Innovative Integration Research Articles

Synergizing AI and Blockchain: Innovations in Decentralized Carbon Markets for Emission Reduction through Intelligent Carbon Credit Trading

This study aims to enhance the paradigm of decentralized carbon markets by proposing an innovative integration of artificial intelligence (AI) and blockchain technology for intelligent carbon credit trading with the goal of attaining sustainable emission reduction. Blockchain systems powered by artificial intelligence (AI) have the potential to boost the effectiveness of current systems and expedite the global implementation of emissions trading. Although still in its infancy, blockchain artificial intelligence (AI) presents a promising solution to some of the world's most pressing environmental issues. Environmental sustainability is greatly affected by artificial intelligence because of its decentralized computation architecture. The Artificial Intelligence and blockchain are outstanding direction for today’s environmental issues starting from carbon footprint emission to earth market unstable management, whereby the AI facilitates the best possible operational control of power systems and the blockchain offers decentralized trading platforms for the energy markets. The paper's theoretical framework, based on advanced mathematical models, serves as the foundation for this study, in which AI algorithms are methodically constructed to anticipate carbon emissions with unprecedented accuracy. Using sophisticated coding simulations and complicated mathematical formulas, the study boldly transitions into a realistic digital implementation that builds on this theoretical foundation. This complex experiment not only validates the theoretical ideas but also illustrates the complex relationship between blockchain and AI in the decentralized carbon market ecosystem. This experiment's mathematical basis is the creation of an integrated pricing model that seamlessly blends blockchain-based trading dynamics with AI-driven forecasts. The model incorporates a dynamic, self-adjusting system that responds to current market conditions, in addition to optimizing the pricing calculation of carbon credits. Complex market dynamics, player tactics, and the overall equilibrium of the carbon credit market are all modeled by mathematical simulations. The project goes deeper into building blockchain-based smart contracts, which enable safe and transparent transactions. The comprehensive mathematical results of the experiment shed light on the best way to price carbon credits while underscoring the disruptive potential of blockchain and artificial intelligence in terms of sustainable emission reduction strategies used in carbon markets. Major conclusions about the potential advantages of Blockchain AI for guaranteeing emissions reduction are drawn from the current study. Additionally, it presents a roadmap for future research in this area.

Innovative Educational Game: Designing and Developing Interactive Web-Based Memory Card Games as an Educational Media for Children

In the dynamic landscape of children’s education, innovation plays a pivotal role in shaping engaging and effective learning experiences. This research focuses on the design and development of an interactive web-based memory card game as an innovative educational tool. Grounded in the waterfall method, the development process prioritizes sequential stages to ensure robustness. Performance testing, using PageSpeed Insights, evaluates the game’s speed and efficiency, aiming to enhance user experience. The results, showcased by a remarkable score of 99, affirm the careful consideration given to optimization techniques. This research not only contributes to the broader understanding of game-based learning but also provides concrete insights for developers. The memory card game stands as a testament to the seamless integration of technological innovations into children’s education, promising enduring contributions to the future of learning.

Introduction to the Special Issue: Leveraging Global Value Chains for innovation and industrialization in Africa

The global nature of innovation has led to the emergence of Global Innovation Networks (GINs) and Global Value Chains (GVCs), facilitating knowledge exchange and economic activities across borders. GVCs offer platforms for accessing external knowledge and fostering innovation. They have evolved through three eras of innovation models: the Linear and Closed Model (LCM), the Interactive and Closed Model (ICM), and the Interactive and Open Model (IOM). The IOM emphasizes open innovation principles, intersecting with GINs and GVCs. Lessons from East Asian and Latin American countries highlight strategic practices for GVC integration, focusing on export-oriented industrialization, education, technology policies, infrastructure, and SME support. In Africa, GVC engagement spans agriculture, manufacturing, and services sectors, driving economic growth, job creation, and technological advancements. To advance GVC participation in Africa, a research agenda, policy directions, and action plans are proposed. The research agenda emphasizes GVC governance, value addition, sustainability, innovation, and regional integration. Policy directions focus on infrastructure development, trade facilitation, SME support, human capital development, and promoting sustainable and inclusive growth. African businesses are encouraged to conduct value chain mapping, form strategic partnerships, embrace technology, ensure quality assurance, invest in capacity building, and diversify markets to leverage the potential of GVCs for economic transformation and global competitiveness.

Uncertainty analysis of Altantic salmon fish scale’s acoustic impedance using 30 MHz C-Scan measurements

Understanding the biomechanics of fish scales is crucial for their survival and adaptation. Ultrasonic C-scan measurements offer a promising tool for non-invasive characterization, however, existing literature lacks uncertainty analysis while evaluating acoustic impedance. This article presents an innovative integration of uncertainty into the analytical framework for estimating stochastic specific acoustic impedance of salmon fish scale through ultrasonic C-scans. In this study, the various types of uncertainties arising due to variation in biological structures and aging, measurement errors, and analytical noises are combined together in the form of uncertain reflectance. This uncertain reflectance possesses a distribution which is derived using a theory of waves by assuming suitable stochasticity in wavenumber. This distribution helps in development of a stochastic-specific acoustic impedance map of the scales which demonstrates the possible deviations of impedance from mean value depending on uncertainties. Furthermore, maximal overlap discrete wavelet transform is employed for efficient time–frequency deconvolution and Kriging for spatial data interpolation to enhance the robustness of the impedance map, especially in scenarios with limited data. The framework is validated by accurately estimating the specific acoustic impedance of well-known materials like a pair of target medium (polyvinylidene fluoride) and reference medium (polyimide), achieving over 90% accuracy. Moreover, the accuracy of the framework is found superior when compared with an established approach in the literature. Applying the framework to salmon fish scales, we obtain an average specific acoustic impedance of 3.1 MRayl along with a stochastic map visualizing the potential variations arising from uncertainties. Overall, this work paves the way for more accurate and robust studies in fish scale biomechanics by incorporating a comprehensive uncertainty analysis framework.

Innovative Integration of Digital Content in Legal Education

The landscape of legal education is undergoing a profound transformation propelled by digital content, ushering in innovative methods to enrich both learning and teaching experiences. This paper delves into the myriad applications of digital content within legal education, encompassing online courses, e- books, legal databases, and virtual simulations. It scrutinizes the manifold benefits these digital resources confer, ranging from heightened accessibility and flexibility to immersive and interactive learning environments. Furthermore, the paper navigates through the challenges and constraints entwined with the integration of digital content in legal education, including concerns pertaining to digital literacy, access inequalities, and the imperative for pedagogical adaptations. Conclusively, the paper furnishes recommendations aimed at orchestrating the effective assimilation of digital content into legal education, thereby harnessing its full potential to enrich the educational journey.

Auto-machine learning for opportunistic thyroid nodule detection in lung cancer screening chest CT.

e13639 Background: Automated Machine Learning (Auto-ML) in medical imaging is a process that allows non-experts to utilize machine learning techniques, opening the door for non-coder physician-driven exploitation of the technology. Auto-ML was applied for opportunistic detection of thyroid nodules in the context of low-dose lung cancer screening chest CT, facilitated by an innovative platform integration. By leveraging scans originally intended for lung cancer screening, suspicious appearing asymptomatic thyroid nodules can also be screened for where technically feasible. Methods: CT scans from the National Lung Screening Trial (NLST) dataset were utilized. This dataset contains low-dose chest CT examinations initially used for lung cancer screening. Annotation was carried out within a no-code web-based platform, Gesund.ai. A board-certified diagnostic radiologist annotated 100 CT examinations. A case was labeled "suspicious" if it presented a thyroid nodule of 1 cm or greater in diameter, while cases with no such nodules were deemed "not suspicious." The Auto-ML feature was then engaged within the software platform for parameter adjustments to refine the model without the need for manual coding. This phase was characterized by an iterative exploration of model performance, facilitated by the platform’s robust validation tools. Results: Upon training and validation, the Auto-ML model, cultivated from 100 annotated cases and assessed against a separate set of 130 cases, demonstrated a discernment accuracy of 0.51 in identifying "suspicious" thyroid nodules with a sensitivity and precision of 0.51 and 0.62 respectively. The AUC for this class stands at 0.69, indicating a moderate ability to distinguish between the presence and absence of thyroid nodules. This metric, while foundational, offers insight into the model's potential efficacy in real-world diagnostic scenarios, reflecting the initial capabilities of the platform's Auto-ML integration in enhancing diagnostic processes. Conclusions: The findings from this study highlight the potential of Auto-ML in revolutionizing opportunistic screening for thyroid nodules via lung cancer CT. The initial accuracy rate underscores the necessity for further refinement and validation of the model, but also demonstrates the promising potential of Auto-ML. Institutional Auto-ML would include extraction of appropriate studies through the no-code software platform integrated directly within the PACS framework. This process would ensure data immobility and foster a secure environment for dataset creation. Annotation would be carried out by multiple physician imagers within the same facility, enhancing the data’s accuracy and comprehensiveness specific for the institution’s patient population. This exploration highlights the strategic role of Auto-ML in advancing cancer patient care through innovative technological integration.

Stabilization analysis for nonlinear interconnected system with memory based coupled sampled data control via quantum-inspired genetic algorithm

In the realm of modern interconnected systems, achieving stability represents a pivotal challenge due to the complex dynamics among various components. This paper presents a new approach to stability analysis, leveraging linear matrix inequalities and Lyapunov-Krasovskii functionals. The framework of memory-based coupling sampled data control is enhanced by a quantum genetic algorithm. By incorporating linear matrix inequality approach, this study establishes a robust mathematical foundation for characterizing stability conditions and synthesizing control gains. This innovative integration provides a powerful toolset to optimize control parameters while mitigating the impact of disturbances. The simulation findings are explicitly based on experimental values of two-area interconnected power systems with doubly fed induction generator based wind farm, which guarantees the asymptotic stability of the proposed controller.

Towards a Sustainable Future: Exploring the Integration of Architecture Education, Innovation and Sustainability

Towards a Sustainable Future: Exploring the Integration of Architecture Education, Innovation and Sustainability

Unveiling the Economic Impact of Technology Trade in Leading Global Economies

Abstract In today's rapidly evolving technological era, high-tech trade is not just a part of international commerce but a key factor that shapes the economic landscapes of nations. The objective of this paper is to analyze how technological innovation, as represented by high-tech exports, contributes to economic growth and to identify the disparities in economic progress among countries with different levels of integration into the global high-tech market. This paper focuses on the importance of high-technology trade in economic development. Specifically, it aims to establish a connection between technology-intensive exports and Gross Domestic Product in three major global economies: China, the United States, and the European Union. The study's empirical evidence strongly supports a notable association between high-technology exports and the growth of gross domestic product, thereby reaffirming the crucial role of technological trade in enhancing economic development. Furthermore, the analysis provides additional insight by highlighting those economies with extensive involvement in the global high-technology market experience superior economic growth. This underscores the vital significance of technological innovation and trade integration in attaining a competitive advantage in the global economic landscape. This study adds to the existing literature by providing empirical evidence of the economic consequences of technology trade, highlighting the need for policy frameworks that encourage innovation and integration into the global high-technology market.

Research on the influence of mineral heterogeneity under different CO2 injection schemes in low permeability reservoirs

In the pursuit of sustainable oil and gas resource extraction, the innovative integration of carbon capture, utilization, and storage (CCUS) technology has emerged as the most promising approach. During the CCUS process, intricate physicochemical interactions between the injected CO2, facilitated through various injection strategies (Water Alternative Gas: WAG/Continue Gas Injection: CGI) and the formation fluids and heterogeneous mineral assemblages within the reservoir trigger alterations in mineral structures, consequently impacting permeability and recovery factors, constituting a pivotal aspect. Precisely delineating and quantifying these interactions is paramount for optimizing process design and evaluating reservoir dynamics in the successful implementation of CCUS operations. This study has carried out qualitative and quantitative characterization of mineral heterogeneity, different pore types, and mineral combination characteristics from a low-permeability sandstone reservoir. Additionally, the effect on the physical properties of minerals from different development methods (WAG/CGI) was investigated using numerical simulation for CCUS applications. The results indicate that the saturated CO2 fluid selectively dissolves the potassium feldspar (orthoclase) in intergranular pores, while the intergranular pores are filled with illite and secondary precipitated clay minerals. It initially dissolves the sensitive mineral (ankerite) in the intergranular pores. The decrease of ankerite and increase of illite result from the prolonged contact period between saturated CO2 and minerals, which changes the mineral cementation to argillaceous type, thus affecting permeability in the context of CCUS. The spatial impact on reservoir physical properties depends on the spatial heterogeneity of the original sensitive minerals (ankerite, anorthite, illite, etc.) distributed in the study area. In the WAG scheme, the physicochemical interaction between saturated CO2 and reservoir minerals is more intense than in the CGI scheme for CCUS operations, significantly impacting cumulative production.

CRISIS MANAGEMENT IN HOSPITALITY

This paper presents a comprehensive review of crisis management in the hospitality sector, focusing on key themes, theoretical frameworks, empirical findings, and practical implications. The review highlights the significance of proactive planning and preparedness, emphasizing the importance of risk assessment, scenario planning, and business continuity strategies to enhance organizational resilience. Effective communication emerges as a cornerstone of crisis management, underscoring the necessity for transparent, timely, and empathetic communication with stakeholders. Collaboration with external stakeholders is essential for effective crisis response, emphasizing the benefits of information sharing and joint training exercises. Additionally, the integration of technology and innovation presents opportunities for enhancing crisis management capabilities, albeit accompanied by challenges. Overall, the findings underscore the importance of proactive, holistic approaches to crisis management that encompass preparedness, communication, collaboration, and innovation to safeguard organizational sustainability in the face of adversity.

Technological Advancements in e-Healthcare: A Comprehensive Assessment in orthopaedics-patient relationship management

Healthcare has witnessed a profound transformation over recent decades, one catalysed by the relentless march of technology. As the healthcare landscape evolves, the integration of technological innovations has become increasingly ubiquitous, revolutionising traditional care delivery models, and introducing novel paradigms. Amidst this advanced landscape, a particular focal point emerges, centred on the symbiotic relationship between technology and healthcare, embodied by the burgeoning field of e-healthcare.Within the broader expanse of healthcare, orthopaedics represents a significant frontier where the confluence of technological advancement and patient care has garnered attention. Orthopaedic practice, by its nature, often necessitates precision, innovation, and multidisciplinary collaboration. From diagnostic imaging modalities that offer unparalleled insights into musculoskeletal structures to wearable devices that empower patients to engage actively in their recovery, technology within the orthopaedic field catalysed a change.The main objective of this paper is to conduct a comprehensive assessment, shedding light on the state of the art in technological advancements within e-healthcare, with orthopaedics as our focal domain. At the heart of our research is a central concern: the orthopaedic-patient relationship. The importance of this relationship cannot be underestimated, as it is the foundation on which healthcare is provided. Our aim, therefore, is twofold: to elucidate the extent to which technology influences the orthopaedics-patient relationship and to uncover novel avenues through which this pivotal relationship can be fortified and optimised.The impetus behind this assessment is rooted in the dual importance of technological innovation and the orthopaedics-patient relationship. As orthopaedic practitioners increasingly harness technology and platforms, the potential for improved patient care, more informed decision-making, and enhanced outcomes beckons. However, these opportunities come hand in hand with challenges, including ethical and privacy concerns and the imperative to ensure that technology augments rather than diminishes the patient-physician bond. In this context, our assessment aims to contribute to the ongoing discourse of technology integration into healthcare, highlighting the unique dynamics that play within orthopaedic practice.

МОВЛЕННЄВА КОМПЕТЕНТНІСТЬ ЯК СКЛАДОВА ПРОФЕСІЙНОЇ ПІДГОТОВКИ МАЙБУТНІХ ПЕДАГОГІВ ПРОФЕСІЙНОГО НАВЧАННЯ

Relevance: The prioritization of developing speech competence in future teachers of vocational education is necessitated by the need to equip learners with competencies for lifelong learning, aligning with the qualification requirements of higher education graduates amidst the reforms in the national higher education system and its integration into the European space. Objective: To substantiate the importance of speech competence as a component of professional training for future teachers of vocational education in higher education institutions. Methods: Analysis, synthesis, and generalization of scientific works and legislative and regulatory documents were used for the theoretical analysis of key concepts of the study, defining the term "speech competence as a component of professional training for future vocational education teachers"; comparison was employed to identify approaches to solving the researched problem; generalization and systematization were used to formulate conclusions and prospects for further research. Results: Analysis of scientific works revealed that the speech competence of future vocational education teachers develops through the integration of the main tenets of linguistic sciences, psychology, and pedagogy. It is hierarchically subordinated to the principles of implementing a competence-based approach in the education system and integrates into the system of key (general) competencies as a distinct component. Speech competence, as defined in the Standard of Higher Education at the Bachelor's level, field of study 01 – "Education / Pedagogy", specialty 015 – "Professional Education (by specializations)" and in educational-professional programs, is determined by specific learning outcomes, mastery of which provides the ability to obtain an educational qualification. The level of speech competence within the system of general competencies for lifelong learning is a guarantee for the systemic further personal and professional development of a vocational education teacher according to the standards and socio-cultural trends. Conclusions: Innovative and European integration development of educational systems, current scientific research in pedagogy and psychology, technological changes, and the digitalization of society require a systemic update of educational content components to effectively develop the speech competence of future vocational education teachers and to ensure their ability to organize and conduct pedagogical activities effectively.

Comparative analysis of translation platforms: Accuracy, fluency, and unambiguity from a technological perspective

This study aims to evaluate and compare the performance of leading translation platforms (Youdao, Google, Baidu, and ChatGPT) for translating news texts. The evaluation focuses on three key dimensions: accuracy, fluency, and unambiguity. Each platform was tested by translating a Chinese news article, and their outputs were analyzed for their handling of specialized terminology, linguistic fluency, and cultural adaptability. The findings reveal significant performance differences, reflecting varying levels of technological maturity, data quality, and cultural adaptability among the platforms. ChatGPT emerged as the top performer due to its high accuracy and fluency, while Youdao, Baidu, and Google exhibited varying strengths and limitations in handling complex news texts. Ultimately, the results highlight the importance of continuous technological innovation and theory-practice integration in improving translation quality, particularly for accurate and effective global news dissemination.

Thermo-Regulated Cotton: Enhanced Insulation through PVA Nanofiber-Coated PCM Microcapsules

The innovative integration of phase change materials (PCMs) into textiles through microencapsulation presents a transformative approach to developing thermally regulated fabrics. This study explores the synthesis and characterization of microcapsules containing a coconut oil core and an ethylcellulose shell, and their application on cotton fabrics coated with polyvinyl alcohol (PVA) nanofibers. The dual-layer system involving microcapsules and nanofibers is designed to enhance the thermal insulation properties of textiles by regulating heat through the absorption and release of thermal energy. The microencapsulation of PCMs allows for the effective incorporation of these materials into textiles without altering the fabric’s inherent properties. In this study, the coconut oil serves as the PCM, known for its suitable phase change temperature range, while ethylcellulose provides a robust shell, enhancing the microcapsules’ structural integrity. The application of a PVA nanofibers layer not only strengthens the thermal regulation properties but also protects the microcapsules from release while the fabric is manipulated, thereby prolonging the functional life of the fabric. Comprehensive testing, including scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), confirms the successful application and durability of the microcapsules on the textiles. Thermal imaging studies demonstrate the fabric’s enhanced capability to maintain a consistent temperature, highlighting the potential of this technology in applications ranging from smart clothing to energy-efficient building materials or automotive isolation. The integration of PCMs in textiles via microencapsulation and nanofiber technology marks a significant advancement in textile engineering, offering new opportunities for the development of smart and sustainable materials. The study demonstrates the promising potential of integrating PCMs into textiles using microencapsulation and nanofiber technologies. Despite the initially modest insulation improvements, the methodology provides a robust foundation for further research and development.

Innovations in architectural and environmental design: From methodologies to technology and cultural sensitivity

This comprehensive paper delves into the multifaceted world of contemporary architectural and environmental design, exploring an array of methodologies that emphasize sustainability, technological innovation, and socio-cultural integration. It begins by discussing the Integrated Design Process (IDP), which fosters early-stage collaboration across various disciplines, illustrated by the example of Seattles Bullitt Center. The paper then explores the principles of Biophilic Design, highlighting its positive impacts on human health and well-being, as seen in Singapores Khoo Teck Puat Hospital. The concept of Adaptive Reuse is explored next, emphasizing its role in sustainable urban development, with the Tate Modern in London serving as a case study. Further, the paper examines Green Building Technologies, Digital Design and Simulation Tools like BIM, and Smart Building Systems, showcasing their contributions to sustainable and efficient building management. The final sections discuss the importance of Community Engagement, Cultural Sensitivity in Design, and Inclusive Design, stressing the need for architecture that resonates with community values and is accessible to all. Through these discussions, the paper provides an insightful look into the current trends and future directions in architecture and environmental design, advocating for a holistic approach that balances functionality, sustainability, and cultural relevance.

Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites

AbstractA novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online.Highlights Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online.

MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network

Abstract Metasurface has garnered extensive attention across multiple disciplines owing to its profound capability in electromagnetic (EM) manipulations. To determine its EM characteristics accurately, full-wave simulations are essential. These simulations necessitate significant amounts of time and memory resources, hindering the efficiency of the design process. In this article, we propose MetaPhyNet, a novel physics-driven neural network based on temporal coupled-mode theory (CMT) to address the challenges of low efficiency and high memory consumption in large-scale metasurface design. In the proposed approach, a surrogate model is developed to achieve rapid prediction of the EM responses of ultra-large-scale metasurfaces. In comparison with the full-wave EM simulation, the proposed model reduces the simulation time of the ultra-large-scale metasurface by up to two orders of magnitude and the memory consumption by more than two orders of magnitude. Our proposed approach aims to enhance the efficiency and intelligence in metasurface design by leveraging the principles of CMT within a neural network framework. Through this innovative integration of physics-based modeling and machine learning, we seek to achieve significant advancements in the design efficiency of metasurfaces. We apply the proposed model to optimize the design of two metasurface absorbers to showcase the effectiveness of our proposed approach. Simulations and experimental results are provided to demonstrate the value and impact of our approach in addressing existing challenges in full-wave EM simulation-based design optimizations of metasurfaces.

Optimization of heat management and utilization in batch processes

Batch plants boast operational flexibility in manufacturing process and resource sharing, making batch processes suitable to fluctuating-demand or small-scale production. However, given time dependence of operational behavior, batch processes are more demanding in energy integration than continuous processes, a task which is especially important amid the global net zero-emission current. It is, therefore, imperative to develop sustainable design strategies for intermittent processes. The study introduced a targeting method and developed an innovative over-time heat integration technique to optimize energy utilization in batch processes, involving use of heat transfer media for heat recovery over various time intervals and a heat makeup strategy to heat the media over different periods, in order to enhance heat-energy utilization efficiency. The technique is applicable to both fixed and unspecified batch schedules, the latter of which involves simultaneous design of production schedule and heat recovery systems, increasing flexibility and improving heat energy utilization. Case studies are conducted to demonstrate the proposed method. With the heat makeup technique, energy efficiency can be significantly enhanced, achieving energy savings of 51% and 93.1% in hot and cold utility, respectively. Meanwhile, the simultaneous design of production schedule and heat integration can boost savings up to 53% and 99%. Results have proven the benefits of the technique, including significant reduction of energy consumption and carbon emission, contributing to clean production, environmental protection, and sustainable development.

Research on Rural Tourism Development in Ganzi Tibetan Area under the Perspective of "Agriculture, Culture and Tourism" Integration

With its unique resource endowment, rural tourism in the Ganzi Tibetan region has developed rapidly. From the early stage of "big development, small integration" to "big integration, small development", it ultimately presents a new situation of "each with its own characteristics, fully blooming". When developing rural tourism, especially in the process of combining agriculture, culture, and tourism, the Ganzi Tibetan region faces many difficulties such as resource independence, single function, technological barriers, and market competition. To this end, efforts can be made to strengthen the integration of resources, functions, technologies, and markets in rural tourism in the Ganzi Tibetan region through regional linkage and mutual assistance, innovative integration forms, coordinated planning of industries, and improved market access, in order to promote the deep integration of agriculture, culture, and tourism in the Ganzi Tibetan region and drive the leapfrog development of rural tourism industry.