Nowadays, remote sensing images provide a wealth of information for various applications, including land-use mapping, environmental monitoring, and disaster management. Pan-sharpening refers to the process of enhancing the spatial resolution of a multispectral image by fusing it with a panchromatic image. However, these images often suffer from low spatial resolution due to the limitations of the sensors used. To address this issue, image fusion methods, such as pan-sharpening, have been developed to merge high-resolution panchromatic images with low-resolution multispectral images to produce high-resolution multispectral images. Deep learning-based pan-sharpening techniques have recently emerged as a promising approach to achieve high-quality pan-sharpening results. In this survey, we provide a comprehensive overview of recent advancements in deep learning-based pan-sharpening techniques for remote sensing images. We review and compare various deep learning architectures and approaches, including autoencoder-based approaches such as denoising autoencoders, generative adversarial networks (GANs), conditional GANs, convolutional neural networks (CNNs), deep residual networks and more. We also discuss the challenges and future directions of this field and the advantages of using deep learning techniques for pan-sharpening and presents an in-depth analysis of different state-of-the-art methods, including their network architectures and experimental results. Additionally, the paper covers evaluation metrics for assessing the quality of pan-sharpened images and presents a comparative analysis of the surveyed methods.

Pedestrian detection is a challenging field due to intrinsic problems in detection such as differences in size, colour, background, pose, and clothing. In recent years, various methods have been introduced and evaluated, with some showing reasonable performance, but the problem remains unresolved. In this research, we propose using a Multi Classifier system/fusion (MSC/MCF) that combines the output of different classifiers to obtain a better detection rate. Pedestrian detection has drawn attention due to its wide range of applications, particularly in the vehicle industry where it can increase safety and prevent collisions on roads. As a result, pedestrian detection is a significant part of the safety systems of autonomous vehicles, and combining different classifier outputs will provide better precision in the final decision if certain criteria are considered in our MSC system. In this article, we use three well-known object detectors for pedestrian detection and combine the depth map output of stereo vision to enhance our proposed classifier, which fine-tunes a famous deep network object detector (RCNN). By combining the results of these classifiers, we improve the detection rate by 25 percent compared to each individual classifier.

This paper presents a study in which the divorce predictor scale (DPS), based on Gottman couples therapy, was used to predict divorce. The 54 items in the DPS were used as features or attributes in a machine learning model. In addition to the DPS, a personal information form was utilized to gather participants’ personal data in order to conduct this study in a more structured and traditional manner. Out of 148 participants 116 participants were married whereas 32 were divorced. With the use of algorithms Artificial Neural Network (ANN), Naive Byes (NB) and Random Forest (RF), the effectiveness of DPS was examined in this study. The correlation based feature selection method was used to identify the top 6 features from the same dataset and the highest accuracy rate was 96.61% with ANN. The results show that DPS can predict divorce. This scale can help family counsellors and therapists in case formulation and intervention plan development process. Additionally, it may be argued that the Hail region, KSA sampling confirmed the Gottman couples treatment predictors.

During forest monitoring conducted in 2017 and 2025, the larvae of a lepidopteran species were observed to damage a large number of Avicennia marina mangrove trees located in Khamir port (Hormozgan province). Following intensive feeding of larvae on different portions of the canopy, many trees exhibited severe dieback. In order to identify the pest, samples of larvae and pupae were collected and transferred to the laboratory for morphological and molecular identification. The nucleotide sequence of a 685 bp portion of the DNA barcode for the mitochondrial Cytochrome Oxidase subunit I amplified using LCO 1490 and HCO 2198 primers was compared with other sequences in the GenBank database. The insect was identified as Streblote solitaria on the basis of morphological and behavioural characteristics, as well as phylogenetic analysis of molecular data. This is the first report of the presence of S. solitaria in this forest in southern Iran and subsequent mangrove damage.

The objective of this study was to examine the relationship between perceived job insecurity and employee task performance. In addition, the moderating influence of emotional intelligence was also examined in this relationship. By focusing on the employees of (3) commercial banks in the Nigerian Banking industry, this research argued that perceived job insecurity exercises an effect on employee task performance and that this relationship is moderated by emotional intelligence. Through the multi-stage sampling technique, a total of 385 employees were proportionately selected from the cluster that represents each bank. Furthermore, the close ended and structured questionnaire was utilized in a descriptive cross-sectional research design to elicit responses from these employees. Based on the hierarchical moderated regression analysis conducted, it was revealed that perceived job insecurity exercises a significant and negative effect on task performance. In addition, these relationships were both found to be moderated by emotional intelligence in such a way that that the negative relationship between job insecurity and task performance was weaker among respondents who reported higher level of emotional intelligence. To conclude, recommendations were made on the need for organizations to recognize the importance of emotional intelligence whenever there is need to maintain a superior level of employee performance most especially in a working environment characterized by high level of job uncertainty.

In this article, the turbulent flow characteristics of graphene oxide-water nanofluids with 0.0%-1.0% of volume fraction for a double tube counter flow heat exchanger in the Reynolds number range of 80,000 to 180,000 have been fully investigated. The effect of graphene oxide nano catalysts on thermophysical properties has been studied. Compared to the base fluid (water), the viscosity of the nanofluid increases by a maximum of 23%. In the current nanofluid, 25.4% growth of thermal conductivity was observed compared to its lowest value for water. The maximum increase in the friction factor, convective heat transfer coefficient, Nusselt number and thermal performance coefficient of nanofluid compared to the water are 23%, 69.7% and 37%, 1.8 respectively. In order to validate the data, the results of this study have been compared with previous studies and experimental data. According to the results obtained from the present study, the investigated nanofluid is suitable for use in heat exchangers.