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  • 1. Enhanced mechanical properties and microstructure of Incoloy 825 components fabricated using pulsed cold metal transfer in wire arc additive manufacturing

    Prasanna Nagasai Bellamkonda., Maheswar Dwivedy

    Journal, Welding in the World, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    To address the challenges of heat input in wire arc additive manufacturing (WAAM), this study employed the pulsed cold metal transfer (PCMT) technique to fabricate Incoloy 825 (IN825) components. PCMT, characterized by controlled droplet transfer and reduced heat input, enhanced mechanical performance and microstructural quality. Comprehensive analyses, including microstructural examination, X-ray diffraction, energy-dispersive X-ray spectroscopy (EDS), and element mapping, were performed. Titanium and molybdenum-rich secondary particles were identified through EDS. The mechanical properties of PCMT-fabricated components were compared with both wrought IN825 and those produced by gas metal arc additive manufacturing (GMAAM). Results demonstrated that PCMT components, particularly those fabricated at a 45° orientation, achieved approximately 113% of the ultimate tensile strength (UTS) and 131% of the elongation compared to wrought IN825. This marked a significant improvement over GMAAM-fabricated components. The reduced heat input and enhanced cooling rates in the PCMT process contributed to finer microstructures and superior mechanical properties. Fractography studies revealed that PCMT components exhibited ductile fractures with significant plastic deformation and some brittle regions. These findings underscored the advantages of PCMT in producing high-performance IN825 components compared to traditional GMAAM.

  • 2. Exploration of the Structural, Optoelectronic, Thermoelectric, and Photovoltaic Characteristics of K2Tl(As/Sb)I6 via DFT and SCAPS-1D Simulations

    M T Islam., Deepshikha Burman

    Journal, Journal of Inorganic and Organometallic Polymers and Materials, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    This study highlights K2Tl(As/Sb)I6 as a thermodynamically stable, lead-free double perovskite with exceptional bifunctional photovoltaic and thermoelectric performance, making it a promising candidate for next-generation clean energy applications. We have explored the structural, optoelectronic and thermoelectric properties by using DFT while their photovoltaic properties have been explored with the help of SCAPS-1D simulations. The predicted negative formation energy and the lower fluctuation in RMSD obtained through DFT calculations, indicates that K2Tl(As/Sb)I6 is thermodynamically stable. Furthermore, electronic property analysis using the TB-mBJ method reveals that both K2TlAsI6 and K2TlSbI6 possess a desirable direct band gap of 1.16 eV and 1.04 eV, respectively—ideal for optoelectronic applications. The optical analyses unveil remarkable absorption coefficients, soaring to the impressive magnitude of 10? cm?¹, beyond the threshold energy of 1.18 eV for K2TlAsI6 and 1.05 eV for K2TlSbI6. These compounds exhibit notable electrical conductivity and minimal reflectivity, attributed to their well-dispersed band structures and optimally aligned band gap values. The thermoelectric evaluation highlights exceptional ZT values of 0.79 and 0.74 at 510 K for K2TlSbI6 and K2TlAsI6, respectively, owing to their ultra-low thermal conductivity (??/?~1014 W m?1?1 s?1) and remarkably high electrical conductivity (?/?~1019 ? m?1 s?1) over a wide temperature range of 200–650 K. Furthermore, SCAPS-1D simulations unveil outstanding photovoltaic performance, showcasing peak power conversion efficiencies of 30.01% (31.77%) for Ag/Cu2O/K2TlAsI6/TiO2/FTO (Ag/Cu2O/K2TlSbI6/TiO2/FTO), with corresponding JSC values of 40.69 mA/cm2 (45.07 mA/cm2), VOC of 0.93 V (0.81 V), and fill factors of 84.07% (82.21%). These remarkable figures surpass those of recently reported lead-free halide double perovskite solar cells, driven by the excellent electronic and optical characteristics of K2Tl(As/Sb)I6. This study provides a promising pathway toward the realization of next-generation high-efficiency solar cells and thermoelectric devices based on eco-friendly materials

  • 3. 2D MoTe memristors for energy-efficient artificial synapses and neuromorphic applications.

    Rajwali Khan., Naveed Ur Rehman., Sundaravadivel Elumalai., Appukuttan Saritha., Muhammad Fakhar-E-Alam., Muhammad Ikram., Sherzod Abdullaev., Nasir Rahman., Sambasivam Sangaraju

    Journal, Nanoscale, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    2D-materials for memristor-based low-power neuromorphic computing.

  • 4. Critical agendas for the areal linguistics: locating Sindhi within South Asia

    Journal, Critical Inquiry in Language Studies, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    As a concept within applied linguistics, areal linguistics concerns itself with investigating the nature of structural similarities among languages produced by contact rather than by history or by genetic similarities. A critical look at its descriptive linguistic agendas reveals that the domain needs to be revisited in terms of questions of power relations and linguistic inequalities within specific linguistic areas. Such investigations reconfigure the dynamics of geography and regionality within language as a site of power. This study seeks to make an intervention into India as a linguistic area with a focus on Sindhi, a non-regional language in India. Given that the language and the community do not have a state or a linguistic territory within India, the condition of Sindhi is characterized by a sense of precarity. Seen through the prism of India as a linguistic area, this precarity is not quite visible. In revisiting the celebrated concept of India as a linguistic area, this study suggests ways of asking contemporary questions about areal linguistics that go beyond describing the nature of contact among languages, and instead ask how this contact impacts the markers of hegemony over minor languages in terms of technological, epistemological, and aesthetic leverage.

  • 5. Structural engineering of bimetallic NiMoO for high-performance supercapacitors and efficient oxygen evolution reaction catalysts

    Sayali Ashok Patil., Pallavi Bhaktapralhad Jagdale., Mallamma Jinagi., Parasmani Rajput., Amanda Sfeir., Sébastien Royer., Akshaya Kumar Samal., Manav Saxena

    Journal, Journal of Materials Chemistry A, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Advancing energy storage and conversion research on 2D nanostructures hinges on the critical development of bifunctional electrodes capable of effectively catalyzing oxygen evolution reactions and facilitating charge storage applications.

  • 6. Does Excellence Correspond to Universal Inequality Level?

    Bikas K Chakrabarti., Asim Ghosh., Máté Józsa., Zoltán Néda

    Journal, Entropy, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    We study the inequality of citations received for different publications of various researchers and Nobel laureates in Physics, Chemistry, Medicine and Economics using Google Scholar data from 2012 to 2024. Citation distributions are found to be highly unequal, with even greater disparity among Nobel laureates. Measures of inequality, such as the Gini and Kolkata indices, emerge as useful indicators for distinguishing Nobel laureates from others. Such high inequality corresponds to growing critical fluctuations, suggesting that excellence aligns with an imminent (self-organized dynamical) critical point. Additionally, Nobel laureates exhibit systematically lower values of the Tsallis–Pareto parameter b and Shannon entropy, indicating more structured citation distributions. We also analyze the inequality in Olympic medal tallies across countries and find similar levels of disparity. Our results suggest that inequality measures can serve as proxies for competitiveness and excellence.

  • 7. Mother’s maladies: understanding the intricacies of postpartum psychosis and motherhood through Jerry Pinto’s .

    Rajni Mujral

    Journal, Medical Humanities, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Motherhood, a familiar yet complex phenomenon, is informed by many factors whose consequences for women are often detrimental yet undermined. Particularly in India, discourse surrounding mothers’ health often disregards the social and familial expectations and impositions that threaten women’s authority over their own bodies. Amidst this, postpartum disorders, particularly the concept of postpartum psychosis, embody the anomalies of medical and social knowledge bases. Addressing the ambiguities and interconnectedness of motherhood and madness, this paper discusses the simplification of postpartum concerns as a biological condition alone and explores the complexities of diagnosis based on Em’s aetiologies. Addressing the psychopathological and social nuances of postpartum psychosis, this paper also advocates for destigmatising women’s apprehensions regarding the structural obligation of motherhood and broadening the discourse surrounding their reproductive autonomy.

  • 8. A New Method for Multilevel Thresholding of Crop Images Using Coronavirus Herd Immunity Optimizer

    Anil Kumar|Amit Vishwakarma|G K Singh

    Journal, IEEE Transactions on Consumer Electronics, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Due to the multimodality and uneven distribution of intensity levels in crop images, multilevel thresholding is a complicated job. In this paper, a new technique is proposed to segment the complex background color crop images (CBCCI) using recursive minimum cross entropy (R-MCE) and coronavirus herd immunity optimizer (CHIO). In the proposed method, CBCCI is converted into CIE lab color space, then pre-processed using Gaussian and Guided filters to smooth the flat part as well as preserve the color information on the edges. Finally, CHIO is applied with R-MCE to select the best possible threshold values. The accuracy of the proposed method is evaluated using peak signal-to-noise ratio, feature similarity index, structural similarity index, root mean square error, fitness value, and CPU time. To investigate the performance, a comparative study with bacterial foraging optimization, artificial bee colony, differential evolution, wind-driven optimization, firefly algorithm, sparse particle swarm optimization, and cuckoo search algorithm is made. The proposed method shows better average fidelity parameters than the above-reported algorithms. It also takes less computational time to obtain segmented images. Further, a graphical user interface is developed for consumer electronics applications which would be fast enough to process accurately and respond in real-time.

  • 9. Optimal Charging of Lithium-Ion Batteries: An Electro-Thermal Model Approach Using Maximum Possible Optimization

    Syed Ali Hussain|Asha Anish Madhavan|Sangaraju Sambasivam

    Journal, Advanced Theory and Simulations, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Electric vehicle (EV) charging has recently become one of the most pressing issues. Given the growing demand for lithium-ion batteries (LIBs) in electric vehicles, this study analyzes optimization methods for improving existing approaches to speed up charging while reducing temperature rise. This work formulates a double-objective function for battery charging based on an electrothermal model. The focused objective function is comprised of a combination of two different fitness functions. Optimization of charging current is made dynamically following a battery's temperature. These experimental findings validate the proposed charging strategy's effectiveness in delivering the optimal current profile. This approach demonstrably achieves a well-calibrated balance between competing performance objectives. By adopting the suggested strategy, any increase in the battery's temperature can be maintained within an acceptable temperature range. The proposed constant current constant voltage (CCCV) charging method takes a total charging time of 1874 s, with a temperature shift from 26 to 45.78 .

  • 10. Boosting the Simultaneous Conversion of Glycerol and CO<sub>2</sub> to Lactate and Formate Using ZrO<sub>2</sub>?Supported NiO Catalyst

    Sudip Bhattacharjee|Unnikrishnan Pulikkeel|Vipin Amoli|Biswajit Chowdhury|Thomas E Müller|Praveen Kumar Chinthala|Asim Bhaumik

    Journal, Advanced Functional Materials, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Glycerol, a by?product of biodiesel production, and CO2, a major greenhouse gas, are abundant but underutilized feedstocks. Their simultaneous conversion into formic acid and lactic acid presents an innovative and sustainable approach to addressing environmental challenges. Formic acid, a versatile compound in multiple industries, and lactic acid, a versatile platform chemical used in food, pharmaceuticals, and biodegradable plastics, hold immense commercial value. In this work, a NiO?ZrO2 catalyst synthesized through incipient wetness impregnation is employed to achieve the simultaneous conversion of CO2 and glycerol in an alkaline medium. Comprehensive characterisation of the catalyst using PXRD, Raman spectroscopy, XPS, BET surface area, CO2/NH3?TPD, H2?TPR, and UHR?TEM analysis revealed its unique properties, including weak Lewis acid sites critical to its performance. Under optimal reaction conditions, 200 °C, 40 bar CO2, and KOH as the base, the catalyst achieved yields of 3.26 mmol of formate and 11.20 mmol of lactate. The synergistic interaction between NiO and ZrO2, along with the in situ formation of carbonate salts, is key to the high efficiency. An initial economic assessment demonstrates the commercial viability of co?producing formic and lactic acid, with the glycerol price and the efficiency of converting the formate and lactate salts to the corresponding acids being critical factors for the economic feasibility of the process.

  • 11. Unlocking Sustainability: Integrating Omics for Advanced Wastewater Treatment

    Shiwangi Dogra|Nilotpal Das|Ashutosh Sharma|Aurea Karina Ramírez Jiménez |Alfredo Díaz Lara |Shane A Snyder |Futoshi Kurisu

    Journal, Journal of Environmental Chemical Engineering, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Owing to the urgent and escalating environmental crisis of water pollution through anthropogenic wastewater generated from various sources, the development of novel and innovative bioremediation strategies that are equally sustainable is highly necessitated. The present study embarks on an integrated omics-based exploration, complemented by a thorough literature synthesis, to critically evaluate and enhance hybrid algal-bacterial systems for effective wastewater treatment. Drawing on case studies and research from diverse geographic regions, we explore how these technologies inform the design and optimization of both engineered and natural treatment systems. The review emphasizes the integration of multi-omics data to support sustainable, targeted bioremediation strategies and underscores the cross-disciplinary convergence of environmental engineering, molecular biology, and systems ecology. This global and holistic perspective positions omics as a cornerstone for advancing the next generation of wastewater treatment solutions. Comprehensive analyses of the efficacies of different treatment methods used to remediate organic pollutants, heavy metals, nutrients, and contaminants of emerging concern (CECs), including antibiotic resistance genes (ARGs), were carried out, thus underscoring the pivotal role of microbial diversity and metabolic activity in the complex process of contaminant elimination. While prior research has predominantly focused on isolated components, the current study presents a holistic approach, merging state-of-the-art high-throughput metagenomics and transcriptomics techniques. This innovative combination illuminates the functional dynamics of microbial communities operating within the hybrid system under a range of operational conditions. The primary critical findings reveal significant shifts in microbial community structure and gene expression patterns, which are intricately linked to enhanced efficiencies in nutrient uptake and contaminant removal. In addition, the study also situates these findings within the expansive framework of omics-based bioremediation research, providing a clear and structured pathway for identifying prevailing knowledge gaps and directing future optimization efforts. Collectively, these contributions not only deepen our understanding of microbial community functions but also pave the way for designing next-generation bio-based wastewater treatment systems driven by the intricate interplay of microbial dynamics.

  • 12. Complicating opioid access in global health

    Nick Surawy Stepney

    Journal, The Lancet Global Health, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    -

  • 13. ZIF-67 templated Co3O4/NiCo2O4@Mn0.2Cd0.8S p-n heterojunction with a perfect d-band alignment boost photocatalytic hydrogen evolution reaction

    Saad Mehmood|Lincoln Einstein Kengne Fotso|Saddam Sk|Ujjwal Pal

    Journal, International Journal of Hydrogen Energy, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Designing suitable heterojunctions effectively addresses challenges like rapid electron-hole recombination, limited mobility, restricted absorption, and insufficient active sites. Thus to improve the photocatalytic performance, we synthesized a p-n heterojunction photocatalyst, Co3O4/NiCo2O4@Mn0.2Cd0.8S, by coupling ZIF-67-derived p-type Co3O4/NiCo2O4 double-shelled nanocages with n-type Mn0.2Cd0.8S nanoneedles via a template-assisted method. Analytics revealed the judicious anchoring of the Mn0.2Cd0.8S over the Co3O4/NiCo2O4 surface, reinforcing the photocatalytic activity. The resultant heterostructure exhibits superior photocatalytic performance, achieving an HER rate of ?14.34 mmol h?1 g?1 with an AQY of ?35.1 %. This represents an enhancement of ?72-fold compared to pristine Mn0.2Cd0.8S. The synergistic interplay within the heterostructure, facilitated by abundant active sites, enhanced light absorption, and an efficient charge transfer channel at the p-n heterojunction interface, promotes efficient photoexcited charge separation and transfer. Furthermore, the DFT calculations reveal that the incorporation of NiCo2O4 and Mn0.2Cd0.8S into the Co3O4 framework significantly reduces the HER overpotential from |?GH| = 0.32 eV for pristine Co3O4 to 0.20 eV for the Co3O4/NiCo2O4@Mn0.2Cd0.8S heterostructure. This enhancement is attributed to the optimized charge distribution at the active sites and a downward shift in the d-band centre from ?2.15 eV to ?2.30 eV, which weakens the adsorption of reaction intermediates, thereby accelerating HER kinetics.

  • 14. MATSFT: User query-based multilingual abstractive text summarization for low resource Indian languages by fine-tuning mT5

    Journal, Alexandria Engineering Journal, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    User query-based summarization is a challenging research area of natural language processing. However, the existing approaches struggle to effectively manage the intricate long-distance semantic relationships between user queries and input documents. This paper introduces a user query-based multilingual abstractive text summarization approach for the Indian low-resource languages by fine-tuning the multilingual pre-trained text-to-text (mT5) transformer model (MATSFT). The MATSFT employs a co-attention mechanism within a shared encoder–decoder architecture alongside the mT5 model to transfer knowledge across multiple low-resource languages. The Co-attention captures cross-lingual dependencies, which allows the model to understand the relationships and nuances between the different languages. Most multilingual summarization datasets focus on major global languages like English, French, and Spanish. To address the challenges in the LRLs, we created an Indian language dataset, comprising seven LRLs and the English language, by extracting data from the BBC news website. We evaluate the performance of the MATSFT using the ROUGE metric and a language-agnostic target summary evaluation metric. Experimental results show that MATSFT outperforms the monolingual transformer model, pre-trained MTM, mT5 model, NLI model, IndicBART, mBART25, and mBART50 on the IL dataset. The statistical paired t-test indicates that the MATSFT achieves a significant improvement with a -value of 0.05 compared to other models.

  • 15. Deep learning BiLSTM and Branch-and-Bound based multi-objective virtual machine allocation and migration with profit, energy, and SLA constraints

    Ravi Uyyala|Anup Kumar Maurya|SARU KUMARI

    Journal, Sustainable Computing: Informatics and Systems, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    This paper highlights a novel approach to address multiple networking-based VM allocation and migration objectives at the cloud data center. The proposed approach in this paper is structured into three distinct phases: firstly, we employ a Bi-Directional Long Short Term Memory (BiLSTM) model to predict Virtual Machines (VMs) instance’s prices. Subsequently, we formulate the problem of allocating VMs to Physical Machines (PMs) and switches in a network-aware cloud data center environment as a multi-objective optimization task, employing Linear Programming (LP) techniques. For optimal allocation of VMs, we leverage the Branch-and-Bound (BaB) technique. In the third phase, we implement a VM migration strategy sensitive to SLA requirements and energy consumption considerations. The results, conducted using the CloudSim simulator, demonstrate the efficacy of our approach, showcasing a substantial 35% reduction in energy consumption, a remarkable decrease in SLA violations, and a notable 18% increase in the cloud data center’s profit. Finally, the proposed multi-objective approach reduces energy consumption and SLA violation and makes the data center sustainable.

  • 16. Deep learning-driven channel estimation for Intelligent reflecting surfaces aided networks: A comprehensive survey

    Jaya Singh|Kuldeep Singh|Sandeep Kumar|Ghanshyam Singh

    Journal, Engineering Applications of Artificial Intelligence, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Intelligent reflecting surfaces (IRS) technology has demonstrated considerable potential in enhancing wireless communication by improving signal quality and extending coverage. However, IRS-assisted systems face unique issues in channel estimation caused by their passive nature and the complexity of the channel environment. Deep learning-driven methods provide powerful tools to address complexities such as non-linearities and the high dimensionality inherent in these systems. This paper offers an extensive survey of existing channel estimation techniques in IRS-assisted systems, laying a foundation for future research. To achieve this, a comprehensive literature search was conducted across eight reputable databases and search engines, including IEEE Xplore, Google Scholar, and Scopus etc. After applying rigorous inclusion criteria, 57 key articles were identified as highly relevant, forming the basis of this review. The survey covers traditional methods, such as least squares (LS), minimum mean squared error (MMSE), and linear MMSE (LMMSE), and contrasts them with advanced approaches, including matrix decomposition, compressed sensing, and deep learning techniques. The survey then systematically categorizes the selected studies into three groups: discriminative (supervised learning), generative (unsupervised learning), and hybrid learning. This study reveals that convolutional neural networks (CNNs) are well-suited for resource-constrained or real-time applications, while transformers provide excellent adaptability and accuracy, albeit with higher computational demands. The survey concludes with insights into future research directions, emphasizing the need for improved estimation efficiency and robustness in next-generation wireless systems.

  • 17. Bio inspired feature selection and graph learning for sepsis risk stratification

    D Siri|Raviteja Kocherla|Sudharshan Tumkunta|GOGINENI KRISHNA CHAITANYA|Gowtham Mamidisetti|Nanditha Boddu

    Journal, Scientific Reports, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machine learning models for sepsis prediction often suffer from poor interpretability, limited generalizability across diverse patient populations, and challenges in handling class imbalance and high-dimensional clinical data. To address these gaps, this study proposes a novel framework that integrates bio-inspired feature selection and graph-based deep learning for enhanced sepsis risk prediction. Using the MIMIC-IV dataset, we employ the Wolverine Optimization Algorithm (WoOA) to select clinically relevant features, followed by a Generative Pre-Training Graph Neural Network (GPT-GNN) that models complex patient relationships through self-supervised learning. To further improve predictive accuracy, the TOTO metaheuristic algorithm is applied for model fine-tuning. SMOTE is used to balance the dataset and mitigate bias toward the majority class. Experimental results show that our model outperforms traditional classifiers such as SVM, XGBoost, and LightGBM in terms of accuracy, AUC, and F1-score, while also providing interpretable mortality indicators. This research contributes a scalable and high-performing decision support tool for sepsis risk stratification in real-world clinical environments

  • 18. Effect of curing methods on strength and microstructure development in rice husk ash-based magnesium silicate binders

    K V L Subramaniam

    Journal, Cement and Concrete Composites, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    The environmental impact of Portland cement production has intensified the search for alternative low-carbon cement. Reactive magnesium oxide cement has emerged as a promising option. The current study investigates the hydration behavior, strength development, and phase evolution of MgO and MgO-RHA blends cured under sealed and carbonation conditions. Two RHA sources with differing amorphous content and particle size were used. A detailed investigation was conducted using various techniques, including calorimetry, TGA, FTIR, XRD, Raman spectroscopy, and SEM. Results showed that higher glassy content and finer particles in RHA enhanced cumulative heat release, hydration product formation, and compressive strength. Carbonation curing further improved strength consistently by promoting the formation of nesquehonite and magnesium silicate hydrate. Quantitative XRD revealed that M-S-H formation was influenced by the consumption of periclase and unreacted glassy phase. Raman and FTIR analyses confirmed significant chemical and structural transformations, including the formation of brucite, nesquehonite, and carbonate phases. The D and G-band features in MgO-RHA samples suggested variations in carbonated products, influenced by processing conditions. Finally, SEM analysis revealed various carbonated products, M-S-H, and a dense microstructure. Overall, the study emphasizes the critical role of RHA properties and curing strategies in optimizing the performance of MgO-RHA systems for sustainable binder applications.

  • 19. Influence of DNA Sequences on the Thermodynamic and Structural Stability of the ZTA Transcription Factor?DNA Complex: An All-Atom Molecular Dynamics Study

    Journal, The Journal of Physical Chemistry B, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    The Epstein–Barr virus (EBV) is one of the cancer-causing gamma-type viruses. Although more than 90% of people are infected by this virus at some point, it remains in the body in a latent state, typically causing only minor symptoms. Our current understanding is that a known transcription factor (TF), the ZTA protein, binds with dsDNA (double-stranded DNA) and plays a crucial role in mediating the viral latent-to-lytic cycle through binding of specific ZTA-responsive elements (ZREs). However, there is no clear understanding of the effect of DNA sequences on the structural stability and quantitative estimation of the binding affinity between ZTA TF and DNA, along with their mechanistic details. In this study, we employed classical all-atom molecular dynamics and enhanced sampling simulations to study the ZTA-dsDNA structural properties, thermodynamics, and mechanistic details for the ZTA protein and for two different dsDNA systems: the core motif and the core motif with flanking end sequences. We conducted residue-level and nucleic acid-level analyses to assess the important protein residues and DNA bases forming interactions between the ZTA and dsDNA systems. We also explored the effect of adding flanking end sequences to the core motif on DNA groove lengths and interstrand hydrogen bonds. Our results indicate that the flanking sequences surrounding the core motif significantly influence the structural stability and binding affinity of the ZTA–dsDNA complex. Among ZRE 1, ZRE 2, and ZRE 3, particularly when paired with their naturally occurring flanking ends, ZRE 3 exhibits higher stability and binding affinity. These findings provide insights into the molecular mechanisms underlying EBV pathogenesis and may indicate potential targets for therapeutic intervention. A detailed explanation of the binding mechanisms will allow for the design of better-targeted therapies against EBV-associated cancers. This study will serve as a holistic benchmark for future studies of these viral protein interactions.

  • 20. Effect of Palm Oil Fuel Ash and Granite Dust Inclusion on the Performance of Slag Based Alkali Activated Binders: an Innovative Step Towards Sustainable Development

    Mehar Sai Komaragiri|Sk M Subhani

    Journal, International Journal of Pavement Research and Technology, 2025, Quartile: Q1, DOI Link, View abstract ⏷

    The utilization of river sand in the construction industry increases the demand and forms a several environmental impacts by degradation of river beds. It is very crucial to find the alternative materials for the sustainable development. Granite dust, M-sand, and palm oil fuel ash (POFA) are three of the most abundantly produced industrial by-products, yet their potential use as precursors and alternative fine aggregates has not been fully explored. This study comprehensively investigates the effects of incorporating varying proportions (0–50%) of granite dust into M-sand within both heat and ambient-cured alkali-activated binders (AABs) based on sodium (NaOH+Na?SiO?) and potassium (KOH+K?SiO?). POFA is utilized as a source material, replacing slag at levels of 10%, 20%, and 30% for the optimized granite dust composition. Extensive laboratory tests were performed to evaluate the physio-mechanical and durability performance of the resulting AABs, including compressive strength, water absorption, sorptivity, acid resistance, and microstructural characterization of the POFA-based alkali-activated samples using advanced analytical techniques. The results indicate that, irrespective of curing temperature, M-sand-based AABs exhibited enhanced setting behavior, compressive strength, water absorption, and porosity properties with up to 30% granite dust substitution. This improvement is attributed to the development of C-A-S-H, N-A-S-H, and K-A-S-H gel phases. In POFA-based AABs, the dense packing of POFA resulted in reduced water absorption and sorptivity compared to control specimens. Overall, the findings suggest that POFA, as a pozzolanic material, significantly improved the properties of granite dust-based AABs, offering a sustainable solution for mitigating the environmental impact and disposal challenges of industrial waste