Automation Capabilities Research Articles

The Salesforce Marketing Cloud (SFMC) platform allows e-commerce businesses to create personalized, data-driven marketing experiences on a scale. The research investigates how SFMC addresses major e-commerce problems by utilizing its marketing automation capabilities and its third-party system integration features. The research builds upon existing knowledge by studying methods for developing custom solutions through API-based workflows, data analytics, and AI-powered personalization, as well as their security and compliance requirements. The paper demonstrates how emerging trends like augmented reality (AR) and AI-driven analytics are transforming e-commerce operations within the Salesforce ecosystem. By synthesizing established best practices with real-world business examples, this study demonstrates how customized Salesforce integration enables businesses to build scalable, efficient, and customer-focused e-commerce experiences.

Business entities throughout various industries gather substantial collections of documents, communications, contractual materials, and administrative records, necessitating robust organizational and access frameworks. Traditional management approaches depend on human-driven classification, keyword-based location methods, and rigid procedural architectures that struggle amid rapidly escalating information volumes. Computational intelligence provides significant automation capabilities for document operations, yet fully automated systems create vulnerability in regulated contexts where errors produce serious consequences. Augmented intelligence frameworks address this tension by combining machine processing power with human judgment, framing technology as an enhancement rather than a replacement of professional expertise. Automated classification generates suggested categories and metadata that knowledgeable personnel verify before adoption, improving accuracy while decreasing repetitive effort. Intelligent redaction identifies potentially protected information requiring expert review and approval before release, maintaining control over legally significant choices. Voice and text-based conversational systems allow staff to retrieve materials, launch approval processes, and generate summaries using natural language queries instead of complex navigation paths. Insurance operations provide concrete evidence of practical benefit through streamlined policy handling, faster claims processing, and seamless connections between content systems and underwriting platforms. Deployment outcomes show meaningful productivity improvements while maintaining accuracy standards. Achieving an effective balance between automation benefits and oversight requirements demands careful framework construction, determining which decisions operate autonomously and which require human involvement, supported by ongoing monitoring of system performance and user experience measures. Intelligent redaction mechanisms identify potentially sensitive materials requiring human examination and authorization before document distribution, preserving oversight in legally consequential decisions. Conversational interfaces utilizing voice recognition and natural language interpretation enable personnel to locate documents, initiate approval sequences, and produce content summaries through intuitive spoken or typed requests rather than navigating elaborate menu architectures. Insurance sector implementations demonstrate tangible value through automated policy document processing, accelerated claims handling, and cross-platform integration connecting content repositories with underwriting and adjudication infrastructure. Implementation results indicate substantial productivity enhancements alongside sustained accuracy benchmarks. Balancing automation advantages against oversight necessities requires deliberate framework design specifying which determinations machines manage autonomously and which demand human participation, supported by continuous performance monitoring and user satisfaction assessment.

Integrating intelligent1 sensors in embedded systems2 has revolutionized various industries by enhancing automation, efficiency, and real-time decision-making3 capabilities. This paper explores the development and implementation of intelligent sensors within embedded systems, highlighting their architecture, functionality, and benefits. Smart sensors, equipped with advanced data processing and communication abilities, facilitate critical information collection, analysis, and transmission, enabling embedded systems to operate with higher precision and autonomy. The discussion includes the design considerations for embedding intelligent sensors, the role of machine learning algorithms in sensor data interpretation, and the challenges associated with power consumption, data security4, and system integration. Case studies across diverse applications such as industrial automation, healthcare monitoring, and environmental sensing illustrate the transformative impact of intelligent sensors. The findings underscore the potential of these technologies to drive innovation in embedded systems, paving the way for smarter, more adaptive, and more efficient solutions.

Accurate and automated identification of water bodies from satellite imagery is critical for environmental monitoring, water resource management, and disaster response. Current deep learning approaches, however, suffer from a strong dependence on manual hyperparameter tuning, which limits their automation capability and robustness in complex, multi-scale scenarios. To overcome this limitation, this study proposes a fully automated segmentation framework that synergistically integrates an enhanced U-Net model with a novel hybrid evolutionary optimization strategy. Extensive experiments on public Kaggle and Sentinel-2 datasets demonstrate the superior performance of our method, which achieves a Pixel Accuracy of 96.79% and an F1-Score of 94.75, outperforming various mainstream baseline models by over 10% in key metrics. The framework effectively addresses the class imbalance problem and enhances feature representation without human intervention. This work provides a viable and efficient path toward fully automated remote sensing image analysis, with significant potential for application in large-scale water resource monitoring, dynamic environmental assessment, and emergency disaster management.

This study aims to analyse the benefits of implementing the Lean Manufacturing concept in improving the performance and competitiveness of the manufacturing industry, as well as to propose strategies for its effective implementation. The research method used is a qualitative literature review, by collecting, analysing, and synthesising findings from 20 journal articles published between 2020 and 2025 that discuss Lean Manufacturing practices in various industrial sectors. The research results show that the application of Lean Manufacturing provides significant benefits, including reducing waste (muda), increasing production efficiency, improving product quality, and enhancing customer satisfaction. In addition, the integration of Lean principles with digital technologies in the Industry 4.0 era—known as Lean 4.0—has been shown to increase operational flexibility, process automation, and real-time decision-making capabilities. However, several challenges such as limited lean culture, lack of management commitment, and employee resistance remain obstacles to sustainable implementation. The conclusion of this study states that Lean Manufacturing is an effective strategic approach for improving industrial performance and competitiveness when supported by strong organizational commitment and continuous improvement culture.

Supernovae (SNe) are among the most energetic and transient events in the universe, offering crucial insights into stellar evolution, nucleosynthesis, and cosmic expansion. Optical observations have historically played a central role in the discovery, classification, and physical interpretation of SNe. In this review, we summarize recent progress in the optical study of SNe, with a focus on advancements in time-domain surveys and photometric and spectroscopic follow-up strategies. High-cadence optical monitoring is pivotal in capturing the diverse behaviors of SNe, from early-time emission to late-phase decline. Leveraging data from ARIES telescopes and national/international collaborations, we systematically investigate various SN types, including Type Iax, IIP/L, IIb, IIn/Ibn and Ib/c events. Our analysis includes light curve evolution and spectral diagnostics, providing insights into early emission signatures (e.g., shock breakout), progenitor systems, explosion mechanisms, and circumstellar medium (CSM) interactions. Through detailed case studies, we demonstrate the importance of both early-time and nebular-phase observations in constraining progenitor and CSM properties. This comprehensive approach underscores the importance of coordinated global efforts in time-domain astronomy to deepen our understanding of SN diversity. We conclude by discussing the challenges and opportunities for future optical studies in the era of wide-field observatories such as the Vera C. Rubin Observatory (hereafter Rubin), with an emphasis on detection strategies, automation, and rapid-response capabilities.

3D-printed microfluidic integrated magnetic robot for biofluid analysis.

With the growth of trusted computing demand for big data analysis, cloud computing platforms are reshaping trusted data infrastructure by integrating Blockchain as a Service (BaaS), which uses elastic resource scheduling and heterogeneous hardware acceleration to support petabyte level multi-institution data security exchange in medical, financial, and other fields. As the core hub of data-intensive scenarios, the BaaS platform has the dual capabilities of privacy computing and process automation. However, its deep dependence on smart contracts generates new code layer vulnerabilities, resulting in malicious contamination of analysis results. The existing detection schemes are limited to the perspective of single-source data, which makes it difficult to capture both global semantic associations and local structural details in a cloud computing environment, leading to a performance bottleneck in terms of scalability and detection accuracy. To address these challenges, this paper proposes a smart contract vulnerability detection method based on multi-modal semantic fusion for the blockchain analysis platform of cloud computing. Firstly, the contract source code is parsed into an abstract syntax tree, and the key code is accurately located based on the predefined vulnerability feature set. Then, the text features and graph structure features of key codes are extracted in parallel to realize the deep fusion of them. Finally, with the help of attention enhancement, the vulnerability probability is output through the fully connected network. The experiments on Ethereum benchmark datasets show that the detection accuracy of our method for re-entrancy vulnerability, timestamp vulnerability, overflow/underflow vulnerability, and delegatecall vulnerability can reach 92.2%, 96.3%, 91.4%, and 89.5%, surpassing previous methods. Additionally, our method has the potential for practical deployment in cloud-based blockchain service environments.

This article examines the dual impact of SAP S/4HANA as both a catalyst for organizational transformation and an enabler of positive societal change. The article explores how this advanced enterprise resource planning solution transcends traditional ERP functionality through its in-memory computing architecture, integrated analytics, and intelligent automation capabilities. The article shows progress through five interconnected dimensions: the foundational transformation of business operations, the enhancement of organizational efficiency through real-time analytics and process automation, the advancement of sustainable business practices through resource optimization and environmental compliance tools, the evolution of human capital management strategies, and the broader societal impacts spanning from customer experience to corporate social responsibility initiatives. Through synthesis of contemporary research, the article demonstrates how SAP S/4HANA implementations facilitate not merely technological upgrades but fundamental reimagining of business-society relationships, creating value that extends beyond implementing organizations to broader economic ecosystems and social systems.

Abstract Background Therapeutic Drug Monitoring (TDM) plays a crucial role in optimizing drug therapy and toxin testing. Traditional sample preparation techniques, such as protein precipitation, often encounter challenges, including low automation, matrix effects, and interference from large molecules, which can impact both accuracy and sensitivity. While immunoassays offer speed and sensitivity, they are limited in terms of drug versatility and may suffer from cross-reactivity. To address these limitations, magnetic bead-based automated sample preparation has emerged as a promising solution. This method facilitates drug extraction, purification, and minimizes matrix effects, making it ideal for complex biological matrices. With its high efficiency, broad compatibility with various drug types, and automation capabilities, magnetic bead-based preparation is well-suited for TDM applications in both clinical and pharmaceutical research, as well as toxin management. Methods Magnetic beads functionalized with silica materials were employed to selectively extract multiple target drugs from serum samples. This process was integrated into an automated system capable of processing up to 96 samples within 10 minutes. The high-throughput method eliminates the need for centrifugation and vortexing, reducing interference from phospholipids. Drug extraction efficiency and specificity were evaluated across a broad range of drug classes. The system was tested with various drugs, including antipsychotics, antidepressants, antiepileptics, analgesics, and others, demonstrating excellent extraction efficiency and sample purification. LC-MS/MS was used for precise quantification of the extracted drugs. Results The magnetic beads demonstrated high efficiency in extracting a wide range of therapeutic drugs from serum samples. The beads, with their unique surface structure, efficiently captured both non-polar and polar compounds, resulting in superior extraction performance compared to traditional protein precipitation. The method optimized extraction conditions, such as pH adjustments, and minimized interference from phospholipids, improving sensitivity and accuracy in therapeutic drug monitoring (TDM) applications. Conclusion Magnetic bead-based automated sample preparation offers a rapid, high-throughput solution for TDM. The method ensures efficient drug extraction, minimizes sample matrix interference, and improves the precision of drug quantification using LC-MS/MS. With reduced manual labor, shorter processing times, and enhanced sensitivity, this approach is ideal for clinical and pharmaceutical applications, providing a reliable and standardized tool for therapeutic drug monitoring across diverse drug classes.

The financial services industry has undergone a profound architectural transformation as institutions abandon traditional monolithic systems in favor of distributed microservices architectures that better align with modern business demands for agility, scalability, and regulatory compliance. This comprehensive article examines the cutting-edge innovations in microservices architecture specifically tailored for financial environments, exploring how container orchestration technologies, service mesh implementations, and event-driven patterns address the unique challenges of maintaining data consistency, ensuring robust security, and meeting stringent regulatory requirements in distributed systems. The article reveals that successful microservices adoption in financial services requires sophisticated approaches to distributed data management, performance optimization for latency-sensitive operations, and comprehensive observability frameworks that enable effective monitoring and troubleshooting across complex service topologies. Contemporary implementations demonstrate innovative solutions for managing distributed transactions through saga patterns, implementing zero-trust security models, and achieving regulatory compliance through automated audit trails and policy enforcement mechanisms. The article of real-world case studies from major financial institutions and fintech organizations illustrates both the transformative potential and inherent complexities of microservices architectures, highlighting critical success factors including gradual migration strategies, organizational restructuring, and substantial investments in platform automation and team capabilities. Emerging trends toward serverless computing, artificial intelligence integration, and quantum-safe security preparations indicate that the architectural evolution will continue accelerating, requiring financial institutions to develop adaptive technology strategies that balance innovation with the stability and compliance requirements fundamental to financial services operations.

Cell therapies represent a transformative approach for treating diseases resistant to conventional therapies, yet their development and manufacturing face significant hurdles within the biopharmaceutical sector. A critical parameter in the production of these therapies is cell confluency, which serves as both an indicator of biomass in adherent cultures and a determinant of product quality. However, existing methods for measuring confluency are often inadequate for the large-scale cultivation systems used in industry, and current software solutions lack comprehensive automation capabilities necessary for a manufacturing environment. This article introduces a novel image-based software application designed for accurate cell confluency estimation, integrated with a high-throughput microscopy system. Utilizing a machine-learning model for pixel classification, the application facilitates efficient image and metadata processing in a cloud environment, delivering results through an interactive web interface. By incorporating methods from process analytical technologies, manufacturing data digitalization, and data science, this platform enables automated image acquisition, storage, analysis, and reporting in near-real time. The proposed solution aims to streamline the manufacturing process of cell therapeutics, ultimately enhancing the reliability and speed of delivering these innovative treatments to patients.

Large-scale measurement of key feature points (KFPs) on an aircraft is essential for coordinated movement of locators, which is critical to the final assembly accuracy. Due to the large number and wide distribution of KFPs as well as line-of-sight occlusion, network measurement of laser trackers (LTs) is required. Existing approaches rely on operational experience for the configuration of stations, sequences and station transitions of LTs, which compromises both efficiency and automation capability. To tackle this challenge, this article presents an autonomous mobile measurement method for KFPs in complex scenes of aircraft assembly, incorporating path self-planning and self-positioning capabilities, thereby substantially diminishing temporal expenditure. Firstly, a simultaneous self-planning method of measurement stations and tasks is proposed to determine the minimum number of stations, optimal locations, and their specific KFPs at each station. Secondly, considering obstacles and turning time, a path planning model of mobile LTs combining coarse and fine localization is established to realize automatic station transitions. Finally, an optimal sequence of series of KFPs with a wide spatial distribution is generated to minimize total distance. Aircraft component assembly experiments validated the method, cutting measurement error by 37% and total measurement time by over 50%.

The evolving landscape of business-to-business financial operations demands a fundamental reimagining of payment strategies that transcends traditional approaches constrained by delays, inefficiencies, and fragmented workflows. This comprehensive article examines the transformational potential of converging three critical payment technologies—real-time payments, virtual cards, and dynamic discounting—into integrated ecosystems that simultaneously address speed, control, and cost optimization challenges facing modern organizations. Through extensive mixed-methods analysis combining quantitative performance metrics with qualitative organizational insights across multiple industry sectors, this article reveals that organizations adopting siloed approaches to payment innovation forfeit significant synergistic benefits that emerge from coordinated technology deployment. The article demonstrates how integrated payment solutions create compound value through enhanced cash flow predictability, streamlined operational processes, and strengthened supplier relationships that extend beyond mere transaction processing to strategic competitive advantage. Drawing upon longitudinal case studies from Fortune 500 companies, comprehensive industry surveys, and expert interviews spanning twelve months, this study establishes evidence-based frameworks for successful implementation while addressing critical challenges including technical integration complexity, organizational change management, and supplier readiness considerations. The article indicates that convergence strategies enable sophisticated payment optimization through data integration, workflow automation, and intelligent decision-making capabilities that position organizations for sustained competitive advantage in increasingly digital business environments. This article contributes both theoretical understanding of payment technology convergence and practical guidance for organizations seeking to harness these innovations for transformational financial performance improvement, ultimately arguing that integrated payment ecosystems represent an evolutionary leap toward more agile, efficient, and strategically valuable B2B financial operations.

Purpose This paper aims to present a digital maturity model for intelligent automation, specifically designed for small and medium-sized accounting firms. Intelligent automation facilitates the execution of complex accounting tasks, leading to notable improvements in operational efficiency, client satisfaction and employee engagement. Despite these benefits, smaller firms often face challenges in adopting automation due to constrained resources and limited expertise. The proposed maturity model supports firms in evaluating their current level of digital advancement and provides a structured pathway for progressing towards more sophisticated automation capabilities. Design/methodology/approach The model was developed following a design science-based procedure model. The development began with a systematic literature review. Subsequently, the model was refined on the basis of feedback from 15 expert interviews conducted in Finland. Finally, it was evaluated in three accounting firms. Findings The final digital maturity model encompasses 11 dimensions. Four of the dimensions are typical for digital maturity models: technology, processes, vision and leadership and data and cybersecurity. The remaining dimensions are tailored to accounting firms: role of the accountant, sales to cash, purchase to pay, payments, general ledger, reporting and customer communication and payroll. Each dimension is scaled across five levels: non-existent, initial, proficient, advanced and continuous improvement. Originality/value The developed digital maturity model advances the limited academic literature on digital maturity models in accounting, demonstrates the application of intelligent automation within accounting firms and serves as a practical tool to support these firms in their automation efforts.

Reinforcement Learning (RL) has been considered a promising method to enable the automation of contact-rich manipulation tasks, which can increase capabilities for industrial automation. RL facilitates autonomous agents’ learning to solve environments with complex dynamics with little human intervention, making it easier to implement control strategies for contact-rich tasks compared to traditional control approaches. Further, RL-based robotic control has the potential to transfer policies between task variations, significantly improving scalability compared to existing methods. However, RL is currently inviable for wider adoption due to its relatively high implementation costs and safety issues, so current research has been focused on addressing these issues. This paper comprehensively reviewed recently developed techniques to improve cost and safety for RL in contact-rich robotic manipulation. Techniques were organized by their approach, and their impact was analysed. It was found that current research efforts have significantly improved the cost and safety of RL-based control for contact-rich tasks, but further improvements can be made by progressing research towards improving knowledge transfer between tasks, improving inter-robot policy transfer and facilitating real-world and continual RL. The identified directions for further research set the stage for future developments in more versatile and cost-effective RL-based control for contact-rich robotic manipulation in future industrial automation applications.

Metal additive manufacturing techniques have seen technological advancements in recent years, fueled by their ability to provide industrial use parts with excellent mechanical properties. Wire Arc Additive Manufacturing is a technology that is being widely used in critical industries, and much research is conducted in this field due to the multiple factors involved in the overall process. Within WAAM, gas metal arc welding stands out for its low cost, high production volume, high quality and capability for automation. In this study, a CNC router was retrofitted with a gas metal arc welding setup to facilitate precise metal printing. The flexibility in this process allows for rapid repairs on site without the need to replace the entire part. The literature predominantly focuses on the macro-mechanical properties of GMAW parts, and very few studies try to study the interaction and influence of different process parameters on the mechanical properties. Thus, this study focused on the GMAW WAAM of stainless-steel parts by studying the influence of the wire feed rate, arc voltage and strain rate on the UTS, yield strength, toughness and percentage elongation. ANOVA and interaction plots were analyzed to study the interaction between the input parameters on each output parameter. Results showed that printing stainless steel through the gas metal arc welding process with an arc voltage of 18.7 V and a wire feed rate of 6 m/min resulted in poor mechanical properties. The input parameter that influenced the mechanical properties the highest was the wire feed rate, followed by the arc voltage and strain rate. Printing with an arc voltage of 18.7 V and a wire feed rate of 5 m/min, tested at a crosshead speed of 1 mm/min, gave the best mechanical properties.

Comparative assessment of a multiplex micro-chip immunoassay, VaxArray, and meso scale discovery assay for serotype-specific coronavirus IgG quantitation.

Machine Learning has emerged as a transformative force in supply chain management within the technology industry, fundamentally reshaping traditional paradigms through intelligent automation and predictive capabilities. The integration of ML algorithms enables organizations to transition from reactive operational models to proactive, data-driven decision-making frameworks that enhance competitive advantage through significant improvements in operational efficiency and cost reduction. Advanced ML technologies, including neural networks, ensemble methods, and reinforcement learning algorithms, demonstrate substantial improvements in demand forecasting accuracy, inventory optimization efficiency, and supplier risk management effectiveness. Deep learning applications process vast datasets to identify complex patterns and correlations that conventional statistical methods cannot detect, enabling superior market alignment and improved resource allocation. Multi-echelon inventory optimization systems consider entire supply networks as interconnected ecosystems, optimizing allocation across distribution centers and warehouses while reducing carrying costs. Natural language processing algorithms analyze extensive documentation to maintain current supplier risk assessments, providing early warning systems for potential disruptions. However, ML implementation presents considerable challenges, including data privacy concerns, security vulnerabilities, algorithmic bias issues, substantial implementation costs, and integration complexities with legacy systems. Organizations must navigate vendor lock-in risks, over-reliance on automation, and the need for skilled personnel while maintaining human oversight for critical decisions. Successful ML adoption requires comprehensive data governance frameworks, hybrid human-machine collaboration models, vendor diversification strategies, and continuous monitoring mechanisms to ensure sustained performance and ethical operation.

This technical review examines architectural solutions implemented during a pharmaceutical pilot transformation initiative, focusing on the complex challenges of delivering compliant, scalable data architectures in highly regulated environments. The transformation addressed critical integration challenges across heterogeneous data sources spanning clinical, commercial, and regulatory domains while maintaining strict compliance with global healthcare regulations, including GDPR, HIPAA, and FDA requirements. A comprehensive hybrid cloud architecture was developed, leveraging containerized microservices for processing capabilities while preserving on-premises storage for sensitive patient data, ensuring regulatory compliance across multiple jurisdictions. The initiative implemented sophisticated metadata management frameworks using enterprise-grade governance solutions to address fragmented data landscapes and incomplete data catalogs that historically hindered operational efficiency. Cross-functional stakeholder alignment emerged as a fundamental success factor, requiring matrix governance structures and tailored communication strategies to bridge technical architecture decisions with diverse business objectives across therapeutic areas. Automation capabilities proved essential for managing compliance monitoring, data quality assurance, and regulatory audit preparation while maintaining auditability and explainability for regulatory authorities. The pilot demonstrated that adaptive data governance frameworks capable of accommodating evolving pharmaceutical regulations without fundamental architectural redesign are critical for long-term success in dynamic regulatory environments.