Some Fruit Characteristics of Iranian Cornelian Cherries (Cornus mas L.)

Although the Cornelian Cherry is widely grown in the north-eastern areas of Iran, it is not recognized as an important fruit crop as are many other fruit species. Large variability has been observed in all morphological and chemical compositions under study. Fruit weight varied from 1.499 to 3.29 g, whereas seed weight ranged from 0.249 to 0.425 g. The average lengths of fruits were between 15.22 and 22.31 mm, and the average widths of them were between 10.26-16.3 mm. The content of ascorbic acid ranged from 240-360 mg/ 100 g fresh weight. The total soluble solids and total acidity were 5-12.5% and 0.43-1.86% respectively. Grouping of Cornelian Cherry accessions based on 5 factors was performed and were divided into three sub-clusters. The results obtained from this study might be helpful for Cornelian Cherry breeders trying to develop new genotypes and varieties

Quantifying morphologies of developing neuronal cells using deep learning with imperfect annotations

The functionality of human intelligence relies on the interaction and health of neurons, hence, quantifying neuronal morphologies can be crucial for investigating the functionality of the human brain. This paper proposes a deep learning (DL) based method for segmenting and quantifying neuronal structures in fluorescence microscopy images of developing neuronal cells cultured in vitro. Compared to the majority of supervised DL-based segmentation methods that heavily rely on creating exact corresponding masks of neuronal structures for the preparation of training samples, the proposed approach allows for imperfect annotation of neurons, as it only requires tracing the centrelines of the neurites. This ability accelerates the preparation of training data by several folds. Our proposed framework is built on a modified version of PSPNet with an EfficientNet backbone pre-trained on the CityScapes dataset. To handle the imperfectness of training samples, we incorporated a weighted combination of two loss functions, namely the Dice loss and Lovász loss functions, into our network. We evaluated the proposed framework and several other state-of-the-art methods on a published dataset of approximately 900 manually quantified cultured mouse neurons. Our results indicate a close correlation between the proposed method and manual quantification in terms of neuron length and the number of branches while demonstrating improved analysis speed. Furthermore, the proposed method achieved high accuracy in neuron segmentation, as evidenced by the evaluation of the neurons’ length and number of branches

Fully automatic classification of breast cancer microarray images

AbstractA microarray image is used as an accurate method for diagnosis of cancerous diseases. The aim of this research is to provide an approach for detection of breast cancer type. First, raw data is extracted from microarray images. Determining the exact location of each gene is carried out using image processing techniques. Then, by the sum of the pixels associated with each gene, the amount of “genes expression” is extracted as raw data. To identify more effective genes, information gain method on the set of raw data is used. Finally, the type of cancer can be recognized via analyzing the obtained data using a decision tree. The proposed approach has an accuracy of 95.23% in diagnosing the breast cancer types

APPRAISAL OF TAKAGI–SUGENO TYPE NEURO-FUZZY NETWORK SYSTEM WITH A MODIFIED DIFFERENTIAL EVOLUTION METHOD TO PREDICT NONLINEAR WHEEL DYNAMICS CAUSED BY ROAD IRREGULARITIES

Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire–obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire–obstacle contact interface. A new Takagi–Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics

AAGLMES: an intelligent expert system realization of adaptive autonomy using generalized linear models

Abstract—We earlier introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI). This study presents an expert system for realization of AA, using Support Vector Machine (SVM), referred to as Adaptive Autonomy Support Vector Machine Expert System (AASVMES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions, here modeled as Performance Shaping Factors (PSFs), based on the extracted rules from the experts’ judgments. SVM is used as an expert system inference engine. The practical list of PSFs and the judgments of GTEDC’s (the Greater Tehran Electric Distribution Company) experts are used as expert system database. The results of implemented AASVMES in response to GTEDC’s network are evaluated against the GTEDC experts’ judgment. Evaluations show that AASVMES has the ability to predict the proper LOA for GTEDC’s Utility Management Automation (UMA) system, which changes in relevance to the changes in PSFs; thus providing an adaptive LOA scheme for UMA. Keywords-Support Vector Machine (SVM); Adaptive Autonomy (AA); Expert System; Human Automation Interaction (HAI); Experts’ Judgment; Power System; Distribution Automation; Smart Grid

Effects of Silicon and AgNO3 Elicitors on Biochemical Traits and Antioxidant Enzymes Activity of Henbane (Hyoscyamus reticulatus L.) Hairy Roots

Lattice henbane (Hyoscyamus reticulatus L.) is an herbaceous, biennial plant belonging to Solanaceae family. H. reticulatus hairy roots were established from two-week-old leaves infected by A7 strain of Agrobacterium rhizogenes on solid Murashige and Skoog (MS) medium. In this study, abiotic elicitors including; Sodium silicate (Na2SiO3) with different concentrations (0, 1, 5 and 7 mM) and silver nitrate (AgNO3) concentrations (0, 0.5, 1 and 2 mM) were added to hairy roots culture media. The results showed that, Na2SiO3 and AgNO3 significantly affected hairy roots fresh weight after 24h. Also, the highest hairy root fresh weight was observed in the control, and with broadening elicitor concentrations, fresh weight was decreased in both treated hairy roots with AgNO3 and Na2SiO3 but the effect of exposure duration was not significant. Biochemical analysis showed that total antioxidant activity (TAA), total phenol (TP), catalase (CAT), ascorbate peroxidase (APX) and Guaiacolperoxidase (GPX) activities were enhanced in elicitated hairy roots compared to non elicitated hairy roots. The highest CAT, APX and GPX activities were observed in hairy roots treated with 7mM Na2SiO3 and 2mM AgNO3. Our results suggest that, Na2SiO3 and AgNO3 can stimulate the antioxidant defense systems and protect the plants from subsequent stresses

Hydrogel-integrated graphene superstructures for tissue engineering: From periodontal to neural regeneration

Hydrogel-integrated graphene superstructures (GSSs) represent a promising platform for applications in tissue engineering and regenerative medicine. Graphene, a two-dimensional carbon-based material, possesses remarkable mechanical, thermal, and electrical characteristics, making it a strong candidate for application in biomedicine. Researchers have pursued the integration of graphene with hydrogels, known for their biocompatibility and ability to provide a conducive environment for cellular growth, to craft sophisticated scaffolds tailored to tissue engineering needs. The integration of hydrogels and graphene enables the construction of 3D frameworks that closely mimic the natural extracellular matrix (ECM) found in biological tissues. Hydrogels furnish a biocompatible, well-hydrated environment, while the graphene component bolsters the scaffold's mechanical integrity and electrical conductivity. This amalgamation enhances cellular adhesion, differentiation, and proliferation, thereby facilitating tissue regeneration. A notable advantage of hydrogel-integrated GSSs lies in their capacity to support the growth and differentiation of a variety of cell types such as PC12, MG-63, U-87, and MC3T3-E1 cell lines. Overall, hydrogel-integrated GSSs exhibit great potential for advancing biomimetic tissue engineering and regenerative medicine. The combination of the unique properties of graphene with the biocompatibility of hydrogels enables the development of advanced scaffold systems for tissue regeneration. Further research and development in this domain will play a crucial role in advancing regenerative medicine and the treatment of various diseases and injuries

Optimising spread-layer quality in powder additive manufacturing: assessing packing fraction and segregation tendency

Powder bed fusion (PBF), a subset of additive manufacturing methods, is well known for its promise in the production of fully functional artefacts with high densities. The quality of the powder bed, commonly referred to as powder spreading, is a crucial determinant of the final quality of the produced artefact in the PBF process. Therefore, it is critical that we examine the factors that impact the powder spreading, notably the powder bed quality. This study utilised a newly developed testing apparatus, designed specifically for examining the quality of powder beds. The objective was to analyse the influence of various factors, including the recoater shape, recoater gap size, and the different powder flow properties, on the powder bed relative packing fraction. Additionally, the study aimed to assess the variation in the particle size and shape across the build plate. The results indicated that all of the variables examined had an impact on the relative packing fraction, as well as the size and shape variations observed across the build plat