The primary goal of picture compression is to reduce the amount of unused image data while still storing or transmitting it in a format that is appropriate. The compression of raw binary data is quite different from the compression of a picture, and these differences may be rather substantial. In light of this, compression is often regarded as an essential technique for the purposes of both data storage an d transmission in order to mitigate the excessive amounts of data that are generated by these images. In order to transmit enormous datasets, particularly for the purposes of telemedicine and teleradiology, one needs a significant amount of storage capacity as well as an expansive network. As a result, compression is an important aspect of medical imaging. In addition to the importance of compression, the quality of the photos themselves is also an essential factor in the success of analysis. In addition to this, the amount of time necessary to compress the photographs before sending them should be reduced.

The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to train deep learning models effectively. Privacy and security concerns associated with using real-world organization data have made cybersecurity researchers seek alternative strategies, notably focusing on generating synthetic data. Generative Adversarial Networks (GANs) have emerged as a prominent solution, lauded for their capacity to generate synthetic data spanning diverse domains. Despite their widespread use, the efficacy of GANs in generating realistic cyber attack data remains a subject requiring thorough investigation. Moreover, the proficiency of deep learning models trained on such synthetic data to accurately discern real-world attacks and anomalies poses an additional challenge that demands exploration. This paper delves into essential aspects of generative learning, scrutinizing their data generation capabilities, and conducts a comprehensive review to address the above questions. Through this exploration, we aim to shed light on the potential of synthetic data in fortifying deep learning models for robust cybersecurity applications.

Forests, integral to human civilization, hold immense value and play a vital role in maintaining ecological harmony. Despite India's goal of extensive forest coverage, significant progress is still needed. Uncontrolled forest fires pose a severe threat, particularly in Uttarakhand's Pauri Garhwal district. To address this challenge, a comprehensive study examined surface and subsurface hydrological factors influencing the forest fire occurrences, such as elevation, aspect, slope, vegetation, proximity to human settlements, proximity to waterbodies, Active faults and lineament density. A total of 15 such factors were integrated with advanced techniques of remote sensing and GIS and coupled with historical fire data to create a precise forest fire risk map using the support vector machine algorithm. Forest fire risk map was classified into 5 distinct risk zones, Very High Risk (47.38 Km2), High Risk (275.98 km2, Moderate Risk (985.49 km2), Low Risk (1741.17 km2) and Very Low Risk (2374.11 km2) aiding in proactive fire management. By embracing this innovative tool, decision-makers can protect forests, preserve biodiversity, and ensure a sustainable future for generations to come.

Aquaculture is rapidly becoming one of the pivotal sectors in the farm economy, driven by the increasing demand for high-quality animal protein at an affordable cost, especially with the escalating human population. However, the expansion of high-density fish populations also brings forth a challenge – the rapid transmission and spread of infectious disease agents among them. To combat this, vaccination is emerging as a reliable and standardized method for providing immunity against viral and bacterial outbreaks. The ideal vaccine is expected to be safe, effective, economical, and easily administered. The fish vaccination industry continually publishes new information on fish immunology and vaccinology, contributing to the improvement of vaccine formulation and efficacy. This review aims to offer insights into the current status of bacterial, viral, and parasitic diseases, discuss existing vaccinations, and address potential industry-threatening diseases like infectious edwardsiellosis, motile aeromonas septicemia, TiLV, salmon anemia, vibriosis, and white spot disease. Technological advancements have played a crucial role in enhancing our understanding of fish immunological mechanisms, leading to improved vaccine administration and the development of recombinant live attenuated, subunit, DNA, and RNA vaccines. However, challenges such as oral tolerance, vaccine degradation, and stressful environments persist, impacting vaccine efficacy. Addressing these challenges and gaining a deeper understanding of the fish immune system and host-pathogen interactions will be pivotal for future improvements, contributing to the sustainability of aquaculture and enhancing global food security.

Chronic Hepatitis B (CHB) Virus infection is major etiological factor for liver cirrhosis and/ or liver cancer. The viral protein, major contributor in predisposition of chronicity and Hepatocellular Carcinoma (HCC) is Hepatitis B x (HBx) protein. Its dynamic subcellular distribution to an extent determines its multifactorial role. It is a regulatory protein which modulates viral as well as host machinery in favours to HBV persistence. An insight on HBx stabilising factors is critical for therapeutic purpose. The precise role of HBx in the pathogenesis of Chronicity of HBV is not known. Summary: This review comprehensively summarizing different mechanisms and their regulation by HBx protein with respect to chronicity and HCC emphasising viral persistence. Key Messages 1. HBx is a key protein for viral persistence. 2. Dynamic subcellular distribution of HBx determines its function. 3. HBx modulates cellular machinery to favours HBV survival. 4. HBx affects various intermediary mechanisms contributing to disease progression. 5. HBx may be a potent target to prevent the disease progression towards HCC.

Antimicrobial resistance (AMR) is a major global concern; antibiotics and other regular treatment methods have failed to overcome the increasing number of infectious diseases. Bacteriophages (phages) are viruses that specifically target/ kill bacterial hosts without affecting other human microbiome. Phage therapy provides optimism in the current global healthcare scenario with a long history of its applications in humans that has now reached various clinical trials. Phages in clinical trials have specific requirements of being exclusively lytic, free from toxic genes with an enhanced host range that adds an advantage to this requisite. This review explains in detail the various phage engineering methods and their potential applications in therapy. To make phages more efficient, engineering has been attempted using techniques like conventional homologous recombination, Bacteriophage Recombineering of Electroporated DNA (BRED), clustered regularly interspaced short palindromic repeats (CRISPR)-Cas, CRISPY-BRED/Bacteriophage Recombineering with Infectious Particles (BRIP), chemically accelerated viral evolution (CAVE), and phage genome rebooting. Phages are administered in cocktail form in combination with antibiotics, vaccines, and purified proteins, such as endolysins. Thus, phage therapy is proving to be a better alternative for treating life-threatening infections, with more specificity and fewer detrimental consequences.

Geothermal energy is likely to be a significant contributor in achieving sustainable energy goals and net-zero emissions targets. Within geothermal power plants, heat exchangers play a critical role in harnessing this renewable energy source. However, these heat exchangers encounter significant challenges when exposed to geothermal fluids, including erosion, corrosion, and scaling, which adversely affects their performance and longevity. The current review focuses on surface engineering techniques, particularly coatings, as a highly effective and economically viable solution to address these challenges in geothermal heat exchangers. The review begins by providing an overview of geothermal energy, its significance in the context of sustainability and the important role played by heat exchangers in geothermal power generation, followed by the challenges and their impact on heat exchangers. The subsequent section focuses on surface engineering by coatings and its types employed to enhance the performance of heat exchangers. In the final part, the reader is presented with an overview of the challenges associated with the application of coatings in geothermal heat exchangers and potential future directions in this field. This review offers a detailed understanding of the critical role coatings play in improving the efficiency and service life of heat exchangers in geothermal power plants.

In recent years, there has been growing interest in thermal spray techniques using suspension or solution-based coatings. These techniques offer precise control over particle size and microstructure, improving feedstock flowability and allowing for high-quality coatings customization. Spray parameters, such as stand-off distance (SOD) and feedstock flow rate, can alter the performance and characteristics of these coatings. Geothermal power plant heat exchangers often face issues like corrosion, scaling, and fouling. These issues could be mitigated, at least in part, by the use of spray coatings. In this study, TiO2 coatings were applied to a carbon steel substrate using suspension plasma spray (SPS) to enhance the performance of geothermal heat exchanger materials. The impact of SOD (50, 75, and 100mm) and feedstock flow rate (10, 20, and 30 ml/min) on these coatings was examined through various techniques, including SEM, profilometry, XRD, and adhesion testing. The results demonstrated that coatings deposited using 10ml/min feedstock flow rate were well-adhered to the substrate due to efficient melting of coating material but as the SOD and feedstock flow rate increase due to poor thermal and kinetic energy exchange between the torch and feedstock particles, adhesion between coating and substrate decreases. smaller SODs led to more compact coatings, while higher feedstock flow rates increased coating porosity. Smaller SODs and lower flow rates resulted in well-adhered coatings, offering valuable insights for advancing geothermal heat exchanger coatings.