Jaypee University of Information Technology (also J. P. University of Information Technology and JUIT) is a private university in Waknaghat, Solan, Himachal Pradesh, India.
This study investigates the use of fermented banana stem (FBS) extract as a sustainable bio-based admixture in M-20 concrete. The novelty of this work lies in utilizing fermentation-derived banana stem extract, which contains organic compounds that may influence cement hydration and particle dispersion. The FBS extract was prepared through a controlled seven-day fermentation process and characterized in terms of basic physical properties, including pH, specific gravity, viscosity, and total dissolved solids. It was then incorporated as a bio-based admixture through partial replacement of mixing water at levels of 0
Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.
In the present era, the adoption of hydrogen fuel plays a crucial role in the development of eco-friendly and sustainable transportation, where the decision-making process frequently faces challenges due to uncertainty in the form of incompleteness, vagueness, and impreciseness in the information. To address these types of challenges and conflicts in the information, the present communication introduces the utilization of a new “p,q,r-cubic quasi-rung neutrosophic fuzzy set (p,q,r-CQNFS),” and related “distance and cosine similarity measures” which are synchronized with the TOPSIS “Technique for Order of Preference by Similarity to Ideal Solution” method to investigate the decision-making problem under consideration in a systematic and enhanced way. This provides a more profound understanding of various choices for adopting hydrogen fuel with their suitability and priority. The proposed methodological approach has further been combined with a practical case study on hydrogen fuel adoption in transportation, which has been presented to illustrate the usefulness of the proposed method. The obtained results demonstrate that the presented model provides enhanced accuracy, flexibility, and reliability in the decision-making process. Sensitivity and comparative analysis have also been carried out to validate the effectiveness of the proposed approach. It is firmly believed that the present work would be helpful to policymakers and industry stakeholders in making informed decisions and supporting the transition toward cleaner and greener transportation systems.
Geopolymer concrete (GPC) is a novel environmentally sustainable substitute for Ordinary Portland Cement (OPC), providing superior strength, durability, and markedly reduced carbon emissions. The present study assesses the efficacy of several GPC systems, highlighting the influence of critical factors such as activator molarity, water-to-binder (W/B) ratio, silica modulus, and alkali concentration. Research indicates that elevated NaOH molarity and optimized sodium silicate to sodium hydroxide (SS/SH) ratios improve early-age strength, while increased W/B ratios extend setting durations. Combinations such as FA-GGBFS and FA-MK exhibit synergistic enhancements in compressive strength, durability, and microstructural density. Heat curing expedites geopolymerization, although ambient curing is viable with particular mix designs. Applications encompass structural components, pavement blocks, and geopolymer bricks, exhibiting enhanced resistance to sulfate attack, thermal stress, and chloride infiltration. This review emphasizes the potential of GPC in sustainable building while simultaneously confronting issues associated with precursor variability, activator costs, and setting regulation. The study adopts the approach of a systematic-narrative review in order to ensure methodological rigor and clarity regarding how the existing knowledge on geopolymer concrete is synthesized. Literature was then collected using defined keywords related to industrial byproducts and geopolymer materials from Scopus, Web of Science, ScienceDirect, and Google Scholar databases. Selected studies were thematically categorized on the basis of precursor characteristics, activator chemistry, mechanical behavior, and environmental benefits. This structured approach will ensure that the review is comprehensive, unbiased, and technically robust. The results endorse GPC as a feasible, low-carbon alternative to OPC, facilitating the worldwide transition towards more sustainable infrastructure solutions.
Bacterial biofilms are organized multicellular structures enmeshed in a self-secreted extracellular matrix (ECM). The communities present an alarming challenge in the fight against antimicrobial resistance (AMR). They act as a protective niche for microbes, provide chemical and physical protection to the resident cells, allow bacteria to endure host immune responses, and undermine the standard antimicrobial treatments. Despite advancements in microbiological research, biofilms remain an invisible frontier that complicates diagnostics and treatment. This perspective article provides insights into the enigmatic nature of biofilms and examines their role in human infections and diseases. It scrutinizes biofilm AMR mechanisms, including altered metabolic states, ECM-linked decreased antibiotic penetration, and augmented horizontal gene transfer. Further, it delves into the innovative anti-biofilm interventions for mitigating impact of bacterial biofilm on human health. The article also highlights the challenges in engineering ECM for eradicating the recalcitrant biofilms. The article emphasizes critical urgency to integrate biofilm-related research with the comprehensive AMR response, and advocates for interdisciplinary collaborations to transform laboratory discoveries into healthcare advancements. Research uncovering the complexity of biofilms and intriguing therapeutic approaches can address the requirement of revolutionary solutions to combat biofilm-associated infections and ensuing AMR. Overall, this perspective serves as a call to action, underscoring the compelling need to prioritize collective efforts in biofilm research to promote public health.
