Articles

This study examines curriculum reform in the "Comprehensive Jewelry Material Techniques" course, a core component of the jewelry design program at West Yunnan University of Applied Sciences, drawing on curriculum theories and Torrance's principles of creative thinking, it addresses persistent challenges in applied undergraduate art education: outdated content, pedagogical monotony, limited material diversity, superficial applications, and constrained instructional time. These impede students' innovatnment with industry demands in a digital-era art and design landscape. This study adopts a progressive, student-centered pedagogy integrating material cognition, deep practice, themed jewelry creation, multi-dimensional evaluation, and achievement exhibitions. This fosters interdisciplinary knowledge synthesis, psychological safety, and holistic competencies—enhancing material mastery, aesthetic literacy, and practical innovation. Empirical outcomes demonstrate elevated teaching efficacy, as evidenced by student works exhibiting novel material fusions, creative potential. The reforms model a transformative paradigm for vocational art curricula, bridging experiential transmission with autonomous exploration to cultivate adaptable, creatively resilient designers responsive to societal and technological shifts.

Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

This Practice-based research explores the innovative application of textiles in the creation of decorative flower vases, positioning them at the intersection of functional design and contemporary art. The study investigates the potential of techniques such as weaving, embroidery, and applique to transcend the conventional boundaries of the medium. Through a methodological framework combining material experimentation interviews with textile artisans and pottery producers in Accra, and critical reflection, the research examines the interplay of materiality, form and aesthetics. It integrates traditional Ghanaian motifs with modern design principles to create culturally resonant, sustainable artworks. The findings demonstrate textiles' significant versatility and creative capacity for producing unique decorative objects. This study contributes to discourses on material innovation and sustainable design by highlighting textiles as a dynamic medium for artistic expression. It offers practical insights for artisans and designers, underscoring the role of textiles in evolving traditional crafts for contemporary contexts.

The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.

Modern organizations leverage highly distributed, global deployments to provide high availability and resiliency for cloud-first applications. By hosting these applications across multiple geographic locations and relying on highly available services, organizations can prevent disruption to their business and reduce complexity by employing the scale of infrastructure offered by major cloud providers. Global deployments in the cloud are built on well-known models such as failover, load balancing, and scalability. However, traditional methods used to recover from regional failure—while effective—can be complex. Typical multi-region recovery and high availability system architectures have latency and cost risks that should be considered when facing other limitations such as deployment models in the cloud. This document describes the different traffic management techniques that can be applied to multi-region strategies, focusing on trade-offs and costs. The introduction of new traffic management techniques being applied to the traditional global architectures now allows organizations to adopt cloud services more efficiently. Traffic management is much more straightforward in some environments, while others have started to leverage their traffic management platform via routing. In multi-region deployments, active-active and active-passive are the most common architectural models, allowing organizations to seamlessly handle failover, scalability, and global distribution based on business goals and requirements. However, traffic management for these infrastructures is critical to ensure just data distribution and efficiency, maintaining costs under control and workloads rerouted when necessary. Using the new traffic management techniques will allow organizations to evolve system architectures easily based on business requirements, taking advantage of cost benefits from multiple infrastructures. In these scenarios, traffic management becomes a crucial backbone of success to ensure that traffic is being efficiently and intelligently distributed [1].

M&A is a strategic concept of business growth through consolidation, gaining market access, increasing strategic positions, and increasing operational efficiency. To understand the dynamics of M&A, this paper looks at aspects such as targeted firm identification, evaluation, bidding for the target firm, and post-acquisition integration. All forms of M&A, including horizontal, vertical, conglomerate, and acquisitions, are discussed in terms of goals and values, including synergy, cost reduction, competitive advantages, and access to better technology. However, issues such as cultural assimilation, adhesion to regulations, and calculating an inaccurate value are also resolved. The paper then goes deeper to provide insight into how predictive analytics applies to M&A, using ML to improve decision-making with forecasting benefits. Including healthcare, education, and construction industries, the presented predictive models using regression analysis, neural networks, and ensemble techniques help to make decisions. Through time series and real-time data, PDA enables sound M&A strategies, effective risk management and smooth integration.

Encryption is a method of encoding data so that only authorized parties can read or access that data. With the fast improvement of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. In this paper, the authors work on improving the security of data using a combination of Hill cipher and a Transposition cipher (<b>G</b><SUB><b>p</b></SUB>) , the plaintext will be encrypted using ordinary Hill cipher technique and encrypted again using <b>G</b><SUB><b>p</b></SUB><SUB><b> </b></SUB>cipher there by producing a more complex ciphertext that will be very difficult for cryptanalyst to intrude, and the decryption process is done in two phases, the first decryption will return the ciphertext of the ordinary Hill cipher method and the second decryption will return the original plaintext.

Failure prediction can be achieved through prognostics, which provides timely warnings before failure. Failure prediction is crucial in an effective prognostic system, allowing preventive maintenance actions to avoid downtime. The prognostics problem involves estimating the remaining useful life (RUL) of a system or component at any given time. The RUL is defined as the time from the current time to the time of failure. The goal is to make accurate predictions close to the failure time to provide early warnings. J S Grewal and J. Grewal provide a comprehensive definition of RUL in their paper "The Kalman Filter approach to RUL estimation." A process is a quadruple (XU f P), where X is the state space, U is the control space, P is the set of possible paths, and f represents the transition between states. The process involves applying control values to change the system's state over time.

The complexity of manufacturing photolithography has increased significantly. The increase in the level of integration has driven smaller feature-sized integrated circuits (ICs). The evolution in stepper technologies has been geometric. This has enabled the printing of printed ICs with a 45 nm feature size. Improvement in lithographic technology is moving towards 32 nm. This feature-size roadmap poses many challenges to semiconductor manufacturing technology. Advanced photomask synthesis, high-NA steppers, and computational lithography are some examples of the solution space. Optical proximity correction (OPC) and model-based optical proximity correction (MBOPC) are subsets of this solution space. OPC has matured significantly and is the de facto solution for manufacturing photomasks up to the 65 nm node. The OPC technique has been further refined as model-based OPC and has been applied to advanced printing technology of 45 nm. The OPC solution for 45 nm technology has limitations of mask rule check (MRC) and manufacturability restrictions. These restrictions are inevitable in OPC and MBOPC solutions because of the limits in lithographic technology. The technology evolution towards 32 nm has equally challenged the non-linear treatment of wafer-level problems in OPC solutions. PBOPC has limitations in reducing the wafer optical proximity error of the granny's issue, edge placement, mask rule check, etc. PBOPC also has limitations in reducing the mask error enhancement factor. With all these challenges, it is still a formidable solution methodology to address the wafer and mask level issues. Such a formidable solution architecture can result in a limited number of PBOPC solutions. This text looks at the performance of advanced PBOPC features on exposure tuning and the effects of higher-order wafer and aerial image effects. This text also discusses the performance of continuous process correction of masks, lenses, and scanners.

The building construction industry holds a crucial role in the reduction of greenhouse gas emissions globally. The targets for greenhouse gas emissions may not be achieved without a defined strategic plan to meet up with the set targets from various sectors of the economy. Recognizing the enormous potential that the building industry holds in contributing to global greenhouse gas GHG emission reduction, this study describes a framework on how optimization techniques can be used as a guide for emission reduction targets for the housing sector using illustrations of the onsite and offsite building construction industry. Given that some of the GHG gases are also sources of air pollution, this study includes a discussion on how the effort to address air pollution can be used to find a consensus towards addressing the concern about GHG emissions. This study presents procedures for simplified methods of estimation of GHG emissions that various municipalities around the globe can use to estimate and report the emissions from the building construction industry. The study presents a unifying strategy for emission management. The study also demonstrates how programming methods can be applied to GHG emissions management. The approach used in this study is transferable to other industries. The study recommends a unifying strategy for the management and control of emissions in the building construction industry. The study also recommends a coordinated effort in sharing best practices for emission control and management from all jurisdictions globally. In the effort to reduce global emission targets, further studies like this and its expansion is recommended for all sectors of the global economy. It is recommended that these studies should be followed by a concrete effort to achieve good implementation of sustainable emission reduction targets globally.

The purpose of the study was to examine Colleges of Education students’ attitudes toward the teaching and learning of Social Studies in the Ashanti region. Quantitatively, descriptive survey research design was employed for the study to examine the attitudes of Colleges of Education students toward the teaching and learning of Social Studies in the Ashanti region. The population for the study comprised all the second year (form 2) Social Studies students from the four (4) selected Colleges of Education in the Ashanti Region. Purposive and simple random sampling techniques were used to select four (4) Colleges of Education (St. Louis, Wesley, St. Monica's and Offinso Colleges of Education), and two-hundred seventy-seven (277) second year Social Studies students for the study. The main instrument used for data collection was questionnaire. The quantitative data obtained was coded into Statistical Product for Service Solution (SPSS) (version 26.0) and analyzed using descriptive statistics involving tables, frequencies and percentages for each item. “Strongly Agree”, “Agree”, “Strongly Disagree”, “Disagree” were all used in the analysis for easy presentation and understanding. The study revealed that students were able to dedicate much time to studying the subject in order to acquire relevant knowledge, positive attitudes and skills as well as solve societal problems stems from their ability to have good perception of the subject. The study also concludes that varying techniques of teaching is a key factor in enabling students to develop keen interest in Social Studies. It arouses and sustain students’ interest in the subject, they must not neglect employing methods that are student-centered. It is recommended that teachers should vary their methods of teaching to focus more on the use of child-centered techniques of teaching. It is also recommended that, teaching universities and colleges of education should collaboration with Ministry of Education, T-TEL and other Non-governmental educational organizations to organise workshops, and seminars to help Social Studies teachers on the use of vary teaching techniques in evaluating students learning.

The purpose of the study was to examine techniques and strategies use in assessing Social Studies concepts in the three senior high schools in Sefwi Wiawso Municipality in the Western North Region of Ghana. Qualitatively, the research design for this study was a Case study. The population for this study consisted of all ten (10) Social Studies teachers in three public Senior High Schools (Wiawso Senior High School, Wiawso Senior/Technical school, and Asawinso senior high school) in Sefwi Wiawso municipality in the Western North Region of Ghana. Purposive and convenient sampling techniques were used to select municipalities, schools, and Social Studies teachers for the study. The main instrument used for data collection was interview. The data were edited before being analyzed in themes and pattern. Being a case study design in qualitative research, the researcher read through the data several times so as to familiarize with the data, making notes, referring back to the research question, making decisions whether to focus on individual responses or on topics. The interview data were interpreted to articulate meaning, making decisions on whether to use direct quotes or the summary of respondents’ responses and reporting the data considering the interest of various audiences. The study revealed that teachers used various teaching techniques such as brainstorming, simulation, discovery-learning, role-play discussion, dramatization, problem-solving, and building a community implies that they believed in child-centered method of the teaching and learning of Social Studies. The study also concluded that Social Studies teachers use techniques and strategies like concept attainment, field trips, and debate co-operative learning in motivating and providing learners with the opportunity to interact with their own community or environment. It is recommended that Social Studies teachers should make effort either to go on field trips or make use of available experts that are in their communities by inviting them into the Social Studies classrooms to tap into their rich knowledge to enhance concept learning. It is also recommended that frequent seminars and periodic or regular in-service training should be organised by the Ghana Education Service to help Social Studies teachers to acquaint themselves with the modern teaching techniques and strategies that promote the teaching and learning of Social Studies concepts.

Crime is a serious and widespread problem in their society, thus preventing it is essential. Assignment. A significant number of crimes are committed every day. One tool for dealing with model crime is data mining. Crimes are costly to society in many ways, and they are also a major source of frustration for its members. A major area of machine learning research is crime detection. This paper analyzes crime prediction and classification using data mining techniques on a crime dataset spanning 2006 to 2016. This approach begins with cleaning and extracting features from raw data for data preparation. Then, machine learning and deep learning models, including RNN-LSTM, ARIMA, and Linear Regression, are applied. The performance of these models is evaluated using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RNN-LSTM model achieved the lowest RMSE of 18.42, demonstrating superior predictive accuracy among the evaluated models. Data visualization techniques further unveiled crime patterns, offering actionable insights to prevent crime.

Data warehousing is a technique for collecting, managing, and presenting data to help people analyze and use that data effectively. It involves a large database designed to support management-level staff by providing all the relevant historical data for analysis. This chapter begins with a definition of data warehousing, followed by an overview of large-scale policy management to highlight the need for data warehousing. Next, an overview of an ETL framework is presented, along with a discussion of advanced ETL techniques. The chapter concludes with an outline of performance optimization techniques for data warehousing. Data warehousing is considered a key enabler for efficient reporting and analysis, with implementation choices ranging from cost-effective desktop systems to large-scale, mission-critical data marts and warehouses containing petabytes of data. Extract, transform, and load (ETL) systems remain one of the largest cost and effort areas within data warehouse development projects, requiring significant planning and resources to build, manage, and monitor the flow of data from source systems into the data warehouse. The technology and techniques used for ETL can greatly influence the success or failure of a data warehouse. Complex business requirements for data cleansing, loading, transformation, and integration have intensified, while operational plans for real-time and near-real-time reporting add additional challenges. Parallel loading mechanisms, incremental data loading, and runtime update and insert strategies not only improve ETL performance but also optimize data warehousing performance, particularly for large-scale policy management.

The Murray Darling Basin Authority (MDBA) currently has been using a discrete field sampling technique for water quality monitoring that is expensive, time consuming and may not adequately represent the spatial variability of water quality relative to the entire water body. A pilot project was executed to assess the effectiveness of using earth observation data, supported by archived field-based observations for quantitative estimation of Water Quality Parameters (WQP) and detection of algal blooms in the River Murray. The selected pilot study area includes a 100km stretch of the River Murray between the Hume Dam and Yarrawonga Weir. The time frame for the archived field samples was between November 2008 and March 2011, when major algal blooms were occurring in this stretch of the Murray River.Analysis of the 2009 data shows that waters in sites in the Murray River downstream of the Hume Dam to the Yarrawonga Weir show more temporal than spatial variability in Chl-a and PC levels. The Chl-a concentration is relatively less in the Yarrawonga Weir than in the Murray River. The scatter plot of PC vs. Turbidity suggests that PC is a more significant parameter for the detection of Cyanobacteria than Chl-a. The field data represents the temporal bio-optical variability across the 2009 algal bloom events by successfully capturing the co-variations among Chlorophyll-a, Chycocyanin and turbidity at pre, during and post bloom conditions. The methodology has proved that the usefulness of an integrated earth observation and field based WQP technique to accurately map algal bloom events. The long term MDBA RMWQMP data for the 2009 bloom event is found partially compatible to the NOW Pilot study data in that only the data for the Heywood site that was used together for testing the WQP monitoring technique. The incompatibility of the RMWQMP data downstream of Yarrawonga Weir may be due to differing techniques used for determining Chlorophyll. The 2010 data was suitable for testing the technique for complex spatial bio-optical variability during the peak of the bloom in a large water storage. Lack of Chlorophyll measurements in 2010 data poses challenges in interpreting the relationship of bio-optical variability with the spatial distributions of bio-optical parameters. As relational parameters are absent, local information and expert advice will be required to develop plausible assumptions between the Chlorophyll - Phycocyanin relationship. The field sampled data for the 2010 bloom event acquired from the Hume Dam was used for comparative investigation of both moderate resolution sensors (MODIS and MERIS) and high resolution sensors (TM/TM+). The 2009 bloom event field samples of sites in the Yarrawonga Weir was used as an input with MODIS and MERIS and the data from all the sites was applied with TM/TM+. This paper will present an integrated earth observation and field based WQP technique to accurately map algal bloom events, and discuss challenges for real time earth observation data initiatives and future collaborative projects.

The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is done through a bag of words method, and 80-20 data is split using training and testing. The experimental findings demonstrate that the LSTM model outperforms the conventional models, such as SVM and Naïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of 99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, it performs well the model consumes heavy computational resources and longer training time. In summary, the results show that deep learning performs well in sentiment analysis and can be used to social media monitoring, customer feedback evaluation, market sentiment analysis, etc.