Volume 13 | Issue 7

 Abstract

Now a days, implantation is becoming a common phenomenon to maintain oral health. Implants are mainly used to replace broken or spoiled teeth and also to support the denture in case of no teeth on the bone. Since biomaterials (bone is a natural material) can't be replaced, and its decay is related to higher stresses and so there is a need to analyse the effect of thread shapes on the stress generation which is linked to decay of bone. Due to difficulty in application of either theoretical or experimental application to find the stress condition of the bone, in the present work, finite element analysis was carried out to find the stress generation with different thread profiles. 4 types of thread profiles were considered based on the literature of implants used in the dental applications. They are square, V-thread, buttress and Reverse Buttress threads. Literature has ambiguity in finding the best implant thread for better stability of the system which is mainly based on overall deformation and stress conditions. Here geometry is built using Ansys pre-processing module based on standard thread proportions along with meshing. After application of boundary conditions, results were extracted and represented in the tables for 100 N vertical loading. The results shows better stability with square threads compared to other form of threads considering important parameters of structural stability

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thread profiles, fea, ansys ,stress concentration.

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Hybrid Metal Matrix Composites (HMMCs) represent an advanced class of materials that combine the beneficial properties of both metallic and non-metallic reinforcements, enhancing their mechanical, thermal, and wear-resistance properties. This review paper focuses on the evaluation of hybrid composites formed by incorporating cenosphere (a lightweight, hollow alumino-silicate microsphere) and granite powder into Aluminium LM-6 alloy matrix. The study explores the synergistic effects of combining these reinforcements to produce a composite material with superior characteristics suitable for applications in the aerospace, automotive, and manufacturing industries[1]. The paper provides an in-depth analysis of the various reinforcement techniques, including the preparation of cenosphere-granite reinforced composites, and the mechanical and metallurgical properties of the resulting HMMCs. Attention is paid to the uniformity of reinforcement dispersion, microstructural analysis, and the impact of processing parameters on the final properties of the composite material. The mechanical properties, such as hardness, tensile strength, wear resistance, and thermal stability, are discussed in relation to the microstructural changes induced by the reinforcements. Additionally, the study reviews advancements in the fabrication methods, including stir casting, and the challenges associated with the dispersion of cenosphere and granite powders into the aluminium matrix [2].Through a synthesis of existing literature, this paper aims to present a comprehensive understanding of the potential and challenges associated with the development of cenosphere-granite hybrid composites in the Aluminium LM-6 matrix. The work also suggests future directions for optimizing the properties of HMMCs through innovative fabrication techniques and enhanced reinforcement distribution[3].

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Aluminium-LM6, cenosphere, granite powder, Hybrid metal matrix composites

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This review paper presents a comprehensive examination of hybrid damping systems that integrate viscoelastic materials (VEMs), Acoustic Black Hole (ABH) techniques, and honeycomb sandwich panels to address the persistent challenge of vibration mitigation in engineering structures. By exploring the theoretical principles, experimental validations, and numerical models underlying these advanced damping mechanisms, the study elucidates how the synergistic combination of VEMs, with their frequency and temperature-dependent energy dissipation, ABH's innovative wave-trapping geometries, and the exceptional stiffness-to-weight benefits of honeycomb structures can significantly enhance vibration attenuation. Applications across aerospace, automotive, and industrial domains are discussed, highlighting the potential for improved structural performance and extended service life. The review also identifies critical research gaps, such as the challenges in manufacturing and temperature sensitivity, and proposes future directions including adaptive damping strategies and optimization through machine learning. Overall, this work aims to provide a foundational framework for the development of next-generation hybrid damping solutions in complex engineering systems

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Viscoelastic Materials, Acoustic Black Hole, Honeycomb Sandwich Panels, Vibration Damping, Hybrid Systems

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This review article presents a comprehensive examination of recent studies conducted to enhance the damping characteristics of carbon fibre-reinforced composites. Research indicates that adding multi-walled carbon nanotubes to the composite matrix enhances damping performance, primarily due to interfacial sliding between the nanotubes and the polymer. The integration of barium titanate-based piezoelectric nanowires improves energy absorption through combined mechanical and piezoelectric mechanisms.". Moreover, critical structural elements such as laminate thickness and fiber alignment significantly influence the damping performance of carbon fiber-reinforced composites. Investigations indicate that increased thickness enhances the overall stiffness and fundamental frequency, while fibre orientation affects vibration attenuation, with certain directions exhibiting superior damping capabilities. A through comprehension of these parameters is essential for enhancing the vibrational characteristics of CFRPs in real-world implementations.

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Interfacial skidding mechanism, piezoelectric nanowires, multiwalled carbon nano tubes, CFRC

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Polymer matrix composites (PMCs) augmented with basalt fibers have attracted significant scholarly interest owing to their superior mechanical attributes, thermal resilience, and ecological durability. This review meticulously examines the performance and longevity of basalt-reinforced polymer matrix composites, emphasizing their mechanical, thermal, and chemical characteristics. Moreover, a comparative evaluation with traditional reinforcements such as glass and carbon fibers is provided. In addition, this study addresses the constraints and prospective solutions to improve the practical applications of these composites

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Polymer composites, performance, durability, basalt

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As we know more research is going on to have natural and biodegradable materials in order to upgrade for next upcoming generation on composite applications. Many of the organizations are concerned towards the green resources. Increase in using natural composites has minimized the greenhouse gases. As increase in use of natural fiber has bought some of the problems like poor compactablity between matrix and natural reinforcement and more moisture absorption between the natural fibers and the matrix. Natural fibers can be suits as the alternative source for the petroleum-based products. Moreover, before this the issues need to be solved, including the adhesion between the matrix and the green fibers, poor fire resistance, low impact strength and low durability. The research has led to certain customization on natural fibers and resin. To identify in demand of use of eco-friendly materials in different types of applications on green fibers and the resin type and the resources customization and process techniques, mechanical behaviours and the applications and other properties of natural composite is essential to provide more efficient behavior of natural composite

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Natural fiber composite, Composites, Fiber, Particle, Mechanical Properties, Thermal Properties.

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Various sectors use battery packs and with the increase in use of electrical vehicles the need for a more efficient and smaller battery pack is significant. In the present study; we analyse the flow and heat transfer performance of a battery pack and use nano particles, mainly Graphene-Amine and Graphene-Oxide based nano particles mixed in solution of water and ethylene glycol. The nanofluid is prepared in laboratory and is tested in the experimental setup. The results are collected and then a CFD model is prepared and new designs and different fluids are to be tested. The impact of the stud100y focuses on maintaining or optimizing battery operating temperatures to improve the performance and increase efficiency of the system.

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Battery Pack, Nano particles, CFD fluid flow and heat transfer, Graphene Amine, Graphene Oxide

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In this study, polyethylene glycol (PEG) in various ratios was used to solvolyze renewable biomass, yielding polyol. The presence of functional groups including -CO, C=O, esters, and ethers was revealed when the liquefied wood was analyzed using Fourier Transform Infrared Spectroscopy (FTIR). The resultant polyol was employed to make resin and then polymerized with different proportions of methylene diphenyl diisocyanate (MDI) to create adhesives, wood composites, and polyurethane foam. The FTIR analysis confirmed the successful incorporation of functional groups essential for polyurethane formation. An analysis of the mechanical characteristics of the generated polyurethane foams showed that the NCO/OH ratio has a major impact on the foam's mechanical strength and thermal stability. Higher NCO/OH ratios led to higher glass transition temperature and degradation temperature, which indicated increased rigidity in the polyurethane films. This study advances the evolution of bio-based polyurethane materials made from renewable resources, providing a sustainable substitute for petroleum-based products.

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FTIR Liquefaction, Resin, Solvolysis

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The unique physicochemical properties and interactions within the rhizosphere, copper-based nanomaterials (Cu-NMs) have been identified as efficient agents for boosting plant growth and improving nutrient absorption. This study investigates how Cu-NMs affect microbial activity in the plant rhizosphere, with a focus on how they encourage the synthesis of siderophores by helpful soil microbes. Compounds known as siderophores have a strong affinity for iron and help to solubilize and absorb this essential nutrient, which is essential for plant growth. According to our findings, when Cu-NMs are sprayed at the right quantities, they promote the development of microorganisms that produce siderophores, which raises the amount of iron available in the rhizosphere. Improvements in biomass accumulation, root growth, and chlorophyll levels were linked to the increased iron intake. This study presents a sustainable, nanotechnology-driven approach to increasing crop yield through microbiome-mediated nutrient uptake and provides fresh insights into the indirect channels through which Cu-NMs can improve plant nutrition. Flacourtia montana leaf extract was used in an environmentally benign reaction to produce copper nanoparticles. The ability of bacterial colonies to produce siderophores is demonstrated by the creation of yellow zones. By using a visual observation method, we can identify bacteria that improve the absorption of iron by plants. Evaluation of Cu- NMs-induced siderophore synthesis was made possible by the bacterial growth patterns and medium color changes seen during the study. The Debye-Scherrer equation analysis's 42.28 nm crystallite size result was consistent with the SEM results. The (111), (200), and (220) peaks in the X-ray diffraction investigation showed that the Cu2O nanoparticles have crystalline characteristics. Our scientific studies verified that significant increases in iron availability were brought about by copper nanoparticles and their microbial siderophore enhancing capabilities in rhizosphere circumstances.

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Copper-based nanoparticles (Cu-NMS), Siderophore production, Rhizosphere, PGPR (Plant Growth- Promoting Rhizobacteria), Antimicrobial properties

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CdS thin films, Structural properties, X-ray diffractometer, Scanning electron microscope.

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Additive manufacturing, also known as 3D printing, is a way through which we bring our ideas to life. Instead of using traditional cutting tools, it is a way of manufacturing an object layer by layer directly from digital designs. This cutting-edge technology enables innovators to create intricate components for industries like healthcare, automotive, and aerospace with enhanced speed, precision, and cost efficiency. It fosters endless advancement, sustainability, and flexibility on demand for customization which was witnessed during the COVID-19 pandemic. War fatalities result from combat injuries (i.e., Penetrating Injuries, Blast Injuries, Gunshot Wounds, Burns, Crush Injuries, Traumatic Amputations, etc.). Though mortality rates due to combat injuries have decreased significantly over the past few decades globally, Hemorrhage remains a leading cause of combat casualties. It is caused due to Gunshot Wounds (GSW), Explosive Injuries (IEDs, Bombs, Mines), Sharp Weapon Injuries, and Crush Injuries (Collapsed Buildings, Heavy Equipment Accidents). This underscores the critical importance of effective bleeding control measures in combat situations. Today, many organizations are stepping up to provide military-grade medical solutions to help treat trauma and emergencies when it matters most. 3D printing is playing a key role in prosthetics, implants, and surgical advancements. Its potential in trauma care, especially in combat settings, is immense. Our study focuses on developing personalised 3D-printed medical aids for gunshot wounds (GSW) and similar injuries that provide effective solutions for hemorrhage control, injury stabilization, and increased survival chances in battlefield conditions. Rapid production of customized, biocompatible wound care materials enhances adaptability in emergency situations.

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3D printing, hemorrhage, gunshot wounds (GSW), medical aid.

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Dynamic charging for electric vehicles (EVs) integrates Tesla coils embedded in road surfaces, powered by solar panels and mains electricity. Vehicles equipped with copper coils charge while driving, eliminating the need for traditional charging stations. An ESP module and IR sensor detect vehicles, sending payment links to owner's phones. After payment, a servo motor-driven gate opens for vehicle exit. A 7-segment LED display on the EV indicates charging status. This system offers a seamless, efficient, and sustainable charging experience, reducing range anxiety, saving time, and promoting renewable energy usage, although infrastructure setup and standardization are key challenges.

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Wireless Charging, Power Transfer Inductive Charging, Resonant Charging and Mobility

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Robotic-assisted surgery (RAS) has emerged as a transformative force in modern surgical practices, particularly in minimally invasive surgery (MIS). This survey paper explores the evolution, current state, and prospects of RAS, underscoring its role in augmenting surgical precision and overcoming the constraints of conventional MIS techniques. Despite these advantages, RAS is seen without its challenges. Significant difficulties such as high operational costs, limitations in haptic feedback, and potential latency issues between control interfaces and robotic mechanisms are critically analysed. In this comprehensive review, we identify and discuss key research gaps within the domain of RAS. These include the need for advanced feature extraction methods capable of capturing essential details in surgical procedures, improved temporal and spatial modelling techniques, and the development of more efficient computational strategies to enhance the practicality of RAS systems. Additionally, this paper explores the intersection of RAS with surgical phase recognition technologies, a critical component in refining surgical workflows and augmenting real-time decision-making, as well as the importance of deep learning methodologies in advancing surgical phase recognition, highlighting their potential to significantly elevate the accuracy and efficiency of RAS.

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Robotic-assisted surgery (RAS), Minimally invasive surgery (MIS), Temporal and spatial modeling, Surgical phase recognition technologies, Deep Learning.

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The Animal Herd Welfare Management System (AHWMS) is an online platform designed to help farmers care for cows and buffaloes. It includes features like health tracking, disease prevention, and veterinary support. Farmers can also access government schemes and livestock trading options through the system. By using AHWMS, farmers can enhance animal health, boost productivity, and make informed decisions. This digital solution makes livestock management easier and contributes to rural development by connecting farmers with essential resources

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Animal welfare, herd management, livestock health, technology, digital platform, agriculture.

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In this paper,we introduce and investigate topological spaces called Fuzzy wgr?- Spaces and We get several characterizations and some of their properties. Also we investigate its relationship with other types of functions.

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Fuzzy wgr?-closed sets, Fuzzy wgr?-open sets, Fuzzy wgr? topological spaces

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Waste management system has become an important concern for all globally and nationally. Poor solid waste management practices in schools can lead to significant environmental consequences like pollution of air, soil, and water. In this study, we try to examine the different dimensions of the solid waste management in educational institutions. Effective waste handling also depends on the institutional capacities and how they implement the process. Educational institutions generate large enough amount of waste to justify individual attention. Minimising waste generation by focusing on management practices at the source can save disposal sites, space, reduce illegal dumping, create employment opportunities and therefore cut down on pollution potential from solid waste. Educational institutions are one of the most important and vital social institute for the community. These institutions have significant impact on surrounding community. Also, solid waste management practice in school and colleges can bring about behavioural change in students which they can apply in their home and therefore in community. Waste management in educational institutions has major social, financial, and environmental impacts. A more coordinated effort between schools, local governments, and communities could lead to a significant reduction in waste generation, better waste disposal practices and educational opportunities for students about sustainability and environmental responsibility.

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Solid waste, institutional capacities, educational institutions, sustainability, behavioral change, community.

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 Abstract

: Infertility (Uqr) is a major reproductive health issue affecting about 10-15% of couples, and Polycystic Ovarian Disease (PCOD) is one of its most common causes. PCOD leads to anovulation, menstrual irregularities, obesity, and hormonal imbalance, making conception difficult. In the Unani system of medicine, infertility is recognized as Uqr, and PCOD is described under Marz-e- Akyas Khusytur Rehem and Ihtibas-al-Tams (retention of menses), attributed to Sue Mizaj Barid Balghami (cold and phlegmatic temperament) that disturbs the function of the uterus and ovaries. To evaluate the therapeutic efficacy of Unani formulations in the management of infertility caused by PCOD, with emphasis on their role in inducing ovulation and restoring menstrual regularity. The study was conducted at the Department of Amraz-e-Niswan wa Qabalat (Gynecology & Obstetrics), Nizamia General Hospital, Hyderabad. A total of 30 female patients, aged 20-35 years, with infertility due to PCOD were enrolled and divided into two groups of 15 each: Group A received Ustukhuddoos (Lavandula stoechas), Gaouzaban (Borago officinalis), Gule-Tesu (Butea monosperma), Musli Safed (Chlorophytum borivilianum), and Musli Siyah (Curculigo orchioides) in decoction and powder form.Group B received Aftimoon (Cuscuta reflexa), Afsanteen (Artemisia absinthium), Sadab Khushk (Ruta chalepensis), Khulanjan (Alpinia galanga), Bozidan (Tanacetum umbelliferum), and Asgandh Nagori (Withania somnifera). Treatment was continued for three consecutive months, with follow-ups every 15 days. Clinical evaluation, hormonal assays, and follicular studies via ultrasonography (USG) were conducted to monitor ovulation and conception outcomes. The study concludes that Unani formulations are effective in inducing ovulation and regulating menstrual cycles in women suffering from PCOD-related infertility. The combination of herbs with emmenagogue, deobstruent, aphrodisiac, anti-inflammatory, and insulin-sensitizing actions plays a vital role in correcting hormonal imbalance and improving reproductive function. These findings reaffirm the Unani concept of restoring E-Tedal-e-Mizaj (temperamental balance) and Taqwiyat-e-Rahem (uterine tonicity) as key principles in treating infertility.

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 Keywords

Infertility; Uqr; PCOD; Ihtibas-e-Tams; Unani medicine; Ovulation induction; Ustukhuddoos; Gaouzaban; Musli Safed; Gule Tisu, Musli Siyah, Reproductive health.

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The purpose of the study was to find out the effect of combined ballistic training and yogic practices on psychomotor profiles among college men cricket players. To achieve the purpose of the study thirty cricket players have been randomly selected from PSG college of arts & science, coimbatore, Tamil Nadu and India. The age of the subjects were ranged from 18 to 25 years. The selected subjects were divided into two groups with fifteen subjects in each group. The experimental group underwent a combined ballistic training and yogic practices programme for six weeks. The control group was not participating in any other training during the course of the study. Reaction time was taken as criterion variable in this research. The selected subjects were tested on reaction time was measured through Stick drop method (Distance on ruler 20 centimeters). To determine whether there had been a significant improvement, the dependent't' test was used to analyse the data, and an ANCOVA was used to see whether there had been any significant differences between the groups. To examine the level of significance difference, if any, across groups, the.05 level of confidence was fixed. The difference is found due to effect of combined ballistic training and yogic practices given to the experimental group on reaction time when compared to control group.

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Ballistic training, yogic practices, reaction time

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In today's fast-paced apparel retail industry, data-driven decision-making is a critical competitive differentiator. While self-service business intelligence (SSBI) tools have empowered non-technical users with analytical capabilities, they have also introduced challenges related to data governance, consistency, and scalability. This review investigates the application of federated governance models to support scalable SSBI in apparel retail organizations. Drawing on academic literature, case studies, and industry reports, the review outlines a conceptual model that combines centralized policy enforcement with decentralized, domain-specific governance roles. Experimental data from leading retail case studies demonstrate significant improvements in time-to-insight, data quality, user adoption, and governance compliance post-implementation. The paper also explores future directions including AI-augmented governance, cross-enterprise collaboration, and the integration of data ethics. The findings suggest that federated governance, when implemented with cultural and technological alignment, can deliver sustainable agility and trust in self-service analytics environments.

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Self-Service BI (SSBI); Federated Governance; Apparel Retail; Data Governance; Business Intelligence; Data Stewardship; AI in Governance; Data Culture; Scalable Analytics; Organizational Agility.

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The advent of Big Data has revolutionized the way economic behavior is observed, analyzed, and predicted, offering transformative insights into the field of behavioral economics. Big Data encompasses vast, complex, and diverse datasets generated from digital interactions, social media, online transactions, sensors, and various digital platforms. Its integration with behavioral economics provides researchers with unprecedented opportunities to understand human decision-making processes at a granular level. Traditional behavioral economics relied heavily on controlled experiments and surveys to study biases, preferences, and irrational choices. However, the availability of Big Data enables the examination of real-world behaviors across large populations and varied contexts in real time. By applying advanced analytical tools such as machine learning, artificial intelligence, and predictive modeling, economists can uncover patterns of consumer behavior, financial decision-making, and social influence that were previously hidden. Big Data also enhances the predictive accuracy of behavioral models, helping policymakers and organizations design better interventions, such as nudging strategies, to promote desirable economic and social outcomes. Furthermore, it aids in refining theories of bounded rationality and prospect theory by validating them with large-scale empirical evidence. Despite these advantages, the fusion of Big Data with behavioral economics also raises critical ethical and methodological challenges. Issues such as data privacy, algorithmic bias, and the potential misuse of behavioral insights for manipulation must be addressed to ensure that data-driven economic policies remain transparent and equitable In today's digital world, we are constantly generating and collecting data. From social media posts to online shopping habits, the amount of data being created and stored is growing at an unprecedented rate. This phenomenon is known as "big data." Big data refers to the large and complex sets of data that are generated by various sources, such as social media, sensors, and other digital devices. These data sets can be analyzed to uncover patterns, trends, and insights that can inform decision-making in a variety of fields, including business, finance, healthcare, and government.

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Big Data, Behavioral Economics, Data Analytics, Artificial Intelligence (AI), Machine Learning (ML), Predictive Modeling, Human Decision-Making, Cognitive Bias, Data-Driven Policy, Real-Time Behavior Analysis, Economic Behavior, Ethical Data Use, Privacy and Security.

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