Announcement: Best Publication Award for "International Water and Irrigation" We are thrilled to announce that "International Water and Irrigation" has been honored with the prestigious Best Publication Award for our outstanding contributions to water preservation. This recognition highlights the significant impact of our journal's research and insights in promoting sustainable water management practices globally. We extend our heartfelt thanks to our esteemed authors, reviewers, and readers for their continued support and contributions, which have been instrumental in achieving this milestone. Together, we are making a difference in the world of water conservation. Stay tuned for more groundbreaking research and innovations in our upcoming issues! Sincerely, The Editorial Team International Water and Irrigation At the heart of advancing sustainable water management and irrigation practices, the International Water and Irrigation Journal stands as a beacon of knowledge and innovation. Committed to fostering a global community of researchers, scholars, and practitioners, our journal serves as a dynamic platform for the exchange of cutting-edge ideas, research findings, and practical insights in the fields of water resources and irrigation. Our Mission: To be a leading source of high-quality research and information that contributes to the sustainable and efficient use of water resources for agricultural and environmental purposes worldwide. Who We Are: Established with a vision to bridge the gap between theory and practice, the International Water and Irrigation Journal brings together a diverse group of experts, academics, and professionals dedicated to addressing the complex challenges associated with water management and irrigation. Our editorial team comprises seasoned researchers and practitioners with a wealth of experience, ensuring the publication of content that is not only scientifically rigorous but also practically relevant. What Sets Us Apart: Global Perspective: We strive to showcase research and innovations from every corner of the globe, recognizing the diverse contexts and challenges faced by different regions. Interdisciplinary Approach: Embracing the interdisciplinary nature of water management, our journal welcomes contributions that integrate insights from fields such as hydrology, agronomy, engineering, economics, and environmental science. Practical Applications: While rooted in scientific rigor, our focus extends beyond academic discourse. We emphasize the practical applications of research, aiming to provide actionable insights for policymakers, practitioners, and stakeholders. Key Areas of Focus: Sustainable Water Management Agricultural Irrigation Practices Water-Energy-Food Nexus Climate Change and Water Resources Innovative Technologies in Irrigation Policy and Governance in Water Management Water Quality and Environmental Impact Our Commitment: We are committed to maintaining the highest standards of editorial integrity and academic excellence. Rigorous peer-review processes ensure that only the most impactful and well-researched articles find their way into our journal. Get Involved: The International Water and Irrigation Journal invites researchers, scholars, and practitioners to contribute their expertise and share their valuable insights. Whether you are an established professional or an emerging researcher, we welcome your contributions to enrich our collective understanding of water and irrigation issues.
Analysis Of Music Multimedia Retrieval and Measurement Quality Model Using Ranking Learning Algorithm
Reasonable music feature library construction methods are of great significance to the realization of accurate music retrieval. In view of the limitations of current music multimedia retrieval, a ranking learning model is introduced in this paper, constructing corresponding models by fusing local and global indexes, constructing and mapping the attributes and characteristics of music multimedia using feature matrix to clean the data; looking on the global search, nodes are used to search, aiming to improve the efficiency of music multimedia retrieval and measurement quality model construction. The results show that the ranking learning model can effectively support the construction of music multimedia retrieval and measurement quality model.
Analysing Colour Composition in Visual Tracking Psychological Quality Prediction in Robot Images
This study explores the integration of colour composition visualization techniques with robot recognition to design a mental quality prediction system for visual processing and robotics. A six-axis industrial robot serves as the experimental platform, modelling mental quality prediction through exponential multiplication. The system incorporates a Jacobian matrix derived from posture and rotation matrix correlations, establishing a link between the robot's terminal motion and colour-based rotation speed. Utilizing mathematical models of colour composition, a visual tracking psychological quality prediction control system is developed, enabling the transformation of motion rates from the colour frame to joint movements. By applying differential mental quality prediction and speed transformation, the relationship between coordinate systems in visual tracking is determined. A detailed analysis of the visual servo system supports the development of a fast-motion vision follow-up controller. Experimental results highlight the practical significance of the proposed fast feedforward control method, demonstrating enhanced accuracy and efficiency in visual tracking and position-based vision applications. This work provides valuable insights into the synergy between visual processing and robotic control systems.
Robotics Assists Automatic Terminology Translation
With the integration of global economy, cultural exchanges between different countries are becoming more and more frequent. The cultural difference between China and the West is the key to our intercultural communication. Robot technology is one of the most advanced technologies in the world. Translation is a transformation of different cultures and languages. Because of the cultural differences between China and the West, the translator must understand and grasp the relevant culture in advance and realize that language and culture are closely related and mutually reinforcing. In the translation of English words, the ways of expression and life in both Chinese and Western languages will have some negative effects on the translation of English words. In the translation of mother tongue and foreign language, the differences between different nationalities should be analyzed. Using robot technology can make the two different languages to convert, can understand and master different cultures of different countries, so that English communication more accurate. Starting with the translation of English words, this paper expounds the application of robot technology in the translation of English words, and discusses some problems in the translation of English words under machine technology.
Evaluation of Sentiment Analysis in College English Writing Teaching Strategies
The popularity of emotion analysis has led to a rapid increase in the range of remarks. This study explores the potential of sentiment analysis to enhance collegiate English writing instruction. This is achieved by methodically gathering and examining performance statistics, instructor remarks, and student feedback. However, interpreting the vast majority of comments to determine people's sentiments is challenging. Sentiment analysis (SA) is a rapid, simple, and automated method for determining the thoughts and emotions of individuals. This essay scrutinizes the objective of sentiment analysis, assesses diverse methodologies, delves into the domains where sentiment analysis finds its application, pinpoints the obstacles and constraints students face, and proposes potential solutions. The study proposed the use of sentiment analysis to examine college English writing instruction strategies using the Gradient Function Optimization (SGFO) algorithm. The results demonstrate that the participants' use of emotion terms improved significantly with the suggested approach. Those with more skill demonstrated greater benefits from the SGFO algorithm. The tool support, which enables them to use emotion phrases in their work more skillfully, also made the participants happy. The suggested model yielded the maximum accuracy of 98.5%, precision of 84%, and recall of 95%, according to the study's findings.
Analysis of Agricultural Electrical Automation Mapping Using XGS Decision Model
Energy conservation is crucial for intelligent agricultural decision-making, including plant development, automation of equipment, and cultivation. Industrial 4.0 technologies include the Internet of Things (IoT), artificial intelligence (AI), and big data. We use these technologies to control energy consumption and enhance the environment. This study proposes to apply an extended galactic swarm (XGS) decision model for agricultural electrical automation mapping by optimizing the effectiveness of the energy consumption forecasting process. We collect energy-related data from a range of environmental and agricultural production sensors to assess and train the XGS model. We normalize the raw data samples by eliminating the noisy data using the min-max normalization strategy. We then predict the energy usage amount using the random forest (RF) technique. Using the XGS model to modify the hyper-parameters can enhance the performance of the RF approach. We implement the proposed model on the Python platform and assess its success using several metrics. When compared to other existing models, the suggested XGS-based prediction framework provides the best efficiency in the energy consumption forecasting process. This article provides a practical agricultural electrical automation mapping solution for astute agricultural managers or farmers who wish to solve agricultural energy concerns more affordably and sustainably. When compared to current techniques, our proposed XGS-RF produced the lowest results in terms of RMSE (1998.12), RMPSE (6.53), and MAE (1384.15).
Analysis of Syntactic and Word Vector Using Cross Language Translation Algorithm
Digital technologies have created the need to preserve intellectual originality in translated works has arisen as a result of digital technologies. To measure the degree of resemblance between textual pairs created in various languages and identify plagiarism, a cross-lingual syntactic link is essential. This study investigates plagiarism detection and evaluation, as well as cross-lingual syntactic analysis, using a dataset of university students. The Natural Language Process (NLP) imports the data into a research model for translation and plagiarism detection. We then extract the data into features using a word vector. The study suggested using the term embedded fast recurrent network (WE-FRN) for plagiarism detection and syntactic analysis. The study presented numerous neural network designs to address the problem of plagiarism detection, including binary classification (syntactic regression analysis of documents) and regime proposals (plagiarism or independently authored). The experimental results showed that using WE-FRN with rich syntactic characteristics yielded better results than the baseline. Additionally, we used the regression of the source and suspicious documents to analyze the classification's loss function. We trained the dataset using a five-fold accuracy of WE-FRN, yielding the highest accuracy values for English (90.9), Chinese (88.87), Japanese (87.49), and Korean (87.26).
Evaluation of Decision Optimization Model using Deep Reinforcement Learning in Collaborative School-Enterprise-Local Education Decision Making
The school-enterprise model fosters cooperation between academic institutions, local businesses, and communities to enhance students' hands-on learning experiences. This study investigates the application of Deep Reinforcement Learning (DRL) in analyzing the mechanisms and current status of school-enterprise collaboration. We propose a novel Distance-Driven Proximal Policy Optimization (DD-PPO) framework to optimize this cooperation. Case studies and assessments were conducted in Hangzhou City's higher vocational institutions to evaluate the model's effectiveness. Findings reveal that 55% of businesses prioritize recruiting fresh graduates, while half of the schools perceive the collaboration as universally beneficial. The professional orientation of schools, driven by business demands, enhances graduate employability but lacks systematic planning, policy support, and alignment in evaluation metrics. These disparities complicate the establishment of a seamless and efficient school-enterprise linkage. Additionally, the inconsistencies in practical skill development among students from different programs highlight the need for a more structured and adaptable system. The proposed DD-PPO model addresses these challenges, offering a promising solution for enhancing school-enterprise partnerships.
A Valuation Model of Agricultural Biotechnology Enterprises Under Digital Transformation
Business group specialists examine a company's corporate structure to understand its financial health. Agricultural biotechnology permits profit-sharing, yet the distinctions between large and small businesses impact both biotech companies and the industry overall. Using data obtained from the company, this study aimed to examine the business models used by small and medium-sized firms (SMEs) and evaluate factors influencing the expansion of agricultural biotechnology businesses and the industry as a whole. The study examined the tactics and business models used by agricultural biotechnology companies across several international borders. We arranged the interviews based on predetermined survey questions. We completed twenty surveys, and more than fifty-five percent of the participating firms expressed acceptance. The study highlights the risky agricultural biotechnology research and development system that limits the emergence of specialised businesses and causes a large amount of turnover. SMEs also face challenges with copyright asset development and licensing procedures.
Applying Text Mining Technology in Mining Thematic Trends in Chinese Literature
Combining text mining technology with mining themes has emerged as a valuable tool for analyzing Chinese literature. This work presents a novel technique to quantify the features of text mining by merging mining algorithms. Determining the significance and language of ancient Chinese literature becomes less subjective as a result. This work demonstrates that the model can classify text mining words in Chinese literature accurately, up to the maximum degree of precision. The study proposes the use of text mining to analyze thematic trends in Chinese literature, employing a model known as the Enhanced Hierarchical Based Gradient Boost Algorithm (TEHGBA). For every Chinese literature outcome, the new TEHGBA model receives higher marks. According to the study's findings, the suggested model produced outcomes with 98.6% accuracy, 95.7% precision, and 90% recall. The study comes to the conclusion that the suggested model aids in the analysis of Chinese literary works and yields excellent recall, accuracy, and precision outcomes.
Implementation Of Emotion Analysis Algorithm in Analysing Public Cognition of Industrial Heritage
Industrial heritage sites hold deep historical and cultural significance, yet public perceptions of their value vary widely. Visitors often form emotional connections to such sites, offering valuable insights for tourism enhancement and cultural preservation. Traditional methods fall short in effectively capturing public sentiment toward industrial heritage. To address this gap, we propose the Advanced Northern Goshawk-Responsive Generative Adversarial Network (ANG-RGAN) technique, which leverages emotion analysis to assess public sentiment. ANG-RGAN combines Responsive Generative Adversarial Networks with attention mechanisms, improving sentiment classification accuracy. Surveys and interviews conducted at Beijing’s industrial heritage sites provided textual data for analysis. By employing tokenization, normalization, and term frequency-inverse document frequency (TF-IDF), emotional expressions such as curiosity, nostalgia, and melancholy were extracted and classified. Experimental results demonstrate superior performance, with an F1-score of 98.3%, accuracy of 98.9%, precision of 98.2%, and recall of 98.5%. Compared to existing methods, ANG-RGAN offers enhanced security and reliability in emotion-based assessments. This approach provides critical insights into public perceptions, fostering the growth of the tourism industry and the preservation of industrial heritage.
Construction of Children's Education Evaluation System Based on Fuzzy Logic
Preschool education is a complicated matter that requires a detailed analysis of many ecological and developmental factors. The existing approaches to educational evaluation lack consideration for the unpredictability bias associated with assessing children's learning and growth. The purpose of this project is to create an improved fuzzy logic (IFL)-based system for evaluating children’s education. For the children’s education assessment system, we collected the various preschool data samples. Physical, socio-emotional development, spiritual, and intellectual growth, parental participation, care, behavior control, and attendance are the seven main feature areas that are the focus of the examination. After the data collection, min-max normalization is employed, to preprocess the data. Principal component analysis (PCA) is performed to remove the irrelevant features, to identify major features. These inputs are translated into fuzzy sets and evaluated using a fuzzy inference system. A prototype of the system is implemented and tested with a dataset of children's educational records. An education evaluation system based on fuzzy logic improves traditional methods by addressing vagueness and subjectivity in assessments. This comprehensive and accurate evaluation enhances in preschool educational strategies and preschool children performance analysis interventions, leading to better outcomes for preschool children.
Forecasting the Effectiveness of Children's Learning Based on Data Mining Technique
This study used data mining (DM) technologies to estimate how well toddlers would learn. Both the public and commercial sectors are rapidly expanding the number of academic institutions. Despite this, learners who are classified as medium- and comparatively low-risk still face unemployment. This work employed a novel fine-tuned seagull-optimized weighted k-nearest neighbor (FSOA-KNN) method to improve the efficacy of the children's learning. We included three hundred students in this study, collected their features, and analyzed them. We apply preprocessing techniques to the collected data samples to enhance prediction performance. We implement the suggested method and assess its effectiveness using measurements for accuracy, recall, f-measure, and precision. According to the study's findings, the suggested model can predict children's learning efficiency with accuracy (97%), precision (96.5%), recall (96%), and F-Measure (95.5%). This article also helps identify students who need additional advice or counseling from a high-quality teaching provider.
Market Trend Prediction of Digital Economy Based on Machine Learning Algorithm
The focus of digitization on helping enterprises, organisations, and governments accomplish their sustainability goals is a defining feature of the contemporary economic landscape. Machine Learning has recently been included into company models and plans along with the use of the newest technology, which has allowed the environment for business in the digital economy will get even better. The digital economy is extensively covered, extremely innovative, and highly porous. It serves as both a fresh hub for economic growth and a pivot for modernising and changing established sectors. The digital economy has the power to provide new employment opportunities, increase consumer demand, and boost investment activity. It is a crucial component in building a contemporary economic structure. Based on data pertaining to the raise of the digital economy in Zhejiang Province, China, between 2014 and 2023, this study finds that taking two samples—the bare minimum needed for node splitting—gets the best results. The research data is then analysed to create a Multi Linear based Random Forest Regression Algorithm (ML-RFR), where training data comprises 70% and testing data has 30%. The study concluded that rather than heedlessly following economic trends, the expansion of the digital economy necessitates raising the standard of the sector's digital economy, extending its level of development, and optimising its development model.
Analysis of Intelligent Recommendation System for Literature Education Using Deep Learning
Teaching literature is essential for fostering critical thinking, creativity, empathy, and cultural understanding among pupils. However, traditional methods face challenges, including incomplete instruction, limited access to diverse resources, and insufficient engagement, particularly in poetry education. To address these issues, this study proposes an Advanced Sand Cat Swarm-driven Consecutive Convolutional Neural Network (ASCS-CCNN) approach that enhances poetry fluency and literature strategies. The model integrates a recommendation system using collaborative filtering to optimize the sequencing of educational resources. Data preprocessing involves tokenization and feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF). The proposed ASCS-CCNN method evaluates students’ performance in poetry instruction based on metrics like similarity, tone correctness, and rhyme accuracy. The findings reveal that incorporating well-chosen texts aligned with curriculum objectives significantly improves student engagement and appreciation of poetry. The ASCS-CCNN model demonstrates superior performance, achieving enhancements in consistency (4.88%), fluency (5.02%), meaningfulness (4.94%), and poutiness (4.82%). This innovative approach offers a promising solution to improve literature education for primary and secondary students.
Application of Convolution Neural Network in Landscape Painting Style Transfer and Feature Extraction Technique
The study of style transfer and feature extraction in computer-based painting is a difficult topic. The majority of research employs manual segmentation to select local regions, leading to ineffective final extracted features and an inadequate capacity to discern the aesthetic approach of the artist. This research proposes a unique style loss and content loss-guided two-channel VGG network for feature extraction and landscape painting style transfer, aiming to address this issue. After convolving the input image, we employ the feature maps across the two-channel network's third through fifth layers. To achieve feature extraction, feature constraint addition, and parameter control for traditional Chinese paintings with local style transfer, we construct a loss function for content and style from the higher layers to the lower layers, which a decoder then decodes to the next layer after each layer matches. We continue this process until we obtain the final synthetic map. We observe significant differences in feature information, such as lines, textures, and frequencies, between landscape paintings and other painting styles. As a result, it is possible to extract, constrain, and learn this feature information while losing style and content.
Current Status and Progress of Supply Chain Model and Its Optimization Research
A multi-objective linear programming (MOLP) model is suggested as a way to build a long-lasting supply chain in today's competitive world. With this, using the particle swarm optimisation (PSO) way to buy things will be cheaper, and buyers and sellers will be able to build lasting relationships. People who want to buy pick one or more sellers and place an order for the right number of goods. This first study looks at the main standards for things like cost, shipping, and delay. It used data from a well-known fuzzy inference system but was still able to solve problems with making decisions based on more than one factor. For example, it could figure out how much something cost, how many things were broken, and how many days the delivery was late. Pioneer Cars gave us some information that we used to make sure the results were correct. With the MOLP cost-optimization model by PSO, it was 3.11 percent cheaper to buy things.
Application Of Convolutional Neural Network in Industrial Product Design
The term "industrial design" describes the process of creating products using design principles for mass production. Design is the pre-production mental exercise that defines the form and attributes of an object. The development of novel primary commodities is increasingly dependent on comprehensive industry evaluation. We present a proposal for industrial product design (IPD) that makes use of convolutional neural networks (CNN). Incentives may encourage firms to develop new technologies, but too many of them might stifle innovation, according to data from China's electronic manufacturing sector. As a result, we collected the Chinese manufacturing data set. As a preliminary step in processing the collected data, normalization might be employed. Another approach that has been proposed is the CNN method. These are compared to more conventional approaches on a number of metrics, such as precision, accuracy, recall, implementation cost, and energy consumption. Compared to more conventional methods, the suggested methodology appears to have a lower implementation cost of 70%.
Implementation of Kano Model in Graphic Design of Architectural Environment Art
Industrial, landscape, graphic, and interior designers are all part of environmental design, which aims to improve the aesthetics of a given location by combining elements from these and other fields. When done right, ecological design can boost a location's practicality, influence, and impact on those who come. The central focus of this endeavour is the implementation of the Kano model (KM) in the realm of architectural environmental art visual design. The Kano model provides a satisfaction-based classification system based on five quality factors: one-dimensional effectiveness, mandatory performance, neutral quality, alluring quality, and reversing quality. Using the Kano model, this investigation seeks to identify the most crucial aspects of graphic design that architects and designers should consider when crafting works of art for the built environment. This research first takes a close look at KM and its uses in graphic design, before moving on to assess the model's potential in the field of architectural environment art. Based on the study's findings, architects and designers can enhance satisfaction and create exceptional graphic design for architectural environments by focused on these key graphic design aspects
A Performance Appraisal Analysis of Statistical Computation in Hospital Performance Management
This multi-institutional study evaluates the effectiveness of six statistical methods for benchmarking hospital performance in quality improvement projects. A dataset of 48,521 patients undergoing craniotomy surgeries at 180 hospitals (2019–2023) was analyzed to assess mortality and serious complication rates. Non-contraction methods included direct standardisation with fixed effects and indirect standardisation without hospital effects, while contraction techniques comprised direct and indirect random-effects standardisation and Exponential Smoothing. The study highlights substantial reductions in adjusted mortality rates (1%-2% vs. observed 0%-10%) and adjusted serious complication rates (7%-17% vs. observed 3%-35%) when using contraction methods. These approaches aligned hospital performance closer to the average. Notably, 17%-39% of hospitals experienced significant changes in quintile rankings after applying random-effects direct standardisation. Logistic and fixed-effect methods identified outliers in smaller hospitals, whereas random-effects indirect standardisation did not. The choice of statistical method significantly impacts hospital rankings and the identification of performance outliers. These findings emphasize the importance of statistical selection in evaluating quality improvement efforts and guiding healthcare benchmarking.
Evaluation of Practical Applications of Artificial Intelligence in Industrial Design
Industrial product design is an involved procedure that demands optimising several variables to achieve the desired performance and quality standards. How well the model works could depend on how complicated and unpredictable the industrial design problem is. The effectiveness of optimising industrial product design processes is demonstrated in this study by introducing a novel model called Firefly Algorithm Fine-Tuned Random Forest (FA-FRF). Several industrial product design criteria, including capacity, material composition, production techniques, and market segmentation, are included in the vast dataset that the research uses. One of the data pre-processing steps used to normalise the features within a given range is min-max normalisation. Feature extraction makes use of Independent Component Analysis (ICA), which seeks to identify and extract the most relevant attributes from the data. The investigation is conducted using the Python software and measures performance using RMSE (0.0490), MAE (0.0320), MSE (0.0020), MAPE (3.4137), and SMAPE (3.9871), following the technique recommended. The FA-FRF model provides improved quality and performance, allowing designers and technicians to optimise industrial decision-making and design outputs.
Landscape Visualization Technology in Landscape Architecture Design using LUMENS Model
The capacity to assess and optimise design ideas has been greatly improved with the use of cutting-edge visualisation technology into landscape architecture. The purpose of this research is to examine how landscape visualisation tools, and the Lumens Model in particular, might be used to evaluate and enhance landscape architectural projects. We explore how this technology allows for a more complex understanding of the influence and efficacy of design through a thorough case study. To help designers see possible results and make data-driven revisions, the Lumens Model is used to simulate different design scenarios. The model is known for its accurate rendering and analysis. The findings demonstrate that the Lumens Model is useful for enhancing the aesthetic and functional results, easing communication among stakeholders, and optimising design features. This research highlights the significance of using state-of-the-art visualisation technologies into landscape architecture for the purpose of achieving better and more informed design solutions.
Analysis of Multiple Attribute Decision Making in Artistic Photography
Artistic photography is a complex kind of visual art that relies on the careful selection of materials to accomplish intended aesthetic and operational results. The substance used to make photographic prints has a considerable impact on their quality, longevity, and appearance. Given the wide range of material properties like texture, longevity, colour reproduction, expense, and impact on the environment, meticulously identifying and ranking the best materials for artistic photography is critical. Previous methods for material selection frequently lack complete frameworks for assessing and comparing materials across numerous aspects at once. This paper overcomes these restrictions by developing the Multiple Attribute Decision Making for Artistic Photography Material Ranking (MADART) algorithm. The MADART algorithm combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Sum Model (WSM) techniques to present a systematic strategy for material choice, addressing the limitations of previous techniques. Experimental outcomes reveal that the MADART algorithm provides higher material selection effectiveness compared to existing methodologies, guaranteeing that photographers and industry professionals may make informed selections that maximize both artistic quality and practical implications.
Taking Into Account the Impact of Resident Tourism Consumption and Sustainable Tourism Management
This research explores the relationship between Resident Tourism Consumption (RTC) and Sustainable Tourism Management (STM), focusing on how resident tourism expenditure influences environmental, social, and economic sustainability in tourism destinations. Using a dataset of 200 survey responses, the study investigates demographic characteristics, RTC, and their links to Environmental Sustainability Practices (ESP), Economic Sustainability Practices (ECSP), Social Sustainability Practices (SSP), and Local Economic Impact (LEI). A comprehensive questionnaire was utilized to gather data, and statistical techniques, including regression analysis, reliability testing, factor analysis, and Principal Component Analysis (PCA), were employed to examine the relationships between RTC and STM. The findings reveal a positive association between RTC and STM, with ANOVA tests identifying significant connections between predictor factors and LEI. These results emphasize the critical role of RTC in enhancing sustainability and boosting local economic development. The study provides valuable insights for tourism destination managers, advocating the promotion of resident tourism expenditure to foster long-term environmental and economic sustainability. This work contributes original evidence to the literature, underscoring the importance of RTC for sustainable tourism development.
Personalized Learning Mode Research Based on Intelligent Technology in Adult Education and Occupational Training
Today, when people are smarter than ever, they learn in a lot of different ways. There are a lot of people who choose to learn on the internet. Even if there are more great training materials available, students may still have issues, such as having too much knowledge. A multi-algorithm shared, changeable learning route suggestion model is suggested as a way to fix this issue and help students who use online learning tools. The learner model is built around four points of view: the student's thought level, their ability to learn, their preferred learning style, and their energy for learning. The second step is to use the association rule method to plan how trainees will learn each piece of information and make a list of them.
During the first half of the evaluation, the students attended the school by taking courses online; for the second half of the evaluation, they attended classrooms in person. For the period of the trial, the students visited the school. Following that, they proceeded to physically attend lessons. In this study, we examine the students' participation over the course of the three months, examine the impact of using the platform in-person versus virtually, examine the students' self-report data regarding their practice habits, and compare it with the collected data. However, the in-class procedure revealed a decrease in the utilisation of the platform, despite the fact that the data reveals that there was a considerable rise in student involvement during the lockdown period. This specific data demonstrates that students who are engaged in remote learning need a platform like this as a supplemental learning channel in order to be successful in reaching their educational goals.
Predicting Academic Success of Students Through Educational Data Mining
This study uses four well-known machine learning methods—Lassis, Random Forest, AdaBoost, and Support Vector Regression—that are good at making predictions. It does this to look into the accuracy of different models and groups of variables. For this study, the China Education Panel Survey statistics were used. This survey took place between 2013 and 2015. The level of effort put in by each group has different effects on academic performance. Parents' strict expectations are the most important individual predictor of academic performance. School effort has a bigger effect on academic achievement than parental and student effort when different social background factors are taken into account. There are also big differences between male and female junior high school students in China, with school effort having a bigger effect on academic achievement. The results have big effects on strategies that are meant to get students to work harder in school. They show that treatments that are tailored to each gender are needed to help all kids do better in school.
Enhancing the Information Security of Distribution Automation System in Low Voltage Area Using Quantum Cryptography
Power transmission and utilization systems are an integral part of the electrical grid's link to the end user. Ensuring the safe and sound transfer of data across all systems is closely tied to safeguarding personal information. This work presents a quantum cryptography solution that can improve the data safety of distribution automation systems in the low-voltage unit region. All three levels of the system—base station, access, and terminal—were considered throughout its design. The information that was delivered across the quantum key distribution (QKD) system was encrypted using a quantum key that was created by the key production modules on both ends of the transmission. This study accomplished the goal of facilitating the flexible and cost-effective transfer of the terminal-end quantum key in the context of QKD.
Research On Green Marketing Methods Under the Guidance of The New Era of Artificial Intelligence and Its Impact on Green Purchase Intention
Specifically, it seeks to determine the influence of people's past behaviour on their intentions to buy these products and on their decision to switch to buying environmentally friendly food. With certain modifications, the Theory of Planned Behaviour (TPB) served as the theoretical foundation. In particular, additional factors were included, such as prior conduct, expertise, and belief in plant-based diets. The information was gathered from 650 Polish consumers of green goods. We used an online surveying method. The study's conclusions indicate that past conduct is a strong indicator that, for the most part, characterises consumer purchase patterns for sustainably produced food. This article aims to explore the notion that intentions to purchase ecologically friendly food are also significantly correlated with attitudes. Additionally, the propensity to buy environmentally friendly food items is favourably influenced by several factors, including knowledge, trust, and the standards of society. There is a statistically significant relationship between perceived behavioural control and the strength of the perceived control. 57% of the variance in customers' intentions to purchase ecologically friendly food may be explained by the enlarged model. This study offers a new viewpoint on the discrepancy between positive sentiments towards ecologically friendly food and actual purchase patterns by including past behaviour into the TPB.
Research On Online Aesthetic Education Course Teaching Platform In Universities
Within the realm of industrial production, the Kruskal method, which is a minimum spanning tree optimisation analysis algorithm, has been utilised extensively for the purpose of determining the optimal parameters employed. Both the Kruskal algorithm and the ecological aesthetic education theory are used to make it work. This study uses this theory to build a dynamic training tool. Secondly, the interactive platform is able to automatically generate more effective teaching courses and related methods by utilising multiple internet of things systems. This is accomplished by conducting an analysis of the issues that are present in conventional classroom instruction and combining this information with the characteristics of ecological aesthetic education. In conclusion, the purpose of this research is to validate the dependability and stability of the platform described above by means of trials and practices. The experiments showed that using the theory and method of ecological aesthetic education in an interactive teaching platform based on the Kruskal algorithm can combine traditional classroom instruction with ecological aesthetic education while still letting students fully participate in the classroom setting.
Research on Quality Evaluation and Improvement Strategies of College English Teaching Analysis Based on Data Mining
In this study, the qualitative assessment is appropriately measured using the fuzzy comprehensive evaluation model, which is based on the bat algorithm. It also offers a practical and user-friendly method of evaluating the calibre of English instruction by fusing objective index data with numerical review. The fuzzy comprehensive evaluation analysis method is used to create the model for evaluating the level of English training and figure out how much each factor is worth. Then, each of the model's three types of data is dealt with separately. One way to do this is to make objective indicators like students' course grades more reliable and lessen the impact of how hard the course is on these indicators. With the help of the bat algorithm and a flexible complete evaluation model, the personal evaluation is turned into a number. In the end, this makes the decision
Research On the Integration of Virtual Reality with Chinese Cultural Heritage and Traditional Art: A Focus on Virtual Simulation
This type of folk art has been around for a long time in China. We need to accept and protect this one-of-a-kind, hidden form of traditional culture work that uses virtual reality. This study brings together the digital tools of glove theatre to meet the needs of people who want to act. A cloud-based virtual reality glove theatre that more than one person can use was made for this study. In general, it makes hand puppets work better. Everyone can use the virtual world from afar and see it in their own way. People and computers are both used in this way to send and receive data. It also explains how glove toys are made and how to play with them. Virtual reality is a cool new way to have fun for everyone. Someone asked thirty people who played this game how it works and how they felt about it. The study found that the suggested cloud-based VR system is not only simple to use, but it also helps pass on traditions that people can't see or touch. Our work can be used a lot in schools and can also help glove toys stay important. The cloud-based virtual reality method we use gives us a new way to share and protect cultural material that isn't easy to see.
The Impact of Using Social Media Applications on Students Vocabularies
The growth of mobile devices has had an impact on a wide range of human pursuits, including education. The objective of this research was to explore the opinion of students on the use of a mobile application to teach students new English vocabulary and phrases. After downloading and using the application for a period of one semester, a total of twenty-eight college students responded to a questionnaire survey using pen and paper. They were able to better prepare for the final achievement test, learning was accessible from any place and at any time, students loved the corrective feedback, and students would like to see the mobile app utilised in further courses taught by the faculty. On the other hand, the data suggest that the students stated that the app did not provide a very supportive environment with respect to the performance of communication interactions.
Transformative Trends in Supply Chain Finance Digitization and Financial Ecosystems
A lot of attention has been paid to supply chain finance since it is a crucial financial tool to promote the sustainable growth of the real economy. The study results on 3181 non-financial companies that were listed on the Chinese A-share market between 2012 and 2021 are presented in this article. A method known as multiple regression analysis is used to look at the connection between supply network finance and business financialization. It also looks at how supply chain finance affects company financialization and the processes that support it.The study's data specifically show that the supply chain finance model, which is controlled by core firms, makes it much harder for Chinese companies to get loans. A big part of companies are small and medium-sized ones. Because of this, supply chain financing is more strongly linked to the "financialization" of businesses. This has a big calming effect on state-owned companies and gets worse over time when the main companies have more knowledge than they would otherwise. This dependence also has a big effect on the rise of the supply chain finance boost, which is an important factor. This paper adds to the academic conversation by sharing its findings on how to improve supply chain finance and help the real economy grow in a way that is sustainable. In order to get the results that are wanted, an excellent supply chain finance model must be matched with the needs for China's real economy to grow. Businesses should get financial help, but the government should also encourage the growth of a positive loop in the industry chain and put making things ahead of investing money.
Numerical Simulation Study on Ground Subsidence and Structural Stress Characteristics of Open Caisson Construction
This paper studies the stress characteristics of caisson construction in complex strata and its influence on the surrounding environment, and focuses on the application effect of structural reinforcement technology of pipe jacking working well. Through the numerical simulation analysis of the Nanniwan power tunnel project in Wuhan, this paper analyzes the settlement deformation law of the surrounding environment during the sinking process of the caisson structure, and studies the stress characteristics of the caisson itself after the reinforcement measures. The results show that the construction of the reinforced structure can effectively reduce the settlement of the surrounding environment caused by the sinking of the caisson and improve the stability and safety of the structure. The contact pressure between the outer wall of the caisson and the soil increases sharply and then slowly during the sinking process, and the maximum value appears at the edge angle at the bottom of the caisson. The stress distribution on the section of the caisson is more uniform. As the depth increases, the stress decreases first and then increases. The research in this paper provides theoretical guidance and reference for disaster prevention and field construction of caisson construction in complex strata.
Production and Characterization of Traditionally Distilled Alcohol (Raksi)
Raksi is an ethnic alcoholic drink, distilled from traditional fermented cereal beverages. The objective of this work was to characterize the traditionally fermented alcohol (raksi). We processed six raksi samples at home using traditional methods, and collected four locally from hotels in various districts. During the distillation of raksi, raksi samples were physically analyzed and found to be colorless, strongly concentrated, and alcoholic in nature. While examining the collected alcohol samples, they were found to be light yellow, cloudy, with strong concentration, and unpleasantly alcoholic in nature. All the samples were acidic with a pH range of 3.6-4.5, turbidity range from 3.5-7.8, and TDS less than 14, TSS ranging from 35-850 ppm, and TA ranging from 0.016-0.234%. The alcohol percentage was found in the range 8-46%. Alcohol percentage was highest in sample LAR10, i.e., 45% using a hydrometer, 45.32% using a pycnometer, and 35.95% using HS-GC. Sample LAR4 showed a minimum alcohol % of 10% using a hydrometer, 10.11% using a pycnometer, and 8.68% using HS-GC. There was no detection of methanol and propanol from the HS-GC result; only the ethanol was detected. Almost all the distillates lied in the score panel of “good” to “very good” by using the hedonic scale. The total bacterial count was found in the range of 0-30 CFU/100 ml and 0-7 CFU/ml from the MF technique and pour plate method, respectively. Morphological and biochemical studies confirmed that the isolated organisms were Bacillus spp. We found most of the alcohol samples effective against Pseudomonas aeruginosa. The substrates used for the fermentation of alcohol and their antibacterial efficacy showed insignificant association, i.e., p = 0.82 > 0.05. But the association between the alcohol percentage of samples and their antibacterial effectiveness was significant, i.e., p = 0.01 < 0.05. These findings highlight the characterization and detection of methanol and propanol in raksi, and they enhance the further findings of any hazards.
A Study on Nature Documentaries Involving Environmental Conservation: Raising Awareness of The Environment Among College Students In Chennai, India—With A Special Reference To The Netflix Series 'Our Planet.’
Netflix released ‘Our Planet, a popular environmental documentary. Sir David Attenborough narrates the English version, but 10 other languages had simultaneous releases with their narrators. This documentary stands out by comprehensively depicting current environmental challenges. The series examines eight rare ecosystems' biodiversity and features. Human impact on the environment distinguishes it from other traditional documentaries and it discusses how climate change affects all life. Our survival depends on nature. But the world is in more peril than ever. This documentary raised awareness of the world's rarest animals and most valuable habitats. This study examines how informed and influential college students in Chennai, Tamil Nadu, are about environmental issues including global warming. This study measures the impact of six factors that indicate people's views on environmental degradation. Dimensions produced via Schwartz norm activation were hope, connectivity to nature, shifting responsibility, etc., The documentary's impact on participants' opinions was assessed using a pre-test and post-test control group design. Due to the limited availability of video-on-demand providers with documentaries, participants were assigned based on OTT subscription availability. The findings suggest that the mere transmission of information without the use of storytelling and visual elements carries the danger of failing to capture attention and, thus, convey the intended message. While documentaries employing storytelling, imagery, and narrative techniques may not bridge the attitudinal and behavioral divide between a sustainable and unsustainable society, they can contribute toward a solution. Practitioners should create combinations of strategies that influence attitudes and behaviors to communicate the scientific consensus to the general community effectively.
Burnout in Suspension: Navigating the Waters of Performance Appraisal and Employee Retention in Private Colleges and Universities
With the continuous advancement of Chinese-style modernization and the rapid development of higher education in China, private colleges and universities are facing serious teacher management challenges beneath the Chinese sense of suspended life. Based on burnout theory, this study explores the mechanism of performance appraisal on employee retention in private colleges and universities, focusing on the mediating role of compensation and benefits and employee empowerment. In this study, a questionnaire survey was conducted among 350 teachers in five private universities, and 270 valid samples were obtained. Descriptive statistics, correlation analysis and structural equation modeling (SEM) analysis were conducted using ADANCO software. It was found that: a) performance appraisal has a significant direct positive effect on employee retention; b) compensation and benefits play a partially mediating role between performance appraisal and employee retention, and reduce the risk of burnout by increasing teachers' sense of identification with and suspension to the organization; and c) employee empowerment plays a partially mediating role between performance appraisal and employee retention, and prevents burnout by enhancing teachers' sense of self-efficacy and job resources . This study provides three recommendations for private colleges and universities: optimize the performance appraisal system, improve compensation and benefit packages, and enhance employee empowerment management as a way to prevent teacher suspension burnout and improve employee retention. The findings enrich the theoretical framework of the relationship between performance appraisal and employee retention, and provide a theoretical basis for human resource management practices in private colleges and universities under suspension era.
Re-Examine the Connotation and Nature of NIMBY
NIMBY is a common phenomenon in the field of environmental protection. Scholars have different understandings of the connotation and nature of NIMBY. In a narrow sense, NIMBY means that some residents resist a facility near their habitat when it has not been built or has not polluted the environment. In a broad sense, NIMBY refers to the resistance of some residents to the proposed facilities near their habitat or the facilities that have polluted the environment. NIMBY in a narrow sense belongs to precautionary environmental protest. NIMBY, broadly defined, can be both remedial and precautionary environmental protest. By tracing the history of academic research on NIMBY, this paper finds that the NIMBY studied by scholars in the early stage was a narrow sense of NIMBY, which mainly studied the conflicts caused by the location or construction of public facilities such as waste treatment facilities. This makes many scholars have long believed that the nature of NIMBY was the conflict between community interests and public interests. The narrow sense of NIMBY is usually understood by the academic and practical fields. The nature of NIMBY depends on the attributes of NIMBY facilities, the rationality of facility planning and design, the procedural justice of site selection process and other factors. Re-examining the connotation and nature of NIMBY will help all sectors of society to view the phenomenon of NIMBY more fairly, pay more attention to the interests and environmental rights of community residents, and avoid one-sided labeling of NIMBY. This study will contribute to environmental protection and sustainable development.
Study on Environmentally Friendly New Mode of Land Requisition and Demolition in the Construction of Long Mileage Expressway
Construction of long mileage expressway is an important part of China's transportation infrastructure construction. The efficient and high-quality completion of the task of land acquisition and demolition is of great significance to ensure the smooth opening of long mileage expressway. In view of the problems of large workload, many influencing factors, difficult coordination, environmental protection, and difficulty in maintaining agricultural and water resources stability in the construction and demolition in the construction of long mileage expressway, a environmentally friendly new model of long mileage expressway construction land acquisition and demolition based on full staff land acquisition and demolition was proposed by combining theoretical analysis with field research. In this paper, the characteristics and difficulties of long mileage Expressway and its land acquisition and demolition work were firstly analyzed, then the problems existing in the traditional land acquisition and demolition mode of long mileage expressway were analyzed, and finally a environmentally friendly new land acquisition and demolition mode of long mileage expressway construction based on full staff land acquisition and demolition was put forward. Nan Zhan expressway with 186.821 km long was taken as the background, the environmentally friendly new mode of long mileage expressway construction land acquisition and demolition proposed was adopted to carry out the land acquisition and demolition work. The results show that the review of more than 1000 mu land acquisition and demolition data in Pubei County was completed in only three days, and the completion rate of submission for review was as high as 100%, which has achieved good economic and social benefits, which can provide reference for the land acquisition and demolition of similar projects.
Application of Green and Low-Carbon Concepts in Logistics Engineering: Education, Scientific Research and Practice
The world's unavoidable development trend is the green and low-carbon (GLC) development mode, which aims to address the issues of resource and energy scarcity and environmental contamination. The logistics industry (LR) is the basic service industry of national economy and social development, and its energy consumption and environmental pollutant emissions are increasing year by year. In order to meet the demand for GLC talents of the LR, colleges and universities should integrate the concept of green and low-carbon (GLCC) into the professional teaching and talent training process. This study discusses how to integrate the GLCC into the logistics engineering profession from three aspects of university teaching, research and practice. First, based on the analysis of the basic connotation of GLC development, it discusses the current problems and challenges of GLC development and the urgency of integrating the GLCC development into the logistics engineering profession. Second, the basic strategy of integrating green low-carbon into the teaching of logistics engineering majors is discussed. Then, by analyzing the current scientific research status and main research direction of GLC logistics, the scientific research method of GLC logistics is proposed. Finally, combined with the case and practice of integrating the GLCC into the internship training courses of logistics engineering majors in our school, we discussed how to apply the green low-carbon concept into practice. The integration of the GLCC into the teaching, scientific research and practice of logistics engineering not only enriches the curriculum system of logistics engineering, but also provides university teachers with new ideas and methodological support for scientific research, and at the same time meets the needs of enterprises and the society for talents with the awareness and ability of GLC.
Development of a Blended Teaching Module on Integrated Moral-Art Education to Promote the Improvement of College Students' Moral Level, Psychological Health, and Environmental Awareness
Both social and ecological environments have a significant impact on the moral development of young students, and environmental protection moral awareness is increasingly becoming a part of social morality. Environmental awareness is a moral reflection of human beings on the negative effects of modern industrialization development; Social morality, psychological health and environmental awareness are important contents and goals of personality education for college students. In the face of the increasingly prominent moral decline and psychological health problems of young college students around the world, such as the frequent occurrence of violence, crime and suicide, it has become a common topic of global education reform and development. Emphasizing and strengthening the moral education and psychological health of young college students has become a common topic of global education reform and development. Similar social cases are still on the rise, reflecting a deviation from the correct moral outlook. These cases also show that the traditional, single theory indoctrination teaching method of moral education can no longer achieve the goal of moral education for college students. Moral education and psychological health education need more attractive and emotional teaching materials and methods. Art education can make up for the "requirements" of students. This is because art education includes a variety of vivid, visual, and infectious materials and methods, such as painting, music, and calligraphy. This study selected a class of 50 students as a sample to conduct pre - and post testing by effectiveness questionnaire. The questionnaires will be completed before the start and after the end of the courses respectively. This study used SPSS v 28.0 to analyse and collecte data. It was found that the students who were taught using blended teaching module on integrated moral-art education had significantly higher scores than those who were not taught using the blended teaching module. They have significantly improved social and environmental moral literacy as well as psychological well-being. They learn to better protect the natural environment, respect others, care for society.
Dripping Water Penetrates the Stone: The Innovative Dynamics of Employee Performance and Supervisor Leadership in the Hospitality Industry
Even small forces (such as the reverse impact of employee performance on leadership) can have a profound effect, just as a drop of water can penetrate a rock. Under the wave of economic globalization, the hospitality industry, as an important part of the service industry, is experiencing profound changes. Digital transformation and demand upgrading have made the hospitality industry face serious challenges in human resource management innovation. Based on the path-goal theory, this study explores the mechanism of reverse influence of employee performance on supervisor leadership in star-rated hotels in Henan Province, focusing on the mediating role of career development and work environment. Through a questionnaire survey of employees in several star-rated hotels in Henan Province, 120 valid samples were obtained, and SPSS software was used for reliability, validity and regression analysis. The study found that: employee performance has a direct positive impact on supervisor leadership; career development and work environment both play a partial mediating role between employee performance and supervisor leadership. This study provides theoretical basis and practical suggestions for management practice in the hospitality industry and enriches the path-goal theory.
Evaluating the Impact of Water User Associations on Irrigation and Agricultural Productivity in Assam: A Case Study of Nalbari District
Transforming institution-building for the attainment of irrigation is crucial to the economy's balanced development of agriculture. Over the years, agriculture's performance in the irrigation sector has sunk due to inadequate management and poor farmer participation, affecting the economy's balanced development. Therefore, Govt of Assam has been pushing to form a Water user association (WUA) under the Irrigation Water User Act, of 2004. Participatory management, involving farmers as users, is widely used in water distribution, overseeing operation and maintenance. The PIM approach proposes the equitable and economic distribution of water for irrigation, with local participation. It results in a spike in productivity in agriculture. The study explores water usage among WUA members in Assam, focusing on irrigation schemes and their socio-economic benefits for local farmers. The study reveals that due to effective coordination between regional irrigation departments and WUAs, farmers are better informed about PIM and canal construction, thereby enhancing production, but still requires rural areas in Assam to be connected with PIM for adequate capacity. The study recommends that continuous policy reforms are needed to improve community participation in Assam, tackle the current challenges in PIM, and enhance the effectiveness of WUAs.
Blockchain for Transparent Irrigation Water Allocation and Management
Water scarcity and inefficient irrigation practices pose significant challenges to sustainable agriculture and water management globally. Blockchain technology, with its potential for transparency, immutability, and decentralization, offers a transformative approach to irrigation water allocation and management. This paper reviews the application of blockchain technology in creating transparent, efficient, and equitable irrigation systems. Key features, benefits, challenges, and future directions are explored to provide a comprehensive understanding of how blockchain can revolutionize water resource management in agriculture.
Micro Level Flood Vulnerability Assessment for Enhance Community Resilience in Lower Brahmaputra Valley Assam India
The flood vulnerability index (FVI) is a critical tool for assessing the susceptibility of areas to flooding, integrating various dimensions such as social, economic, and physical factors. Recent studies have developed diverse methodologies to create FVIs that reflect local conditions and enhance community resilience. These methodologies utilize advanced techniques, including artificial intelligence, geographic information systems (GIS) and participatory approaches, to generate comprehensive vulnerability assessments. This study aims to assess flood vulnerability in the lower Brahmaputra River Valley of Assam. Four vulnerability indices i.e. social, economic, environmental, and physical was calculated using twenty-one parameters and Analytic Hierarchy Process (AHP) was used for parameters weightage calculation. Farther these indices were combined to derive a final flood vulnerability index (FVI). The twenty-one parameters represent economic, social, environmental and physical aspects of the study area. The study finds that 58% of the villages in the study area are economically vulnerable, 57% are socially vulnerable, 56% are environmentally vulnerable, and 48% are physically vulnerable. It requires a holistic flood vulnerability planning and management approach for reducing vulnerability and increasing resilience.
Metal Ions Analysis of Water in the Industrial Area in Gaya District of Bihar State - India
The groundwater quality assessment conducted in the Manpur Block near Falgu River was aimed at evaluate the suitability of the water for drinking purposes. The study measured various water quality parameters, including hardness, turbidity, conductivity, pH, TDS (total dissolved solids), alkalinity, chloride, content and dissolved metals. These parameters were compared with the acceptable limits prescribed by the BIS (Bureau of Indian Standards) to determine if the water meets the required standards for safe consumption. The study included analysis of the acceptability of groundwater sample, metal contamination, potential risks, and analysis of surrounding groundwater. Some of the groundwater samples collected were found to be acceptable within the BIS limits for drinking water quality, while others exceeded these limits. The average concentrations of metals in the groundwater samples followed the trend: Mn (Manganese) > Zn (Zinc) > Fe (Iron) > Ni (Nickel) > Cu (Copper) > Cr (Chromium). This indicates that manganese and zinc are the most prevalent metals, while chromium and copper were found in the least amounts. Presence of certain metals such as nickel and iron beyond the acceptable limits can invite a serious health hazard to the local population, especially if the water is consumed regularly without proper treatment. The study assessed not only the water quality of the Falgu river but also the quality of nearby groundwater, which may be affected by industrial activities in the congested area. This relationship is important to understand the broader environmental impact on water resources.
The Intersects of AI and Film: How Machine Learning Determines Narrative Structures and Creates Cultural Representation
Artificial intelligence is among the most dynamic transformative forces in industries nowadays. It is a field of filmmaking where AI penetrates further than mere automation and spreads into complex and artistic involvement in film and content distribution (Barron, 2023). However, it leans on the key role of machine learning algorithms regarding what kind of content should be produced, how they are produced, and even what gets delivered to the audiences. Be it scripting, editing concepts, or any other intermediate process between pre-production, production, and post-production, AI-driven tools are interconnected in and out at each stage. Today, Netflix and Amazon Prime are two of the many streaming services that are using AI algorithms for analyzing data on user usage and curating content for viewers to maximize engagement (Hallur, Prabhu, & Aslekar, 2021). All these algorithms have rewired viewing habits into data-driven models that determine a great part of what is created and pushed. In brief, such technical developments enable higher quality output in terms of viewing experience and offer film creators some critical insights, and there also exists a paradox at this point. The same algorithms that streamline and maximize production processes can stifle creative diversity in return. The likelihood for such decision-making is that a preference in content for reaffirming already established formulaic expressions of success will orient AI toward formulaic narratives and reserve access to alternative or underrepresented voices and stories. In this sense, critical thinking about the future of creative freedom in filmmaking asks how much AI-driven decision-making affects the range of voices and stories presented to audiences. Hence, how do the directors and filmmakers manage through such a new landscape where these data-driven recommendations heavily influence a creative choice? The impact of AI also extends far beyond individual production choices and touches the whole veneer of cultural representation (Liu, 2024). Data that trains such machine learning models is, by default, a reflection of historical tastes; in some way, this might mean that algorithms that are being perpetuated are multiplying existing biases. This means stories closely associated with the mainstream or fitting the popular conventional tropes are likely to get algorithmic support while innovative or diverse narratives remain unrepresented (Frey, 2021). This can create a landscape of homogenized media where particular genres of the story outshine others. This paper explores the interaction between AI and film, with an emphasis on how machine learning informs the conditions of narrative structures as well as impacts cultural representation. It draws from the in-depth case study of major streaming platforms' recommendation algorithms complemented by AI-driven tools such as ScriptBook to engage with this complex interplay between technological innovation and creative decision-making. The research seeks to explore how AI impacts the viewing patterns, popular cultural traditions, and production process, and thus to comprehend prospects as well as problems of the latest changes in this ongoing trend. The paper focuses on the deeper comprehension of the duality of AI as it is both an efficient implementor and a potential gatekeeper in creative and cultural diversity.
SO2 Gas Concentration Detection System Combined with Photoacoustic Spectroscopy Technology
Traditional photoacoustic spectroscopy uses a microphone as a photoacoustic detector in SO2 gas concentration detection. The signal response will be affected by environmental factors, which will affect system stability. This paper builds a cavity enhanced photoacoustic spectroscopy technology gas detection system, so as to improve SO2 gas concentration detection effect and system stability. For the cavity enhanced photoacoustic spectroscopy gas detection system and the influence of buffer gas on photoacoustic resonance frequency, helium with high sound velocity property is used as buffer gas to compensate for the drift of resonance frequency. Moreover, this paper builds a CE-PAS gas detection system to test the SO2 gas concentration. The experimental results show that the sensor has four orders of magnitude linear dynamic range (1.2 ppb-10ppm) and 1.37 x10-11 W • cm-1Hz-1/2 NNEA, which can be used for high-sensitivity SO2 detection. In addition, the long-term stability experiment shows that the signal fluctuation of the system for the same standard SO2 gas is within ±2%, which verifies the reliability of the technology and the stability of the system. Therefore, this study can provide more reliable technical support for subsequent SO2 gas concentration detection, and can also provide technical reference for the monitoring of other gas concentrations.
Monthly Fluctuation in Physico-Chemical Characteristics of Dohan River, Ganwali Neem ka Thana : A Case Study
Water quality monitoring is crucial for environmental and public health management. This study evaluates seasonal variations in key physicochemical parameters of the Dohan River, Ganwali Neem ka Thana, across different months during 2023-24. Parameters such as air and water temperature, pH, turbidity, dissolved oxygen, total dissolved solids (TDS), and various ions were analyzed over a year. The results indicate significant seasonal variations, highlighting the impact of climatic conditions on water quality.
Eutrophication Inversion of Reservoir Waters Based on Deep Learning and UAV Hyperspectral Remote Sensing
Eutrophication of reservoir water poses significant risks to human life and production, necessitating large-scale, regular monitoring and early warning. This work comprehensively evaluated the eutrophication status of reservoir water quality using four water quality parameters: chlorophyll a (Chl-a), Total Phosphorus (TP), Total Nitrogen (TN) and Permanganate Index (CODMn). A water quality parameter inversion scheme based on deep learning and UAV hyperspectral remote sensing images is proposed. Based on the optimized feature combinations and sample sets, a water quality parameter concentration regression prediction model based on deep convolutional residual neural network (WQR-DCRNN) is designed and constructed for the inversion of the water quality parameters of Chl-a, TP, TN, and CODMn. Then, the parameters of above WQR-DCRNN models are optimized by reinforcement learning. Taking Shanzai Reservoir in Fuzhou City as an example, the average inversion accuracy of the four water quality parameters reached 0.91, 0.80, 0.81, and 0.78, respectively, and the mean relative error (MRE) can meet the requirement of the industrial standards for spectral water quality detection. The concentration spatial distribution patterns of the four water quality parameters provided by this work can provide useful decision-making references for the assessment, prevention and control of eutrophication in large-scale reservoir water bodies.
Research on the Integration of Structural Design of Urban Irrigation Buildings and Water Saving Technology Based on the Concept of Sustainability
In the process of urban development, the shortage of water resources has brought obstacles to urban development, and it is urgent to realize the structural innovation design of urban irrigation buildings and the effective application of water-saving technical measures. This paper establishes a structural system of urban irrigation buildings by combining the concept of sustainable development with highly efficient water-saving irrigation technology as an entry point. For the comprehensive benefits of water saving under this structural design, the evaluation index system of water saving benefits of irrigation buildings is designed, and the weights of the evaluation indexes are solved by the improved entropy weighting method. Combining the improved entropy weight method with the close value method, an evaluation model for the comprehensive benefits of water conservation of urban irrigation building structures was established, and the relevant factors affecting the structural design and water conservation benefits of urban irrigation buildings were analyzed through the obstacle degree factor model. The weight of each evaluation index of water saving benefits of urban irrigation building structures varies greatly, among which the weight of social benefits accounts for the largest proportion of 0.2049. After the implementation of the irrigation building structure system in the sample cities, their comprehensive benefits of water saving showed an overall increasing trend during 2015-2023. The social and economic benefits become important factors hindering the deep integration of urban irrigation building structure design and water-saving technology. Therefore, further development of high-standard urban irrigation building structure design, combined with sustainability impact assessment and increased investment in irrigation building structure renovation, is needed to provide a guarantee for realizing the improvement of water-saving benefits of urban irrigation building structures.
Research on the Influence Mechanism of Government Regulation and Media Attention on Corporate Water Information Disclosure
Enterprises as an important component of modern economy and society, on the one hand is the direct creator of the social wealth, promoted the rapid development of our industrial economy. On the other hand, it is also the main producer of environmental pollution, which has caused serious ecological problems, which forces human beings to explore the road of common development of economic construction and environmental governance. The full disclosure of environmental management results is a prerequisite for environmental governance. Enterprises make selective disclosure for the purpose of satisfying external legitimacy, so external environmental supervision has become an important driving factor for corporate water information disclosure. On the basis of relevant research results, this paper combines related concepts of environmental information disclosure and basic theories such as organizational legitimacy theory, signal transmission theory, information asymmetry theory and stakeholder theory. Firstly, in the theoretical analysis part, from the perspective of government, media and enterprise utility, based on the principle of utility maximization, this paper introduces the following methods: A mixed game theoretical framework of government supervision, media reports and corporate water information disclosure was constructed to clarify the heterogeneous disclosure behavior of enterprises under the pressure of external environmental supervision. Secondly, A-share listed companies in water-sensitive industries in Shanghai and Shenzhen from 2015 to 2019 are selected as samples to construct mandatory and selective disclosure content indicators of corporate water information, and empirically analyze the influence of external pressures of government supervision and media reports on the disclosure content of heterogeneous water information of enterprises. Finally, according to the relevant theories and empirical analysis results, the research conclusions and relevant suggestions are put forward.
A Study on Water Resources Marketing and Economic Benefit Maximization by Integrating Multiple Regression Analysis
The purpose of marketing activities carried out by enterprises is to improve economic benefits, and the evaluation of water resources marketing activities provides support for maximizing the economic benefits of water resources enterprise marketing. Based on STP marketing and 4Ps marketing theory, this paper selects GZ Water Company as a research sample, analyzes the competitive environment of water resources marketing through Porter's Five Forces Competition Model, and explores the internal and external factors of water resources marketing in combination with SWOT model. Based on the results of SWOT analysis, SO, WO, ST and WT strategies are proposed to be applied in the formulation of water resources marketing strategies. In order to investigate the impact of each strategy on maximizing economic efficiency, an empirical analysis is carried out through multiple linear regression model. The results show that the external and internal environment scores of water resources marketing are 3.27 and 4.24, respectively, with external opportunities outweighing threats and internal strengths outweighing weaknesses, and that there is a highly significant effect at the 1% level between the SO strategy and the maximization of economic benefits, and that for every 1% implementation of the SO strategy of water resources marketing, the enhancement of its effect on the maximization of economic benefits rises by 0.426%. Water marketing needs to focus on its strengths and promote technological transformation to better empower economic performance.