The aim of this review paper is to critically analyze and compare traditional and emerging theoretical approaches to the travel behavior of Generation Z as the most recent tourist segment. The analysis will focus on five key domains: motivation, accommodation, marketing communication, values (sustainability), and technological integration. The comparative framework highlights the gap between the product-oriented model (e.g., standardized hotels, mass advertising) and the demand for experiential, ethical, and digitally seamless offerings. The synthesis of findings will result in a proposal for transforming CR from a 'product seller' to an 'experience curator'. This new role requires the implementation of a model based on the experience economy and connectivity (eWOM), which effectively targets the search for authenticity and social validation among young travelers (D'Acunto et al. (2025). The study identifies key directions for future research and recommendations for practice to address this dominant and formative group of travelers.

Radiology In Training effectively trains skilled scientific reviewers, and the majority of its members continue serving as Radiology reviewers after graduating from the program.

Atmospheric water harvesting (AWH) has been recognized as a promising technology to address global freshwater scarcity in a decentralized manner. Nevertheless, conventional AWH sorbents are often associated with high energy consumption, toxic synthesis procedures, and short operational lifetimes. To address such limitations, a comprehensive review paper develops a unified framework to bridge the gap between nanoscale material properties, such as synthesis routes, structural architecture, and adsorption thermodynamics, and macro-scale environmental and economic performance. This review paper rigorously examines emerging nanomaterials such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), mesoporous metal oxides, and graphene oxide derivatives. By highlighting benchmark materials such as MOF-303 and passive solar-regenerated COF-ok, the review paper emphasizes the advantages of bio-assisted "green" synthesis routes. Crucially, this review extends beyond traditional water uptake figures and incorporates comprehensive Techno-Economic Assessments (TEA) and Life-Cycle Assessments (LCA). It examines various real-world influences, such as cumulative energy demand, levelized costs of water, and ton-scale manufacturing viability, to name a few. This report bridges atomic-level mechanics with industrial economics, and by so doing, offers design criteria to guide researchers in crafting a new generation of sustainable AWH infrastructure, with a focus on hierarchical pores, surface chemistry, and photothermal design.

The Birth companionanyperson chosen by the pregnant woman according her choice to give continuous support during labour and childbirth. Whom she trust greatly .The person maybe pregnant woman’s husband ,family member ,healthcare provider, community member. The objective of this review paper to assess andexplore perceptions and experiences of pregnant women, health care providers (obstetrics and gynecology specialists, residents, and midwives), female birth companions, volunteer birth companion. The review paper emphasizes birth companionsencourage and support given to pregnant women during labour and child birth and make communication bridge between pregnant women and healthcare team. Birth companions providenon-pharmacologicalmeasure to pregnant mother for pain relief. Acceptance ofa birth companion by healthcare providers one of the cause develop role and responsibilities of birth companion . Birth companions play a very important rolefor witness during disrespectful and abusive behavior to women in labour. Health professionals have knowledge about benefit ofbirth companions. But it is not routine practicedue to lack of adequate space and difficulty to ensure privacy. In this article, more over discuss continuous and effective support to the parturient women,bybirthcompanion is important that she increase their knowledge regarding birth that will help the future another pregnant women ,her family and the health care staff

The Photon which is a fundamental entity of electromagnetic radiations is a boson. According to Maxwell equations, it is nothing but an electric field which propagates through space with the speed of light and produce orthogonal magnetic field during its movement. Photoelectric effect and Compton effect suggested its particle nature. In this review paper, it is tried to build a comprehensive and concise description of photon. Various theories and results are indicated to generalize specific properties of the photon. Quantum entanglement, bunching effect, comparison of photon with hypothetical particle Graviton, pair production, Schwinger effect, photon-photon collision and polarization of photons are some remarkable phenomena that are related to photon; these terms are concisely explained in this review article. These phenomena are not only vital for future technology but also will have the central key for our understanding of the sub-atomic world.

This review paper provides a comprehensive overview of the functional materials and assembly technologies used in intracardiac echocardiography (ICE) and intravascular ultrasound (IVUS) transducers. ICE and IVUS are advanced medical imaging technologies that play significant roles in the diagnosis and treatment of cardiovascular diseases, involving material selection and fabrication processes for miniature piezoelectric ultrasonic transducers. The review begins with an introduction to the principles and applications of ICE and IVUS, highlighting their advantages over other imaging modalities, then delves into the materials and assembly processes of the transducers, presenting the mainstream trends and research progress in various directions in this field in recent years. Finally, the paper summarizes the future technological development and clinical application trends of ICE/IVUS ultrasonic transducers.

Carbon monoxide (CO), as a colorless, odorless, and highly toxic gas, which poses a significant public health risk in Nigeria, due to increasing anthropogenic activities, rapid urbanization, vehicular emissions, biomass burning, and industrial growths. The high affinity of CO for hemoglobin impairs oxygen transport in the human body, leading to hypoxia and numerous cardiovascular and neurological complications. “Regardless of its harmfulness, CO consistently remains under-monitored and poorly regulated in many parts of Nigeria, contributing to a silent, but increasing public health afflictions.” This review evaluates the sources, distribution, health impacts, and regulatory responses to carbon monoxide pollution across Nigeria. Data were sourced from peer-reviewed articles, environmental agency reports, WHO air quality records, and field observations. Emphasis is placed on urban centers like Lagos, Abuja, Kano, and Port Harcourt, where CO levels frequently exceed WHO standards. The analysis also explores CO exposure pathways, including indoor pollution from generators, charcoal stoves, and gas heaters. Findings from previous studies reveal a strong correlation between elevated CO levels and hospital admissions due to respiratory and cardiovascular conditions. The paper concludes by highlighting policy lapses, weak enforcement of emissions standards, and lack of real-time air quality monitoring as major challenges. Recommendations include integrated CO surveillance systems, public awareness campaigns, and transition to cleaner energy sources. The article calls for a nationwide strategy to address the neglected but lethal implications of carbon monoxide pollution for sustainable environmental and public health governance in Nigeria.

Cardiovascular disease (CVD) is currently among the leading causes of morbidity and mortality across the world, which is why there is an excellent demand of reliable, effective, and exact predictive mechanisms. In a typical machine learning/deep learning, the class imbalance, high-dimensional clinical data, noisy or redundant features, and computational inefficiencies are some of the key weaknesses that tend to deteriorate predictive reliability. To address these problems, scientists have resorted more and more to hybrid optimization methods, which combine machine learning models with metaheuristic methods in order to advance the process of feature selection, parameter optimization, and model generalization. In this survey paper, a detailed analysis of the hybrid optimization methods used to predict heart diseases is given, especially the way in which they improve the predictive capabilities. The paper examines popular metaheuristic algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) and explains their efficiency in the search of the most informative features of complicated medical data. Special focus is put on the classifier of the Random Forest that is optimized with the help of the following metaheuristic strategies in order to enhance precision and decrease overfitting. The survey identifies the practical challenges of hybrid optimization, including the cost of computation, convergence problems, and the heterogeneous healthcare data, as well. These limitations are mitigated by suggesting possible solutions and research directions. Lastly, the paper provides a comparison of the performance of different machine learning algorithms that have been optimized using different metaheuristic methods and it is established that hybrid models will always have a high predictability of cardiovascular disease. The results emphasize the relevance of hybrid optimization as a strong direction in the formation of high-performance and clinically reliable models of heart disease prediction.

This review article explores the nutritional and therapeutic potential of millet-based recipes, emphasizing their relevance in both traditional Ayurveda and modern dietary practices. Millets, small-seeded cereals including ragi, bajra, and little millet, are highlighted for their richness in fiber, minerals, and phytochemicals, making them suitable for managing iron deficiency anemia, diabetes, and obesity. The article presents detailed preparation methods for recipes such as Ragi Laddu, Bajra Laddu, Little Millet Daliya, and Finger Millet-Palm Jaggery Cookies, focusing on techniques like germination and roasting that enhance nutrient bioavailability and safety. Nutritional tables provided illustrate the carbohydrate, protein, mineral, and vitamin profiles of various grains, alongside their glycemic indices. The discussion underscores the value of millet integration into mainstream diets for combating malnutrition and non-communicable diseases, while advocating for further innovation in ready-to-cook millet products suited to contemporary lifestyles.

The escalating complexity of health insurance processes has led to the need to find new solutions to realize the efficiency, cost-reduction, and service delivery. The analytics technique known as predictive analytics is a statistical model based analytics using machine learning to predict the outcome and it can deliver gigantic potential in the area of maximizing the time in the operation of the insurance business to include the underwriting processes, claims management processes, and fraud detection processes. The review article investigates the way predictive analytics can be integrated into health insurance operation and proposes a template on the optimization of the cycle time. Based on the synthesis of the recent literature, the paper identifies a collection of core aspects of integration, which include data consolidation, model development, workflow embedding, feedback-driven learning, and ethical governance. It highlights the importance of predictive systems in helping the insurers to automate decision making, concentrate on low-risk claims, detect fraudulent activity, and customize customer interaction. The given model is aimed at the cycle of constant improvement with the help of monitoring performance and open governance which will ensure the long-term enhancement of the operational efficiency and compliance with the laws. Additionally, the article discusses the issues related to data privacy, algorithm bias, and interpretability and provides the ethical and technical solutions to eliminate the risks. The future research directions are to bring artificial intelligence, blockchain, and Internet of Things (IoT) into the picture to obtain more interoperability, transparency, and real-time flexibility. Overall, this paper has demonstrated that predictive analytics is not only a groundbreaking technology, but also a wonderful strategic enabler to efficiency, accountability and innovation in the health insurance sector.

The rising incidence of anxiety, depression, academic stress, and socio-emotional problems among students has led to an increased focus on mental health as an essential area of concern for education today. Teacher education institutions form an essential foundation for providing pre-service teachers with essential knowledge, skills, and attitudes that enable them to support students mental health and well-being alongside their academic performance. This review article critically explores existing research literature(2015-2025), international and national policy initiatives, and pedagogical innovations related to integrating mental health education into pre-service and in-service teacher education programs. The review draws on interdisciplinary research literature from education, psychology, and public policy and critically synthesizes some of the emerging themes and concerns related to teachers preparedness, curriculum reform, mental health literacy, and systemic challenges. The review also proposes a multi-tiered conceptual framework for mainstreaming mental health education into pre-service and in-service teacher education programs through curriculum embedding, experiential learning, institutional support systems, and research-informed evaluation.

The repertoire of disease-associated, formalin-fixed, paraffin-embedded (FFPE) samples collected and archived over decades is vast. When combined with frequently available and detailed clinical information on patients, disease progression, and outcomes, these samples represent aunique resource for investigating disease mechanisms at the molecular level. However, due to the specific sample preparation process, which involves chemical modifications of the tissue, the direct use of FFPE material in many analytical procedures has been limited.This review article reports on advances in analytical methods from the fields of proteomics and glycomics for the use of FFPE samples. Aparticular focus is placed on the analytical approach we developed for glycan headgroups of glycosphingolipids. Glycosphingolipids are components of the outer plasma membrane whose sugar chains extend into the extracellular space and are highly diverse and complexly regulated. Their glycosylation reflects the cellular state and mirrors pathological changes.The analytical characterization of glycosphingolipid glycans is challenging and has not yet been part of routine diagnostics. In this article, we describe asample preparation strategy for FFPE tissue that preserves the characteristic glycosphingolipid repertoire and enables analysis by capillary gel electrophoresis coupled to laser-induced fluorescence detection (CGE-LIF). Using this approach, we identified anovel glycosphingolipid-based biomarker for bladder cancer in patient urine, which is also enriched in corresponding tumor FFPE samples.The goal of this article is to demonstrate the potential of FFPE and autopsy samples for systematic molecular analyses. This methodological combination opens access to extensive FFPE archives and enables the study of glycosphingolipid glycosylation in various tissues and diseases, facilitating the identification of new biomarkers and providing deeper insights into disease mechanisms.

Obsessive compulsive disorder (OCD) is a psychiatric disorder characterized by the persistence of obsessions and compulsions, which negatively impact functionality and may become chronic if left untreated. Although the efficacy of pharmacological treatment and Cognitive Behavioral Therapy (CBT) involving exposure and response prevention techniques has been proven in OCD, treatment discontinuation rates and residual symptoms following treatment have led researchers to explore different therapeutic approaches. One such approach gaining increasing interest is Acceptance and Commitment Therapy (ACT), a third-generation therapeutic model, which is proving to be an innovative and effective treatment for OCD. ACT primarily aims to guide individuals toward leading a life aligned with their chosen values, by accepting the pains that exist in the natural flow of life instead of avoiding them. In the treatment of OCD, with the ACT approach, interventions aimed at enhancing psychological flexibility have been shown to positively impact the prognosis of the disorder. In addition to engaging in compulsions to avoid the anxiety caused by obsessions, OCD patients may follow a wide variety of experiential avoidance strategies. With the ACT approach, these strategies can be reduced and the anxiety caused by obsessions and compulsions can be eliminated through the acceptance of negative internal experiences. This review article addresses experiential avoidance in OCD within the framework of ACT. Based on recent publications, the article discusses how ACT targets experiential avoidance in OCD treatment and the effects of interventions on the relationship between OCD symptom severity and psychological flexibility.

Guided Endodontics is a modern, digitally assisted technique that integrates cone beam computed tomography (CBCT), intraoral scanning, and computer-aided design/manufacturing (CAD/CAM) to achieve precise and minimally invasive access to root canals. This review article explores the historical evolution of endodontic practice leading to guided approaches, defines the core principles of digital planning and precision navigation, and highlights its clinical applications in managing calcified canals,retreatments, complex anatomical variations, and microsurgical procedures. Evidence from recent studies demonstrates that guided endodontics improves accuracy, reduces iatrogenic risks, and preserves tooth structure compared to conventional freehand methods [1-3,6,7]. Advantages include predictability, efficiency, and enhanced patient outcomes, while limitations such as cost, technical demands, and restricted applicability in certain anatomical situations remain challenges. Future perspectives emphasize the integration of artificial intelligence, dynamic navigation systems, advanced guide materials, and broader accessibility, alongside its growing role in dental education. This review aims to summarize the historical evolution, core principles, clinical applications, advantages, limitations, and future perspectives of guided endodontics, supported by evidence from recent studies.[8,12]

Efficient financial management has become increasingly important in the digital economy where individuals and organizations manage various financial activities such as expenses, investments, savings, and asset portfolios. Traditional financial tracking methods such as manual bookkeeping and spreadsheet-based record keeping often lack automation, analytical capabilities, and structured financial monitoring. With the rapid advancement of web technologies and financial technology platforms, intelligent financial management systems have emerged to provide automated financial monitoring, real-time dashboards, and financial analytics. These systems integrate web technologies, cloud databases, and graphical visualization tools to improve financial awareness and decision-making. This review paper analyzes modern financial management platforms and technologies that support personal and organizational financial monitoring. The study reviews existing research related to web-based financial systems, mobile financial applications, cloud-based financial platforms, and artificial intelligence-driven financial analytics. In addition, the paper evaluates the architecture and features of integrated financial monitoring platforms such as XO Finance, a web-based financial management system designed to track savings, expenses, asset investments, and profit/loss analytics in a unified platform. Comparative analysis and interpretation of existing systems highlight both technological advantages and limitations. The paper concludes by identifying future research opportunities involving artificial intelligence integration, blockchain-based financial transparency, and advanced financial visualization techniques for next-generation financial management systems.

Human emotion recognition has become a significant research focus within artificial intelligence due to its growing importance in human–computer interaction, affective computing, and intelligent decision-support systems. Conventional emotion recognition methods have largely relied on unimodal data sources, such as text, speech, or facial expressions. Although effective in controlled settings, unimodal approaches often provide an incomplete and ambiguous understanding of emotional expression, as human emotions are inherently multimodal. This review paper critically examines a dissertation that proposes a deep learning-based multimodal sentiment analysis framework for human emotion detection by integrating textual, acoustic, and facial expression modalities. The reviewed framework employs a Long Short-Term Memory (LSTM)-based architecture to effectively model temporal and contextual dependencies present in multimodal data. Textual information is encoded using embedded word sequences, audio data captures emotional prosody through acoustic features, and visual inputs represent facial expression patterns. These modality-specific features are fused within a unified deep learning framework to perform binary emotion classification. Experimental evaluation using standard performance metrics, including accuracy, precision, recall, F1- score, confusion matrix analysis, and training–validation curves, demonstrates an overall classification accuracy of 82.22 percent, along with balanced precision and recall values. The review highlights the robustness, methodological soundness, and practical relevance of multimodal sentiment analysis, emphasizing its advantages over unimodal approaches and its contribution to the advancement of affective computing research.

In today’s online world, website generate large amount of user interaction, data , including click stream, browsing patterns, and engagement metrics. Proper analysis of this information is essential for predicting website visits and improving internal pages navigation. Machine learning techniques helps automate the process of analyzing user behavior and discovering browsing patterns. One widely used most commonly used method is the K-Nearest Neighbor (KNN) algorithm, known for its simplicity and effectiveness in similarity-based prediction. KNN compares a current user session with and finds the most similar patterns to estimate future page visits and traffic distribution. This review paper explains how KNN models can be used to predict website hits and increase internal page traffic. It discusses web usage mining, feature selection, prediction processes, recommendation method, advantages, Limitation, and possible improvements. The study shows that KNN-based system can improve personalization, reduce bounce rate, and enhance website organization when used with suitable preprocessing and hybrid approaches. Existing work: KNN-based ML techniques analyze user browsing behavior and to predict website visits, recommend pages, improves personalization, reduce bounce rate, & increase engagement

Student transportation safety is a critical concern for educational institutions, parents, and government authorities. Conventional school bus systems rely on manual attendance recording, fixed schedules, and verbal communication, which often leads to inefficiencies, delays, and safety risks. With the rapid advancement of Internet of Things (IoT) technology, smart transportation systems have emerged as reliable solutions for real-time monitoring and automated data management. This review paper presents a comprehensive analysis of IoT-based smart school bus and student monitoring systems, with a primary focus on the base research paper titled “IoT Based Smart School Bus and Student Monitoring System.” The study reviews earlier approaches based on RFID, GPS, GSM, and IoT architectures, highlighting their advantages and limitations. The paper further explains the working principles, components, and benefits of the proposed IoT-based system using ESP32 microcontroller, GPS module, RFID reader, Real-Time Clock (RTC), LCD display, and cloud integration through Google Sheets. The review emphasizes improvements in safety, attendance accuracy, communication, and data transparency. The findings suggest that IoT-based school transportation systems offer scalable, cost-effective, and future-ready solutions for modern educational environments.

Radiation has emerged as a crucial component of modern technological development and is extensively used in medicine, industry, agriculture, scientific research, communication systems, and nuclear energy production. The growing dependency on both ionizing and non-ionizing radiation has significantly improved the quality of human life; however, uncontrolled or excessive exposure poses serious risks to human health, ecological stability, and environmental sustainability. Ionizing radiation such as alpha, beta, gamma rays, X-rays, and neutrons can cause genetic mutations, DNA damage, cancers, and long-term biological alterations, while non-ionizing radiation including ultraviolet rays, radiofrequency radiation, and microwaves can also contribute to cellular stress, skin disorders, and ecological imbalance. This research article provides a comprehensive analysis of the sources of radiation, environmental exposure pathways, health impacts, global safety standards, and modern radiation protection practices. Special emphasis is placed on radioactive waste management, which remains one of the most critical environmental challenges due to the long half-life and persistence of radionuclides in soil and water ecosystems. The study explores major international guidelines such as those of the International Atomic Energy Agency (IAEA), the Atomic Energy Regulatory Board (AERB), and UNSCEAR to highlight the scientific principles of radiation protection, including the ALARA (As Low as Reasonably Achievable) approach. Furthermore, the article underscores the importance of developing integrated radiation governance, advanced technological safeguards, real-time environmental monitoring systems, emergency response mechanisms, and public awareness programs. Strengthening institutional capacity and promoting sustainable nuclear practices are essential to minimize long-term environmental and health risks. Overall, the study argues that while radiation offers undeniable benefits to society, its safe and responsible use is vital for ensuring human well-being and maintaining ecological balance.

In this review article, various cutting-edge strategies are addressed that aim to enhance the mechanical and functional properties of Biocompatibility metal, which are manufactured using additive manufacturing techniques, in particular for biomedical applications such as implants and prostheses. Additive manufacturing has several advantages, including time saving and cost reduction, especially in small product manufacturing processes and prototypes, freedom of design for complex shapes that are difficult to achieve by traditional methods, the advantage of reducing waste and material waste, the possibility of customizing products to order, enhancing sustainability and reducing environmental impact, and others. It can be said that most of the previous studies focused either on the biological properties of biometallics that were manufactured using addition techniques or improving mechanical properties, while comprehensive strategies that integrate the two aspects together have not been reviewed, and this article shows how to achieve synergistic improvements between the two performances. Any modern integrative revision combines various strategies (alloying, microscopic, surface, computational).