtained analytical results correspond well with the solved numerical results. The capability of the current solution is examined by application to a few relevant industrial cases. [ABSTRACT FROM AUTHOR]

Analyzing different drugs for various purposes is an important issue in the area of computational biology. We categorize the previous computational studies into Individual and Network approaches. While the Individual approach focuses on one specific drug without considering its relationship with other drugs, the Network approach considers also the drugs relationships. In this paper, we apply a Network approach, previously proposed for discovering the relationships among diseases, to drug data. We construct a Human Drug Network (HDN) for 200 different drugs based on functional and structural information available in the PPI network. For evaluating our proposed HDN, first, we analyzed the literature to prove that the proposed HDN is biologically meaningful. Second, we used the HDN to augment the initial prior knowledge of different drugs. As an example of prior knowledge, we considered the initial seed proteins (a set of proteins which are previously known to be drug targets) of each drug. We clustered the HDN nodes using the Markov CLustering Algorithm (MCL) and then, we augmented the seed proteins of each drug based on the cluster it belongs to. In the end, we concluded that our proposed HDN enables us to generate novel hypotheses (in terms of potential drug target proteins) and produce complementary results comparing to existing methods. [ABSTRACT FROM AUTHOR]

Predicting novel drug side-effects, or Adverse Drug Reactions (ADRs), plays an important role in the drug discovery process. Existing methods consider mainly the chemical and biological characteristics of each drug individually, thereby neglecting information hidden in the relationships among drugs. Complementary to the existing individual methods, in this paper, we propose a novel network approach for ADR prediction that is called Augmented Random-WAlk with Restarts (ARWAR). ARWAR, first, applies an existing method to build a network of highly related drugs. Then, it augments the original drug network by adding new nodes and new edges to the network and finally, it applies Random Walks with Restarts to predict novel ADRs. Empirical results show that the ARWAR method presented here outperforms the existing network approach by 20% with respect to average Fmeasure. Furthermore, ARWAR is capable of generating novel hypotheses about drugs with respect to novel and biologically meaningful ADR. [ABSTRACT FROM AUTHOR]

Graphene oxide (GO) was functionalized using three different diamines, namely ethylenediamine (EDA), 4,4′-diaminodiphenyl sulfone (DDS) and p -phenylenediamine (PPD) to reinforce an epoxy adhesive, with the aim of improving the bonding strength of carbon fiber/epoxy composite. The chemical structure of the functionalized GO (FGO) nanosheets was characterized by elemental analysis, FT-IR and XRD. Hand lay-up, as a simple method, was applied for 3-ply composite fabrication. In the sample preparation, the fiber-to-resin ratio of 40:60 (w:w) and fiber orientations of 0°, 90°, and 0° were used. The GO and FGO nanoparticles were first dispersed in the epoxy resin, and then the GO and FGO reinforced epoxy (GO- or FGO-epoxy) were directly introduced into the carbon fiber layers to improve the mechanical properties. The GO and FGO contents varied in the range of 0.1–0.5 wt%. Results showed that the mechanical properties, in terms of tensile and flexural properties, were mainly dependent on the type of GO functionalization followed by the percentage of modified GO. As a result, both the tensile and flexural strengths are effectively enhanced by the FGOs addition. The tensile and flexural moduli are also increased by the FGO filling in the epoxy resin due to the excellent elastic modulus of FGO. The optimal FGO content for effectively improving the overall composite mechanical performance was found to be 0.3 wt%. Scanning electron microscopy (SEM) revealed that the failure mechanism of carbon fibers pulled out from the epoxy matrix contributed to the enhancement of the mechanical performance of the epoxy. These results show that diamine FGOs can strengthen the interfacial bonding between the carbon fibers and the epoxy adhesive. [ABSTRACT FROM AUTHOR]

The aim of this work was to investigate the prediction of the elastic constants of carbon fibre-reinforced epoxy laminated composites using a simple mathematical approach. For this purpose, a Matlab Calculation Code (MCC) has been developed in MATLAB, which provides sufficient capabilities for analysis of the mechanical behaviour of composite materials. The MATLAB code enables a variety of fibre orientation angles to be generated and a wide variety of fibre volume fractions to be analyzed under mechanical loading. In order to validate the accuracy of prediction results, a commercial grade epoxy resin (Araldite LY-5052) and unidirectional carbon fibre (Torayca T700) were used to fabricate some samples of laminated composites with cross-ply configuration using the hand lay-up method. Experimental results such as elastic modulus, tensile strength as well as elongation at break were compared with the theoretical data extracted from MCC. The theoretical analysis were verified with experimental results for the same type of composites. Although the values were different, excellent agreement in the trends of the changes was found for the two approaches. Therefore, it was concluded that the MCC is capable of predicting the elastic constants of laminated composites with a high level of confidence for a wide range of fabricating conditions. The results also showed that the tensile properties of composites made with 3-layer were generally greater than those of the other evaluated composites. This kind of behaviour may be explained by bondline defects, which adversely influence the mechanical properties. [ABSTRACT FROM AUTHOR]

The cellular metabolism of a living organism is among the most complex systems that man is currently trying to understand. Part of it is described by so-called protein-protein interaction (PPI) networks, and much effort is spent on analyzing these networks. Recently, there has been much interest in predicting involvement of network nodes (in this case, proteins) in different diseases. Many approaches to this problem exist. We categorize the previous studies into Individual and Network approaches. While the Individual approach focuses on one specific disease without considering its relationship with other diseases, the Network approach considers also these relationships. In this paper, we construct a Human Disease Network (HDN), using a novel approach for discovering relationships among different diseases. We built the HDN for 20 different diseases based on functional and structural information available in the PPI network. We showed that the proposed HDN is biologically meaningful and is capable of augmenting the initial prior knowledge of different diseases by sharing information across highly-related diseases. Furthermore, comparing to previous Individual and Network approaches, our proposed HDN increases the accuracy of predictive models and discovers more and still informative relationships among different diseases, respectively. [ABSTRACT FROM AUTHOR]

The objective of this research work was to gain a better understanding of the mechanical properties of epoxy resin composites reinforced with carbon fiber. For this purpose, the effects of fiber orientations, resin types, and number of laminates on mechanical properties of laminated composites have been investigated. In the sample preparation, composites were manufactured by hand lay-up process, using a fiber-to-resin ratio of 40:60 (w:w). To investigate the effect of fiber orientation, angles of 0°, 35°, 45°, and 90° were selected. Results show that the mechanical properties, in terms of tensile, flexural and impact strengths, were mainly dependent on the fiber orientations followed by the number of laminates. At a similar fiber orientation, the composites made with EM500 epoxy resin showed the highest mechanical properties (such as tensile and flexural and impact strengths) compared to other evaluated composites. However, the differences were not highly significant. The results indicated that the mechanical properties of composites made with five-ply were generally slightly greater than three-ply composites. It may be due to the bondline defects, which adversely influence the mechanical properties. Scanning micrographs of the composites showed that the epoxy matrix material was fully adhered to the fibers, indicating a strong interface. It can be concluded that the order of increment parameters in the mechanical properties of the composites is fiber orientation > number of laminates > resin type. In addition, the tensile and flexural properties were superior in case of ±35° fiber orientations. [ABSTRACT FROM PUBLISHER]

Based on the super twisting concept, this article develops an integral sliding mode controller with nonlinear disturbance observer for position control and extending the traveling range of an initially curved micro-beam. The nonlinear damped model of the curved micro-beam is modeled based on the non-classical continuum theory. The single-mode assumption is used to transform the nonlinear governing PDE of the system into a nonlinear state-space form. The robust controller is designed to overcome the position control problem in the presence of non-parametric uncertainties and unknown non-symmetric input saturation due to the existing constraints in the electrostatic actuation. The effects of the uncertainties are considered as a compound disturbance term which can be estimated in finite time. Furthermore, due to the estimation properties of the observer, knowledge about the bounds of the uncertainties is not required. Extensive simulation results clearly verified the effectiveness of the controller in position tracking and extending the traveling range of the initially curved micro-beam to the unstable zones under smooth control effort. [ABSTRACT FROM AUTHOR]

Halomethylated iminolactones have been synthesised from alkyl isocyanides, acetylene dicarboxylic acid esters and halogenated acetophenones in solvent free microwave assisted multi-component reactions. [ABSTRACT FROM AUTHOR]

Abstract: Disk scheduling is an operating system process to service disk requests. It has an important role in QOS guarantee of soft real-time environments such as video-on-demand and multimedia servers. Since now, some disk scheduling algorithms have been proposed to schedule disk requests in an optimized manner. Most of these methods try to minimize makespan by decreasing the number of disk head seeks as one of the slowest operations in modern computers and crucial for system performance because it usually takes some milli-seconds. In this paper, we propose a new disk scheduling method based on genetic algorithm that considers makespan and number of missed tasks simultaneously. In the proposed method, a new coding scheme is presented which employs simple GA procedures such as crossover and mutation and a penalty function in fitness. To get the best performance of the proposed method, its parameters such as number of chromosomes in initial population, mutation, and crossover probabilities, etc have been adjusted by applying it on some sample problems. The algorithm has been tested on several problems and its results were compared with well-known related methods. Experimental results showed that the proposed method worked very well and excelled most related works in terms of miss ratio and average seeks. [Copyright &y& Elsevier]

• Chitosan based materials (CBMs) have great potential application in wound healing. • CBMs have great biodegradable, biocompatible and antimicrobial activities. • Several approaches have been presented to modify chitosan using amino acids. • Amino acids provides more re-epithelization and cell adhesion deposition. Chitosan, a well-known biopolymer due to its unique properties, has received considerable attention as a result of the amine group activity that locates on the backbone of chitosan. To improve the mechanical and antibacterial characteristics of chitosan, various modifications have been used. Amino acids, the monomeric units of proteins, among all other compounds have been chosen to discuss as promising materials for wound healing in combination with chitosan. This review aims to provide an up-to-date overview of the methods used for modification of chitosan and the potential biomedical application, in particular wound healing, reported in the literature during the last five years. [ABSTRACT FROM AUTHOR]

Chaos is one of the most significant findings in physics and engineering. Fractional-order chaotic systems are nonlinear systems with special features which, some of them include sensitivity to initial condition and the order of fractional derivative, unpredictable and complex dynamic behavior, high bandwidth and controllable noise-like behavior. By accepting this truth that any irregular behavior in a dynamic system is a sign of chaos and that a chaotic system is a deterministic system with pseudo-random behavior, chaos can be observed in different fields of science and engineering like mathematics, physics, mechanical, chemical and electrical engineering. Recently, control of fractional-order chaotic systems has been one of the most interesting topics that attracted many researchers' idea. This paper deals with the comprehensive study of control and synchronization of fractional order chaotic systems, and shows how chaos is formed in developing inter-disciplinary researches of fractional order systems from the first research till today. [ABSTRACT FROM AUTHOR]

The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus. [ABSTRACT FROM AUTHOR]

Regularization is an essential aspect in the context of deep learning as it mitigates the risk of overfitting in deep neural networks. This study presents a novel deep learning regularization method, referred to DL-Reg, which effectively reduces the nonlinearity of deep networks by enforcing linearity to a certain extent. The proposed method is based on the incorporation of a linear constraint into the objective function of deep neural networks, which is defined as the error of a linear mapping from the inputs to the outputs of the model. Specifically, DL-Reg imposes a linear constraint on the network, which is further adjusted by a regularization factor, thereby preventing the network from overfitting. The effectiveness of DL-Reg is evaluated by training state-of-the-art deep network models on several benchmark datasets. The results of the experiments demonstrate that the proposed regularization method provides significant improvements over existing regularization techniques and enhances the performance of deep neural networks, particularly when dealing with small-sized training datasets. The main code of DL-Reg written in PyTorch is also available here: https://github.com/m2dgithub/DL-Reg.git. • Regularizing deep networks using a linear regression model. • Allowing supervised deep networks to be trained using the semi-supervised learning. • Increasing the performance of deep networks on the small-sized dataset. • Conducting extensive experiments to certify the significance of the proposed method. [ABSTRACT FROM AUTHOR]

Mustard Gas is one of chemical warfare agent that used in Iraq-Iran War. The first effects of SM are on the epithelial tissues of the eye, the respiratory system and the skin but in chronic phase patients suffer from systemic inflammation due to this exposure. Some of this patient suffer from cardiovascular disease and need to Coronary artery bypass grafting (CABG). Cardiac surgery evocates generalized inflammatory response in all patients and in this patient monitoring inflammatory before surgery is necessary because this mediator in this patients is more higher than other patients and preoperative inflammatory help us to better predict of this patients outcome and better manage on pump CABG. [ABSTRACT FROM AUTHOR]

Amyloid-β (Aβ) peptide and tau protein are two hallmark proteins in Alzheimer's disease (AD); however, the parameters, which mediate the abnormal aggregation of Aβ and tau, have not been fully discovered. Here, we have provided an optimum method to purify tau protein isoform 1N4R by using nickel-nitrilotriacetic acid agarose chromatography under denaturing condition. The biochemical and biophysical properties of the purified protein were further characterized using in vitro tau filament assembly, tubulin polymerization assay, circular dichroism (CD) spectroscopy and atomic force microscopy. Afterwards, we investigated the effect of tau protein on aggregation of Aβ (25–35) peptide using microscopic imaging and cell viability assay. Incubation of tau at physiologic and supra-physiologic concentrations with Aβ25–35 for 40 days under reducing and non-reducing conditions revealed formation of two types of aggregates with distinct morphologies and dimensions. In non-reducing condition, the co-incubated sample showed granular aggregates, while in reducing condition, they formed annular protofibrils. Results from cell viability assay revealed the increased cell viability for the co-incubated sample. Therefore, the disassembling action shown by tau protein on Aβ25–35 suggests the possibility that tau may have a protective role in preventing Aβ peptide from acquiring the cytotoxic, aggregated form against oxidative stress damages. [ABSTRACT FROM AUTHOR]

Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term global information that is complementary to the local action information of the partial sequences and helps improve action prediction. We show that action prediction can be improved by combining the latent global information with the local action information. We test the proposed method on three challenging skeleton datasets and report state-of-the-art performance. [ABSTRACT FROM AUTHOR]

• Training of a deep neural network for dehazing with unmatched training data. • Improvements over existing methods from based on objective PSNR and SSIM measures. • Demonstration of training method that can be applied to vision tasks. • Demonstration of effects of Haze on Object Detection Models. The rapid growth in computer vision applications that are affected by environmental conditions challenge the limitations of existing techniques. This is driving the development of new deep learning based vision techniques that are robust to environmental noise and interference. We propose a novel deep CNN model, which is trained from unmatched images for the purpose of image dehazing. This solution is enabled by the concept of the Siamese network architecture. Using object performance measures of image PSNR and SSIM we are able to demonstrate a quantitative and qualitative improvement in the network dehazing performance. This superior performance is achieved with significantly smaller training datasets than existing methods. [ABSTRACT FROM AUTHOR]

Background and aims: Pain management is one of the most important responsibilities of nurses in an intensive care unit (ICU). It is difficult to perform pain assessment appropriately in patients who are unable to report their pain. This study is aimed to determine the impact of implementing the critical care pain observation tool (CPOT) on the amount and frequency of analgesics' administration in ICUs. Materials and methods: This interventional study was conducted in 2014. Sixty nurses and 240 patients were studied. This study was carried out in three phases: first the data about amount and frequency of analgesic administration were extracted from patients' medical files. Then the CPOT was implemented into the nursing assessment process and finally, nurses' performance regarding the amount and frequency of analgesic administration was recorded. This data obtained before and after intervention were analyzed using chi-square and independent t-test p values less than 0.05 were considered significant. Results: In this interventional study, we found that there was no difference in the demography and cause of ICU admission before and after implementation of CPOT (age p = 0.937, gender p = 0.996, and the cause of admission p = 0.996). We found that after implementing the CPOT into the nursing assessment process, the amount of analgesics administered (7.95 ± 8.77 mg vs. 11.01 ± 11.04 mg, p = 0.018) and the frequency of administration (2.91 ± 1.38 vs. 4.16 ± 0.99, p <0.001) increased significantly. Moreover, there was a significant increase in the frequency of pain assessment per patient per day in nursing practice after implementation of CPOT as compared to the practice before (7.2 ± 2.48 vs. 1.03 ± 1.63, p <0.001). The mean pain scores before and after the intervention (5.5 ± 1.08 vs.2.2 ± 0.48) were also significantly different. Conclusion: Applying CPOT, as an objective mean of pain assessment, was effective in improving the performance of ICU nurses in assessment and management of patients' pain. It increased the amount and frequency of analgesic administration. We can recommend that COPT is a useful tool for assessment and management of pain in ICU patients and should be implemented in all ICUs. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the relationship between river water quality and the distribution of benthic macroinvertebrate communities in the Haraz River in Iran. Using a surber net sampler, benthic macroinvertebrate communities along the stream was sampled in wet and dry seasons of 2015 at nine stations with three replications. The physicochemical water quality parameters were measured in the field by water checker. Hilsenhoff biotic Indices, Shannon Wiener Diversity Indices, Average Score per Taxon (ASPT) Index and Pielou Evenness Index were applied to carry out a biological assessment of water quality. A total of 3781 (spring 769, summer 1092, autumn 1095 and winter 825) benthic macroinvertebrate specimens belonging to 4 orders, 11 classes and 16 families were identified. The lowest number of taxa was recorded in spring while the highest was recorded in autumn. Station 9 had the lowest number of taxa while the highest number of taxa was recorded at station 3. The average values (±SD) of the water quality parameters were temperature 14.75 ± 4.38 °C, pH 7.93 ± 0.62, water flow 14.11 ± 9.04 m3 s−1, electric conductivity 532.75 ± 161.35 μmohs cm−1, total dissolved solids 296.61 ± 76.21 mg L−1, salinity 0.28 ± 0.07 mg L−1, turbidity 580.77 ± 149.92 NTU and dissolved oxygen 8.08 ± 0.75 mg L−1. The assessment of stations 1 to 6 indicated that water quality conditions were suitable. In addition, substantial level of organic pollution was observed in stations 7 and 8. In station 9 water quality was fairly poor, requiring a more favourable management based on the capacity of the self-purification of the Haraz River. [ABSTRACT FROM AUTHOR]

Sulfur mustard (SM) is a toxic chemical agent that belongs to a class of vesicant compounds. In the 1980s it was used by the Iraqi army against Iranian forces. Sulfur mustard severely irritates the skin, eyes and lungs. The highest side effects seen in patients affected by this gas are pulmonary complications including different types of lung diseases such as bronchiolitis. It has also led to a certain type of chronic obstructive pulmonary disease called mustard lung. Similar extra-pulmonary, molecular and hormonal effects can be observed in these patients and patients with chronic obstructive pulmonary disease. Here cardiovascular complications may be one of the most dangerous visible effects. And atherosclerosis is probable following the direct effects or consequential long-term effects of SM. The development of atherosclerosis in these patients is associated with an increased risk of cardiovascular and coronary artery disease. Coronary artery bypass grafting surgery is the treatment of coronary artery disease. Doing this surgery by bypass pump has its own morbidity and due to local and systemic inflammation changes in patients with SM pulmonary disorders it may have more side effects. Therefore, detailed knowledge of inflammatory diseases as well as the serum level or even the local lung fluid of the inflammatory factors in these patients before surgery are needed so that it would be possible to reduce the rate of morbidity and mortality by normalizing the inflammatory conditions of the patients before cardiac surgery. [ABSTRACT FROM AUTHOR]

Despite extensive research, 3D face reconstruction from a single image remains an open research problem due to the high degree of variability in pose, occlusions and complex lighting conditions. While deep learning-based methods have achieved great success, they are usually limited to near frontal images and images that are free of occlusions. Also, the lack of diverse training data with 3D annotations considerably limits the performance of such methods. As such, existing methods fail to recover, with high fidelity, the facial details especially when dealing with images captured under extreme conditions. To address this issue, we propose an unsupervised coarse-to-fine framework for the reconstruction of 3D faces with detailed textures. Our core idea is that multiple images of the same person but captured under different viewing conditions should provide the same 3D face. We thus propose to leverage a self-augmentation learning technique to train a model that is robust to diverse variations. In addition, instead of directly employing image pixels, we use a set of discriminative features describing the identity and attributes of the face as input to the refinement module, making the model invariant to viewing conditions. This combination of self-augmentation learning with rich face-related features allows the reconstruction of plausible facial details even under challenging viewing conditions. We train the model end-to-end and in a self-supervised manner, without any 3D annotations, landmarks or identity labels, using a combination of an image-level photometric loss and a perception-level loss that is identity and attribute-aware. We evaluate the proposed approach on CelebA and AFLW2000 datasets, and demonstrate its robustness to appearance variations despite learning from unlabeled images. The qualitative comparisons indicate that our method produces detailed 3D faces even under extreme occlusions, out of plane rotations and noise perturbations where existing state-of-the-art methods often fail. We also quantitatively show that our method outperforms SOTA with more than 30.14%, 9.87% and 11.3% in terms of PSNR, SSIM and IDentity similarity, respectively. [ABSTRACT FROM AUTHOR]

Graphene oxide (GO) was functionalized using three different diamines, namely ethylenediamine (EDA), 4,4′-diaminodiphenyl sulfone (DDS) and p -phenylenediamine (PPD) to reinforce an epoxy/glass fiber (EP/GF) composite laminate, with the aim of improving the overall composite mechanical performance. Different mechanical characterization techniques were used to determine the mechanical performance, including: tensile stress strain, double cantilever beam (DCB) mode-I fracture toughness and dynamic mechanical thermal analysis (DMTA). Scanning electron microscopy (SEM) was used to support the results and conclusions. The results demonstrated remarkable enhancements in the mechanical performance of EP/GF composite laminates by incorporation of functionalized graphene oxide (FGO) nanofiller, whilst the mechanical performance of the GO reinforced composite only improved marginally. Finally, the mechanical performance of the EP/GF/FGO multi-scale composites was found to be dependent on the type of FGO functional groups; of which EDA exhibited the highest performance. These investigations confirmed that the EDA-FGO-reinforced EP/GF composites possess excellent potential to be used as multifunctional engineering materials in industrial applications. [ABSTRACT FROM AUTHOR]

The knuckle creases present on the dorsal side of the human hand can play significant role in identifying the offenders of serious crime, especially when evidence images of more recognizable biometric traits, such as the face, are not available. These knuckle creases, if localized appropriately, can result in improved identification ability. This is attributed to ambient inclusion of the creases and minimal effect of background, which lead to quality and discerning feature extraction. This paper presents an ensemble approach, utilizing multiple object detector frameworks, to localize the knuckle regions in a functionally appropriate way. The approach leverages from the individual capabilities of the popular object detectors and provide a more comprehensive knuckle region localization. The investigations are completed with two large-scale public hand databases which consist of hand-dorsal images with varying backgrounds and finger positioning. In addition to that, effectiveness of the proposed approach is also tested with a novel proprietary unconstrained multi-ethnic hand dorsal dataset to evaluate its generalizability. Several novel performance metrics are tailored to evaluate the efficacy of the proposed knuckle localization approach. These metrics aim to measure the veracity of the detected knuckle regions in terms of their relation with the ground truth. The comparison of the proposed approach with individual object detectors and a state-of-the-art hand keypoint detector clearly establishes the outperforming nature of the proposed approach. The generalization of the proposed approach is also corroborated through the cross-dataset framework. [ABSTRACT FROM AUTHOR]

Abstract: This study was conducted to determine the replacement value of faba bean meal (FBM) for soybean meal (SBM) in practical diets of Oncorhynchus mykiss fingerlings. Four isonitrogenous (42% crude protein) and isolipidic (20% total lipid) diets were formulated using commercial ingredients. Faba bean meal was added in the diets at levels of 0% (control) and 15, 30 and 45%. When the FBM content was increased in the diets, the soybean meal, fish meal and wheat flour were gradually reduced to adjust protein and carbohydrate contents. A completely randomized experimental design was developed with four treatments and three replicates. Two hundred and forty rainbow trouts with average initial weight of 12.5±0.09g were assigned to twelve experimental tanks. The experiment lasted for seven weeks. Based on results, survival did not differ among treatments (p>0.05). Growth performance and feed conversion ratio of rainbow trout fingerlings fed diets containing 15 and 30% FBM were similar (p>0.05) to the control diet. However, 15% FBM diet showed better performance (p<0.05) compared to the 30% FBM diet. The poorest growth performance and FCR were observed in the fish fed the diet containing 45% FBM. The apparent digestibility of dry matter and protein decreased with increasing levels of FBM from 30% to 45% in diet. No significant difference (p>0.05) was found in carcass protein and ash contents among the dietary treatments. The present results demonstrated that rainbow trout fingerlings can tolerate up to 30% FBM in diet, but maximum growth performance was found at the 15% FBM diet. [Copyright &y& Elsevier]

The present study proposes an exact analysis for unsteady anisotropic conduction heat transfer in heterogeneous composite conical shells. The fibers in the composite conical shell are wound in arbitrary directions so that a defined fiber angle changes between 0° and 90°. The cone's base is considered to have a general linear boundary condition, while the effects of internal heat generation, thermal convection, and external radiation heat flux on the temperature distribution are investigated. The heterogeneity of the heat transfer problem is caused by a linear dependence of conduction coefficients on the temperature. To solve the governing equations, first, the Kirchhoff transformation followed by an appropriate finite integral transform is applied. The resulting nonhomogeneous equation is then separated into two equations, i.e., steady equation and unsteady equation. The solution of the steady case is derived using Green's function, while the separation of variables method is utilized to solve the unsteady part. Eventually, the temperature distribution is achieved by applying the inverse transforms on the subsequent solutions. The verification of this solution is based on the comparison between the present analytical results and numerical computations with a second-order finite difference code. Applicability of the present solution is evaluated for resolving an industrial case problem. [ABSTRACT FROM AUTHOR]

Sulphur mustard (SM) is a well-known chemical warfare agent. It was first used during World War I and more recently in the Iran/Iraq conflict causing more than 100,000 Iranian casualties. Previous study showed that this gas can raised inflammatory marker but in this current case that suffered from cancer. After examine current case for candidate CABG, we found that Hs-CRP and IL-6 was very low than the other and this result show that this patients have bad inflammatory status and need to more tests. Result of the inflammatory marker in this patient was very abnormal and showed unknown effect of Sulphur mustard on patient and need to more research on mustard gas mechanism on inflammatory markers. [ABSTRACT FROM AUTHOR]

Risk screening tools play a crucial role in identifying potential high‐risk non‐native (NN) fish species. In this study, potentially invasive NN fish species in the Anzali Wetland Complex (AWC), which is located on the south coast of the Caspian Sea (Iran), were identified using the Aquatic Species Invasiveness Screening Kit (AS‐ISK). Twenty‐nine freshwater fish species were screened of which 13 exist in the AWC and 16 in close proximity to it ("horizon" species). Receiver operating characteristic curve analysis showed that AS‐ISK could discriminate reliably between non‐invasive and invasive fish species for the AWC. Mean threshold scores were 3.25 for the Basic Risk Assessment (BRA) and 11.75 for the BRA + CCA (BRA + Climate Change Assessment), and these, respectively, classified 89.7% and 86.2% of the species as high risk. The CCA resulted in an increase in the BRA scores for 86.2% of the species, suggesting the need to account in future NN species management for a likely increased invasiveness of those species under future climate conditions. These results suggest that AS‐ISK could prove an effective tool for identifying potentially invasive NN freshwater fishes in other wetlands of the Caspian Sea basin. [ABSTRACT FROM AUTHOR]

• Heat conduction in heterogeneous composite conical shells is solved analytically. • Fiber angle varies from zero to 90 degrees. • Heat conduction coefficients are temperature-depended. • Heat convection and external radiation are modeled. In present paper, problem of anisotropic heat conduction in heterogeneous composite conical shells is solved analytically. Arbitrary values for fiber angle (ranging from zero to 90 degrees) cause anisotropy for the heat conduction problem in composite conical shells. In our analysis, heat convection between conical shell and ambient flow is taken into account. In addition, an external source of radiative heat transfer is modeled. Herein, the heat conduction problem is assumed to be heterogeneous which is due to dependence of the conductivity coefficient on temperature. Kirchhoff transformation followed by an integral transform method are used to solve present heterogeneous heat conduction problem. Green's functions are applied to find the solution of final ordinary differential equations. Verification of the present analytical solution is obtained through comparing the analytical results with those of a second order finite difference method. Good agreement between the analytical and numerical results has been found. In order to evaluate the capability of our analytical solution in solving real industrial problems, the temperature distributions in a typical pressure vessel and a pin fin are calculated. [ABSTRACT FROM AUTHOR]