Chandigarh University

Background Although Helicobacter pylori (H. pylori) infections are widespread throughout the world, it is yet unknown whether they are linked to systemic illnesses like dyslipidemia. The purpose of this systematic review and meta‐analysis was to examine the connection between lipid metabolism and H. pylori infection, with a particular emphasis on how it affects dyslipidemia. Methods We conducted a thorough search up until October 10, 2024, across databases such as PubMed, Web of Science, and Embase. Studies that reported lipid profiles in both H. pylori‐infected and non‐infected patients were considered eligible. The primary outcomes were triglyceride, LDL‐C, HDL‐C, and total cholesterol levels, which were examined using a random‐effects model in R software version 4.4. Results There were 17 studies with more than 150,000 participants from 681 screened publications. Higher levels of LDL (MD: 5.32 mg/dL; 95% CI: 1.315 to 9.319) and total cholesterol (MD: 6.28 mg/dL; 95% CI: 0.718 to 11.842), as well as lower levels of HDL (MD: −2.06 mg/dL; 95% CI: −3.212 to −0.915), were the results of the meta‐analysis. Among those infected, triglyceride levels were likewise higher (MD: 7.93 mg/dL; 95% CI: 0.413 to 15.436), but the odds ratio (OR) did not show a significant increase in risk (OR: 1.002; 95% CI: 0.995 to 1.010). Conclusion H. pylori infection is associated with significant dyslipidemia, suggesting a potential link between chronic bacterial infection and lipid metabolism. The findings emphasize the need for further research to explore the mechanisms and potential therapeutic interventions.

This study introduces an integrated experimental and finite element analysis (FEA) simulation methodology for improving the turning process of Inconel 825 using tungsten carbide (WC) cutting tools. The research presents an innovative framework that integrates infrared thermal imaging with numerical simulations to examine transient temperature profiles and cutting forces across different machining settings. This study systematically examines the effects of feed rate, cutting speed, and depth of cut on thermal and mechanical responses, employing an L9 orthogonal array for experimental design, in contrast to usual investigations. This research’s primary innovation is the exact monitoring of interface temperatures with an infrared thermal camera, yielding precise thermal data despite the difficulties posed by expensive materials and real-time heat dissipation assessment. The FEA simulations performed in Abaqus FEA utilize an elastoplastic material model exhibiting nonlinear behavior, effectively capturing yielding in both tension and compression. The results demonstrate a robust connection between experimental and numerical findings, with cutting force predictions differing by less than 5%. The research indicates that raising the cutting speed lowers cutting forces while influencing temperature patterns in a non-linear manner. The research underscores the significance of WC inserts in augmenting heat dissipation and promoting machining stability. The proven FEA framework provides a dependable prediction instrument for optimizing machining settings, hence enhancing process control and precision manufacture of high-strength alloys.

In the era of big data and advanced analytics, the quality and governance of data have become pivotal for organizational success. This chapter explores the strategic importance of data quality and governance, highlighting their roles in ensuring accurate, complete, and reliable data for informed decision-making. It delves into the key components of a robust data governance framework, including data stewardship, data quality management, and regulatory compliance. Through case studies and best practices, the chapter provides actionable insights for organizations aiming to enhance their data governance strategies. By addressing common challenges and presenting solutions, this chapter serves as a comprehensive guide for leaders and data professionals striving to maximize the value of their data assets.

Strategic analytics has become crucial for start-up organizations aiming for growth and sustainability. This chapter explores the core elements of strategic analytics, including its definition, scope, historical development, and key theories and models. It highlights the essential analytics’ function in locating market opportunities, conducting competitive analyses, gaining deep customer insights. By examining case studies of successful start-ups, this chapter demonstrates the transformative power of data-driven decision-making and offers best practices for leveraging analytics to drive innovation, optimize operations, and build resilience. Entrepreneurs are encouraged to invest in strong analytics capabilities, foster a data-driven culture, ensure ethical data use, and remain agile in the face of technological advancements and market disruptions.

In the dynamic landscape of startup ecosystems, strategic decision-making is paramount for sustainable growth and competitive advantage. This chapter explores the integration of analytics into the strategic framework of startup organizations. It delves into the methodologies, tools, and frameworks that startups can leverage to harness data-driven insights for informed decision-making. Key topics include the identification of strategic goals, data collection and management strategies, analytical techniques for market intelligence and customer segmentation, and the implementation of analytics to optimize operations and enhance scalability. By adopting a strategic analytics approach, startups can navigate uncertainties more effectively, capitalize on emerging opportunities, and chart a path towards long-term success in a rapidly evolving market environment.

This chapter discusses on various optimization approaches that can be used strategically in decision making areas. Therefore it underlines the importance of these techniques in the improvement of the supply chain’s performance and sustainability. When it comes to the modern realities of doing business, strategic analytics are one of the key tools for decision-making that increases companies’ adaptation to their environments. The chapter helps to understand the state-of-practice approaches and identifies the potential of using data analytical methods and presents the performances of multi-objective optimisation methods including evolutionary algorithms, swarm intelligence, hybrid metaheuristic and hyper heuristic methods. The chapter begins with definitions of descriptive, predictive, and prescriptive analytics with fundamental concepts. The latter offers an analysis of how the Particle Swarm Optimization (PSO) mathematical algorithm can be applied to improve a specific logistics schedule. It mainly contains problem definition, data collection, determination of the fitness function and PSO algorithm to determine the best routes. Different configuration of PSO is shown in the context of the case study to understand how it influences optimization results such as cost, transit time and computation time. Thus, this paper aims to focus on stressing tangible benefits, which strategic analytics brings, including increased productivity, competitive advantage, optimization of supply networks and chain and enhancements in financial outcomes. It is always wise to learn from experience and from others and this chapter will be of immense value to any business or student and the guidelines will be very helpful in the implementation of optimization techniques in improving business resilience.

“The Social Media Playbook: Strategies for Building Brand Loyalty and Engagement” is a crucial manual for using social media to increase engagement and brand loyalty. The chapter highlights the need of customizing methods to the specific aspects of each major social media platform by examining its distinctive advantages. Examples of these strengths include Instagram’s visual-centric approach, LinkedIn’s efficacy in B2B marketing, and Instagram’s captivating short-form videos. The chapter emphasizes how crucial it is to produce excellent, pertinent material and to schedule it well in order to preserve consistency. To evaluate the effectiveness of social media campaigns and make data-driven modifications, it provides information on key performance indicators (KPIs) such as engagement rates, reach, impressions, follower growth, and conversion rates. The chapter talks about how new technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) will affect social media involvement in the future. AR and VR provide immersive experiences that can deepen consumer relationships with brands, while AI improves automation and personalization. Together with the expanding contribution of social commerce to the smooth operation of online stores, the evolution of influencer marketing toward more genuine, long-term collaborations is examined. Maintaining client trust is emphasized as requiring adherence to legislation such as the CCPA and GDPR, with a focus on data protection and security. The chapter also emphasizes how important user-generated content (UGC) is for creating a sense of community and repeat business. All things considered, this chapter offers practical advice and techniques for negotiating the ever-changing social media scene, adjusting to new trends, and successfully interacting with consumers to foster enduring brand loyalty.

Future communication paradigms, such as 6G networks, emphasize self-sustainability, intelligent networking, and secure, adaptive communication. This research presents an innovative routing framework tailored for Underwater Sensor Networks (UWSNs) and Underwater Acoustic Networks (UANs), addressing critical challenges like energy constraints, security vulnerabilities, limited bandwidth, and interference. The proposed system integrates a Multi-Agent System (MAS), blockchain technology, and acoustic communication to enhance security, optimize energy usage, and improve data transmission efficiency. Key features include intelligent node mechanisms, proactive bandwidth and interference management, a multi-hop paradigm, distance-aware longevity strategies, and robust cryptographic protocols. The system is benchmarked against established routing protocols such as GCORP, PER, MARL-MC, and MLAR, demonstrating superior performance. The proposed cognitive intelligence (CI) protocol achieves energy consumption below 120 J per transmission, significantly lower than existing methods. It also achieves end-to-end latency under two seconds in multi-hop scenarios, outperforming alternatives like MARL-MC and GCORP. Additionally, the CI protocol exhibits a packet delivery ratio (PDR) exceeding 90% and an extended network lifetime surpassing 1850 s, making it a robust solution for resource-constrained underwater environments. This work not only addresses the unique demands of underwater networks but also contributes to the vision of self-sustainable and intelligent communication systems, aligning with the broader context of 6G paradigms through energy-efficient routing, cognitive intelligence, and secure, adaptive communication frameworks. The results underscore the effectiveness of the CI protocol in enhancing energy efficiency, reducing latency, and ensuring reliable long-term operation, thereby supporting critical applications like disaster management and environmental monitoring.

In the current research, we developed and formulated an innovative therapeutic agent utilizing silver nanoparticles infused with the Ocimum basilicum leaves, aimed at addressing arthritis and osteoporosis. The AgNPs characterization was conducted using techniques such as field emission‐scanning electron microscopy (FE‐SEM), X‐ray diffraction (XRD), ultraviolet–visible spectroscopy (UV–Vis), and transmission electron microscopy (TEM). The distinct peak observed at 448 nm in the UV–Vis spectrum indicated the successful formation of silver nanoparticles. Furthermore, the TEM and FE‐SEM images revealed that these NPs were predominantly spherical (10–50 nm). The potential for anti‐osteoarthritic activity was assessed in vitro through protein denaturation methods involving egg albumin and bovine serum albumin, as well as membrane stabilization techniques, utilizing several concentrations (1–1000 μg/mL). In vivo evaluations were conducted using formaldehyde, CFA, and turpentine oil models at several doses. Additionally, the in vitro antioxidant capacity was evaluated through a reducing power assay. The findings demonstrated concentration‐dependent inhibition of albumin denaturation, along with significant stabilization of RBC membranes, with optimal results achieved at 1000 μg/mL. Similarly, the nanoparticles demonstrated the anti‐osteoarthritic effect with the highest activity noted at 1 mg/kg. The CFA model findings indicated a more pronounced protective efficacy against osteoarthritic lesions and changes in body weight. Furthermore, silver NPs reduced significantly rheumatoid parameters levels, ameliorated the altered hematological factors, and positively influenced both histopathological and radiographic alterations. Additionally, silver nanoparticles showed strong antioxidant qualities. In Wistar rats, osteoporosis was induced by the combination of MPSC (10 mg/kg, subcutaneously, three times a week for 4 weeks) and silver nanoparticles (5 μg/kg/day, oral, for 30 days). This treatment led to an elevation in serum levels of markers associated with bone mineral content, while simultaneously causing a reduction in both urinary and serum levels of bone resorption markers indicative. An increase in the tibia and femur strength was reported, especially at 5 μg/kg of silver NPs. The mechanisms by which silver nanoparticles may counteract glucocorticoid‐induced osteoporosis likely include calcium homeostasis regulation, collagen synthesis, and free radicals neutralization. Collectively, these findings endorse the conventional application of silver nanoparticles as effective agents against osteoarthritis and osteoporosis in humans.

The influence of magnetic fields on plant growth and development has gained increasing attention due to their potential to enhance seed germination, nutrient absorption, and overall crop productivity. Scientific studies indicate that magnetic field exposure can improve the efficiency of photosynthesis by influencing chlorophyll accumulation and electron transport mechanisms. This research investigates the impact of magnetic field treatment on crop resistance to diseases and its role in enhancing agricultural yield. By exploring the effects of magnetization on plant health, this study aligns with Sustainable Development Goal 13, which emphasizes climate action and sustainable agricultural practices to ensure food security. The primary objective is to naturally enhance crop productivity through magnetic field priming, reducing the need for excessive pesticide use. To achieve this, MATLAB v.23 is utilized for mathematical simulations, modeling the relationship between light absorption at specific wavelengths and plant response. This approach helps determine various crop indices and demonstrates how enhanced chlorophyll-a and chlorophyll-b of levels contribute to improved photosynthesis efficiency. The research examines the role of magnetization in promoting plant vitality and disease resistance over a wide cultivation area. Magnetic fields influence biochemical reactions within plant cells by aligning unpaired electrons, which enhances electron mobility and metabolic activity. This biophysical stimulation supports early-stage seed germination and robust plant development. The study specifically investigates DBW-187 and PBW-725 wheat varieties under controlled conditions, comparing root and shoot lengths between magnetically treated and untreated samples. The findings provide valuable insights into how magnetic field treatment can be leveraged as a sustainable agricultural practice to improve crop yield and resilience while minimizing chemical interventions.

Thermal spray methods have grown in popularity across various sectors due to their ability to deposit protective coatings on surfaces. These coatings improve the surface qualities of substrates, including resistance to wear, corrosion, and thermal insulation. However, a number of crucial elements impact the quality and function of the coated substrate surface. This chapter provides a thorough evaluation and effects of significant factors on the coated substrate surface using a range of thermal spray techniques. Powder characteristics, process parameters, substrate preparation, and post-treatment techniques are some of these crucial elements. The effects of several factors, such as powder characteristics, process parameters, substrate preparation, and post-treatment processes, are explored in relation to the coating's microstructure, adhesion, and overall performance. The objective of this study is to examine the effects of various factors influencing the coated substrate’s surface and to provide suggestions for future investigations aimed at improving thermal spray methods.

This chapter provides the depth overview of distinct process elements that affect the quality of thermal spray coating. These factors include stand of distance, the rate at which the material is supplied, the surface’s temperature that is being sprayed from, and the particle velocity. Additionally, various post-processing and surface finishing techniques are discussed. Finally, the effects of different thermal spray processes on the quality of coatings are analyzed. Thus, this chapter becomes useful for the researchers studying the effects of various processes as well as parameters on the coating quality.

Achieving surface coatings on metal substrates poses a complex challenge for researchers, to achieve the specific properties. In recent years, functionally graded coatings (FGCs) have garnered significant interest from researchers globally. This is attributed to their unique combination of thermal, mechanical, electrical, and tribological properties, making them compelling materials for tailored coatings in advanced engineering applications. FGCs represent an innovative departure from traditional composites, featuring a non-uniform distribution of phases that creates a seamless gradient structure. Consequently, the exploration of gradient coatings has opened up a new avenue for research in this field. This chapter emphasis on various key factors influencing the functional graded coated substrate.

This chapter reviews various process and parameters optimization techniques. Further, focusing on the latest coating materials, including ceramics, metals, and composites, with a focus on their improved performance, the research also examines cutting-edge coating quality diagnostics and characterization methods. Finally, the different applications of coatings in various sectors, such as wear resistance, thermal insulation, mechanical strength and corrosion protection etc. are explored and highlighted their critical roles in industries like aerospace, automotive, energy and constructions.

Abstract Background: Oropouche virus (OROV), an emerging arbovirus, poses a significant public health concern in the tropical and subtropical regions of Latin America, as well as in other regions, with imported cases reported in North America and Europe. While OROV is primarily associated with acute febrile illness, especially emerging evidence suggests it may cause neurological complications, though these remain understudied. This systematic review and meta-analysis aim to estimate the prevalence of neurological manifestations in OROV infections. Methods: Following the PRISMA 2020 guidelines, a systematic literature search was conducted across PubMed, Web of Science, and Embase up to January 25, 2025, and registered in PROSPERO (Registration ID: CRD42025634617). Nested Knowledge software was employed for the screening and data extraction processes. Data extraction and quality assessment were performed using a modified version of the Newcastle-Ottawa Scale. A meta-analysis was conducted using R software to estimate the pooled prevalence rates of potential neurological symptoms, with heterogeneity assessed using the I² statistic. Sensitivity analyses and publication bias assessments were also performed. Results: Ten studies from Brazil, Peru, and Colombia were included, encompassing a total of 2,872 patients. The pooled prevalence of neurological symptoms was high, with headache (89.16%), myalgia (70.71%), and eye pain (52.87%) being the most common. Other symptoms included arthralgia (56.5%), back pain (46.1%), and nausea (43.3%). Significant heterogeneity was observed across studies, likely due to variations in geography and diagnostic methods. Sensitivity analyses confirmed the robustness of the findings. Conclusion: Neurological manifestations are prevalent in OROV infections, with headache, myalgia, and eye pain being the most frequent. The clinical overlap with other arboviruses complicates diagnosis, highlighting the need for improved diagnostic tools and surveillance of neurological syndromes associated with arboviruses in endemic regions and new areas with recent circulation of the virus. To improve generalizability, future research should broaden geographic analyses and concentrate on longitudinal and standardised studies to better understand the temporal dynamics of symptoms. Keywords: Oropouche virus, neurological manifestations, arbovirus, headache, myalgia, eye pain.

This research examines the impact of a continuous bias force on the behaviour of a spherical particle that is periodically compelled to move at a constant speed at infinity in a Newtonian liquid at low Reynolds numbers. The dynamics of particle motion are shown on the two-dimensional phase plane, including parameters such as the external periodic force, particle Reynolds number, constant bias force, and constant velocity at infinity. It is found that there is a range of values of the undisturbed far field motion and the constant bias where the effects of both neutralize. In this region the fluid behaves as quiescent. It is shown that the numerical simulation yields the case results with zero constant force and velocity at infinity. A Stokes’ iterative solution is also provided in this article and it is seen that the solutions are largely manifested by the periodic motion. The Reynolds number effects are dominated when it is larger than the amplitude of the periodic motion and smaller than the Strouhal number. A drifting, pulling, pushing of the particle happens due to Reynolds number, constant bias and far field velocity effects respectively. Simulations are validated by the earlier literatures and hence this simulation can be considered as a test case for simulations of more complex flows.

In the context of India, the banking sector stands as a major contributor to economic growth, driving various financial activities and investments. Artificial intelligence is increasingly affecting the lives of everyone. Present study is focused on exploring the different usage and applications of Artificial Intelligence in the Indian Banking Sector. The findings of the study show that AI plays an influential role in the functioning of the Indian banking sector. Indian banking sector is opting for AI-supporting solutions like Chatbots, virtual assistants, smart wallets, automated services, ATMs, CCTV, robots’ advisors, and many more. The study has highlighted how AI-supported technologies are implemented by Indian Banks. As the landscape of banking transforms with innovations such as digitalization and Artificial Intelligence, banks must stay agile and responsive to maintain their pivotal role in sustaining economic stability and fostering growth. Through effective operation and strategic adaptation, banks continue to be instrumental in driving economic progress and prosperity, ensuring that financial services remain accessible and efficient for all segments of society. Future research, preferably descriptive research, should be carried out in the banking sector to explore more.