Birla Institute of Technology and Science - Hyderabad Campus

Flaxseed is emerging as an important functional food ingredient because of its rich contents of α-linolenic acid (ALA, omega-3 fatty acid), lignans, and fiber. Flaxseed oil, fibers and flax lignans have potential health benefits such as in reduction of cardiovascular disease, atherosclerosis, diabetes, cancer, arthritis, osteoporosis, autoimmune and neurological disorders. Flax protein helps in the prevention and treatment of heart disease and in supporting the immune system. As a functional food ingredient, flax or flaxseed oil has been incorporated into baked foods, juices, milk and dairy products, muffins, dry pasta products, macaroni and meat products. The present review focuses on the evidences of the potential health benefits of flaxseed through human and animals' recent studies and commercial use in various food products.

The use of liposomes for drug delivery began early in the history of pharmaceutical nanocarriers. These nanosized, lipid bilayered vesicles have become popular as drug delivery systems owing to their efficiency, biocompatibility, nonimmunogenicity, enhanced solubility of chemotherapeutic agents and their ability to encapsulate a wide array of drugs. Passive and ligand-mediated active targeting promote tumor specificity with diminished adverse off-target effects. The current field of liposomes focuses on both clinical and diagnostic applications. Recent efforts have concentrated on the development of multifunctional liposomes that target cells and cellular organelles with a single delivery system. This review discusses the recent advances in liposome research in tumor targeting.

Nanoparticles as drug delivery system have received much attention in recent years, especially for cancer treatment. In addition to improving the pharmacokinetics of the loaded poorly soluble hydrophobic drugs by solubilizing them in the hydrophobic compartments, nanoparticles allowed cancer specific drug delivery by inherent passive targeting phenomena and adopted active targeting strategies. For this reason, nanoparticles-drug formulations are capable of enhancing the safety, pharmacokinetic profiles and bioavailability of the administered drugs leading to improved therapeutic efficacy compared to conventional therapy. The focus of this review is to provide an overview of various nanoparticle formulations in both research and clinical applications with a focus on various chemotherapeutic drug delivery systems for the treatment of cancer. The use of various nanoparticles, including liposomes, polymeric nanoparticles, dendrimers, magnetic and other inorganic nanoparticles for targeted drug delivery in cancer is detailed.

WikiPathways (http://www.wikipathways.org) is an open, collaborative platform for capturing and disseminating models of biological pathways for data visualization and analysis. Since our last NAR update, 4 years ago, WikiPathways has experienced massive growth in content, which continues to be contributed by hundreds of individuals each year. New aspects of the diversity and depth of the collected pathways are described from the perspective of researchers interested in using pathway information in their studies. We provide updates on extensions and services to support pathway analysis and visualization via popular standalone tools, i.e. PathVisio and Cytoscape, web applications and common programming environments. We introduce the Quick Edit feature for pathway authors and curators, in addition to new means of publishing pathways and maintaining custom pathway collections to serve specific research topics and communities. In addition to the latest milestones in our pathway collection and curation effort, we also highlight the latest means to access the content as publishable figures, as standard data files, and as linked data, including bulk and programmatic access.

Semiconductor heterojunctions are pivotal in determining the overall photocatalytic efficiency. This review explores recent advances in diverse heterojunction types, charge transfer mechanisms and materials.

Abstract It is a subject of exploration whether the phase pure anatase or rutile TiO 2 or the band alignment due to the heterojunctions in the two polymorphs of TiO 2 plays the determining role in efficacy of a photocatalytic reaction. In this work, the phase pure anatase and rutile TiO 2 have been explored for photocatalytic nitroarenes reduction to understand the role of surface structures and band alignment towards the reduction mechanism. The conduction band of synthesized anatase TiO 2 has been found to be more populated with electrons of higher energy than that of synthesized rutile. This has given the anatase an edge towards photocatalytic reduction of nitroarenes over rutile TiO 2 . The other factors like adsorption of the reactants and the proton generation did not play any decisive role in catalytic efficacy.

Oxindole has been shown to be a pharmacologically advantageous scaffold having many biological properties that are relevant to medicinal chemistry. The simplicity and widespread occurrence of this scaffold in plant-based alkaloids have further reinforced oxindole's merit in the domain of novel drug discovery. First extracted from Uncaria tomentosa, commonly the known as cat claw's plant which was found abundantly in the Amazon rainforest, molecules with the oxindole moiety have been shown to be common in a wide variety of compounds extracted from plant sources. The role of oxindole as a chemical scaffold for fabricating and designing biological drugs agents can be ascribed to its ability to be modified by a number of chemical groups to generate novel biological functions. This review is aimed at providing a description of the general chemistry based on existing corresponding structure-activity relationships (SARs) and compile all recent developmentary studies on oxindole-derived compounds as a successful pharmaceutical agent. A substantial group of oxindole derivatives are chiefly being tested as anticancer agents, however, a several oxindole derivatives have been shown to possesses antimicrobial, α-glucosidase inhibitory, antiviral, antileishmanial, antitubercular, antioxidative, tyrosinase inhibitory, PAK4 inhibitory, antirheumatoid arthritis and intraocular pressure reducing activities, to name a few. In this review we show the potential value of developing newer oxindole derivatives with an improved range of pharmacological implications as well as identifying drugs possessing oxindole core, that are showing and serving increased efficacy in clinical practice.

Organic light emitting diodes (OLEDs) have been well known for their potential usage in the lighting and display industry. The device efficiency and lifetime have improved considerably in the last three decades. However, for commercial applications, operational lifetime still lies as one of the looming challenges. In this review paper, an in-depth description of the various factors which affect OLED lifetime, and the related solutions is attempted to be consolidated. Notably, all the known intrinsic and extrinsic degradation phenomena and failure mechanisms, which include the presence of dark spot, high heat during device operation, substrate fracture, downgrading luminance, moisture attack, oxidation, corrosion, electron induced migrations, photochemical degradation, electrochemical degradation, electric breakdown, thermomechanical failures, thermal breakdown/degradation, and presence of impurities within the materials and evaporator chamber are reviewed. Light is also shed on the materials and device structures which are developed in order to obtain along with developed materials and device structures to obtain stable devices. It is believed that the theme of this report, summarizing the knowledge of mechanisms allied with OLED degradation, would be contributory in developing better-quality OLED materials and, accordingly, longer lifespan devices.

Abstract This paper presents a study on the impact of ChatGPT as a formative feedback tool on the writing skills of undergraduate ESL students. Since artificial intelligence-driven automated writing evaluation tools positively impact students’ writing, ChatGPT, a generative artificial intelligence-propelled tool, can be expected to have a more substantial positive impact. However, very little empirical evidence regarding the impact of ChatGPT on writing is available. The current mixed methods intervention study tried to address this gap. Data were collected from tertiary level ESL students through three tests and as many focus group discussions. The findings indicate a significant positive impact of ChatGPT on students' academic writing skills, and students’ perceptions of the impact were also overwhelmingly positive. The study strengthens and advances theories of feedback as a dialogic tool and ChatGPT as a reliable writing tool, and has practical implications. With proper student training, ChatGPT can be a good feedback tool in large-size writing classes. Future researchers can investigate the impact of ChatGPT on various specific genres and micro aspects of writing.

Abstract The paper explores the use of concepts in cognitive psychology to evaluate the spread of misinformation, disinformation and propaganda in online social networks. Analysing online social networks to identify metrics to infer cues of deception will enable us to measure diffusion of misinformation. The cognitive process involved in the decision to spread information involves answering four main questions viz consistency of message, coherency of message, credibility of source and general acceptability of message . We have used the cues of deception to analyse these questions to obtain solutions for preventing the spread of misinformation. We have proposed an algorithm to effectively detect deliberate spread of false information which would enable users to make informed decisions while spreading information in social networks. The computationally efficient algorithm uses the collaborative filtering property of social networks to measure the credibility of sources of information as well as quality of news items. The validation of the proposed methodology has been done on the online social network `Twitter’.

We develop new hyperon equation of state (EoS) tables for core-collapse supernova simulations and neutron stars. These EoS tables are based on a density-dependent relativistic hadron field theory where baryon-baryon interaction is mediated by mesons, using the parameter set DD2 from Typel et al. (2010) for nucleons. Furthermore, light and heavy nuclei along with the interacting nucleons are treated in the nuclear statistical equilibrium model of Hempel and Schaffner-Bielich which includes excluded volume effects. Of all possible hyperons, we consider only the contribution of $\Lambda$s. We have developed two variants of hyperonic EoS tables: in the np$\Lambda \phi$ case the repulsive hyperon-hyperon interaction mediated by the strange $\phi$ meson is taken into account, and in the np$\Lambda$ case it is not. The EoS tables for the two cases encompass wide range of density ($10^{-12}$ to $\sim$ 1 fm$^{-3}$), temperature (0.1 to 158.48 MeV), and proton fraction (0.01 to 0.60). The effects of $\Lambda$ hyperons on thermodynamic quantities such as free energy per baryon, pressure, or entropy per baryon are investigated and found to be significant at high densities. The cold, $\beta$-equilibrated EoS (with the crust included self-consistently) results in a 2.1 M$_{\odot}$ maximum mass neutron star for the np$\Lambda \phi$ case, whereas that for the np$\Lambda$ case is 1.95 M$_{\odot}$. The np$\Lambda \phi$ EoS represents the first supernova EoS table involving hyperons that is directly compatible with the recently measured 2 M$_{\odot}$ neutron stars.

Several industrial by-products are extensively used again as a supplementary cementitious material or aggregates in the interest to reduce environmental footprints in terms of energy depletion, pollution, waste disposition, resource depletion, and global warming related with conventional cement. A remarkable quantity of industrial scrap materials, primarily designated as construction and demolition waste from the construction industry, has transformed into crucial apprehension of governments. In the recent past, substantial explorations have been accomplished to appreciate the distinct characteristics of concrete, employing recycled aggregates from construction and demolition waste. Geopolymer composite is a new cementitious material, and it appears to be a potential replacement for conventional cement concrete. This paper summarises the previous research concerning the utilisation of recycled aggregate as a partial or complete supplants for conventional aggregates in geopolymer concrete. The influence of recycled aggregate addition on the fresh and hardened properties of geopolymer concrete is comprehensively reviewed in this paper. The studies suggest significant improvement in the workability on addition of recycled aggregates to geopolymer concrete. However, the addition results in increased water absorption and sorptivity.

In this work, we propose the modeling of static wormholes within the $f(R,T)$ extended theory of gravity perspective. We present some models of wormholes, which are constructed from different hypotheses for their matter content, i.e., different relations for their pressure components (radial and lateral) and different equations of state. The solutions obtained for the shape function of the wormholes obey the necessary metric conditions. They show a behavior similar to those found in previous references about wormholes, which also happens to our solutions for the energy density of such objects. We also apply the energy conditions for the wormholes' physical content.

The paper describes the development of a DENET computer model that involves the application of an evolutionary optimization technique, differential evolution, linked to the hydraulic simulation solver, EPANET, for optimal design of water distribution networks. A model is formulated with the objective of minimizing cost and this formulation is applied to two benchmark water distribution system optimization problems—New York water supply system and Hanoi water distribution network. The study yielded promising results as compared with earlier studies in the literature and encouraged to reformulate the model for a new objective of maximizing network resilience. The results of the analysis demonstrate that DENET can be considered as a potential alternative tool for economical and reliable water distribution network planning and management.

A complete theory of gravity impels us to go beyond Einstein's general relativity. One promising approach lies in a new class of teleparallel theory of gravity named $f(Q)$, where the nonmetricity $Q$ is responsible for the gravitational interaction. The important roles any of these alternative theories should obey are the energy condition constraints. Such constraints establish the compatibility of a given theory with the causal and geodesic structure of space-time. In this work, we present a complete test of energy conditions for $f(Q)$ gravity models. The energy conditions allowed us to fix our free parameters, restricting the families of $f(Q)$ models compatible with the accelerated expansion our Universe passes through. Our results strengthen the viability of $f(Q)$ theory, leading us close to the dawn of a complete theory for gravitation.

Abstract Global climate models (GCMs) are developed to simulate past climate and produce projections of climate in future. Their roles in ascertaining regional issues and possible solutions in water resources planning/management are appreciated across the world. However, there is substantial uncertainty in the future projections of GCM(s) for practical and regional implementation which has attracted criticism by the water resources planners. The present paper aims at reviewing the selection of GCMs and focusing on performance indicators, ranking of GCMs and ensembling of GCMs and covering different geographical regions. In addition, this paper also proposes future research directions.

f(R, T) gravity is an extended theory of gravity in which the gravitational action contains general terms of both the Ricci scalar R and the trace of the energy-momentum tensor T. In this way, f(R, T) models are capable of describing a non-minimal coupling between geometry (through terms in R) and matter (through terms in T). In this article we construct a cosmological model from the simplest non-minimal matter–geometry coupling within the f(R, T) gravity formalism, by means of an effective energy-momentum tensor, given by the sum of the usual matter energy-momentum tensor with a dark energy contribution, with the latter coming from the matter–geometry coupling terms. We apply the energy conditions to our solutions in order to obtain a range of values for the free parameters of the model which yield a healthy and well-behaved scenario. For some values of the free parameters which are submissive to the energy conditions application, it is possible to predict a transition from a decelerated period of the expansion of the universe to a period of acceleration (dark energy era). We also propose further applications of this particular case of the f(R, T) formalism in order to check its reliability in other fields, rather than cosmology.

Low rank approximation of matrices has been well studied in literature. Singular value decomposition, QR decomposition with column pivoting, rank revealing QR factorization, Interpolative decomposition, etc. are classical deterministic algorithms for low rank approximation. But these techniques are very expensive ( operations are required for matrices). There are several randomized algorithms available in the literature which are not so expensive as the classical techniques (but the complexity is not linear in n). So, it is very expensive to construct the low rank approximation of a matrix if the dimension of the matrix is very large. There are alternative techniques like Cross/Skeleton approximation which gives the low-rank approximation with linear complexity in n. In this article we review low rank approximation techniques briefly and give extensive references of many techniques.

Cosmography is an ideal tool to investigate the cosmic expansion history of the Universe in a model-independent way. The equations of motion in modified theories of gravity are usually very complicated; cosmography may select practical models without imposing arbitrary choices a priori. We use the model-independent way to derive $f(z)$ and its derivatives up to fourth order in terms of measurable cosmographic parameters. We then fit those functions into the luminosity distance directly. We perform the MCMC analysis by considering three different sets of cosmographic functions. Using the largest supernovae Ia Pantheon sample, we derive the constraints on the Hubble constant ${H}_{0}$ and the cosmographic functions, and find that the former two terms in Taylor expansion of luminosity distance work dominantly in $f(Q)$ gravity.

Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However, such general-domain VLMs perform poorly for Remote Sensing (RS) scenarios, leading to inaccurate or fabricated information when presented with RS domain-specific queries. Such a behavior emerges due to the unique challenges introduced by RS imagery. For example, to handle high-resolution RS imagery with diverse scale changes across categories and many small objects, region-level reasoning is necessary alongside holistic scene inter-pretation. Furthermore, the lack of domain-specific multimodal instruction following data as well as strong back-bone models for RS make it hard for the models to align their behavior with user queries. To address these limitations, we propose GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images. Specifically, GeoChat can not only answer image-level queries but also accepts region inputs to hold region-specific dialogue. Further-more, it can visually ground objects in its responses by referring to their spatial coordinates. To address the lack of domain-specific datasets, we generate a novel RS multimodal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. We establish a comprehensive benchmarkfor RS multitask conversations and compare with a number of baseline methods. GeoChat demonstrates robust zero-shot performance on various RS tasks, e.g., image and region captioning, visual question answering, scene classification, visually grounded conversations and referring detection. Our code is available here.