Landscape Dynamics Research Articles

Customer Support Automation of Ticket Creation (RPA) using UiPath

In the dynamic landscape of customer support, manual ticket creation from customer emails is a labor-intensive and error-prone process that impedes operational efficiency. This project proposes an automated system utilizing Robotic Process Automation (RPA) powered by UiPath to streamline the ticket creation process. The solution automates the extraction of critical data from emails, including customer IDs, issue descriptions, and priorities, to create tickets in Customer Relationship Management (CRM) systems. Missing information prompts automated follow-ups, ensuring query resolution. Additionally, the system recognizes structured templates to send predefined responses to frequently asked questions, reduces response time, and handles bounced emails by notifying support teams. Scalability is achieved through parallel bot deployments, enabling efficiency, reduces manual high-volume processing with accuracy and speed. This automation framework enhances operational workload, and improves customer satisfaction, offering a scalable model adaptable across industries.

Agritourism as a catalyst for sustainable rural development: Innovations, challenges, and policy perspectives in the post-COVID-19 era

This study investigates the dynamic landscape of agritourism in Thailand, emphasizing innovations, challenges, and policy implications in the post-COVID-19 era. Employing a qualitative approach, including a comprehensive literature review and semi-structured interviews with stakeholders, the research identifies key agritourism models, such as immersive learning experiences, technology-driven agritourism, and unconventional practices like salt and coconut plantations. Findings reveal that agritourism has adapted to shifting market demands through diversification, technological integration, and a heightened focus on sustainability. Notably, technology adoption in precision farming and hydroponics enhances resource efficiency and visitor engagement, while initiatives like rice paddy field tourism and highland agritourism showcase the cultural and ecological richness of rural landscapes. The study underscores the critical role of policy frameworks, infrastructure development, and community empowerment in fostering sustainable agritourism practices. Key policy recommendations include targeted subsidies, capacity-building programs, and harmonized regulatory frameworks to address challenges such as financial constraints, regulatory ambiguities, and inadequate infrastructure. This research contributes to the broader discourse on sustainable tourism and rural development, aligning agritourism with the United Nations Sustainable Development Goals (SDGs). By synthesizing insights on innovation, resilience, and sustainability, this study offers a comprehensive roadmap for policymakers, practitioners, and academics to leverage agritourism as a vehicle for rural revitalization and global sustainability. Future research directions are proposed to explore the long-term impacts of technological integration, community empowerment, and resilience strategies in agritourism.

Mitigating Cyber Attacks for Organization Assets using UEBA (User and Entity Behavior Analytics) and other Machine Learning Solutions from Cloud Security Threats: A Literature Review

User and Entity Behavior Analytics has been one of the key steppingstones towards protecting valuable data and information for organizations that decided to store their assets in a cloud-based storage. The implications suggest that third-party software may not be as reliable as these are prone to attacks due to its accessibility and vulnerable nature. This study aims to provide a comprehensive review that covers User and Entity Behavior Analytics (UEBA) and other machine learning techniques to effectively mitigate cyberattacks and protect organizational assets from cloud-based security threats. The objective is to identify and analyze anomalous user and entity behaviors that may indicate potential cyberattacks and provide recommendations for organizations to enhance their cloud security posture and minimize the risk of data breaches and other security incidents. The research covers some of the algorithms used in detecting anomalies such as Isolation Forest, Deep Autoencoder, and Linear Regression, and emphasizes the adaptability of these chosen algorithms to the dynamic landscape of cyber threats, especially in cloud environments. By arriving at a cohesive integration of machine learning algorithms, this study advocates for a holistic approach that aligns with evolving security challenges. Ultimately, the significance lies in offering a nuanced perspective on effective cyber threat mitigation strategies, contributing to the broader conversation on securing organizational assets in the face of evolving cybersecurity landscapes.

Financial Sustainability and Influence Dynamics in Yemen's Conflict-Affected NGO Landscape: Unveiling ‘Sheikh’ Organizations

Abstract This study examines the role of Sheikh NGOs as financial gatekeepers in Yemen’s NGO sector, showing how their control over aid distribution reinforces dependency and limits local NGOs' autonomy. Through interviews with 45 stakeholders, the research reveals the challenges that local NGOs face in achieving financial sustainability amid economic instability, political turmoil, and dwindling donor support. The findings indicate a significant asymmetry in funding, with Sheikh NGOs monopolizing resources, which constrains the flexibility and innovation of smaller NGOs. Integrating dependency theory, the study illustrates how reliance on intermediaries reinforces power imbalances, while collective impact theory highlights the potential for transformative partnerships. Successful collaborations, like those between the Youth Leadership Development Foundation and local NGOs, underscore the need for Sheikh NGOs to adopt more supportive roles, empowering smaller organizations. This study calls for a reconfiguration of roles within Yemen's NGO sector, advocating a shift from dependency to partnership. In fostering collaboration and supporting local NGO autonomy, stakeholders can build a more equitable and resilient framework, ultimately enhancing humanitarian efforts in Yemen.

Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks

Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users. Therefore, Intrusion Detection Systems (IDS) remain one of the most used tools for maintaining such flaws against cyber-attacks. The dynamic and multi-dimensional threat landscape in IoT network increases the challenge of Traditional IDS. The focus of this paper aims to find the key features for developing an IDS that is reliable but also efficient in terms of computation. Therefore, Enhanced Grey Wolf Optimization (EGWO) for Feature Selection (FS) is implemented. The function of EGWO is to remove unnecessary features from datasets used for intrusion detection. To test the new FS technique and decide on an optimal set of features based on the accuracy achieved and the feature taking filters, the most recent FS approach relies on the NF-ToN-IoT dataset. The selected features are evaluated by using the Random Forest (RF) algorithm to combine multiple decision trees and create an accurate result. The experimental outcomes against the most recent procedures demonstrate the capacity of the recommended FS and classification methods to determine attacks in the IDS. Analysis of the results presents that the recommended approach performs more effectively than the other recent techniques with optimized features (i.e., 23 out of 43 features), high accuracy of 99.93% and improved convergence.

Editorial: (Ir)Relevance in education: individuals as navigators of dynamic information landscapes

Editorial: (Ir)Relevance in education: individuals as navigators of dynamic information landscapes

Unveiling the impact of COVID-19 on Small Finance Banks in India: An in-depth econometric analysis

This study assesses the performance of Small Finance Banks in India, focusing on their efficiency in the post-COVID-19 period from 2019 to 2023. It explores the influence of bank-specific and macroeconomic factors on the efficiency of Small Finance Banks, with the aim of understanding their role in promoting financial inclusion. The research employs a two-stage Data Envelopment Analysis (DEA) framework to evaluate the efficiency of 10 selected Small Finance Banks. It incorporates both bank-specific variables (such as capital adequacy ratio, credit-deposit ratio, and liquidity ratio) and macroeconomic factors (GDP and inflation) in a Tobit regression model to analyze their influence on efficiency. The study reveals that the majority of Small Finance Banks remained resilient during the pandemic, consistently achieving high efficiency scores, except for a slight dip in 2020–2021 due to lockdown measures. Bank-specific factors indicate a converse association between capital adequacy ratio and efficiency, while liquidity ratio and credit-deposit ratio positively correlate with efficiency. Macro factors, including GDP and inflation, have minimal impact on Small Finance Banks efficiency. This study provides a comprehensive assessment of Small Finance Banks’ performance post-COVID-19, shedding light on the factors influencing their efficiency. It offers valuable insights for policymakers and Small Finance Banks to strengthen their role in advancing financial inclusion in India, contributing to a more inclusive and dynamic financial landscape. The findings suggest that Small Finance Banks should focus on expanding their influence in niche segments by increasing assets, deposits, and revenue streams while managing operating expenses and liquidity risks. Listing on the stock exchange and active policy support for research initiatives can enhance the Small Finance Banks ecosystem and drive financial inclusion.

Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research.

This review explores the current understanding and recent advancements in neuroblastoma, one of the most common extracranial solid pediatric cancers, accounting for ~ 15% of childhood cancer-related mortality. The hallmarks of NBL, including angiogenesis, metastasis, apoptosis resistance, cell cycle dysregulation, drug resistance, and responses to hypoxia and ROS, underscore its complex biology. The tumor microenvironment's significance in disease progression is acknowledged in this study, along with the pivotal role of cancer stem cells in sustaining tumor growth and heterogeneity. A number of molecular signatures are being studied in order to better understand the disease, with many of them serving as targets for the development of new therapeutics. This includes inhibitor therapies for NBL patients, which notably concentrate on ALK signaling, MDM2, PI3K/Akt/mTOR, Wnt, and RAS-MAPK pathways, along with regulators of epigenetic mechanisms. Additionally, this study offers an extensive understanding of the molecular therapies used, such as monoclonal antibodies and CAR-T therapy, focused on both preclinical and clinical studies. Radiation therapy's evolving role and the promise of stem cell transplantation-mediated interventions underscore the dynamic landscape of NBL treatment. This study has also emphasized the recent progress in the field of diagnosis, encompassing the adoption of artificial intelligence and liquid biopsy as a non-intrusive approach for early detection and ongoing monitoring of NBL. Furthermore, the integration of innovative treatment approaches such as CRISPR-Cas9, and cancer stem cell therapy has also been emphasized in this review.

Beyond firewalls: The future of cybersecurity research

The digital revolution has changed our world forever and it brought technology into our lives in every way. Be it for cross-border commerce, critical infrastructures, communications, or the very bonding of us as societies, all part of the paradigm today seem to take a more “inter-connected” flavor. While this brought ubiquitous connectivity into our lives, it also opened Pandora’s Box, unleashing relentless waves of cyber threats that endanger individuals, organizations and even national security. But as with everything, cybersecurity practitioners must understand the advanced threats as they increase in their level of sophistication and scale, making high-quality and innovative cybersecurity research imperative. The intention of this article is to be forward looking in discussing the major threats facing the industry and presenting fruitful lines of inquiry to navigate this complex and dynamic landscape.

Genomic insights into the dynamic antibiotic resistance landscape of Vibrio cholerae during the Cholera outbreak 2022 in Odisha, India

This research delves into the evolving dynamics of antibiogram trends, the diversity of antibiotic resistance genes and antibiotic efficacy against Vibrio cholerae strains that triggered the cholera outbreak 2022 in Odisha, India. The study will provide valuable insights managing antimicrobial resistance during cholera outbreaks. Eighty V. cholerae strains isolated during the outbreak were analysed for genotypic variations in associated drug resistance genes using PCR assays. Antibiogram profiles and MIC gradient analysis were performed according CLSI guidelines to assess antibiotic effectiveness. Substitution of amino acid position in the QRDR Region was examined to understand the development of Fluoroquinolone resistance. Elevated resistances in V. cholerae strains were observed against doxycycline, azithromycin, ciprofloxacin, and chloramphenicol. The average MARI registered 0.63 value, exceeding the threshold value 0.2. PCR assays revealed higher prevalence of antibiotic resistance genes, and MIC values observed have surpassed the previously registered values during any cholera outbreaks in India. Novel mutations in the parC gene, specifically Tyr-88→Cys and Ser-85→Leu implicated Fluoroquinolone resistance in V. cholerae. This study urges moving beyond on antibiotic reliance to control cholera, emphasizing alternative strategies like OCV, rehydration therapy, probiotics and Water, Sanitation and Hygiene (WASH) interventions as effective tools to combat cholera outbreaks and mitigate antibiotic resistance.

Characterizing Microglial Signaling Dynamics During Inflammation Using Single-Cell Mass Cytometry.

Microglia play a critical role in maintaining central nervous system (CNS) homeostasis and display remarkable plasticity in their response to inflammatory stimuli. However, the specific signaling profiles that microglia adopt during such challenges remain incompletely understood. Traditional transcriptomic approaches provide valuable insights, but fail to capture dynamic post-translational changes. In this study, we utilized time-resolved single-cell mass cytometry (CyTOF) to measure distinct signaling pathways activated in microglia upon exposure to bacterial and viral mimetics-lipopolysaccharide (LPS) and polyinosinic-polycytidylic acid (Poly(I:C)), respectively. Furthermore, we evaluated the immunomodulatory role of astrocytes on microglial signaling in mixed cultures. Microglia or mixed cultures derived from neonatal mice were treated with LPS or Poly(I:C) for 48 h. Cultures were stained with a panel of 33 metal-conjugated antibodies targeting signaling and identity markers. High-dimensional clustering analysis was used to identify emergent signaling modules. We found that LPS treatment led to more robust early activation of pp38, pERK, pRSK, and pCREB compared to Poly(I:C). Despite these differences, both LPS and Poly(I:C) upregulated the classical reactivity markers CD40 and CD86 at later time points. Strikingly, the presence of astrocytes significantly blunted microglial responses to both stimuli, particularly dampening CD40 upregulation. Our studies demonstrate that single-cell mass cytometry effectively captures the dynamic signaling landscape of microglia under pro-inflammatory conditions. This approach may pave the way for targeted therapeutic investigations of various neuroinflammatory disorders. Moreover, our findings underscore the necessity of considering cellular context, such as astrocyte presence, in interpreting microglial behavior during inflammation.

Exploring Barriers to Innovative Marketing in MSMEs: An Analysis Using a BWM‐ISM Multi‐Criteria Decision‐Making Framework

ABSTRACTIn today's dynamic business landscape, organisations increasingly realise the pivotal role of innovative marketing approaches (IMA) in gaining a competitive edge and driving sustainable growth. However, many organisations encounter formidable barriers to adopting and developing IMA, despite the growing importance of innovative approaches in marketing. The purpose of this research is to identify, prioritise, establish the inter‐relationships and propose a ‘level‐wise structural model’ of the barriers that an organisation faces in adopting and developing IMA. The research employed a mix of quantitative and qualitative methods. An exhaustive review of related literature and theoretical frameworks (namely the ‘resource‐based view’ (RBV) theory, the ‘social capital theory’ (SCT) theory, the ‘innovation resistance theory’ (IRT) theory, and the ‘institutional theory’ (IT) theory), along with expert feedback, are used to categorise and finalise the barriers. Further, a case study from ‘micro, small and medium enterprises’ (MSMEs) in a developing nation such as India along with hybrid ‘multi‐criteria decision‐making’ (MCDM), that is, best‐worst method (BWM), was applied to prioritise the barriers. While ‘interpretive structural modelling’ (ISM) was used to establish contextual relationships, and ‘matrice d'impacts croisés multiplication appliquée á un classment’ (MICMAC) analysis was employed to cluster the barriers. The findings suggest that among the main category technology‐related barriers and from subcategories, infrastructure supports the top barriers and works as a driving obstacle while building IMA for sustainable development. The findings from this study can help develop effective marketing strategies that align profitability with environmental and social responsibility.

Ribosome profiling reveals dynamic translational landscape following X-ray irradiation.

X-ray irradiation induces widespread changes in gene expression. Positioned at the bottom of the central dogma, translational regulation responds swiftly to environmental stimuli, fine-tuning protein levels. However, the global view of mRNA translation following X-ray exposure remains unclear. In this study, we systematically investigated X-ray-induced translational alternation using ribosome profiling. Our study revealed a temporary translation inhibition in HEK293T cells following X-ray treatment. A subset of mRNAs experienced translational upregulation by bypassing upstream open reading frames (uORFs). The upregulated genes were enriched in the MAPK signaling pathway, such as MAPKBP1. Suppression of MAPKBP1 inhibited X-ray-induced cell apoptosis. Furthermore, we identified the induction of novel peptides encoded by small open reading frames (smORFs) within long non-coding RNAs (lncRNAs) upon X-ray treatment. Overall, our findings provide a comprehensive overview of the translational landscape within eukaryotic cells following X-ray treatment, offering new insights into DNA damage response.

Targeting tumor microenvironment with photodynamic nanomedicine.

Photodynamic therapy (PDT) is approved for the treatment of certain cancers and precancer lesions. While early Photosensitizers (PS) have found their way to the clinic, research in the last two decades has led to the development of third-generation PS, including photodynamic nanomedicine for improved tumor delivery and minimal systemic or phototoxicity. In terms of nanoparticle design for PDT, we are witnessing a shift from passive to active delivery for improved outcomes with reduced PS dosage. Tumor microenvironment (TME) comprises of a complex and dynamic landscape with myriad potential targets for photodynamic nanocarriers that are surface-modified with ligands. Herein, we review ways to improvise PDT by actively targeting nanoparticles (NPs) to intracellular organelles such as mitochondria or lysosomes and so forth, overcoming the limitations caused by PDT-induced hypoxia, disrupting the blood vascular networks in tumor tissues-vascular targeted PDT (VTP) and targeting immune cells for photoimmunotherapy. We propose that a synergistic outlook will help to address challenges such as deep-seated tumors, metastasis, or relapse and would lead to robust PDT response in patients.

Characteristics and spatial-temporal dynamics in agronomic landscape: Case study of Guangrong watershed, Northeast China

Characteristics and spatial-temporal dynamics in agronomic landscape: Case study of Guangrong watershed, Northeast China

Supply and demand prediction by 3PL for assortment planning

To underscore the critical role of predictive capabilities in third-party logistics (3PL) companies for assortment planning, particularly within the rapidly evolving e-commerce sector and business to business (B2B) flows. This study employs a comprehensive literature review on the forecasting capabilities of 3PL firms, enriched by empirical research across nine logistics facilities. It leverages statistical tools and the ARIMA_PLUS algorithm to evaluate the precision and dependability of demand and supply forecasts generated by these companies. The research reveals that 3PLs possess the ability to generate accurate demand and supply forecasts utilizing advanced forecasting tools. The effectiveness of these forecasts is closely linked to the quality of data available, and the expertise of the personnel involved. Challenges arise in forecasting for smaller order volumes, which are more common in e-commerce flows. The study also highlights that technological advancements and investments in data analytics are pivotal in enhancing forecast accuracy. The investigation focuses on a select group of 3PL companies, potentially limiting the generalizability of the findings. Moreover, the study underscores the necessity for further exploration into how technological innovations impact forecasting capabilities. By emphasizing the significance of 3PL firms' predictive abilities, also for e-commerce assortment planning, this paper addresses a notable gap in existing research. Its insights are invaluable for businesses contemplating logistics outsourcing and for 3PL providers aiming to advance their forecasting proficiency. The findings stress the importance of integrating advanced forecasting models and analytics to stay competitive in the dynamic e-commerce landscape.

Empowering Transactions: A Holistic Examination of Digital Payment Adoption in India's Dynamic Financial Landscape

Empowering Transactions: A Holistic Examination of Digital Payment Adoption in India's Dynamic Financial Landscape

Influential factors of cybersecurity investment: A quantitative SEM analysis

In the dynamic landscape of digital enterprises, cybersecurity has emerged as a critical determinant of organizational effectiveness. This study delves into the intricate realm of cybersecurity investment within ASEAN organizations, exploring the key facets that drive decision-making in this domain. Using a quantitative approach through structural equation modeling (SEM), we conducted an in-depth analysis based on a sample of 419 enterprises meeting cybersecurity criteria. Our findings reveal that cybersecurity strategy, financial considerations, and institutional and regulatory conditions are the primary factors influencing cybersecurity investments in the ASEAN region. In particular, financial resources emerged as the most critical determinant, underscoring the importance of adequate funding to address evolving cyber threats. Furthermore, our study highlights the crucial role of institutional and regulatory frameworks in shaping investment behavior, indicating a heightened awareness among firms regarding compliance with legal requirements. By unpacking these dynamics, our research provides deep insights into the intricate interplay of factors shaping cybersecurity investments in ASEAN organizations. This study contributes to the discourse by emphasizing the imperative nature of understanding the impact of risk aversion, organizational structures, and long-term practices on cybersecurity resilience. The implications of our findings extend to policy making, innovation, and future research directions in the cybersecurity domain, offering valuable insights to improve cybersecurity preparedness and resilience against evolving cyber threats.

MCDM Review in marketing and managerial decisions: Practical implications and Future research

This research presents a short review of Multiple Criteria Decision-Making (MCDM) methods and research in various fields, including marketing and business management. The academic literature shows that MCDM methods in the area of marketing are used by academics to solve problems related to the positioning of products and services, market segmentation, brand management, promotion and advertising strategies, product development and market entry strategies, customer relationship marketing and channel distribution. With regard to business and management domains they are used to prioritize various decision-making aspects, like project assessments, resource allocation, strategic planning, risk management, performance evaluation, supplier and vendor selection, human resource management and strategic investment decisions. We can claim that in both domains, MCDM brings a systematic and transparent approach to decision-making, helping marketing managers to make more informed and objective choices. In summary, the continual refinement of these methods and the integration of cutting-edge technologies hold promise for further enhancing the effectiveness and efficiency of decision-making processes in the dynamic landscape of business and management. Further, the analysis highlights emerging trends and challenges for the future of MCDM research.

Transgender Healthcare: A Historical Narrative of Gender-Affirming Plastic Surgery.

This historical narrative presents a comprehensive historical journey delving into the diverse facets of gender identity and expression across ancient cultures. It explores these civilizations' perspectives on gender diversity, highlighting instances that challenged conventional gender norms. Transitioning to the modern era, the narrative examines the dynamic landscape of LGBTQ+ and trans movements, spotlighting influential figures whose advocacy has significantly influenced societal perceptions and advanced the rights of gender diverse individuals. Their contributions continue to shape contemporary affirming care practices. Exploring the emergence of gender-affirming surgery, the narrative highlights pioneering surgeons and original techniques employed in these transformative procedures. It shows the courage and innovation of early surgeons who laid the groundwork for contemporary practices. At the core of this historical narrative lies the pivotal role of plastic surgeons in gender-affirming care. It emphasizes the significance of a collaborative multidisciplinary approach among medical professionals to ensure comprehensive and patient-centered care for transgender individuals seeking affirming interventions. However, intertwined with this narrative are legislative barriers that have hindered progress in gender-affirming care. The narrative navigates these obstacles, highlighting their complexities and emphasizing the urgent need for sustained medical advocacy and legislative reforms to foster an inclusive healthcare landscape. Ultimately, this narrative stands as a testament to the ongoing struggle and resilience within the realm of gender-affirming care, shedding light on its profound impact on individuals' lives, emphasizing the ongoing need for compassionate, comprehensive, and inclusive healthcare practices.