Browse Items (7684 total)
Sort by:
-
Effect of Microbial Consortium on Biodegradation Process of Organic Waste Materials
Solid waste generation has significantly increased in tandem with population growth, presenting substantial challenges to human health and environmental sustainability. In developing nations such as India, traditional management practices, including incineration and open dumping, continue to prevail despite their detrimental environmental effects. There is a paucity of research focusing on microbial consortia specifically engineered for the accelerated biodegradation of heterogeneous organic waste under local conditions. This study addressed the microbial consortium isolated from waste-associated environments enhancing the rate of organic waste degradation and effect of consortium for improving compost nutrient quality compared to natural decomposition. Microbial strains were isolated from a soil sample obtained from a vegetable and fruit waste disposal site in Bengaluru, Karnataka, India. Among the 50 isolates, five exhibiting strong hydrolytic activity were selected and identified through 16S rRNA sequencing as Cronobacter sakazakii LMSC, Stutzerimonas xanthomarina LMSA, Cronobacter sakazakii LMSPR, Pseudomonas pseudoalcaligenes LMSPE, and Staphylococcus sp. LMSL. A consortium was prepared by inoculating equal proportions of each culture, incubating for 48 hours, and applied it to waste degradation. During composting, temperature (2832 C), pH (neutral to alkaline), and moisture (68.44% in treated vs. 18% in control) were monitored. Nutrient analysis revealed higher values in the treated compost compared to the controls, contributing to enhanced soil quality and plant growth. The novelty of this study resides in the formulation of a native microbial consortium from waste-associated bacteria, which demonstrated superior biodegradation efficiency and compost quality compared to natural decomposition. 2026 Widener University School of Civil Engineering. All rights reserved. -
Attitude of Parents Towards Various Behavior Management Techniques Utilized in Pediatric Dental Treatments
Dental experts are trusted to apply the knowledge and abilities they have acquired during their dental education to the diagnosis and effective treatment of any dental illness. When it comes to pediatric patients, however, the dentist's responsibility is different. However, without the right behavior management method (BMT), therapy outcomes would not be effective. Sometimes young children behave disruptively during dental visits, which makes it easier or harder for the dentist to perform dental work. Nonetheless, before being applied to children, behavior management strategies need the parents' acceptance and consent. This review's objective is to evaluate the dentists' use of effective behavior modification techniques (BMT) as well as the parents' attitudes regarding these techniques. RJPT All right reserved. -
Hijab row verdict in India: A sentimental analysis of user responses on Facebook
The hijab controversy, a hot topic in India since January 2022, emerged from a clash between students and college authorities in Karnataka, a state in India. The dispute made its way to Karnatakas High Court, which, on 15 March 2022, ruled that the hijab was not mandatory for religious practices. This decision stirred widespread debates, especially on social media platforms like Facebook. This study is focused on user reactions in The Times of India Dailys official Facebook page, employing sentiment analysis to decipher public emotions, attitudes, and opinions. Nearly 200 comments posted on the verdict day were analyzed, utilizing the routine activity theory (RAT), spiral of silence theory, and online disinhibition effect. The research introduced a unique model, RAT+A, to enhance analysis. As a significant platform for public discourse, Facebook played a crucial role in capturing diverse societal sentiments and reactions regarding this social issue. 2025, First Monday. All rights reserved. -
Systematic Review on Online Brand Advocacy's (OBA) Antecedents and Consequences
Objective: The primary objective of this study is to discern and synthesize the antecedents and consequences of online brand advocacy (OBA) through a systematic review of empirical studies that employ OBA as a measurable construct. In this study, the gap in our knowledge about the drivers and the effect of OBA is identified, with the synthesis of empirical evidence being made on a systematic level to determine the main antecedents and outcomes of OBA. Methodology: A total of 35 peer-reviewed articles were compiled from Web of Science, Taylor and Francis, Springer, and Sage databases. The search keywords included OBA, Online brand advocacy, and brand advocacy. Inclusion criteria were restricted to quantitative, survey-based empirical studies conducted between 2010 to 2021. Findings: The antecedents, consequences and theories of OBA were categorised into six themes: customer-brand relational factors were brand identification, brand equity, trust factors, brand loyalty, brand authenticity, customer satisfaction, individual factors were intrinsic and extrinsic motivations, personality congruence, brand experience, brand love, cognitive and affective image and social factors were community involvement, brand community attachment, social identity, firm performance, wealth creation. Based on theoretical foundations, we have the social identity theory and the social influence theory as the last theme. Originality/value: This review is novel in compiling and categorising the antecedents, consequences, and theories of OBA as used in empirical studies, providing a solid foundation for advancing research on OBA. The Research Publication,. -
Bridging the Skill Gap: Correlating Affective Competencies with Innovation and Technology Preparedness Among Software Professionals
This research focuses on examining the relationship between innovation and technology readiness skills and the affective competencies of IT/ITeS professionals. This research also explores the impact of affective competencies in the work environment. The study highlights how affective competencies like appreciation, motivation, attitudes, and values shape workplace interactions and influence professional success rather than focusing on secondary emotional competencies. A logical connection has been created empirically between affective competencies and their significance in interpersonal relationships, addressing emotional attributes such as feelings, motivation, appreciation, values, and attitudes. This research study is a section of a survey of 457 IT/ITeS professionals, aiming to explore the skills gap analysis on Bloom's paradigms on cognitive, affective, and psychomotor competencies. This article specifically focused on the impact of affective competencies across various IT/ITeS job roles, like Software / Web developers IT/ITeS job roles. The findings reveal a growing realization in the industry: affective competencies are not optional. They are foundational to thriving in dynamic, tech-driven work environments and fostering effective collaboration with teams and clients in the IT/ITeS sector. The Research Publication, www.trp.org.in. -
Enhancing Personalization in Search Engines Through Behavioral Profiling
With the development of search engines, people demand more contextual, relevant, and important results according to their needs and preferences. The current paper will examine the enhancement of search engine personalization through behavioral profiling, which involves capturing user interaction data, such as search histories, clicks, and other similar data, to understand user interests and intentions. The behavioral profiling promotes the ability to adjust the results to the requirements of mutual changes in user behavior and apply machine learning algorithms and advanced data mining techniques. We describe the key aspects of the successful behavioral profiling systems, such as user modeling, data collection frameworks, and privacy boundaries of the data protection. The paper will address the points mentioned by providing behavioral profiling to enhance user satisfaction and effective search and engagement. It will discuss the predictive relevance ranking's triple impacts on socioeconomic gains: time, energy costs, and attention time. We also discuss the ethical issues of user data collection, and the invitation implies achieving the appropriate compromise between individualization and privacy. By the case studies and comparisons, we affirm that the behavioral personalization greatly improves the accuracy of the search when the methods are either static or generic. This study enhances the design of a smart, convenient search engine by cultivating actionable, individual-sensitive recency search. It aims to smoothly aid personalized interactions in real time, inspiring advancement in context-sensitive retrieval systems. The Research Publication,. -
Compressed Data Representation Methods for High-Speed Search
The current age of computing revolves around data; the ability to fetch and store large quantities of information has become imperative for systems such as embedded systems and even search engines. Methods of compressed data representation are vital, as they enable faster query execution while reducing the storage space needed. This paper has analyzed such methods. The authors have reviewed bitmap indexing, inverted index compression, succinct data structures, LZ-based schemes, and compressed tries based on the set criteria of practical usefulness, search performance, and space efficiency. Through qualitative metrics, the authors performed a comparative evaluation, which is then represented in a conceptual figure and through tables. Moreover, the paper analyzes potential use case scenarios in domains such as bioinformatics, log management, edge computing, and AI-powered search pipelines. Other issues that have been explored include a balance between compression and query latency, optimizing for heterogeneous hardware, encrypted data search, and searching through encrypted data. The findings illuminate previously unexplored areas of research, including learned indexing, adaptive compression, and searching with minimal energy expenditure. The Research Publication,. -
Explainable Information Retrieval Techniques in Academic Search Engines
Due to the rapid increase in scholarly publications globally, researchers rely heavily on specialized academic search engines to gather pertinent information. However, the algorithms used in many of these systems create a black box that breaches transparency, significantly eroding user trust and interpretability. Thanks to XIR, Explainable Information Retrieval, this issue has become a thing of the past. Users can now receive easily understood rationales for why documents were retrieved and how the documents were ranked. This work examines the various XIR techniques integrated into academic search tools, assesses their application methods, and analyzes how effectively they enhance users' understanding, satisfaction, decision-making, and information processing. The paper also formulates a central proposal, which incorporates important elements of XIR, and highlights remnant problems that require deeper analysis. The Research Publication, www.trp.org.in. -
Investigating the Role of Intelligent HR Systems in Enhancing the Relationship Between Employee Engagement and Performance: A Computational Perspective for Economic Development
This study examines the impact of Human Capital Management Practices (HCMPs), specifically employment opportunities, training, and rewards, on employee performance. Work engagement is a mediating component in this analysis. The results indicate that HCMPs enhance employee performance by increasing work engagement. This demonstrates the strategic role that HR plays in developing a motivated workforce that propels business success. Engaging employees is essential for every business to succeed in providing its clients with high-quality services; engaged employees treat customers royally and talk about the success of their organization. According to research, engaged workers are 87% less likely to quit than disengaged ones. They tend to maintain regular attendance without absenteeism, treat their work with care, and contribute to the business's growth. As a result, they are often rewarded for their dedication and commitment. The manager must communicate with employees, listen to their concerns, and reward them for improved work. Employee engagement, on the other hand, improves performance and helps the company expand. It is bothering because employees who are less engaged are more likely to leave than those who are more engaged. When workers are engaged, they work hard, do better at their jobs, and stick with the company for years. To measure the level of engagement, research indicates that companies with poor employee engagement experienced an average operational income decrease of almost 32%, created a foundation for engagement to occur easily, set goals and tracked employee engagement, and encouraged continued high engagement levels with routine public recognition for employees who are engaged. The primary task is to break the culture of dependency on leaders and develop teamwork with visibility to accountability and engagement. This empowers employees to solve their issues and provides opportunities for coaching and mentoring. High employee engagement results in high organizational performance. The Research Publication. -
Leveraging AI And Blockchain for Secure Digital Right Management in Libraries
The urge to develop the Digital rights management systems that are well-established, protecting the intellectual property, and still facilitating the reasonable access to information is supported by the faster rate of digitization of library materials. The given paper investigates how Artificial Intelligence and Blockchain can be combined to support the safety, accountability, and effectiveness of Digital Rights Management (DRM) systems utilized in modern libraries. AI allows advanced content recognition, behavioral analytics, and authorization, the most important features of intelligent and dynamic rights enforcement. Conversely, Blockchain offers an immutable and decentralized historical document into which the administration of licensing agreements, property, and transactions can be documented and which will reduce the chances of illegal access and piracy. Smart contracts are issued in Blockchain platforms that allow libraries to provide license compliance with the administered copyright licensing, real-time auditing control of access, and numerous others. Licensing requirements can be forecasted, irregularities detected, and fair use policies recommended using automated algorithms formulated using content metadata and usage patterns. When these technologies are integrated, it forms a very accurate and trustworthy digital rights management (DRM) system, which is advantageous to the authors, libraries, and users. The study will present a model that has a conceptual framework aimed at showing what AI and Blockchain technology can become in library DRM systems. In the model design, the privacy of the data, data accuracy, and the ability of the system to expand and the legal issues are considered to be major concerns. Besides, the deliberation focuses on the complexity of the constituent parts as well as interoperability and technology ethics. The introduction of other new technologies leads to an unparalleled change in the way digital resources are organized, accessed, and secured in the library setting and introduces increasingly powerful and flexible information systems. The Research Publication. -
Gamification Analytics for Enhancing Engagement in Digital Repositories
Digital repositories are critical in storing and distributing of scholarly materials and research data applicable in different fields. One thing, which is a problem despite the academic setting, is the need to sustain a user activity as the motivation to use a repository is not always consistent. One of the issues that could be solved with the help of Gamification is the need to maintain interaction by incorporating game elements into non-game contexts. This article presents a discussion on enhancing interaction with digital repository users and finding information using gamification analytics. By capturing real-time data and making behavioral decisions, we will learn how users engage with the gamification capabilities, such as earning points, badges, leaderboard position, and tracking achievements. This paper will use case study as a research design to investigate the impact of game-like characteristics integrated in an academic repository system. Key performance indicators (KPIs) that are used to measure levels of user motivation and engagement are session time, frequency of visits, and depth of reading. The findings show that the users experienced more active and content-oriented discovery experiences in comparison to the repository when it was configured in relation to their preferences and objectives using user-centered gamification strategies. Also, the study stresses the necessity of constant evaluation and flexible structures to track engagement over a longer period. This study also contributes to the knowledge of digital library sciences by forming a model of introducing gamification strategies into management systems based on data analysis. The results show that analytics can be used as evaluative measures of participation and as evaluative criteria for adjusting designs and enhancing the experience. The Research Publication,. -
Catalysts of Change: The Transformative Journey from HR 1.0 to HR 5.0 Innovations, Challenges, and Strategies in Human Resource Management with Technology and Data-Driven Integration
Human Resource Management (HRM) has evolved significantly, transitioning from HR 1.0 to HR 5.0 due to technological advancements, shifting demographics, and the demands of the global business environment. This chapter highlights the evolution of HRM discussions, focusing on the key changes, concerns, and approaches that have characterized this transformation. In the HR 1.0 phase, the emphasis was on paperwork and routine clerical tasks. This laid the basis for the kind of advancement that was HR 2.0, which brought into account computerization and rudimentary data interconnection. In HR 3.0, SHRM became dominant, meaning that organizational HR practices were oriented only to achieving strategic objectives. There was a shift in the application of digital technology in performing human resource activities under the emergence of HR 4.0. The present phase is called Human Resources 5.0 (HR 5.0), which marked the strain referred to as ''People First." This approach is a mixture of Technologies Focus and Humanity Focus with an ample concern on the experience and emotional state of the employees fused with the capacity of the organizations to deal with competitive issues common to most firms. It also explores the influences that have led to these changes such as; technological developments, geographic expansion, and prospective staff demands. It also looks at possible risks linked to each shift among them, resistance to change in organization cultures, threats to data privacy amongst others plus the need to acquire higher skills. Moreover, the triple-layered paper maps actionable approaches toward the transformation of HR that are supported by cases that focus on learning culture, diversification, inclusion, and the utilization of big data. Reading through this paper to understand how HR has evolved from 1.0 to 5.0, will help HR managers, and organizations get ideas on how to meanwhile shape their human resource management strategies and advancement in the ever-growing global economy. 2025, The Research Publication. All rights reserved. -
Australian bushfire emissions result in enhanced polar stratospheric clouds
Extreme bushfire events amplify climate change by emitting greenhouse gases and destroying carbon sinks. They also cause economic damage, through property destruction, and even fatalities. One such bushfire occurred in Australia in 20192020, and this event injected large amounts of aerosols and gases into the stratosphere and depleted the ozone layer. While previous studies have focused on the drivers behind ozone depletion, the bushfire impact on polar stratospheric clouds (PSCs), a paramount factor in ozone depletion, has not been extensively investigated so far. Therefore, this study focuses on the effects of bushfire aerosols on the dynamics and stratospheric chemistry related to PSC formation and its pathways. An analysis from Auras Microwave Limb Sounder revealed that the enhanced hydrolysis of dinitrogen pentoxide significantly increased nitric acid (HNO3) in the high-latitude lower stratosphere in early 2020. This resulted in an anomalously high areal coverage of PSCs with ice, exceeding 3 standard deviations with respect to background period. Based on Lagrangian backward-trajectory analysis, we find that a predominant fraction (79 %) of the liquidnitric acid trihydrate (NAT) mixture formed via the ice-free nucleation pathway. These NAT particles subsequently acted as nuclei for ice formation, accounting for 95 % of the observed ice PSCs. This rapid conversion from NAT to ice likely contributed to the strong positive anomaly in ice PSC. This highlights the primary formation pathways of ice and liquidNAT mixtures and possibly helps us to simulate PSC formation and denitrification process better in climate models. These findings will contribute significantly to a deeper understanding of the impacts of extreme wildfire events on stratospheric chemistry and PSC dynamics. Author(s) 2025. -
AN ADAPTIVE HYBRID SCHEDULING APPROACH FOR SUSTAINABLE AND RELIABLE CLOUD SERVICES
The modern cloud computing systems have to plan the heterogeneous workloads and balance performance effectiveness, service availability, and sustainability. In this study, an adaptive hybrid scheduling framework is developed Adaptive Ant-guided Min-Max (AAMM) combining ant-guided optimization with dynamic Min-Min and Max-Min in deciding how to allocate cloud tasks as a multi-objective. The scheduler jointly evaluates task completion time, the likelihood of Service Level Agreement violations, energy consumption, and monetary cost within a unified scoring framework, enabling informed trade-offs among competing objectives. AAMM is assessed based on a real disaggregated Deep Learning Recommendation Model workload of 1,544 heterogeneous tasks, running on heterogeneous virtual machines. Comparative experiments are done with Min-Min, Max-Min and ACO-guided Min-Min scheduling strategies. According to experimental findings, the suggested approach has been very effective in reducing energy per task, cost per task, SLA violations are significantly lowered, and flow time stability is enhanced. Though moderate growth in the makespan is witnessed, the accompanying trade-off has created equal distribution of resources and service reliability. 2026 Academy and Industry Research Collaboration Center (AIRCC). All rights reserved. -
Analysis of Mothers Willingness for Age 1 First Dental Visit of Their Child using Andersens Behavioral Model of Health Service Utilization
Background: Early childhood caries (ECC) is a preventable disease among children under 6 years of age.The first dental visit (FDV) is a preventive model endorsed by the American Academy of Pediatric Dentistry and the American Academy of Pediatrics. It is designed to improve oral health outcomes, yet the FDV attendance rate before the age of 1 is low globally, especially in India. Aims: To investigate maternal willingness to attend the FDV within 1 year of age and explore associations with predisposing, enabling, and need factors using Andersens behavioral model for health services utilization. Materials and methods: A cross-sectional survey was conducted among mothers of children aged 915 months. A validated questionnaire was administered to 640 mothers visiting vaccination centers in two hospitals. Statistical analysis involved descriptive statistics and logistic regression to evaluate factors influencing FDV willingness. Results: Willingness to attend FDV within 1 year of age was significantly influenced by predisposing factors, such as oral health knowledge, perceived barriers, and susceptibility to caries. Enabling factors, such as socioeconomic status and family support, showed minimal influence, while need factors, including the perceived oral health of the child, strongly correlated with FDV willingness. Findings revealed low awareness and attendance rates for FDV in the study population. Conclusion: First dental visit attendance among infants in the study population is critically low, highlighting the need for targeted awareness campaigns. Pediatric healthcare professionals should actively promote oral health and FDV as preventive measures during well-baby visits to enhance acceptance and utilization. Clinical significance: This studys focus on analyzing mothers willingness to pursue the FDV at age 1, using Andersens behavioral model of health service utilization, which provides actionable insights into the multifactorial drivers behind health-seeking behavior. Understanding how predisposing, enabling, and need-based factors influence maternal decision-making not only aids in identifying barriers to early dental care but also hi hli hts o ortunities to tailor ublic health interventions The Author(s). -
Comparative analysis of carrier material efficiency in the encapsulation of flavor bioactives from Decalepis hamiltonii extract by using spray-drying and freeze-drying
An aqueous extract from the tuberous roots of Decalepis hamiltonii was encapsulated by spray-drying and freeze-drying for food applications. The study aimed to identify suitable carrier materials among sodium caseinate, maltodextrin, and gum acacia, used alone and in blends, to understand their collective effect during encapsulation. The physicochemical characteristics of freeze-dried and spray-dried samples revealed differences of 14%20% in 2-hydroxy-4-methoxy benzaldehyde, 12%40% in phenolic content, and 7%40% in flavonoid content in the dried powders. Similarly, the methanol extracts of freeze-dried encapsulated samples demonstrated good antioxidant potential compared with those of spray-dried encapsulated powder. Among the carrier materials used, sodium caseinate showed good retention of bioactives and a flavor metabolite (2-hydroxy-4-methoxybenzaldehyde), which was quantified by high-performance liquid chromatography (encapsulation efficiency 82%; yield 40 w/w) and confirmed by1H nuclear magnetic resonance (NMR). However, in this study considering flavor retention and powder yield (encapsulation efficiency 74% and 59 w/w), maltodextrin in combination with sodium caseinate (MS) was observed to be the best carrier material for spray-drying. These "maltodextrinsodium caseinate" microcapsules are stable and show 70% retention of flavor metabolite after 3 months of storage at room temperature, with the microbial load remaining within acceptable limits. The particle size of the carrier materials ranges from 11.1 to 17.6 m. Thus, the current study suggests that a carrier material mixture (sodium caseinate and maltodextrin) can be used as a prospective material for encapsulating Decalepis hamiltonii bioactives with flavor metabolites and may be useful in food formulations. 2025 by the author(s). -
Demographic Determinants of Fire-Safety Behavior in High-Rise Residential Buildings: A Survey-Based Behavioral Analysis from Bengaluru, India
This study explores the role of demographic and experience-based parameters for fire safety behavior among residents of high-rise residential apartment buildings in the city of Bengaluru, a metropolitan capital in India. Data were gathered through a questionnaire-based survey among 262 residents. Multiple regression analysis was used to assess the correlation among demographic parameters and behavioral responses during evacuation. The results show that age (R2 = 0.154, p = 0.004), presence of vulnerable household members (R2 = 0.137, p = 0.022), and prior fire experience (R2 = 0.157, p = 0.004) are statistically significant predictors of fire-safety behavior. In contrast, gender (R2 = 0.117, p = 0.073), educational qualifications (R2 = 0.109, p = 0.136), and chronic health conditions (R2 = 0.121, p = 0.500) do not exhibit significant associations. Cross-tabulation analysis further indicates that residents who have received fire-safety training prioritize immediate evacuation, whereas untrained residents display delay behaviors. By providing empirical behavioral evidence from an Indian metropolitan context, this study highlights the demographic heterogeneity in evacuation behavior and supports the integration of behavioral realism into performance-based fire safety design for high-rise residential buildings. (2026), (Dr D. Pylarinos). All rights reserved. -
Fuzzy Logic Approach to Cold-Start Challenges in Deaf and Hard of Hearing Recommender Systems
An adaptive e-learning environment faces significant challenges in offering personalized learning resources for Deaf and Hard-Hearing (DHH) learners. These learners exhibit diverse preferences in learning and communication, influenced by their characteristics related to deafness, highlighting the need for personalized educational content. A well-defined learning model is essential to map the characteristics of learners to suitable learning resources, enabling effective recommendations within an e-learning system. This study explores the development of a comprehensive DHH learner model, focusing on the presence of multiple learning preferences based on the VARK (Visual, Aural, Read/Write, and Kinesthetic) learning style model and the effectiveness of fuzzy clustering in capturing the diverse but overlapping preferences. Fuzzy-C-Means (FCM) successfully identified six different but overlapping clusters, indicating that most learners exhibit multimodal learning preferences rather than relying solely on a visual learning style. Cluster centroid analysis reveals that the visual learning style is the most preferred, while aural learning is the least favored among DHH learners. By calculating the overall learning style score based on the fuzzy membership value across all clusters on all four dimensions of VARK, learners' learning style preferences were validated against self-reported data. The evaluation involved a survey of 130 higher secondary DHH students from Kerala, India, yielding promising results (precision: 0.90, recall: 0.84, F1-score: 0.84) on the model's efficiency in identifying the dominant learning style. These findings emphasize the need for adaptive content delivery strategies that integrate text, visual, and interactive elements to enhance the engagement of DHH learners. However, the limited sample size, due to the unavailability of publicly accessible datasets, and the limited number of students in higher secondary education, further highlights the need for accessible and standardized DHH data to advance this research domain. by the authors. -
Developing mathematical models to analyze economic growth patterns in emerging market dynamics
A key factor in determining national development and directing successful market strategies is economic growth. Making better judgements in developing countries is facilitated for investors and policymakers by having a better understanding of the main drivers of growth. The purpose of this paper is to use mathematical models to explain how economic growth patterns vary among the major growing nations. It examines the effects of inflation, foreign investment, trade, and current account balances on the GDP growth of five major economies India, China, Russia, Brazil, and South Africa between the year 2005 to 2025. The study presents how these variables connect to growth and vary among nations using techniques like logistic regression, linear regression, and ANOVA. TARU PUBLICATIONS. -
Decoding math : A review of datasets shaping AI-driven mathematical reasoning
Math problem solving is a fundamental part of the modern era, and artificial intelligence (AI) driven mathematical reasoning has become an essential part of data work. In this literature review, we explore the diverse array of datasets intended to improve AI models capacity to solve mathematical word problems. These datasets not only provided diverse problem sets but also served as benchmarks for evaluating the performance of various deep learning models, including recurrent neural networks (RNNs) and graph-based models. The datasets, particularly GSM8K, posed challenges that even the most sophisticated transformer models struggled to overcome, setting a new standard for the study of AI systems in math problem solving. This literature review aims to provide a comprehensive overview of the evolving landscape of mathematical problem solving, paving the way for future advances in AI-driven mathematical reasoning. 2025, Taru Publications. All rights reserved.