A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm.

Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.

Identification of cardiorespiratory toxic components of Nemopilema nomurai jellyfish venom using sequential chromatography methods.

Jellyfish stings pose a significant threat to humans in coastal areas worldwide, with venomous jellyfish species stinging millions of individuals annually. Nemopilema nomurai is one of the largest jellyfish species, with numerous tentacles rich in nematocysts. N. nomurai venom (NnV) is a complex mixture of proteins, peptides, and small molecules that serve as both prey-capture and defense mechanisms. Yet, the molecular identity of its cardiorespiratory and neuronal toxic components of NnV has not been clearly identified yet. Here, we isolated a cardiotoxic fraction, NnTP (Nemopilema nomurai toxic peak), from NnV using chromatographic methods. In the zebrafish model, NnTP exhibited strong cardiorespiratory and moderate neurotoxic effects. LC-MS/MS analysis identified 23 toxin homologs, including toxic proteinases, ion channel toxins, and neurotoxins. The toxins demonstrated a synergistic effect on the zebrafish, leading to altered swimming behavior, hemorrhage in the cardiorespiratory region, and histopathological changes in organs such as the heart, gill, and brain. These findings provide valuable insights into the mechanisms underlying the cardiorespiratory and neurotoxic effects of NnV, which could be useful in developing therapeutic strategies for venomous jellyfish stings.

Comparative Study of Toxic Effects and Pathophysiology of Envenomations Induced by Carybdea brevipedalia (Cnidaria: Cubozoa) and Nemopilema nomurai (Cnidaria: Scyphozoa) Jellyfish Venoms.

Jellyfish stings can result in local tissue damage and systemic pathophysiological sequelae. Despite constant occurrences of jellyfish stings in oceans throughout the world, the toxinological assessment of these jellyfish envenomations has not been adequately reported in quantitative as well as in qualitative measurements. Herein, we have examined and compared the in vivo toxic effects and pathophysiologic alterations using experimental animal models for two representative stinging jellyfish classes, i.e., Cubozoa and Scyphozoa. For this study, mice were administered with venom extracts of either Carybdea brevipedalia (Cnidaria: Cubozoa) or Nemopilema nomurai (Cnidaria: Scyphozoa). From the intraperitoneal (IP) administration study, the median lethal doses leading to the deaths of mice 24 h post-treatment after (LD50) for C. brevipedalia venom (CbV) and N. nomurai venom (NnV) were 0.905 and 4.4697 mg/kg, respectively. The acute toxicity (i.e., lethality) of CbV was much higher with a significantly accelerated time to death value compared with those of NnV. The edematogenic activity induced by CbV was considerably (83.57/25 = 3.343-fold) greater than NnV. For the evaluation of their dermal toxicities, the epidermis, dermis, subcutaneous tissues, and skeletal muscles were evaluated toxinologically/histopathologically following the intradermal administration of the venoms. The minimal hemorrhagic doses (MHD) of the venoms were found to be 55.6 and 83.4 µg/mouse for CbV and NnV, respectively. Furthermore, the CbV injection resulted in extensive alterations of mouse dermal tissues, including severe edema, and hemorrhagic/necrotic lesions, with the minimum necrotizing dose (MND) of 95.42 µg/kg body weight. The skin damaging effects of CbV appeared to be considerably greater, compared with those of NnV (MND = 177.99 µg/kg). The present results indicate that the toxicities and pathophysiologic effects of jellyfish venom extracts may vary from species to species. As predicted from the previous reports on these jellyfish envenomations, the crude venom extracts of C. brevipedalia exhibit much more potent toxicity than that of N. nomurai in the present study. These observations may contribute to our understanding of the toxicities of jellyfish venoms, as well as their mode of toxinological actions, which might be helpful for establishing the therapeutic strategies of jellyfish stings.

Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System.

Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an open medium, the design of secure encryption algorithms becomes essential. Encryption can be considered one of the effective solutions for accomplishing security. Although numerous models have existed in the literature, they could not adaptable to the rising number of medicinal images in the health sector. At the same time, the optimal key generation process acts as a vital part in defining the performance of the encryption techniques. Therefore, this article presents a Pigeon Inspired Optimization with Encryption-based Secure Medical Image Management (PIOE-SMIM) technique. The proposed PIOE-SMIM approach majorly concentrates on the development of secret share creation (SSC) and the encryption process. At the initial stage, the medical images are converted into a collection of 12 shares using the SSC approach. In addition, an elliptic curve cryptography (ECC) scheme is employed for the encryption process. In order to optimum key creation procedure in the ECC model, the PIO technique is exploited with the aim of maximizing PSNR. Finally, on the receiver side, the decryption and share reconstruction processes are performed to construct the original images. The PIOE-SMIM model displayed an enhanced PSNR of 59.37 dB in image 1. Improved PSNR of 59.53 dB is given for image 5 using the PIOE-SMIM model. For demonstrating an enhanced performance of the PIOE-SMIM method, a widespread experimental study is made and the results highlighted the supremacy of the PIOE-SMIM model over other techniques.

Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks.

In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

Influence of ß-phase stability in elemental blended Ti-Mo and Ti-Mo-Zr alloys.

This paper investigated the improvement of mechanical properties for one of the most used biomaterials, titanium-based alloy. To improve its mechanical properties, molybdenum was chosen to be added to Ti and Ti-Zr alloys through a mechanical blending process. After homogenization of Ti (12, 15) Mo and Ti (12, 15) Mo-6 Zr, the compaction pressure and sintering temperature were varied to create pellets. Characterization has been done using scanning electron microscopy (SEM), X-ray diffraction (XRD), Vickers's hardness, Archimedes test and ultrasonic method, and 3-point bending test. Micrograph of each pellet revealed the influence of Mo content that plays a prominent role in the variation of microstructure in the alloys Ti-Mo and Ti-Zr-Mo. The porosity and density were also influenced by changing the ß-phase. EBSD analysis shows the increase in ß-phase with the addition of Zr. The overall results indicated that the percentage of ß-phase greatly affects the mechanical properties for the specimens.

Danio rerio as an alternative vertebrate model for jellyfish venom study: The toxinological aspects of Nemopilema nomurai venom.

Nemopilema nomurai venom (NnV) is severely toxic to many organisms. However, the mechanism of its poisoning has not been properly understood yet. The present work demonstrates that zebrafish (Danio rerio) is an alternative vertebrate model for studying NnV jellyfish venom for the first time. In this model, NnV appears to cause severe hemorrhage and inflammation in cardiopulmonary regions of zebrafish. NnV also altered the swimming behavior of zebrafish accompanied by a significant downregulation of acetylcholinesterase (AChE) activity in brain tissues. Histopathological changes observed for various organs of D. rerio caused by NnV corresponded to an increase in lactate dehydrogenase (LDH) activity in tissues. NnV also significantly altered glutathione S-transferase (GST) activity in cardiopulmonary and brain tissues of D. rerio. SDS-PAGE revealed many protein bands of NnV of various sizes after silver staining. Taken together, these results indicate that Danio rerio can be a useful alternative animal model for jellyfish venom toxicology studies. Findings of the present study also suggest that Danio rerio could be used to develop an effective treatment strategy and discover the mechanism of action of jellyfish venom envenomation.