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Vehicular ad hoc networks (VANETs) are kind of mobile ad hoc networks (MANETs) which are used to provide communications between mobile vehicles in urban and highway scenario. Due to special characteristics of VANETs such as dynamic topology, frequent disconnection, high vehicular speed and propagation model, designing an efficient routing scheme is one of the most important key issues. In this paper, we propose a hybrid opportunistic and position-based routing protocol in VANETs by considering parameters such as position of nodes, link quality and node density. The proposed method uses a greedy forwarding scheme, in which a sender vehicle chooses a neighbor node with the highest geographical progress to increase the least number of hops between source node and destination vehicle node. Based on opportunistic and position based strategy, the proposed scheme selects optimal candidate nodes and determines appropriate priority for transmitting data. Also, the proposed scheme determines and removes the expired nodes from the routing process. The simulation results in ns-2 indicate performance improvement in terms of packet delivery rate (PDR), throughput and end-to-end delay.

This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65%, 92.21%, and 94.20%, respectively. Besides, we show that the proposed method possesses more generalization power than pre-trained CNN models. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets.

The Internet of Things has proliferated, and the number of devices integrated into intelligent networks has made resource management and allocation one of the most critical challenges. The intrinsic constraints of IoT devices, such as energy consumption, limited bandwidth, and reduced computational power, have increased the demand for more intelligent and efficient resource allocation strategies. Numerous current resource allocation methods, such as evolutionary algorithms and multi-agent reinforcement learning, are grossly inefficient at adapting well to IoT networks in light of dynamic and rapid changes due to the inherent computational complexity and high cost. This paper proposes an intelligent resource allocation approach for Internet of Things (IoT) networks that integrates clustering and machine learning techniques. Initially, IoT devices are grouped using the K-Means algorithm based on features such as energy consumption and bandwidth requirements. A Random Forest model is then trained to accurately predict the resource needs of each cluster, enabling optimal allocation. Simulation results show that the proposed approach improves prediction accuracy to 94%, reduces energy consumption by 20%, and decreases response time by 10% compared to existing methods. These results highlight the effectiveness of the approach in managing resources in dynamic and scalable IoT environments.

Liver failure is a common and life-threatening disease, and its early and accurate diagnosis plays a decisive role in improving the treatment process and the quality of life of patients. The complexity of the diagnostic process and the high costs of traditional methods highlight the necessity of utilizing intelligent and efficient systems. In this research, an innovative approach based on a multilayer perceptron (MLP) optimized by a genetic algorithm is presented. First, using principal component analysis (PCA), the data dimensions were reduced, and then the MLP network was trained with weights and biases adjusted by the genetic algorithm (GA). The performance of the proposed system was evaluated on two validated datasets, ILPD and BUPA, in the MATLAB environment and compared with reference methods. The results showed that this system achieved accuracies of 99.55% on the ILPD dataset and 97.85% on the BUPA dataset, demonstrating a significant superiority over previous approaches. This remarkable improvement is due to the optimal tuning of network parameters and can pave the way for the development of a new generation of medical decision support systems with high accuracy and stability.

IntroductionRhoptries proteins (ROPs) are crucial throughout different stages of the Toxoplasma gondii (T. gondii) lifecycle, playing key roles in both the invasion of host cells and their subsequent survival. ROP34 is particularly noteworthy as it significantly influences host gene expression and aids in the transition from the tachyzoite to the bradyzoite form.Materials and methodsThis research utilized various bioinformatics tools to assess physico-chemical properties, allergenic and antigenic characteristics, sites for post-translational modifications (PTMs) and protein's secondary and three-dimensional structures of the ROP34 protein. Furthermore, the study identified potential B-cell, MHC-binding, and cytotoxic T-lymphocyte (CTL) epitopes within the ROP34 sequence.ResultsThe ROP34 peptide comprised 553 amino acid residues, with a calculated average molecular weight (MW) of 61.60149 kDa, an aliphatic index of 73.98, and a GRAVY score of − 0.554. The antigenicity of the multi-epitope peptide was estimated to be 0.526563 and 0.6025 by the ANTIGENpro and VaxiJen servers, respectively, suggesting ROP34 as an immunogenic protein with no allergenic potential. Secondary structure analysis revealed a composition of 52.80% random coil, 36.17% alpha helix, and 11.03% extended strand. The Ramachandran plot for the refined model depicted that 97.46% of the residues were situated in the favored region.ConclusionThis in silico research serves as a foundation for designing effective immunization tactics to target toxoplasmosis. The present article lays the groundwork for future studies and offers perspectives for the advancement of an appropriate toxoplasmosis vaccine.HighlightsToxoplasmosis represents a significant global concern, especially for pregnant individuals and those with weakened immune systemsThis article provided a comprehensive definition of the important aspects of the ROP34 protein using several bioinformatics tools.We present insightful bioinformatics insights regarding the ROP34 protein, demonstrating its potential as a future vaccine choice.

Deviating to the left on two‐way roads can result in fatal head‐on collisions. This article presents an intelligent decision‐making and path‐planning algorithm aimed at avoiding collision with a vehicle that has deviated from the opposing lane. The path‐planning process utilizes the model predictive control (MPC) approach, employing a linear kinematic prediction model with a horizon of 2 seconds. Considering that the deviated vehicle may abruptly return to its original lane at any moment, its motion is associated with significant uncertainty. To address this challenge, the path‐planning algorithm directs the ego vehicle (EV) under specific constraints to ensure that both the left and right sides of the road are symmetrically reachable in future time steps. This enables the decision‐making algorithm to select the safer direction for evasive maneuver at the appropriate moment. The motion prediction of the threat vehicle (TV) is conducted until the potential collision time, taking into account its motion history, and is utilized in the decision‐making process. Once the maneuver direction is determined, the collision‐free path planning continues using the MPC method. To evaluate the algorithm, six simulations are conducted, modeling various distant and close encounter states of the vehicles on roads with left‐ and right‐hand curves. The simulation results indicate the flexibility and appropriate performance of the algorithm in planning safe and maneuverable paths.

Background: Sarcocystis is a genus of coccidian protozoa that at least seven species of it can parasitize cattle. The global prevalence of Sarcocystis is close to 100% in adult cattle. The main aim of this study was to identify the infection rate of Sarcocystis spp. in heart of cattle in Tehran, Iran by microscopy and PCR-RFLP methods. Methods: Totally, 100 bovine heart samples were collected from the main slaughterhouse of Shahriar, Meysam slaughterhouse, west of Tehran in 2016. At first, heart samples were completely examined for the presence of sarcocystic macrocysts. Then, for microscopic examination, 50 g of each heart was digested in sterile condition using pepsin acid digestion method. Then, the species of the parasite were detected by PCR-RFLP technique and sequencing. Results: Overall, 97 of 100 of the heart muscle samples were infected with Sarcocystis. All the samples were detected as S. cruzi through similar patterns in PCR-RFLP. Conclusion: S. cruzi is the most common species in the heart of cattle slaughtered in Shahriar.

Background Saffron has been utilized in numerous studies as an additive to augment egg quality and to enhance the oxidative stability of egg yolk.However, there is limited knowledge on the responses of hens to dietary supplementation with saffron petals on egg chemical composition, fecal mineral excretion, and ammonia emission. Objectives The objective of this study was to investigate the influence of saffron petal extract‐enriched diet on egg quality, blood metabolites and odorous gas emission from excreta in laying hens. Methods The experimental methodology involved a feeding trial conducted over a period of 12 weeks, using 200 Hy‐line W36 laying hens aged 39 weeks. The dietary intervention included a basic diet (serving as a control with no supplementation), as well as diets fortified with 40, 60 or 80 parts per million (ppm) of hydroalcoholic saffron petal extract in a completely randomized design. Results Results showed that the inclusion of saffron petal extract in the diet did not significantly affect the egg crude protein, fat and ash content. However, a significant reduction (p < 0.05) in yolk cholesterol concentration was observed. No substantial effect was noted on feed intake, feed conversion ratio (FCR) and egg weight (p > 0.05). On the other hand, a significant increase (p < 0.05) was documented in the egg production percentage of hens fed on the 80 ppm saffron petal extract diet compared to the control. Furthermore, saffron petal extract supplementation resulted in a significantly lower yolk cholesterol together with reduced serum cholesterol content (p < 0.05). Blood glucose and triglyceride concentrations also demonstrated a decrease subsequent to the inclusion of 80 ppm saffron petal extract. The excretion of faecal minerals did not show any significant alterations due to the dietary interventions (p > 0.05). Notably, hens supplemented with 60 and 80 ppm saffron petal extract displayed significantly diminished concentrations of faecal ammonia (NH3) emissions (p < 0.05) compared to the control. Conclusion The study suggests that the inclusion of 80 ppm saffron petal extract in the diet of laying hens may serve as a functional food source to mitigate cholesterol levels in egg yolk and blood serum, as well as to reduce faecal ammonia emissions. • A total of 200 Hy‐line (W36) laying hens were assigned to four treatments, to investigate the influence of saffron petal extract‐enriched diet on egg quality, blood metabolites and odorous gas emission from excreta in laying hens. • Saffron extract supplementation resulted in a significantly lower serum cholesterol content (p < 0.05). • The inclusion of 80 ppm saffron petal extract in the diet of laying hens may serve as a functional food source to mitigate cholesterol levels in egg yolk and blood serum, as well as to reduce faecal ammonia emissions.

The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved Sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%.

Purpose: This study was conducted to find a suitable growing substrate for the production of crops (corn, cotton, and canola) seedlings.Method: This experiment was conducted as a completely randomized design with six treatments including different levels of organic wastes (peat moss, perlite, rotted cow manure, palm peat, vermicompost, sugarcane bagasse compost, tea waste compost) on the status of the production of corn, cotton and canola seedlings in the research greenhouse of the Soil and Water Research Institute, Karaj Iran in 2022.Results: The results showed that the superior substrate for the production of corn and cotton seedlings is the substrate containing tea waste compost (shoot fresh weight 4.91 and 4.31gr respectively for corn and cotton). But the superior substrate for the production of canola seedlings was the substrate containing vermicompst (shoot fresh weight 5.56 gr). Although tea waste compost has a higher production cost than other treatments, it causes seedlings to ripen up to one week early, and this partially justifies the high cost of its production.Conclusion: It is concluded that the substrates containing tea compost were more effective than the other substrates for the production of corn and cotton seedlings, while the substrates containing vermicompost were superior to the other substrates for the production of canola seedlings. However, more research on its application for different crops and under various conditions is needed to approve the results of this study.