Customer Behavior Analysis for Social Media

It is essential for a business organization to get the customer feedback in order to grow as a company. Business organizations are collecting customer feedback using various methods. But the question is ‘are they efficient and effective?' In the current context, there is more of a customer oriented market and all the business organizations are competing to achieve customer delight through their products and services. Social Media plays a huge role in one's life. Customers tend to reveal their true opinion about certain brands on social media rather than giving routine feedback to the producers or sellers. Because of this reason, it is identified that social media can be used as a tool to analyze customer behavior. If relevant data can be gathered from the customers' social media feeds and if these data are analyzed properly, a clear idea to the companies what customers really think about their brand can be provided

Identifying stakeholder preferences for communicating impact from medical research: a mixed methods study

Background Documentation of research outcomes using impact case studies (ICS) is increasingly required to demonstrate the wider societal benefits of research. However, there is limited evidence of the best way to communicate research outcomes using ICS, especially when highlighting research impact that is not part of a research assessment programme. This study aims, for the first time, to analyse expectations, and methods of communicating impact from medical research across a varied set of stakeholders relevant to the Medical Research Council (MRC). Methods Impact narratives about outcomes and impact from MRC research were evaluated using an online survey and in depth semi-structured interviews. Participants were recruited from internal MRC databases and included early career and senior management academics as well as representatives from industry, healthcare, charities, and the government. Informed consent was gained prior to data collection and the study was approved by the university’s research ethics committee. Qualitative and quantitative analysis determined stakeholder preferences for ICS content, language and presentation as well as capturing themes and perspectives on the concept of research impact. Results 193 participants responded to the online survey exploring definitions of impact and methods of communicating medical research outcomes. The work uncovered expectations of improved health and wellbeing as well as knowledge generation via publications and citations. In depth interviews with sixteen participants demonstrated preferences for clear, easy to read content that focused on facts and evidence and avoided both academic and hyperbolic language. Emergent themes from this work revealed that ICS need to quickly capture imagination and grab attention, while the views and expectations are quite different to press releases and are audience specific. Conclusions The content of ICS often focuses on non-academic impacts; however this work highlighted that evidence of academic impacts were outcomes highly valued by stakeholders relevant to the MRC. This work examined a new typology of ICS attributes, which emphasised that the language and presentation of impact narratives can influence the perception of research outcomes, providing useful information for individuals and organisations using ICS to showcase their research. It also shows that if ICS attempt to communicate challenges and issues around achieving impact from research, they may be more credible and useful to their intended audience

The Long and Short-Run Symmetrical and Asymmetrical Effects of Climate Change on Rice Production in Sri Lanka

Climate change is becoming evident in Sri Lanka due to increasing temperatures and changes in rainfall patterns. As a country with an agriculture-based economy, the impact of climate change on crop production threatens food security. The present study aimed to examine the symmetric and asymmetric effects of climate change on rice production in Sri Lanka. In this study, we used Autoregressive-Distributed Lag (ARDL) and Non-linear Autoregressive Distributed Lag (NARDL) models. Data for the period between 1952 and 2022 was compiled by the World Bank and the Department of Census and Statistics Sri Lanka. Estimated results were also validated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR). The results of the symmetric ARDL model indicate that temperature and cultivated land area have significant long-term effects on rice production. The results of the NARDL model show that positive and negative changes in climate variables have an asymmetrical long-run impact on rice production in Sri Lanka. Positive changes in temperature and rainfall have a significant negative impact on rice production. At the same time, negative changes rainfall has a significant and positive impact on rice production. Changes in cultivated land areas, both positive and negative, have a long-term significant impact on rice production. Policymakers must comprehend both the symmetric and the asymmetric effects of climate change on rice production and implement agricultural policies that promote sustainable agricultural development and to develop improved climate risk management strategies leading to ensuring food security in Sri Lanka

Performance Enhancement of C-V2X Mode 4 Utilizing Multiple Candidate Single-Subframe Resources

Abstract: Prioritization of data streams in cellular vehicle-to-everything (C-V2X) may lead to unfavorable packet delays in low-priority streams. This paper studies allocating multiple candidate single-subframe resources (CSRs) per vehicle as a solution. It proposes a methodology to determine the number of CSRs for each vehicle based on the total number of neighboring vehicles, and to assign the multiple data streams among them for simultaneous transmission. The numerical results highlight the achievable delay gains of the proposed approach, and its negligible impact on packet collisions.Abstract: Prioritization of data streams in cellular vehicle-to-everything (C-V2X) may lead to unfavorable packet delays in low-priority streams. This paper studies allocating multiple candidate single-subframe resources (CSRs) per vehicle as a solution. It proposes a methodology to determine the number of CSRs for each vehicle based on the total number of neighboring vehicles, and to assign the multiple data streams among them for simultaneous transmission. The numerical results highlight the achievable delay gains of the proposed approach, and its negligible impact on packet collisions

Women and citizenship post-trafficking : the case of Nepal

The research for this paper was funded by the Economic and Social Research Council – ESRC Res-062-23-1490: ‘Post Trafficking in Nepal: Sexuality and Citizenship in Livelihood Strategies’. Diane Richardson would like to acknowledge the support provided by the award of a Leverhulme TrustMajor Research Fellowship, ‘Transforming Citizenship: Sexuality, Gender and Citizenship Struggles’ [award MRF-2012-106].This article analyses the relationship between gender, sexuality and citizenship embedded in models of citizenship in the Global South, specifically in South Asia, and the meanings associated with having - or not having - citizenship. It does this through an examination of women's access to citizenship in Nepal in the context of the construction of the emergent nation state in the 'new' Nepal 'post-conflict'. Our analysis explores gendered and sexualized constructions of citizenship in this context through a specific focus on women who have experienced trafficking, and are beginning to organize around rights to sustainable livelihoods and actively lobby for changes in citizenship rules which discriminate against women. Building from this, in the final section we consider important implications of this analysis of post-trafficking experiences for debates about gender, sexuality and citizenship more broadly.Publisher PDFPeer reviewe

Exploring Policy influences on the adoption of 3D Concrete Printing technology: A hypothetical model

The global construction industry faces mounting pressure to innovate for sustainability and efficiency, with 3D concrete printing emerging as a transformative technology. Despite its potential, significant challenges such as technological immaturity and insufficient policy support hider widespread 3DCP adoption. Drawing on lessons from the adoption of Building Information Modeling in the United States and Hong Kong, this paper addresses these policy gaps by developing a conceptual framework that facilitates 3D concrete printing adoption. Through a systematic review of 30 publications and a qualitative content analysis, this study identifies and synthesizes 17 key policy factors across six policy dimensions into a comprehensive framework. The findings underscore the critical role of sustainability, transformative technical development policies, and research and development initiatives in accelerating 3D concrete printing adoption. Additionally, the study highlights a policy shift from profit-driven strategies to eco-innovative and socio-environmental entity as the technology matures, particularly in its early development stages. The proposed conceptual policy framework provides actionable strategies for policymakers and industry stakeholders to address existing barriers and accelerate the widespread adoption of 3D concrete printing. A critical insight suggests that governments leadership, along with a shift to business models based on usage rights rather than ownership, could reduce financial burden on end users. This coordinated approach combining government leadership, targeted R&D support, eco-innovative policies, and new business models can establish 3DCP as a sustainable and scalable solution for future construction needs

A Review of Supply Chain Dynamics of 3D Concrete Printing Construction Practice

3D printing has gained increasing interest as a revolutionary digital technology in the construction industry. This technology has been supported as an effective tool for improving construction efficiency. Through a previous systematic review study, we comprehensively analysed the benefits and challenges of 3D printing technology implementation in the construction industry. This paper will follow the findings of the review study, investigating supply chain management strategies and challenges in 3D concrete printing projects in conjunction with a questionnaire pilot study. This paper is part of an ongoing research programme focusing on the decision-making mechanism of construction supply chain management in 3D printing. The purpose of this study is to identify key challenges in 3D concrete printing and supply chain. This paper adopted data from a literature review and a questionnaire survey pilot study to investigate the challenges of 3D concrete printing implementation and its supply chain management in practical construction projects. A questionnaire survey was conducted with participants who have significant experience in the construction sector. The result of the Five-Point Likert scale questionnaire was statistically analysed and interpreted. As a result, the critical challenges of 3D concrete printing projects have been defined. The findings show that high initial investment, technology awareness, regulation support as well as workforce training significantly affect 3D printing implementation in the construction sector. The study result provides the research programme with practical data in 3D concrete printing practice. The findings of this paper support a follow-up study in discussing the decision-making of the 3D concrete printing supply chain

Assessing AI Techniques for Precision in Property Valuation: A Systematic Review of the Four Valuation Methods

Property valuation is a critical component of real estate management, influencing decisions on investments, sales, and financing. Traditional methods, such as Linear Regression and Multiple Regression Analysis, often struggle to address the complexities of modern property markets. In response, Artificial Intelligence (AI) offers innovative solutions by utilising advanced models for more accurate predictions of property value. This paper presents a Systematic Literature Review (SLR) using Scopus database, focusing on 37 selected papers. It evaluates the effectiveness of key AI models in property valuation, assessing their predictive power, interpretability, robustness, and flexibility, and examines how well these models generate reliable valuations and their accuracy. The key models were referred to as the “Valuation Four”: Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Regression Models (RM). The review highlights each model’s ability to handle complex real estate data, with SVM and RF demonstrating superior accuracy. At the same time, DT excels in interpretability, making it more user-friendly for decision-making. Regression-based models continue to serve as useful benchmarks but are less effective for intricate datasets. The findings of this study provide valuable insights for real estate professionals and policymakers by identifying the AI models that support precise and reliable property valuation on basis of dataset and factors considerations. This contributes to the ongoing evolution of automated valuation methods and helps advance data-driven decision-making in real estate

Charting the potential of brain computed tomography deep learning systems.

Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients. Deep learning has shown promise for facilitating improved diagnostic accuracy and triage. This research charts the potential of deep learning applied to the analysis of CTB scans. It draws on the experience of practicing clinicians and technologists involved in development and implementation of deep learning-based clinical decision support systems. We consider the past, present and future of the CTB, along with limitations of existing systems as well as untapped beneficial use cases. Implementing deep learning CTB interpretation systems and effectively navigating development and implementation risks can deliver many benefits to clinicians and patients, ultimately improving efficiency and safety in healthcare