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Monitoring long-term trends in structural changes within mangrove ecosystems is essential for understanding their response to climate change and devising effective adaptation strategies in coastal regions. This study examines changes in mangrove patchiness within the Hara Biosphere Reserve (HBR) along the northern coasts of the Persian Gulf (PG) over a 31-year period (1986-2017), focusing on variations in rainfall patterns and drought occurrences. Employing a 35-year time series of monthly Standardized Precipitation Index (SPI) values alongside satellite imagery analysis, trends in both the number of patches (NP) and the Largest Patch Index (LPI) were assessed at the mangrove stand level. The analysis reveals a significant correlation between structural changes in mangroves and drought events. Pre-1998, characterized by wetter conditions, witnessed a decrease in both NP and LPI, indicating patch expansion and habitat extension. Conversely, post-1998, during drought periods, both indices increased, indicating habitat degradation due to heightened drought intensity. Pre-1998 structural changes in the HBR signify habitat expansion, with increased patch extent and core areas reflecting enhanced structural integrity. However, post-1998, a concerning trend of habitat degradation emerged, with increased NP and LPI attributed to intensified droughts. These findings highlight a transitional period marked by favourable conditions for mangrove growth followed by habitat degradation linked to increased drought intensity. This underscores the vulnerability of mangrove ecosystems to climate change impacts, particularly exacerbated droughts, necessitating urgent efforts for conservation and management to preserve biodiversity and ecosystem services in coastal regions.

In disaster management, land-use land-cover (LULC) maps are vital for real-time situational awareness and coordinated responses. These maps aid in coordinating field operations, guiding rescue teams, and identifying high vulnerability areas. They ensure accurate spatial information sharing and management during disaster response efforts. Deep transfer learning models have emerged as powerful tools for LULC classification, addressing challenges like insufficient training data and complex classification tasks. In this research instead of building networks from scratch, pre-trained networks that used EuroSAT benchmark dataset are employed. Several deep learning models including 1-layer CNN, 4-layers CNN, VGG16 and an improved ResNet-50 network as proposed method are considered and compared in this study. The results were analyzed in both quantitative and qualitative ways. In the quantitative mode, the measurement criteria such as Overall Accuracy (OA), F1Score, Precision and Recall were calculated, and in the qualitative mode, the class diagram was drawn in the feature space to check the separability of the classes. Finally, the results show high overall accuracy score of 95.9% indicating the high potential of our proposed network for ResNet-50. The proposed method has resolved insufficient training dataset by implementing data augmentation that it can be solved the problem of lack dataset.

Floods are considered among the most destructive natural disasters, requiring precise and timely management. Remote sensing, utilizing diverse satellite imagery data, enables effective monitoring and assessment of flood impacts. In this context, machine learning and deep learning methods, as effective and scalable approaches, can significantly enhance the accuracy of flood detection and management by analyzing remote sensing data, thereby playing a crucial role in mitigating flood-related risks. In this study, to flood detection using Sentinel-1 SAR data, machine learning algorithms, including Random Forest (RF) and Histogram-based Gradient Boosting Decision Tree, were employed, along with two metaheuristic algorithms, Harris Hawks Optimization (HHO) and Ant Colony Optimization (ACO), for hyperparameter optimization. Additionally, to enhance the models' ability to detect flooded pixels and improve overall performance accurately, a pre-trained VGG-16 Neural Network was used as a deep feature extractor. Finally, four ensemble flood detection models—RF-HHO, RF-ACO, HGBDT-HHO, and HGBDT-ACO—were implemented, and their performance was evaluated and compared based on statistical metrics. Based on the obtained results, all four ensemble flood detection models demonstrated excellent performance in the validation and testing phases. The overall accuracy of these models reached over 95% in the validation phase and exceeded 97% in the testing phase. However, the HGBDT-ACO model achieved the highest accuracy and the lowest error rate in detecting flood pixels, making it the best-performing model in this study. Generally, HGBDT models showed a relative advantage over RF models, as they required significantly less time for training while achieving comparable results. Therefore, they were efficient and performed better in terms of computational complexity.

Wildfires are significant environmental threats, requiring precise and prompt assessment to mitigate damage and guide recovery efforts. Remote sensing, mainly through satellite imagery multispectral data, provides practical tools for monitoring and evaluating wildfire impacts. Canada experiences significant wildfires each year, causing substantial damage to the country’s environment, particularly its vegetation. This study proposed a fast and efficient method using Google Earth Engine (GEE) cloud-based computing to rapidly assess burned vegetation following a wildfire in Canada in 2023, utilizing Sentinel-2 imagery data. This method computed NDVI, GNDVI, and EVI spectral indices for classifying pre-fire vegetation cover and NBR, dNBR, and MIRBI for classifying post-fire burn severity. These spectral indices served as input data for machine learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Support Vector Machine (SVM). Ultimately, the results of vegetation cover and burn severity classifications, performed separately by these models, were combined using a decision-level fusion with a weighting approach based on an accuracy approach to produce integrated and final classifications. Subsequently, by overlapping the results of these fused classifications, the burned vegetation was assessed, and its area was estimated. According to the study's results, significant damage was observed in the vegetation after the wildfire. 4489 km2 of the study area, which was a Military Grid Reference System (MGRS) tile with an area of 12,000 km2, was burned due to the wildfire. 34.06% of this area was specifically burned vegetation, equating to approximately 4,088 km2.

Malaria, a severe disease transmitted by Anopheles mosquitoes, presents a substantial public health concern. Since Anopheles mosquitoes thrive in water-rich environments, accurately mapping surface water is essential for assessing malaria risk and managing mosquito populations. This study seeks to identify areas prone to water accumulation, which create habitats conducive to mosquito breeding. Initially, high-risk months were extracted using precipitation and temperature data. Subsequently, the Sentinel-1 Water Index (SWI) was utilized to analyse seven years of monthly Sentinel-1 time-series images via Google Earth Engine (GEE). To enhance our findings, we integrated monthly surface-water maps using a weighted majority voting strategy. Validation efforts included collecting 520 samples, half of which were water bodies identified through field observations and Google Earth Pro, with masks generated using the Segment Anything Model (SAM) algorithm. Object-based evaluation was employed, treating each water body as a distinct entity. The results revealed an overall accuracy of 96.1% and a kappa coefficient of 92.2% in water body detection, underscoring the method's effectiveness. This method, which outperformed other approaches in the domain and machine learning classifiers, is straightforward to implement, rapid, and does not require training data. Furthermore, while field monitoring may be challenging, the findings of this study could aid health authorities in identifying high-risk areas for disease control and prevention efforts.

Background: Trichomonas gallinae is a protozoan parasite responsible for canker in pigeons, a debilitating disease that causes significant economic losses. While metronidazole (MTZ) remains the primary treatment, the emergence of resistance is a growing concern. This study investigated the efficacy of eugenol and its nanoemulsion formulation against T. gallinae in both in vitro and in vivo settings. Aim: To evaluate the anti-trichomonal activity of eugenol, eugenol nanoemulsion, and MTZ against T. gallinae using in vitro and in vivo models. Methods: In vitro, T. gallinae trophozoites were exposed to varying concentrations of eugenol and eugenol nanoemulsion (0.625–10 μg/ml), as well as MTZ (25 μg/ml). Cytotoxicity was assessed using Vero cells. In vivo, 120 pigeons were experimentally infected and treated with either eugenol (10 mg/kg), eugenol nanoemulsion (10 mg/kg), MTZ (25 mg/kg), or left untreated. Treatments were administered daily for 5 days. Results: In vitro, both eugenol and its nanoemulsion at 10 μg/ml achieved 100% lethality of T. gallinae after 48 hours, while MTZ reached the same effect within 24 hours. In vivo, MTZ and eugenol (at 25 mg/kg and 10 mg/kg, respectively) resulted in 100% recovery of infected pigeons 5 days post-treatment. Notably, eugenol nanoemulsion (10 mg/kg) achieved 100% recovery within just 4 days post-treatment. Conclusion: This study highlights the potential of eugenol and its nanoemulsion as alternative treatments for T. gallinae infections in pigeons. The eugenol nanoemulsion, in particular, demonstrated promising results with faster recovery rates compared to both MTZ and eugenol, suggesting it may be especially effective against MTZ-resistant strains. Further research is warranted to explore the efficacy and safety of these agents for treating T. gallinae infections in pigeons. [Open Vet J 2024; 14(10.000): 2618-2627]

Surgery is the main efficacious treatment for many cases of cystic echinococcosis by removing Echi-nococcus granulosus cysts. However, to reduce risk of cyst spillage and insemination of the content, using a scolicidal agent is crucial. Considering side effects of available scolicidals, and growing nano-technological approaches in novel pharmaceuticals, the present study aimed to find out the scolicidal activity of geraniol (GL), geranium oil (GM), and their developed nanoemulsions (Nano-GL and Nano-GM) on the protoscoleces of E. granulosus. Nanoemulsions were developed by ultrasonication emulsification and characterised by dynamic light scattering method (DLS). The scolicidal effect of GL, GM, Nano-GL, and Nano-GM at different concentrations of 1, 2.5, 5, 10, 25, and 50 µg/mL were determined after 0.5, 1, and 2 hours of incubation. Mortality rates were measured by eosin exclusion test. The average droplet size for Nano-GM and Nano-GL were 124.8 and 88.59 nm, respectively. After one hour of exposure, all tested concentrations of GL and Nano-GL resulted in higher than 90% mortality rates, while GM and Nano-GM killed 70.66% and 90.33% of protoscoleces, respectively. Based on 50% lethal concentration, Nano-GL was significantly more potent than Nano-GM (LC50: 4.52 vs 102.95 μg/mL). Results of scanning electron microscopy revealed tegumental disruption in the treated protoscoleces. This study described an easily applicable and eco-friendly procedure of nano-formulating functional phytochemicals, showing promising scolicidal activity in vitro. The developed formulations, especially Nano-GL, showed the characteristics of an ideal scolicidal agent. Further studies are needed to assess in vivo efficacy and safety of this formulation.

Due to the side effect of commonly used chemical scolicidals, advantages of micro drug delivery system and growing interest for the phytoceuticals, the present study aimed to find out the scolicidal activity of two volatile terpenes (α-pinene and β-caryophyllene) and their microemulsions on proto-scoleces of Echinococcus granulosus. The ordinary and microemulsions of α-pinene and β-caryophyllene and their combination at three different ratios (2:1, 1:1, 1:2) were developed and cha¬racterised by dynamic light scattering to confirm micro-dimensions. The scolicidal effect of the de-veloped formulations at the concentrations of 0.5, 1, 2, 5 and 10 μg/mL were measured at 10, 30, and 60-min time points. Mortality rates were recorded by eosin exclusion test. At the 10-min time point, ordinary and micro α-pinene at the concentration of 10 μg/mL killed 94.66% and 100% of the proto-scoleces, respectively. At the same time point and concentration, ordinary and micro β-caryophyllene caused 44.33% and 68.66% mortality rates in protoscoleces, respectively. Based on the 50% lethal concentration (LC50) values, there was no significant difference between ordinary and micro-emulsified α-pinene. For the micro β-caryophyllene, a LC50 value of 2.1 µg/mL was obtained which was significantly lower than that of ordinary β-caryophyllene (49.85 µg/mL). No synergistic interac-tion existed between β-caryophyllene and α-pinene regarding their activity on E. granulosus. Development of microemulsions increased the toxicity of these terpenoids on protoscoleces, especially that of β-caryophyllene, probably due to improved penetration into the parasite. Further studies are needed to assess in vivo efficacy and safety of this specific preparation.

Background: Chronic toxoplasmosis has been strongly implicated in the development of psychosis and schizophrenia. Additionally, the understanding of schizophrenia has been significantly reshaped by insights into N-methyl-D-aspartate receptor (NMDAR) hypofunction. Aim: This study aimed to compare the behavioral, antioxidant, and NMDAR changes in mice subjected to Toxoplasma gondii infection and those treated with ketamine to induce schizophrenia-like symptoms. Methods: Sixty male BALB/c mice were divided into six groups: TOXO (infected), KET (ketamine-induced schizophrenia), TOXO+KET, TOXO+SDT (sulfadiazine-trimethoprim treatment), TOXO+KET+SDT, and CON (uninfected). After 10 weeks post-infection, behavioral tests were conducted, brain antioxidant status and lipid peroxidation were analyzed, and NMDA-NR1/NR2A expressions were assessed. TOXO and KET induced distinct behaviors: hyperlocomotion, anxiety, and memory impairment. Results: Antioxidant enzyme levels decreased, and lipid peroxidation increased in TOXO and schizophrenic mice brains. NMDAR downregulation, especially NR-1 and NR2A, was evident due to T. gondii and ketamine. Sulfadiazine-trimethoprim ameliorated NMDAR downregulation, but not all of the behavioral alterations. Conclusion: Further studies are needed to elucidate specific NMDAR subunit roles in toxoplasmosis-induced pathophysiology, offering potential therapeutic insights. This investigation highlights the intricate relationship between chronic toxoplasmosis, NMDAR dysfunction, and schizophrenia-like behaviors. Insights gained could pave the way for innovative interventions targeting both cognitive and neurological impairments associated with these conditions. [Open Vet J 2024; 14(7.000): 1634-1643]

The generation of photogrammetric point clouds from satellite images is often based on image correlation techniques. Correlation errors can arise for a wide variety of reasons: transient objects, homogeneous areas, shadows, and surface discontinuities. Therefore, a simple 3D Gaussian distribution at the point cloud level is not an appropriate model. In this paper, we propose a new point cloud denoising method integrated into the Multiview Stereo Pipeline CARS, dedicated to satellite imagery. Building upon bilateral filtering principles, our approach introduces a novel utilization of color information, confidence estimation and geometric constraints alongside point positions and normals. While the use of point color increases the level of detail, the addition of geometric constraints and confidence awareness guides processing towards a realistic solution. We propose an ablation study and compare our solution against a previously established bilateral filter with LiDAR data as ground truth.

Chemical injuries to the cornea account for 11 to 22% of all ocular injuries. Acidic injuries are commonly due to sulfuric, hydrochloric, hydrofluoric, and battery acids, while basic injuries are commonly due to sodium hydroxide, chlorine bleach, and ammonia products. We have previously studied potential-driven electrochemical clearing (P-ECC) for alkaline injuries. In this study, we investigated the use of P-ECC on both acidic and alkaline injuries to determine its effect on restoring corneal transparency. Optical coherence tomography (OCT) was performed before and after P-ECC to determine adequate corneal clearing. Severity of chemical injury was measured through second harmonic generation (SHG) imaging. HCl or NaOH was applied to the corneas of New Zealand white rabbit globes. P-ECC was performed on opacified cornea while OCT imaging was simultaneously performed to evaluate depth resolved clarity. SHG imaging evaluated the structure of collagen before HCl or NaOH application and after P-ECC. Irrigation with water served as positive control. Native rabbit corneas were used as a negative control group. P-ECC induced clearing in the rabbit cornea, shown through OCT. Clearing occurred in regions where the working electrode made contact with the cornea. SHG imaging showed restoration of collagen fibril signal in P-ECC treated corneas compared to control. P-ECC is a potentially effective therapy for clearing acidic and alkaline corneal injuries. However, more ex-vivo experiments are required to determine the specific parameter for optimal clearing. In-vivo experiments are necessary to determine its potential for clinical use.

Abstract Superconducting qubits are among the most advanced candidates for achieving fault-tolerant quantum computing. Despite recent significant advancements in the qubit lifetimes, the origin of the loss mechanism for state-of-the-art qubits is still subject to investigation. Furthermore, the successful implementation of quantum error correction requires negligible correlated errors between qubits. Here, we realize long-lived superconducting transmon qubits that exhibit fluctuating lifetimes, averaging 0.2 ms and exceeding 0.4 ms – corresponding to quality factors above 5 million and 10 million, respectively. We then investigate their dominant error mechanism. By introducing novel time-resolved error measurements that are synchronized with the operation of the pulse tube cooler in a dilution refrigerator, we find that mechanical vibrations from the pulse tube induce nonequilibrium dynamics in highly coherent qubits, leading to their correlated bit-flip errors. Our findings not only deepen our understanding of the qubit error mechanisms but also provide valuable insights into potential error-mitigation strategies for achieving fault tolerance by decoupling superconducting qubits from their mechanical environments.

AbstractBackground: Mycobacterium tuberculosis is still one of the most dangerous human pathogens. Identification of the relationships between different clinical strains has remained a high priority for epidemiology research. Methods: In this study, we used MLSA (Multilocus sequence analysis) to generate a highly robust phylogeny of M. tuberculosis. MLSA, based on single nucleotide polymorphism (SNP) was performed on five genes fragments from the Rpsl (302 bp), MprA (559 bp), LipR (322 bp), KatG (488 bp) and Fgd1 (266 bp), in order to identify polymorphic nucleotide sites, and the discriminatory power of each locus for all genes was measured with Hunter‐Gaston Index (HGI). Results: In this study, a sequence type (ST) number was assigned to each unique allelic profile, and 9 sequence types were identified from 20 strains, these imply that there is a high diversity of strains in this area. Conclusion: Our results showed that the presence of high genetic diversity among clinical isolates of M. tuberculosis in Northeast of Iran. There is no evidence for recent transmission. Keywords: Mycobacterium tuberculosis, Multi-locus sequence analysis; Molecular epidemiology; Tuberculosis; KatG; Rpsl1. IntroductionMycobacterium tuberculosis (M. tuberculosis), the causative agents of tuberculosis (TB), is one of the most successful human pathogens, infecting nearly one-third of the people all around the world, causing over 9 million new cases and 1.7 million deaths each year [1-2]. Identification of the relationships between different clinical strains of M. tuberculosis has great significance to the public health [3].

Trichomoniasis is a protozoan disease caused by Trichomonas gallinae that mainly affects the upper digestive and respiratory tracts of columbiforms. Metronidazole has been used for many years for the control of trichomoniasis, however, in recent years, there were several reports on resistant T. gallinae strains. Psidium guajava, a well-known medicinal plant species, possesses several biological activi-ties including anti-protozoal effects. Considering growing interest on plant and plant-derived com-pounds as alternative sources of bioactive chemicals, the present study was performed to evaluate the anti-trichomonal potential of P. guajava leave essential oil (EO) and its main components on T. gallinae trophozoites. EO was extracted from plant fresh leaves by steam distillation using a Clevenger-type apparatus and gas chromatography-mass spectrometry was performed for identification of the constituents. In vitro susceptibility assay was done in sterile multiwell plates incubated with the tro-phozoites and the corresponding concentrations of tested compounds. Metronidazole was used as the standard anti-trichomonal drug. Mortality rates were evaluated by eosin staining and recorded every 12 h for 2 days. The EO composition was dominated by the β-caryophyllene (30.2%) and α-pinene (18.3%). A significant effect of concentration of the tested compound and time of exposure was noted in the toxicity of the EO and its main constituents. Based on the 12 h fifty percent lethal concentrations (LC50), β-caryophyllene was the most potent anti-trichomonal agent, with LC50 of 0.32 µg/mL. Considering the natural source of β-caryophyllene and its reported low cytotoxicity and promising anti-trichomonal efficacy, this compound can be considered as a candidate ingredient for the development of green antitrichomonal agents.

Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learning and medical imaging. The current research investigates a new strategy to diagnose an oral cancer using Gated Recurrent Unit (GRU) networks optimized by an improved model of the NGO (Northern Goshawk Optimization) algorithm. The proposed approach has several advantages over existing methods, including its ability to analyze large and complex datasets, its high accuracy, as well as its capacity to detect oral cancer at the very beginning stage. The improved NGO algorithm is utilized to improve the GRU network that helps to improve the performance of the network and increase the accuracy of the diagnosis. The paper describes the proposed approach and evaluates its performance using a dataset of oral cancer patients. The findings of the study demonstrate the efficiency of the suggested approach in accurately diagnosing oral cancer.

Abstract Vector-borne diseases emergence, particularly malaria, present a significant public health challenge worldwide. Anophelines are predominant malaria vectors, with varied distribution, and influenced by environment and climate. This study, in Ghana, modelled environmental suitability for Anopheles stephensi, a potential vector that may threaten advances in malaria and vector control. Understanding this vector’s distribution and dynamics ensures effective malaria and vector control programmes implementation. We explored the MaxEnt ecological modelling method to forecast An. stephensi’s potential hotspots and niches. We analysed environmental and climatic variables to predict spatial distribution and ecological niches of An. stephensi with a spatial resolution of approximately 5 km2. Analysing geospatial and species occurrence data, we identified optimal environmental conditions and important factors for its presence. The model’s most important variables guided hotspot prediction across several ecological zones aside from urban and peri-urban regions. Considering the vector’s complex bionomics, these areas provide varying and adaptable conditions for the vector to colonise and establish. This is shown by the AUC = 0.943 prediction accuracy of the model, which is considered excellent. Based on our predictions, this vector species would thrive in the Greater Accra, Ashanti Central, Upper East, Northern, and North East regions. Forecasting its environmental suitability by ecological niche modelling supports proactive surveillance and focused malaria management strategies. Public health officials can act to reduce the risk of malaria transmission by identifying areas where mosquitoes may breed, which will ultimately improve health outcomes and disease control.

Fort de son expertise en géométrie et en 3D, le Centre National d’Études Spatiales (CNES) développe de nouveaux outils open-source capitalisant une partie de son savoir-faire. Ils sont destinés à être au coeur du segment sol de la mission CO3D et à servir tout l’écosystème aval. Le traitement de ces données impose de concevoir des chaînes robustes, capables de passer à l’échelle. De ce fait, les outils 3D du CNES sont massivement parallélisables, utilisant des technologies multiprocesseurs voire multi-noeuds tout en restant agnostique au matériel utilisé. Ces logiciels proposent des interfaces simples afin de permettre également leur utilisation en dehors des chaînes de traitement opérationnelles. Ces outils sont capables de produire un Modèle Numérique de Surface (MNS) à partir de couples stéréoscopiques d’images (CARS) dont l’étape majeure repose sur la mise en correspondance (Pandora). Par la suite, ils permettent d’extraire un Modèle Numérique de Terrain (MNT) à partir du MNS produit (Bulldozer) et d’en dériver un Modèle Numérique de Hauteur (MNH). Suite à cela, des comparaisons entre tous ces modèles numériques d’élévation peuvent également être effectuées (Demcompare). Dans l’attente de l’arrivée des données CO3D, le CNES éprouve d’ores et déjà ces outils sur d’autres capteurs, dont Pléiades et plus récemment Pléiades Neo. Cet article qualifie l’amélioration de cette restitution 3D apportée par cette nouvelle génération de satellite. Les outils 3D du CNES sont sous licence libre et non contaminante (Licence Apache v2) afin de notamment permettre l’exploitation et la valorisation des données Pléiades et maintenant Pléiades Neo. Ils offrent enfin la possibilité de reproduire les résultats présentés dans cet article.

Abstract Background Leishmaniasis poses a significant health risk. Objectives This study aimed to evaluate the effects of Boswellia serrata (B. serrata) essential oil nanoliposomes on Leishmania tropica (L. tropica) in vitro. Methods A mixture of B. serrata essential oil, phosphatidylcholine and Tween 80 were used to prepare B. serrata essential oil nanoliposomes, followed by drying, hydration and size characterisation. The promastigotes of L. tropica were cultured in Roswell Park Memorial Institute medium (RPMI‐1640) containing streptomycin, penicillin and fetal bovine serum. Different concentrations of B. serrata essential and nanoliposomes were tested for their antileishmanial properties by 3‐(4,5‐Dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium Bromide tests (MTT). Results Results of Dynamic Light Scattering (DLS) for B. serrata nanoliposomes indicate that they are successful at producing nanoliposomes with dimensions of 74.8 nm. At 1 μg/mL dose, B. serrata essence caused 17 ± 1.73% mortality, while B. serrata nanoliposomes induced 26 ± 1.15% mortality. B. serrata essence achieved a mortality of 55 ± 2.88% at 10 μg/mL, whereas B. serrata nanoliposomes demonstrated a mortality of 63.66±0.88% at 10 μg/mL. Furthermore, there was a significant difference between similar concentrations of B. serrata and B. serrata nanoliposomes. The LC50 of B. serrata essential oil is 7.26 μg/mL in the 95% confidence interval (12.13–5.25). The LC90 value of B. serrata essential oil is 129.37 μg/mL in the 95% confidence interval (50.07–852.58). The LC50 value of B. serrata nanoliposome is 4.20 μg/mL in the 95% confidence interval (6.13–3.10). LC90 value for B. serrata nanoliposome is calculated as 91.89 μg/mL in the 95% confidence interval (37.09–583.29). Conclusions In vitro experiments have shown that B. serrata oil and the nanoliposome suppress the proliferation of L. tropica promastigotes, which suggests it may be a promising option for treating leishmaniasis.

Date palm production is critical to oasis agriculture, owing to its economic importance and nutritional advantages. Numerous diseases endanger this precious tree, putting a strain on the economy and environment. White scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates. When an infestation reaches a specific degree, it might result in the tree’s death. To counter this threat, precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary. This decision is crucial for farmers who wish to minimize yield losses while preserving production quality. For this purpose, we propose a feature extraction and machine learning (ML) technique based framework for classifying the stages of infestation by white scale disease (WSD) in date palm trees by investigating their leaflets images. 80 gray level co-occurrence matrix (GLCM) texture features and 9 hue, saturation, and value (HSV) color moments features are extracted from both grayscale and color images of the used dataset. To classify the WSD into its four classes (healthy, low infestation degree, medium infestation degree, and high infestation degree), two types of ML algorithms were tested; classical machine learning methods, namely, support vector machine (SVM) and k-nearest neighbors (KNN), and ensemble learning methods such as random forest (RF) and light gradient boosting machine (LightGBM). The ML models were trained and evaluated using two datasets: the first is composed of the extracted GLCM features only, and the second combines GLCM and HSV descriptors. The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%. The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease (DPWSD) and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.

The use of linear array pushbroom images presents a new challenge in photogrammetric applications when it comes to transforming object coordinates to image coordinates. To address this issue, the Best Scanline Search/Determination (BSS/BSD) field focuses on obtaining the Exterior Orientation Parameters (EOPs) of each individual scanline. Current solutions are often impractical for real-time tasks due to their high time requirements and complexities. This is because they are based on the Collinearity Equation (CE) in an iterative procedure for each ground point. This study aims to develop a novel BSD framework that does not need repetitive usage of the CE with a lower computational complexity. The Linear Regression Model (LRM) forms the basis of the proposed BSD approach and uses Simulated Control Points (SCOPs) and Simulated Check Points (SCPs). The proposed method is comprised of two main steps: the training phase and the test phase. The SCOPs are used to calculate the unknown parameters of the LR model during the training phase. Then, the SCPs are used to evaluate the accuracy and execution time of the method through the test phase. The evaluation of the proposed method was conducted using ten various pushbroom images, 5 million SCPs, and a limited number of SCOPs. The Root Mean Square Error (RMSE) was found to be in the order of ten to the power of negative nine (pixel), indicating very high accuracy. Furthermore, the proposed approach is more robust than the previous well-known BSS/BSD methods when handling various pushbroom images, making it suitable for practical and real-time applications due to its high speed, which only requires 2–3 s of time.

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods have been developed. However, the current BSS/BSD methods are not efficient for real-time applications due to their complex procedures and interpolations. This paper introduces a new non-iterative BSD method specifically designed for line-type pushbroom images. The method involves simulating a pair of sets of points, Simulated Control Points (SCOPs), and Simulated Check Points (SCPs), to train and test a Multilayer Perceptron (MLP) model. The model establishes a strong relationship between object and image spaces, enabling a direct transformation and determination of best scanlines. This proposed method does not rely on the Collinearity Equation (CE) or iterative search. After training, the MLP model is applied to the SCPs for accuracy assessment. The proposed method is tested on ten images with diverse landscapes captured by eight sensors, exploiting five million SCPs per image for statistical assessments. The Root Mean Square Error (RMSE) values range between 0.001 and 0.015 pixels across ten images, demonstrating the capability of achieving the desired sub-pixel accuracy within a few seconds. The proposed method is compared with conventional and state-of-the-art BSS/BSD methods, indicating its higher applicability regarding accuracy and computational efficiency. These results position the proposed BSD method as a practical solution for transforming object-to-image space, especially for real-time applications.