Effects of stocking density on growth and production of GIFT (Oreochromis niloticus)

The study was carried out to assess the effects of stocking density on growth and production of GIFT for a period of 100 days. Three stocking densities were used 150, 200 and 250 fish/decimal; designated as treatment T1, T2 and T3 respectively having two replicates for each. Commercial pellet feeds were fed at the rate of 30% body weight up to first 10 days and then gradually it was readjusted to 22%, 18%, 15%, 12%, 10%, 8%, 6%, 5% and 4% respectively after every 10 days interval. The result showed that the fish in the treatment T1 stocked with the lowest stocking density (150 fish/dec) resulted in best individual weight gain (148.65g) followed by those in treatment T2 and T3 respectively. The specific growth rates (SGR) at every 10 days were ranged from 6.59 to 1.11 in different treatments during the experimental period. The food conversion ratio (FCR) values ranged between 1.82 to 2.03 with treatment T1 showing the lowest FCR. The survival rate ranged between 84 to 92%. Treatment T1 and treatment T2 showed significantly higher survival than Treatment T3. The fish production rate in treatment T1, T2 and T3 were 18.58, 23.87 and 26.78 kg/decimal respectively

Viewpoint invariant semantic object and scene categorization with RGB-D sensors

Understanding the semantics of objects and scenes using multi-modal RGB-D sensors serves many robotics applications. Key challenges for accurate RGB-D image recognition are the scarcity of training data, variations due to viewpoint changes and the heterogeneous nature of the data. We address these problems and propose a generic deep learning framework based on a pre-trained convolutional neural network, as a feature extractor for both the colour and depth channels. We propose a rich multi-scale feature representation, referred to as convolutional hypercube pyramid (HP-CNN), that is able to encode discriminative information from the convolutional tensors at different levels of detail. We also present a technique to fuse the proposed HP-CNN with the activations of fully connected neurons based on an extreme learning machine classifier in a late fusion scheme which leads to a highly discriminative and compact representation. To further improve performance, we devise HP-CNN-T which is a view-invariant descriptor extracted from a multi-view 3D object pose (M3DOP) model. M3DOP is learned from over 140,000 RGB-D images that are synthetically generated by rendering CAD models from different viewpoints. Extensive evaluations on four RGB-D object and scene recognition datasets demonstrate that our HP-CNN and HP-CNN-T consistently outperforms state-of-the-art methods for several recognition tasks by a significant margin

On quantifying and improving realism of images generated with diffusion

Recent advances in diffusion models have led to a quantum leap in the quality of generative visual content. However, quantification of realism of the content is still challenging. Existing evaluation metrics, such as Inception Score and Fr\'echet inception distance, fall short on benchmarking diffusion models due to the versatility of the generated images. Moreover, they are not designed to quantify realism of an individual image. This restricts their application in forensic image analysis, which is becoming increasingly important in the emerging era of generative models. To address that, we first propose a metric, called Image Realism Score (IRS), computed from five statistical measures of a given image. This non-learning based metric not only efficiently quantifies realism of the generated images, it is readily usable as a measure to classify a given image as real or fake. We experimentally establish the model- and data-agnostic nature of the proposed IRS by successfully detecting fake images generated by Stable Diffusion Model (SDM), Dalle2, Midjourney and BigGAN. We further leverage this attribute of our metric to minimize an IRS-augmented generative loss of SDM, and demonstrate a convenient yet considerable quality improvement of the SDM-generated content with our modification. Our efforts have also led to Gen-100 dataset, which provides 1,000 samples for 100 classes generated by four high-quality models. We will release the dataset and code.Comment: 10 pages, 5 figure

Text-image guided Diffusion Model for generating Deepfake celebrity interactions

Deepfake images are fast becoming a serious concern due to their realism. Diffusion models have recently demonstrated highly realistic visual content generation, which makes them an excellent potential tool for Deepfake generation. To curb their exploitation for Deepfakes, it is imperative to first explore the extent to which diffusion models can be used to generate realistic content that is controllable with convenient prompts. This paper devises and explores a novel method in that regard. Our technique alters the popular stable diffusion model to generate a controllable high-quality Deepfake image with text and image prompts. In addition, the original stable model lacks severely in generating quality images that contain multiple persons. The modified diffusion model is able to address this problem, it add input anchor image's latent at the beginning of inferencing rather than Gaussian random latent as input. Hence, we focus on generating forged content for celebrity interactions, which may be used to spread rumors. We also apply Dreambooth to enhance the realism of our fake images. Dreambooth trains the pairing of center words and specific features to produce more refined and personalized output images. Our results show that with the devised scheme, it is possible to create fake visual content with alarming realism, such that the content can serve as believable evidence of meetings between powerful political figures.Comment: 8 pages,8 figures, DICT

CTGF Loaded Electrospun Dual Porous Core-Shell Membrane For Diabetic Wound Healing

Purpose: Impairment of wound healing is a major issue in type-2 diabetes that often causes chronic infections, eventually leading to limb and/or organ amputation. Connective tissue growth factor (CTGF) is a signaling molecule with several roles in tissue repair and regeneration including promoting cell adhesion, cell migration, cell proliferation and angiogenesis. Incorporation of CTGF in a biodegradable core-shell fiber to facilitate its sustained release is a novel approach to promote angiogenesis, cell migration and facilitate wound healing. In this paper, we report the development of CTGF encapsulated electrospun dual porous PLA-PVA core-shell fiber based membranes for diabetic wound healing applications. Methods: The membranes were fabricated by a core-shell electrospinning technique. CTGF was entrapped within the PVA core which was coated by a thin layer of PLA. The developed membranes were characterized by techniques such as Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and X-Ray Diffraction (XRD) analysis. In vitro cell culture studies using fibroblasts, keratinocytes and endothelial cells were performed to understand the effect of CTGF loaded membranes on cell proliferation, cell viability and cell migration. A chicken chorioallantoic membrane (CAM) assay was performed to determine the angiogenic potential of the membranes. Results: Results showed that the developed membranes were highly porous in morphology with secondary pore formation on the surface of individual fibers. In vitro cell culture studies demonstrated that CTGF loaded core-shell membranes improved cell viability, cell proliferation and cell migration. A sustained release of CTGF from the core-shell fibers was observed for an extended time period. Moreover, the CAM assay showed that core-shell membranes incorporated with CTGF can enhance angiogenesis. Conclusion: Owing to the excellent cell proliferation, migration and angiogenic potential of CTGF loaded core-shell PLA-PVA fibrous membranes, they can be used as an excellent wound dressing membrane for treating diabetic wounds and other chronic ulcers.This article was made possible by the NPRP9-144-3-021 grant funded by the Qatar National Research Fund (a part of the Qatar Foundation). We also acknowledge the support provided by the Central Laboratories Unit (CLU), Qatar University, Qatar. The statements made herein are solely the responsibility of the authors. The publication of this article was funded by the Qatar National Library. The authors also acknowledge Huseyin Cagatay Yalcin and Ala-Eddin Al Moustafa for sharing resources during the initial stage of this project.Scopu

Does pin tract infection after external fixator limits its advantage as a cost-effective solution for open fractures in low-middle income countries, a prospective cohort study

OBJECTIVE: To determine the frequency of pin tract infection in external fixator tibia and its effects on the definite fracture fixation and bone healing. METHODS: The prospective study was conducted at Lady Reading Hospital, Peshawar, Pakistan, from August 2017 to July 2018, and comprised patients regardless of age and gender with open fracture tibia Gustillo-Anderson type II and type IIIA. Pin tract infection was assessed following the application of locally made external fixation of tibia open fractures. Follow-up was done fortnightly till soft tissue healing, removal of external fixator and definite fracture healing. Pin tract infection was classifiedand treated according to the Checketts-Otterburn classification system. SPSS 20 was used for data analysis. RESULTS: Of the 117 patients, 95(81%) were males and 22(19%) were females with an overall mean age of 24.7±9.35 years. Pin tract infection was documented in 28(23.9%) patients. Minor and major pin tract infections were reported in 27(96.4%) and 1(3.5%) patient respectively. Soft tissues healed in 27(96.4%) cases. CONCLUSION: External fixator for initial stabilisation of open tibial fractures in all patients is recommende

SecureSurgiNET:a framework for ensuring security in telesurgery

The notion of surgical robotics is actively being extended to enable telesurgery, where both the surgeon and patient are remotely located and connected via a public network, which leads to many security risks. Being a safety-critical application, it is highly important to make telesurgery robust and secure against active and passive attacks. In this article, we propose the first complete framework, called SecureSurgiNET, for ensuring security in telesurgery environments. SecureSurgiNET is primarily based on a set of well-established protocols to provide a fool-proof telesurgical robotic system. For increasing the efficiency of secured telesurgery environments, the idea of a telesurgical authority is introduced that ensures the integrity, identity management, authentication policy implementation, and postoperative data security. An analysis is provided describing the security and throughput of Advanced Encryption Standard during the intraoperative phase of SecureSurgiNET. Moreover, we have tabulated the possible attacks on SecureSurgiNET along with the devised defensive measures. Finally, we also present a time complexity analysis of the SecureSurgiNET through simulations. © The Author(s) 2019

Inbound Tourism In Malaysia: Unlocking the potential traveling experience of European and Oceanian Tourists

This study explores the memorable traveling experience (MTE) of 143 tourists from Europe and Oceanian in Malaysia. The researchers conducted a survey to investigate the factors that influence their revisit intention to Malaysia as an attractive destination. Partial least square structural equation modeling (SEM-PLS) results indicated that tourist attitudes, destination images, electronic Word of Mouth (eWOM), and perceived quality have positive and significant influences on travel intention. However, the eWOM failed to mediate the tourists’ attitudes, perceived quality, and destination image towards their revisit intentions.     Keywords: eWOM, Malaysia, Memorable Travelling Experience, Revisit Intentions eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA CE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i21.371

Benchmark data set and method for depth estimation from light field images

Convolutional neural networks (CNNs) have performed extremely well for many image analysis tasks. However, supervised training of deep CNN architectures requires huge amounts of labeled data, which is unavailable for light field images. In this paper, we leverage on synthetic light field images and propose a two-stream CNN network that learns to estimate the disparities of multiple correlated neighborhood pixels from their epipolar plane images (EPIs). Since the EPIs are unrelated except at their intersection, a two-stream network is proposed to learn convolution weights individually for the EPIs and then combine the outputs of the two streams for disparity estimation. The CNN estimated disparity map is then refined using the central RGB light field image as a prior in a variational technique. We also propose a new real world data set comprising light field images of 19 objects captured with the Lytro Illum camera in outdoor scenes and their corresponding 3D pointclouds, as ground truth, captured with the 3dMD scanner. This data set will be made public to allow more precise 3D pointcloud level comparison of algorithms in the future which is currently not possible. Experiments on the synthetic and real world data sets show that our algorithm outperforms existing state of the art for depth estimation from light field images

Study of Pharmacologically Active Drugs Containing Quinazoline Pharmacophore: A Brief Overview

Heterocyclic compounds have been the subject of extensive research due to their diverse pharmacological effects. Among these molecules are quinazolinone analogs, which have demonstrated a range of pharmacological potentials. In search of novel therapeutic pharmaceutical molecules, medicinal chemistry researchers are drawn to the quinazolinone nucleus.  This article aims to provide an overview of the many pharmacological activities of the quinazolinone moiety. Based on the quinazolinone moiety, more modern molecules have been developed and synthesized. These compounds show negligible toxicity and outstanding anti-disease capabilities. This paper reviews several different quinazolinone analogs and makes recommendations for future research paths in the quest to create effective quinazolinone drugs for a range of biological objectives