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Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology
Introduction: Neuroradiology encounters considerable difficulties owing to imaging data's intricacy and high-dimensional characteristics. Conventional diagnostic techniques often encounter challenges regarding precision and scalability, resulting in delays and possible misinterpretations. This paper presents the Big-Data Analytics-based Diagnostics (BDA-D) framework, a revolutionary method using computational models derived from neural architectures and sophisticated analytics to tackle these difficulties. Methods: The BDA-D architecture utilizes data mining, pattern recognition, and machine learning to glean useful neuroanatomical characteristics from massive datasets. By simulating human thought processes, this method speeds up clinical decision-making and improves diagnostic accuracy. To evaluate the effectiveness of the framework, it is put to the test in a clinical environment. Results and Discussion: Diagnostic precision, processing speed, and dependability are all enhanced by experimental validation. By detecting even the most minute neuroanatomical changes, BDA-D allows for more accurate diagnosis than traditional approaches. Based on the results, neuroradiologists may improve their practices by using cutting-edge computational methods to close the gap between data-driven analytics and their practical use in the clinic. BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. Its 95.42% increase in processing speed allows rapid study of important disorders such as strokes and neurodegenerative diseases. BDA-D reduces inter-observer variability with a dependable value of 94.96%, increasing clinical confidence in AI-assisted diagnosis. Conclusion: A revolutionary change in neurodiagnostics, the BDA-D framework improves efficiency and reliability. This method has the potential to completely transform neuroradiology by combining big-data analytics with sophisticated computer models. It will allow for more rapid and precise diagnosis. 2025 The Authors -
Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques
Mental stress, a critical concern worldwide, necessitates precise and nuanced characterization. This study introduces a novel approach to effectively characterize mental stress through a multi-label, multi-class classification framework through natural language processing techniques. Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data. The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). The best performer of this comparative study was further evaluated against an LLM. The potential of large language models (LLMs), including their language understanding and prediction capabilities, is another key focus. We explore how these models could augment and advance mental health research, offering new perspectives and insights into the characterization of mental stress. Our findings show that the top model, an LSTM with TF-IDF and LDA (class weights assigned) outperformed the PaLM model with a coefficient of variation as low as 0.87% across all labels. Despite the PaLM model's superior average performance, it exhibited higher variability among different labels. 2025 The Author(s) -
Predicting electric vehicle performance metrics using a convolution neural network-gated recurrent unit-attention based deep learning architecture
The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods. 2026 The Authors -
Machinability and surface integrity for Mg AZ61A alloy composite by employing Taguchi integrated grey relational analysis
The present experimental study seeking to identify the optimal processing parameters in WEDM of Mg AZ61A-ZrB2 composite using Taguchi integrated grey relational analysis (GRA). Wire-cut electric discharge machining (WEDM) is the most effective method of metal removing process which is utilized in a variety of industries, like defence, biomedical, automotive, and aerospace. It is widely used in the machining of conductive and hard materials like composites and super alloys. In this experiment, the Mg AZ61A alloy composite reinforced with 12 wt% ZrB2 particles was fabricated through stir casting method. The scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) mapping ensured the presence of matrix elements and reinforcement in the developed composite. The material removal rate (MRR) and surface roughness (Ra) were examined with relation to the processing factors such as pulse current (Ip), pulse on-time (Ton), and pulse off-time (Toff). The machining was conducted in compliance with Taguchi's L9 array. According to the GRA results, the optimal ranges of factors for achieving the better MRR and Ra were found at 4 amps of Ip, 15 ?s of Ton, and 45 ?s of Toff. The ANOVA results confirm that Ip was the most dominating factor that contributing to 37.58 %, next by Ton (30.96 %) and Toff (12.85 %), respectively. The confirmation test was demonstrated that the actual and predicted GRG values are fairly close to one another with 13.36 % improvement. The morphology of the machined surface was examined and it was shows the formation of a recast layer and the existence of flaws. 2025 The Authors -
Synthesis of polypyrrole silver graphene ternary nanocomposite and its thermal diffusivity study by thermal lens method
Efficient thermal management materials with tunable thermal diffusivity are increasingly required for advanced electronic, photonic, and energy applications. However, simultaneously achieving both thermal conductivity enhancement and thermal insulation within a single material system remains a significant challenge. In this work, we have synthesized polypyrrole silver graphene (PPy/Ag/Gr) ternary nanocomposites with varying concentrations of graphene by a simple one-pot chemical synthesis method.The photoluminescence spectrum of all the samples of PPy/Ag/Gr showed a quenching in the intensity due to the presence of graphene. Raman spectrum analysis confirmed good coordination of silver and graphene in the ternary composite. Morphological study was done using field emission scanning electron microscopy (FESEM). The thermal diffusivity of the binary composites polypyrrole silver (PPy/Ag) and polypyrrole graphene (PPy/Gr) as well as the ternary composites with varying concentrations of graphene were done using the highly sensitive thermal lens method using two laser sources, one as a pump source and the other as a probe beam. The results of the study show that the thermal diffusivity (D) of PPy/Ag/Gr with 0.2 wt.% of graphene is slightly greater than the binary composites and can be used as a coolant. Another exciting result of this study is that at higher concentrations of graphene the D values of the ternary composites decrease below the D value of the base fluid used. This interesting property of the samples can be exploited by using them as thermal insulators 2025 The Authors -
Sustainable synergies: Performance evaluation of cement mortar with red mud and industrial by- products
This study examines the potential of red mud, a by-product of the alumina industry, as a partial replacement for cement in mortar, aiming to advance sustainable construction in line with UN SDGs 9, 11, and 12. Red mud was incorporated at 1090 % by weight, together with 4 % Ground Granulated Blast Furnace Slag (GGBS) or 4 % fly ash as supplementary cementitious materials. The mixes were tested for compressive strength, flexural strength, water absorption, apparent porosity, and bulk density at 3, 7, and 28 days. The optimum performance was achieved with 3040 % red mud and GGBS, yielding a 28-day compressive strength of 19.29 MPa and flexural strength of 4.55 MPa, outperforming both control and fly-ash-based mixes. Higher red-mud levels increased water absorption (approximately 18 %) and porosity (approximately 15 %). Microstructural analyses (SEM-EDX, XRD, FTIR) confirmed the formation of Calcium Silicate Hydrate (C-S-H), Calcium Aluminate Hydrate (C-A-H), and ettringite phases. The research introduces a novel tri-component binder system (cement-red mud-GGBS/fly ash) that transforms industrial waste into a durable, low-carbon construction material, promoting resource circularity and carbon reduction. 2025 The Authors -
Fabrication of LaCoO3/g-C3N5 Z-scheme photo catalyst for Allura Red dye degradation, ascorbic acid sensing and hydrogen evolution studies
Production of hydrogen by water splitting through photocatalytic process under visible light from waste water is one of the potential green energy technologies. In this study, LaCoO3, g-C3N5, LaCoO3/g-C3N5 (1:1), (1:2) and (2:1) weight ratio nanocomposites (NCs) have been successfully synthesized using a solution combustion, hydrothermal and probe sonication method. The X-Ray Diffraction (XRD) confirmed the compound formed with the crystal size range 20 ?30?nm. The studies of Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) verify the morphology of the particles; band gap of the range 1.52?eV identified from Ultraviolet-Visible (UVvis) studies. The findings demonstrate that Z-scheme heterostructures have developed on the interfaces between the layered flake-like g-C3N5 and the perovskite-type oxides LaCoO3, which improve the absorption of visible light, the separation of photogenerated electron-hole pairs, and the transformation of photogenerated electrons. From the different ratio of synthesized nanoparticles (NPs), the LaCoO3/g-C3N5 (2:1) shows enhanced photocatalytic activity of 99.87?% for degradation of Allura red dye in visible light irradiation. For the first time, the produced nanomaterials were tested for ascorbic acid sensing at extremely low concentrations with a 0.12??M detection limit. The prepared nanomaterials were assessed for their electrocatalytic water splitting operation. Especially, the nanomaterial, LaCoO3/g-C3N5 (2:1) reveal exceptional hydrogen evolution reaction (HER) and also oxygen evolution reaction (OER) capabilities with overpotential of 79?mV and 450?mV, respectively. Hence, the prepared nanomaterials are used for multifunctional applications. 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Synergistic utilisation of waste coconut fibers, sugarcane bagasse ash, and recycled aggregates in geopolymer paver blocks
The use of industrial and agricultural waste such as ground granulated blast furnace slag (GGBS), recycled coarse aggregates (RCA), sugarcane bagasse ash (SCBA), and waste coconut fibers (WCF) have great potential in development of sustainable construction materials. In this study, geopolymer paver blocks were prepared with GGBS and SCBA as precursors, natural coarse aggregates (NCA) partially replaced with RCA as filler material, and WCF used as an additive. To understand the synergy of these materials, SCBA and WCF was incorporated in a fixed dosage, with varying replacement rates of RCA. GGBS was replaced with 20% SCBA, NCA was replaced with 25% and 50% RCA, and a constant dose of 1% WCF was incorporated in the geopolymer mix. The developed paver blocks were tested for compressive strength, split tensile strength, flexural strength, resistance to abrasion, and water absorption. The results obtained demonstrate that addition of RCA, SCBA, and WCF do not benefit the compressive strength, split tensile strength, and water absorption of the paver blocks. However, the flexural strength and resistance to abrasion have shown significant improvement in the presence of RCA, WCF, and SCBA. This research also develops linear regression models to predict strength properties and water absorption of the paver blocks. The results indicate that linear regression model can be used to predict the compressive strength, split tensile strength, and flexural strength with an accuracy of 85%, 96%, and 71%, respectively. Overall, the developed geopolymer paver blocks with RCA, SCBA, and WCF meet the standard requirements of IS 15658: 2021 for pavement applications, and a significant improvement in the flexural strength and resistance to abrasion will enhance the durability and longevity of the paver blocks in service. 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Sustainable removal of reactive blue 222 dye from aqueous solution using wheat bran
Water pollution is one of the major concerns, and establishing a potential sustainable approach can ensure effective water resource management. Textile dye pollution poses a major environmental challenge due to its toxicity and persistence in water bodies. Conventional dye removal methods are often costly and inefficient, necessitating the exploration of low-cost, sustainable adsorbent materials. This research explores the potential of natural wheat bran (WB), an agro-industrial by-product, as an economical and environmentally beneficial bio-adsorbent for the removal of RB222 dye. Adsorbent concentration (0.56?g/100?mL), Initial dye concentration (501000?mg/L), pH (3?11), and contact time were among the major influencing variables assessed to optimise the adsorption process. The structural and morphological changes in wheat bran before and after adsorption were examined to confirm their active role in the adsorption process. Some of the characterisation techniques, like Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD) and Zeta Potential Analysis, were performed to highlight the changes in wheat bran due to Reactive Blue 222 (RB222) adsorption. This study aims to evaluate the feasibility of using wheat bran as a low-cost bio-adsorbent for the efficient removal of Reactive Blue 222 dye from wastewater. The findings showed that wheat bran had a high adsorption capacity of 9.65?mg/g with the best dye removal efficiency of 96% under optimal conditions. Adsorption kinetics studies confirmed that the process follows the Freundlich isotherm and the pseudo-second-order model. The desorption study was also performed to examine its regenerative and reusability potential in the removal of dye. The study proves that wheat bran is a cost-effective and environmentally friendly substitute for dye removal from wastewater and emphasises the extension of value addition to agricultural waste in environmental rehabilitation. These findings highlight the potential of agro-waste valorisation in advancing sustainable wastewater treatment technologies. 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/ -
Highly strengthened mechanical, morphological, IR vibrational and optical properties of rationally tailored wheat starchchitosan blends crosslinked with dual UV/acetic acid system for sustainable green packaging
Through this study, wheat starch/chitosan composite (SCB) films were successfully developed via a UV/acetic acid-assisted solution?casting approach with varying chitosan ratios. Tensile testing shows that stress at break remains nearly unchanged at lower chitosan content (1030?wt%) but increases abruptly beyond 30?wt%, reaching an optimum value at 50?wt% due to intensified hydrogen bonding and cross-linking between chitosan NH?? and starch OH? groups. Strain at break follows a similar trend, with markedly elevated elongation and elasticity. Youngs modulus exhibits the highest rigidity at 50?wt%, while stiffness decreases thereafter, consistent with enhanced elastic deformation. Morphological analysis reveals that chitosan incorporation transforms starch films from brittle, cracked surfaces to densely compact structures with fewer pore defects, resulting in superior adhesion and integrity. UVvis results show improved UV shielding, higher transparency, and tunable absorption, while FTIR analysis confirms only physical interactions with no new bonds formed between the components. Overall, chitosan incorporation significantly fortifies the SCB films by improving mechanical strength, structural stability, and optical performance, extending their application as green, multifunctional materials for sustainable packaging, coatings, and wastewater treatment. 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Natural biotic polymer bombyx mori silk fibroin for gold nanoparticles: Fabrication, characterisations and application as colorimetric ammonia detection
Gold nanoparticles (AuNPs) were fabricated using a cost-effective and environmentally benign method, using silk fibroin (SF) as a stabilizing and reducing agent. As a natural, renewable protein polymer, silk fibroin can reduce and stabilize material as well as provide shape-tailoring agent. This resulted bio-polymer with gold nanoparticles was biodegradable, biocompatible, and very stable. Au ions are proficiently reduced to neutral Au atoms by silk fibroin, which governs the size, shape, and distribution of the nanoparticles and, consequently, their optical characteristics. The fabricated nanoparticles are very well characterized using a set of advanced analytical techniques that include UV-Vis Spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM), and Fluorescence Spectroscopy (FL). The sensing performance of ammonia in aqueous ammonia using the SF-AuNPs is presented herein based on an optical sensing approach utilizing surface plasmon resonance (SPR). It is obvious from the results that the materials synthesized have exhibited some unique plasmonic properties due to the interaction between silk fibroin and gold surfaces, hence enhanced sensitivity in colorimetric detection. The AuNPs acted as an ammonia optical sensor with a detected limit of about 1 parts per billion (ppb), very outstanding performance. Ammonia and AuNPs interact to cause a change in their surface plasmon resonance simple rapid and low-cost to realize, eco-friendly, and can find applications towards environmental monitoring and biomedical research fields for ammonia detection. 2026 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Experimental data-driven machine learning approach for predicting workability in sustainable concrete using green material replacements
Concrete workability is a critical factor governing the placement, compaction, and durability of fresh concrete, yet it remains less explored in data-driven studies compared to hardened properties. This study presents an experimentally validated machine learning framework for predicting fresh concrete workability, namely Compaction Factor Equivalent (CFE) and Vee Bee Time Equivalent (VBTE), using a newly generated laboratory dataset comprising 300 concrete mixes. The dataset was developed through controlled experiments by systematically varying the waterbinder ratio (W/B), aggregatebinder ratio (A/B), type of green material, and replacement percentage, with fly ash and ground granulated blast-furnace slag (GGBS) used as partial cement replacements to promote sustainability, aligning strategies with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). To capture the nonlinear relationships between mix design parameters and workability indicators, three ensemble learning modelsRandom Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)were developed and evaluated. Model performance was assessed using standard statistical metrics, including R, RMSE, MAE, and MAPE. The results indicate that boosting-based models outperform baseline approaches, with XGBoost achieving the highest prediction accuracy for both CFE and VBTE. By shifting the focus from hardened properties to fresh-state performance, this study addresses a critical research gap and demonstrates that ensemble machine learning models, when combined with experimentally generated datasets, can significantly reduce experimental workload while supporting intelligent and sustainable concrete mix design for practical engineering applications. 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/ -
Therapeutic profiling of Saraca indica bark oil silver nanoparticles: Bioactivity and cytocompatibility in human keratinocyte (HaCaT) cells
This study explores the potential of silver nanoparticles synthesized from Ashoka (Saraca indica) bark oil, which has properties as a natural therapeutic agent. The silver nanoparticles (Ag-NPs) were produced using a green synthesis method from the Saraca indica bark oil and characterized through UV-Vis spectrophotometry, FTIR, and SEM techniques. Fungal infections are mainly caused by Candida spp., especially Candida albicans, which significantly contributes to diseases like candidiasis. The antifungal and antibacterial activities were tested against Candida albicans and Bacillus subtilis. Using the disk-diffusion method, different concentrations of Ag-NPs were evaluated and compared with fluconazole and streptomycin. Results showed that the inhibition zones were concentration-dependent, with a maximum inhibition zone of 21.751.768 mm, 21.751.06 mm at 100 g/mL against C. albicans and B. subtilis. The DPPH assay showed 62.17 % antioxidant activity at 80 g/mL, and IC?? values were 36.43 g/mL for AO-Ag NPs compared to 26.88 g/mL for crude oil. The increasing resistance to antifungal drugs and limited effective treatments highlight the need for alternatives. The DPPH antioxidant assay confirmed the nanoparticles free radical scavenging ability, indicating antioxidant potential. An albumin denaturation anti-inflammatory assay revealed notable inhibition by the nanoparticles compared to Ascorbic acid. Cytotoxicity was assessed on human keratinocyte (HaCaT) cells, showing dose-dependent cytocompatibility, with > 90 % viability at lower concentrations and 12.31 1.62 % viability at 100 g/mL. Compared to crude bark oil and positive controls, the nanoparticles exhibited enhanced bioactivity with reduced cytotoxicity to normal skin cells. Morphological observations also suggested apoptosis, possibly linked to ROS-mediated oxidative stress pathways. Overall, this research indicates that Saraca indica-silver nanoparticles are cost-effective, eco-friendly, and biocompatible, with antimicrobial, antioxidant, anti-inflammatory, and low cytotoxic properties. These properties support their potential use in developing nanomedicine treatments for infections and inflammation. 2025 The Authors -
Optical properties of MnTe2 few-layer quantum dots
Quantum dots (QDs) are gaining attention as a possible emissive material that might be used in flexible optoelectronic and photonic systems. In the present work, the temperature-dependent photoluminescence (TDPL) property of manganese di-telluride (MnTe2) QDs was investigated. The room-temperature PL is attributed to the abrupt breakage of the large-area MnTe2 nanosheets by ultrasonication, which integrates defect-mediated localized trap states inside the electronic bandgap. As a result, deliberately generated defect states ultimately generate such PL emission of QDs. Density functional theory (DFT) results further validate the experimental interpretations of the origin of TDPL. In addition, through an in-situ liquid diffusion approach, the QDs were also integrated into a NaCl matrix. Due to light scattering properties, the hybrid crystals exhibit fluorescence centres at various excitation wavelengths. These results suggest that these MnTe2 QDs can be used as an effective basis for future flexible optoelectronic applications. 2024 Elsevier B.V. -
Cr2C MXene quantum dots for selective detection of mercury ions
Mercury (Hg), a highly toxic environmental contaminant that presents significant ecological and health risks, even at low concentrations. There is an urgent need for precise and sensitive methods for detecting Hg2+ ions in the environment. In recent years, a new class of 2D layered materials, MXene, has gained enormous attention due to their unique properties, such as high surface area, oxidation resistance, thermal and chemical stability, electrical and thermal conductivity. This study presents the synthesis and characterization of Cr2C MXene quantum dots (MQDs) derived from Cr2CTx nanolayered MXene sheets via the probe-sonication method. The Cr2C-MQDs were characterized using XRD, FTIR, SEM-EDS mapping, and Zeta potential analysis. The vibrant green fluorescence material, Cr2C-MQDs, was investigated for Hg2+ detection, which exhibited high selectivity and stability with a limit of detection of 30.7 nM. The sensing mechanism is attributed to the strong affinity of Cr2C MXene quantum dots for Hg2+ ions. 2025 Elsevier B.V. -
Study of multilayer flow of non-Newtonian fluid sandwiched between nanofluids
This theoretical investigation examines the nonlinear convective heat transport and multilayer flow of a non-Newtonian fluid within a vertical slab, incorporating viscous heating effects. The middle layer of the slab contains a third-grade fluid, while the outer layers are filled with a water-based Ag-MgO hybrid nanoliquid. Continuity in temperature, heat flux, velocity, and shear stress is maintained at the interfaces of the fluid layers. The thermal buoyancy force is modeled using the nonlinear Boussinesq approximation. The governing system comprises conservation equations for mass, momentum (Navier-Stokes), and energy for each of the three layers. These differential equations are non-dimensionalized, and the resulting dimensionless four-point nonlinear boundary value problem is transformed into a two-point boundary value problem before being solved numerically. For limiting cases, analytical and semi-analytical solutions are computed and used as benchmark results to validate the numerical method employed. Entropy generation analysis indicates that higher third-grade fluid parameters reduce the magnitude of velocity and temperature fields, as well as entropy production across all regions. The third-grade fluid parameter shows a decreasing influence on velocity and temperature fields throughout the system. The continuity of interfacial conditions induces a dragging effect; despite the absence of third-grade fluid parameters in regions I and III, their influence is apparent in these regions. The Bejan number slightly decreases at the walls with increasing third-grade fluid parameters, exhibiting a dual effect in the third-grade fluid layer. Near the walls, the Bejan number decreases as the nanoparticle volume fraction increases. Findings of this work may have applications in polymer industries and processes involving high temperatures. 2024 -
Inter-relational dynamics of factors affecting the emergence of orphan drugs; [Dynamique interrelationnelle des facteurs influennt lergence des micaments orphelins]
Orphan drugs are medications that are produced for the treatment of rare diseases. As there is less number of patients, the drug manufacturing companies are not keen in producing these drugs. Due to high costs of research and development and low profitability, companies do not want to invest in manufacturing of orphan drugs. Several laws have been passed by Governments of different nations to encourage the development of orphan drugs and make it available to patients. This study explores the interrelation dynamics of factors that has resulted in the greater availability of orphan drugs in recent times. Ten factors: internet technology, legislation, online patient support groups, government subsidiary, biotechnological advancements, corporate social responsibility, awareness and diagnosis of rare diseases and exclusive budgeting by pharmaceutical industries for orphan drugs related research and development and production were taken for the study. With a sample size of 38 experts, the technique of decision-making trial and evaluation laboratory (DEMATEL) was used for the study. It was found that information technology, legislation, support groups, and budget were the causes and the factors awareness, diagnosis, medicine availability, subsidiary, CSR and biotechnology emerged to be the effect. 2024 Acadie Nationale de Pharmacie -
Quantum corrections in general relativity explored through a GUP-inspired maximal acceleration analysis
A maximun acceleration analysis by Pati dating back to 1992 is here improved by replacing the traditional Heisenberg Uncertainty Principle (HUP) with the Generalized Uncertainty Principle (GUP), which predicts the existence of a minimum length in Nature. This new approach allows one to find a numerical value for the maximum acceleration existing in Nature for a physical particle that turns out to be [Formula presented] that is, a function of two fundamental physical quantities such as the speed of light c and the Planck length lp. An application of this result to black hole (BH) physics allows one to estimate a new quantum limit to general relativity. It is indeed shown that, for every real Schwarzschild BH, the maximum gravitational acceleration occurs, without becoming infinite, when the Schwarzschild radial coordinate reaches the gravitational radius. This means that quantum corrections to general relativity become necessary not at the Planck scale, as the majority of researchers in the field think, but at the Schwarzschild scale, in agreement with recent interesting results in the literature. In other words, the quantum nature of physics, which in this case manifests itself through the GUP, appears to prohibit the existence of real singularities, in this current case forbiddiing the gravitational acceleration of a Schwarzschild BH from becoming infinite. 2025 The Authors -
Effects of DESI and GW observations on f(T) gravitational baryogenesis
Baryogenesis refers to the physical process responsible for generating the observed baryon asymmetry in the early universe. The presence of a nonzero baryon number density suggests a surplus of matter over antimatter. In this study, a novel approach is proposed to verify the direct consequences of late-time observations on gravitational baryogenesis. The incorporation of two key data sets, DESI and gravitational wave observations, makes the analysis more intriguing. In the teleparallel framework, the methodology connects the primordial time to the late time. The intermediating epochs are also investigated with the help of the deceleration parameter. Our results show that the net remaining asymmetry yields a baryon-to-entropy ratio in excellent agreement with observations. 2025 The Authors. -
Photocatalytic and antimicrobial properties of Boerhavia diffusa bio-callus synthesized Silver nanoparticles
Plant tissue culture plays a pivotal role in plant biotechnology, and offers innovative and reliable avenues for synthesizing nanoparticles. The approach is safe, replicable, and efficient for therapeutic and environmental sustainability. Despite the proven efficiency of green synthesis approaches, plant callus extracts for nanoparticle synthesis remain moderately investigated. The current study bridges the gap by synthesizing ecofriendly silver nanoparticles (Ag-NPs) using callus extracts of Boerhavia diffusa (Punarnava), an important medicinal plant with proven potential pharmacological properties. These synthesized Boerhavia diffusa-mediated Ag-NPs (BD-Ag-NPs) were characterized using UV-Vis spectroscopy, SEM, FTIR, and XRD. Spectral analysis showed spherical-shaped BD-Ag-NPs with an average size of 9 nm at wavelength 420 nm. Energy-dispersive X-ray (EDX) analysis revealed that silver ions constituted 51.78 % of the total weight of the nanoparticle solutions, while the crystalline structure of the BD-Ag-NPs was confirmed through XRD. Phytoconstituents present in the callus were utilized for capping and the reduction of Ag ions to Ag-NPs was confirmed through FTIR analysis. In addition, BD-Ag-NPs exhibited functional properties like textile dye degradation and broad-spectrum antimicrobial activities against bacterial and fungal pathogens. The current study highlights the potential of employing callus-derived nanoparticles for sustainable environment and biomedical applications. This study advances the application of green nanoparticle synthesis using tissue culture systems and makes significant contributions to addressing global challenges. 2025 The Authors