Automation Strategies Research Articles

Conceptual Design of Simulation-Based Approach for Robotic Automation Systems: A Case Study of Tray Transporting

This study investigated the application of robotic automation in food manufacturing, focusing on enhancing tray transporting operations through a simulation-based approach. The findings primarily focused on bakery production but also demonstrate broader applicability to other sectors that involve repetitive and labor-intensive tasks. The researchers analyzed worker fatigue and limited productivity associated with manual tray handling. To evaluate these issues, simulations were conducted for two scenarios (Case A and Case B), applying robotic automation systems at different stages of production. Key performance indicators (throughput and utilization rates) were analyzed to assess improvements in process efficiency and reductions in worker strain. The results showed that robotic automation significantly increased throughput by 83.7% in simpler processes and by 27.1% in more complex ones, highlighting the impact of task complexity on automation effectiveness. Workforce demands decreased and demonstrated the potential of automation to alleviate physical strain in repetitive tasks. Simulations provided insights into workflow optimization, confirming their value as reliable tools for planning and refining automation strategies. The proposed framework offers a flexible and scalable solution for enhancing efficiency and consistency in manufacturing. Future research should apply similar approaches to other industries and explore the integration of human and robotic labor to further optimize safety, productivity, and cost effectiveness.

Navigating Cloud Computing: Exploring Fundamental and Advanced Concepts, Architectures, and Security Mechanisms

This comprehensive article explores the evolution and current state of cloud computing, addressing fundamental concepts, advanced architectures, and security mechanisms. The article examines the transformation from traditional infrastructure to modern cloud environments, highlighting key technological advancements in virtualization, containerization, and service delivery models. It investigates critical aspects of cloud security, management mechanisms, and specialized cloud architectures while analyzing various deployment patterns and their implications. The article encompasses technical and operational perspectives, providing insights into infrastructure components, automation strategies, and delivery model considerations. Through a detailed examination of reference architectures, design patterns, and implementation strategies, this study offers a holistic view of cloud computing's impact on enterprise operations and its role in driving digital transformation.

A Comparative Framework for Intent Classification Systems: Evaluating Large Language Models versus Traditional Machine Learning in Contact Center Applications

Modern contact centers face increasingly complex decisions when selecting appropriate technologies for intent identification systems. This article presents a systematic comparative analysis of Large Language Models (LLMs) and traditional Machine Learning (ML) approaches in contact center environments, examining their relative efficacy across various operational contexts. Through a comprehensive evaluation framework, we assess seven critical dimensions: data complexity, training requirements, performance metrics, resource utilization, customization capabilities, deployment considerations, and hybrid implementation strategies. Our findings indicate that LLMs demonstrate superior performance in scenarios involving complex linguistic patterns and contextual understanding, while traditional ML models maintain advantages in resource-constrained environments and clearly defined intent categories. We propose a novel decision framework that enables organizations to optimize their technology selection based on specific operational requirements, resource availability, and performance needs. The article contributes to both theoretical understanding and practical implementation by providing evidence-based guidelines for selecting and implementing intent identification systems. The results suggest that hybrid approaches, combining the strengths of both LLMs and traditional ML models, offer promising solutions for organizations seeking to balance sophisticated language understanding with operational efficiency These findings have significant implications for contact center automation strategies and provide a foundation for future research in adaptive intent classification systems.

Multi-Cloud Automation : A Strategic Approach to Cloud Infrastructure Management

This article examines the evolution and impact of multi-cloud automation strategies in modern enterprise environments, supported by comprehensive industry research and case studies. Drawing from multiple industry reports, including Flexera's 2024 State of the Cloud Report and PwC's Cloud and AI Business Survey, the research reveals that 89% of enterprises now employ multi-cloud strategies, with the global multi-cloud management market projected to grow at a CAGR of 27.3% through 2030. The article analyzes key components of successful multi-cloud automation, including deployment automation, operational excellence through monitoring, and incident management, while providing detailed metrics on their effectiveness. Through examination of real-world implementations across retail, financial services, and SaaS sectors, the research demonstrates how organizations achieve significant improvements in operational efficiency, cost optimization, and business agility through comprehensive automation strategies.

Test Case Automation: Transforming Software Testing in the Digital Era

Purpose: This research aims to explore the limitations of manual software testing, the effectiveness of test case automation in addressing these challenges, and the strategies for successful implementation of automation. It also examines the broader impact of adopting test automation on the quality, speed, and efficiency of software delivery. Methodology: The study investigates the challenges of manual testing in agile and DevOps environments and explores the role of automation as a solution. By analyzing various automation tools, frameworks, and best practices, the research provides insights into successful implementation strategies for test case automation. Additionally, it reviews the associated benefits and potential challenges organizations may face when transitioning to automated testing. Findings: Test case automation significantly mitigates the shortcomings of manual testing, particularly in fast-paced development cycles. The findings reveal that automation not only enhances the quality of software by catching defects earlier but also accelerates the testing process, improving overall efficiency. However, the study also identifies challenges, such as the initial investment in tools and skills required for automation, as well as the ongoing need for maintenance of automated test cases. Unique Contribution to Theory, Policy and Practice: This research contributes to the theoretical understanding of software testing (Tom Badgett, Corey Sandler & Glenford J. Myers, 2011) by highlighting the essential role of automation in modern software development methodologies like agile and DevOps. It bridges the gap between traditional manual testing and contemporary automated practices, providing a framework for understanding how automation fits within these rapid development paradigms. In practice, the study encourages organizations to adopt test case automation as a critical policy for improving the quality and speed of software delivery. It offers practical insights into the implementation of automation, including tool selection, training, and process integration, which can guide companies in transitioning to automated testing. Additionally, it highlights the long-term benefits of automation, making it a valuable policy consideration for organizations aiming to optimize their development and testing processes.

Exploring Built Environment Visual Interactions: A SoftBIM Data-Driven Approach for a Database About the Outdoor View

Windows and glazed facades provide outdoor views, serving as vital sources of visual information that aid navigation and interaction within buildings. These views can trigger psychological and physiological responses, affecting individual well-being. However, optimizing outdoor view quality is challenging due to the complex interplay of factors influencing the building’s experience of vision. Managing the complexity of optimizing outdoor view quality within current digital frameworks for building design presents significant challenges. A key issue lies in the ambiguity of certain visual metrics, which are often difficult to translate into explicit descriptors of spatial configurations. Even when such metrics are available, their practical use as guiding tools in the design process is frequently obstructed by complex data interoperability procedures. These procedures are necessary to enable seamless data transfer across the multiple software environments involved in the design process. This study advocates for the softBIM paradigm, which optimizes workflows by embedding visual analysis results into target geometries. Supported by this process, the calculation of a metric to measure the impact of existing and planned visual obstructions on the vision of the targeted landmarks is proposed and analysed. This metric is specifically applied to assess the visual information incoming to the vertical facades of building envelopes, a context of application that denotes criteria of assessment different from the ones usually applied in the most established frameworks for visual analysis (e.g., isovist analysis). SoftBIM enables effective automation strategies to aid the metric computation and the processing of the results to implement seamless export and data implementation. The visual metric is built upon implementing the Ladybug suite and addresses the different limitations in the target-based visibility calculation supported by the tool.

Continuous Integration and Continuous Deployment (CI/CD) Optimization

The advent of Continuous Integration and Continuous Deployment (CI/CD) has fundamentally altered the landscape of software development, enabling teams to deliver updates with unprecedented speed and reliability. By automating the integration of code changes from multiple developers into a central repository, CI/CD practices ensure that software is continuously tested and deployed. This ongoing cycle not only facilitates quicker release cycles but also enhances collaboration among team members and fosters a culture of shared responsibility for code quality. Despite these advancements, organizations face significant challenges in optimizing their CI/CD pipelines. As software systems grow in complexity, the demand for swift and dependable deployments intensifies. This paper explores various techniques and strategies for optimizing CI/CD processes to minimize deployment times while maintaining system reliability. Key optimization methods discussed include: Parallelization of Build Processes: This technique involves breaking down the build process into smaller, independent tasks that can be executed concurrently. By leveraging distributed computing resources, organizations can significantly reduce build times, allowing for faster iterations and deployments. Dependency Caching: Caching dependencies can drastically decrease build times by reusing previously downloaded components. This approach not only speeds up the build process but also minimizes network load and enhances the overall efficiency of the CI/CD pipeline. Incremental Builds: Unlike full builds that compile the entire codebase, incremental builds focus on compiling only the changes made since the last build. This strategy reduces the amount of work needed for each build, accelerating the overall development process. The paper also delves into advanced rollback mechanisms such as blue-green deployments and canary releases. Blue-green deployments allow teams to maintain two identical production environments, enabling smooth transitions and quick rollbacks in case of issues. Canary deployments, on the other hand, introduce new features to a small subset of users before a full rollout, allowing teams to monitor the impact and catch potential failures early. Additionally, automated rollback mechanisms play a vital role in maintaining system reliability, ensuring that any failed deployments can be reverted swiftly to avoid downtime and user disruption. The impact of automation tools on deployment speed and error reduction is another critical aspect examined in this research. Automation frameworks can streamline various stages of the CI/CD process, from code integration to testing and deployment, minimizing human error and ensuring consistent, repeatable processes. By implementing robust automation strategies, organizations can not only accelerate their deployment cycles but also improve overall software quality.

AIOps in Action: Automating AI Deployment and Management of Large Language Models for Scalable and Ethical Operations

This comprehensive article explores the transformative role of Artificial Intelligence for IT Operations (AIOps) in the deployment and management of Large Language Models (LLMs). It delves into the automation strategies that streamline LLM deployment, including data preparation, model training optimization, and continuous integration and deployment practices. The article addresses the unique challenges in LLM management, such as resource allocation complexities and latency issues, presenting AIOps-driven solutions that leverage predictive analytics and dynamic scaling techniques. A significant focus is placed on the synergies between AIOps and MLOps, highlighting how their integration enhances model versioning, governance, and performance monitoring. The article also examines the critical aspects of real-time monitoring and incident management, showcasing how AIOps enables sophisticated anomaly detection and automated incident response. Ethical considerations in AIOps-driven LLM deployment are thoroughly discussed, emphasizing the importance of bias mitigation, transparency, and accountability. Looking ahead, the article explores future trends in AIOps for LLM management, including advancements in automation technologies and their implications for operational scalability and efficiency. Through a combination of theoretical analysis and practical case studies, this article provides a comprehensive overview of how AIOps is revolutionizing the landscape of AI operations, offering insights into both the current state and future potential of automated, scalable, and ethically responsible LLM management.

Автоматизований метод перевірки та налагодження тестових сценаріїв на основі формальної моделі

Introduction. Model-based test cases generation is a popular strategy for test automation. It helps to reduce time spent on the development of a test suite and can improve level of coverage. However, many reports show shortage of such test cases in poor quality and doubtable efficiency. Purpose. The main goal of the proposed method is cost-effective validation, assessment, debugging and concretization of generated test cases. The method helps improve quality and efficiency of the test cases, make their scenario meaningful and goal-oriented. The method also develops debugging facilities and simplifies data dependency analysis and test scenario editing. Methods. We propose an automated post-processing method which allows to evaluate path that is examined by the test case, and to make safe changes to the path which will eliminate the shortcomings while leaving the coverage targets of the test case unharmed. The method is based on visualization of the path along the control flow graph of the model with additional information about factual evaluation history of all variables and possible alternative variants of behavior. For consistent substitution of certain values in the signal parameters, which would determine the artifacts of the test environment (such as, for example, files, databases, etc.) and check boundary cases (in predicates of conditions, indexing of arrays, etc.), a method of interactive specification of symbolic traces has been developed. Results. The role of the user in deciding whether to add a test case to the project test suite and make changes to it remains crucial, but to reduce labor intensity, the following processes are automated: evaluation of test scenarios according to certain objective characteristics (level of coverage, ability to detect defects, data cohesion, etc.); highlighting of possible alternatives for making corrections; consistent updating of computations for the corresponding corrections. A prototype was developed based on the proposed methods. The empirical results demonstrated a positive impact on the overall efficiency (ability to detect defects and reduce resource consumption) and quality (meaningfulness, readability, maintenance, usefulness for debugging, etc) of the generated test suites. The method allows to make automatically generated test cases trustable and usable. Conclusion. The proposed toolkit significantly reduces the time spent on researching the results of test generation and validation of the obtained tests and their editing. Unlike existing simulation methods, the proposed method not only informs about the values of variables, but also explores the history of their computations and additionally provides information about admissible alternatives. Further we plan to improve the process of localizing the causes of test failure at execution phase to speed up the search for defects.

RESEARCH OF MODERN DIGITAL MARKETING TRENDS

Today's digital marketing is undergoing significant changes due to digital transformation. The article explores the key trends shaping the modern digital marketing paradigm, including process automation, content personalization, the use of big data, as well as the latest digital technologies such as VR and AR, artificial intelligence. The article pays special attention to the challenges and opportunities that companies face in the process of adapting to the digital environment. The article analyzes modern digital marketing tools. Today's digital marketing tools are essential for any business because they allow you to reach your target audience through a variety of digital channels. They provide effective interaction with customers, including personalization of communications and offers. During the study, the practical aspects of the integration of digital technologies into marketing activity with the aim of increasing its efficiency and effectiveness were determined. Modern digital marketing trends play a primary role in the development of a company's digital marketing strategy. A digital marketing strategy allows you to respond more quickly to changes in the market and adapt to new conditions, which ensures the sustainable development of the company in the conditions of the digital economy. In today's world, where digital transformation is transforming business processes and consumer habits, digital marketing is becoming a necessity for businesses in all sectors of the digital economy. An analysis of modern digital marketing trends demonstrates that automation strategies, the use of big data, personalization of content and the introduction of the latest technologies are critical for successful competition and attracting the modern consumer. Modern digital marketing tools are analyzed, the advantages of the development of digital tools in the context of digital marketing are determined and substantiated, practical aspects of the integration of the latest technologies in marketing activities are proposed to increase their effectiveness and efficiency. The integration of digital technologies and tools allows companies to quickly adapt to changing market conditions and remain competitive. This contributes to the creation of more flexible, adaptive and personalized marketing strategies that meet the modern needs of consumers. The results of the conducted studies emphasize the importance of constant improvement of marketing strategies and adaptation to rapidly changing market conditions to effectively use the potential of digital tools in marketing activities.

A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs

Specifying legal requirements for software systems to ensure their compliance with the applicable regulations is a major concern of requirements engineering. Personal data which is collected by an organization is often shared with other organizations to perform certain processing activities. In such cases, the General Data Protection Regulation (GDPR) requires issuing a data processing agreement (DPA) which regulates the processing and further ensures that personal data remains protected. Violating GDPR can lead to huge fines reaching to billions of Euros. Software systems involving personal data processing must adhere to the legal obligations stipulated both at a general level in GDPR as well as the obligations outlined in DPAs highlighting specific business. In other words, a DPA is yet another source from which requirements engineers can elicit legal requirements. However, the DPA must be complete according to GDPR to ensure that the elicited requirements cover the complete set of obligations. Therefore, checking the completeness of DPAs is a prerequisite step towards developing a compliant system. Analyzing DPAs with respect to GDPR entirely manually is time consuming and requires adequate legal expertise. In this paper, we propose an automation strategy that addresses the completeness checking of DPAs against GDPR provisions as a text classification problem. Specifically, we pursue ten alternative solutions which are enabled by different technologies, namely traditional machine learning, deep learning, language modeling, and few-shot learning. The goal of our work is to empirically examine how these different technologies fare in the legal domain. We computed F2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} score on a set of 30 real DPAs. Our evaluation shows that best-performing solutions yield F2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} score of 86.7% and 89.7% are based on pre-trained BERT and RoBERTa language models. Our analysis further shows that other alternative solutions based on deep learning (e.g., BiLSTM) and few-shot learning (e.g., SetFit) can achieve comparable accuracy, yet are more efficient to develop.

IoT-based prediction and classification framework for smart farming using adaptive multi-scale deep networks

Agriculture is one of the prime economical sources of India and most of the people directly or indirectly depend on farming. The researchers are focusing on plant ailment detection and managing the imbalanced nutrition in plants. Automation is introduced in agricultural fields and most of these automation strategies use the Internet of Things (IoT) for enhance productivity and automate processes. With the help of several deep and machine learning approaches the endless decision-making performance is performed. Here, the endless decision performance shows appropriate outcomes which helps to solve the unstructured problems in smart farming. It is monitored that the traditional analysis doesn’t have enough decision-making ability in the selection of fertilizer quantity that is to be used in farming. This inability leads to crop ailments and that affects the lifestyle of humans too. So, the prior detection of ailments in crops is essential. Enforcing Smart Agriculture is a hot topic in research nowadays to overcome crop damage in the future. Therefore, a new IoT-based smart farming model using deep learning is proposed to increase crop yield. By detecting disease, pests, smart irrigation, and yield, the smart farming model can reduce the amount of water and chemicals used in agriculture. This smart farming model consists of four phases a) disease prediction, b) pest detection c) smart irrigation, and d) yield prediction. In the first phase, the crop images are gathered from online datasets. The diseases in crops are predicted using Multi-scale Adaptive CNN with LSTM layer (MA-CNN-LSTM), where the parameters in MA-CNN-LSTM are optimized using Advanced Mountaineering Team-Based Optimization Algorithm (AMTBO). In the second phase, the input images are given to MA-CNN-LSTM to detect crop pests. Here, the AMTBO is utilized for tuning parameters. In the third phase, the soil quality and environment data are fed into the Multi-scale Adaptive 1DCNN with LSTM layer (MA-1D CNN-LSTM) to predict the smart irrigation, where the parameter optimization is done using the AMTBO. Smart irrigation enhances the growth of crops and minimizes water usage. In the final phase, the input data such as crop quality, soil quality, and environment data are given to the MA-1D CNN-LSTM to check the overall yield prediction in an agricultural region. Here, the parameters in MA-1D CNN-LSTM are optimized via the AMTBO. The simulation results are compared with other algorithms and classification techniques to check the performance of the developed model.

Three-Dimensional Concrete Printing as a Construction Automation Strategy and Assessments from a Case Study Building

Three-Dimensional Concrete Printing as a Construction Automation Strategy and Assessments from a Case Study Building

Enhancing U.S. workforce productivity through strategic data automation: Key insights and implications

The review explores how strategic data automation can enhance workforce productivity in the U.S., providing key insights and implications for organizations. As businesses increasingly rely on data-driven decision-making, the role of automation in streamlining processes and improving efficiency becomes paramount. This review delves into the potential benefits and challenges of implementing strategic data automation strategies and offers recommendations for organizations looking to enhance their workforce productivity. Strategic data automation has the potential to revolutionize how businesses operate, leading to significant improvements in efficiency and productivity. By automating repetitive tasks and streamlining data processing, organizations can free up valuable time and resources, allowing employees to focus on more strategic and value-added activities. However, the successful implementation of strategic data automation requires careful planning and consideration of various factors, including data security, privacy, and regulatory compliance. Key insights from this review include the importance of aligning data automation initiatives with organizational goals and objectives, as well as the need for ongoing monitoring and evaluation to ensure effectiveness. Additionally, the review explores the implications of strategic data automation for the future of work, highlighting the potential for increased collaboration between humans and machines and the need for new skills and competencies. Overall, this review provides valuable insights into how strategic data automation can enhance workforce productivity in the U.S. It highlights the benefits of automation, such as improved efficiency and reduced costs, while also addressing potential challenges and considerations for organizations. By leveraging the power of strategic data automation, organizations can position themselves for success in an increasingly data-driven world.

A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.

Evaporation automation at the Central de Mieles de Útica, Colombia, for non-centrifugal sugar cane production: Sustainable optimization strategies

Non-centrifugal sugar cane production is important worldwide for its economic, cultural, culinary, and sustainable impact. Colombia contributes to the Latin American sugar industry and the country's economy, preserving cultural traditions and rural employment. This research analyzed the productive variables of the non-centrifugal evaporation process of sugar cane automation and their relationship based on a descriptive-correlational quantitative methodology. It was found that the sugar levels in the cane granules must be <1.5 %, for which the cane syrup must have between 65.2 and 66.4 °Bx, pH between 4.56 and 5.7, temperature dependent on the altitude of the area and the boiling and sedimentation point <10 %. Automation strategies were proposed with sensors to optimize technological resources in evaporation processes. This will improve operational efficiency, productivity, workplace safety, product quality, and sustainability within the non-centrifugal sugarcane industry, contributing to Sustainable Development Goal 9, which focuses on promoting innovation and industrial infrastructure.

Strategy formulation to support automation and digitalization initiative: A case study of an aerospace component manufacturer in Indonesia

Purpose: Strategy formulation is part of organization design study to evaluate decision thoroughly, then enable organization to achieve company goal. This study aims to formulate strategy of automation and digitalization for PT XYZ, to be ready for welcoming future growth of demand by maintain competitive cost within the same industry. Method: The study collects primary data from focus group discussion with selected participant of PT XYZ, located in Bandung, Indonesia, with participant selection based on information rich person reference. Data collection and analysis utilize qualitative and quantitative approach, and utilize three steps of strategy formulation (input stage, matching stage and decision stage). Results: Strategy formulation steps as per theory, applicable to finalize strategy to support industry 4.0 transformation at PT XYZ. Focus Group Discussion able to collect organization aspiration, to ensure strategy will align to company objective. Combine steps with latest internal and external factor analysis method, such as PESTLE (Political Economy Social Technological Environment), VRIO (Value Rarity InImitable Organize), Porter Value Chain analysis and QSPM (Quantitative Strategic Planning Matrix) help broaden the perspective of the participant during discussion. The study resulted eight strategies, which then simplified by data reduction approach to be four. A decision of sequence implementation also agreed during focus group discussion with participants. Contributions: By conducting this study, author expect it will prove the practical implementation of strategy formulation in industry 4.0 transformation for aerospace industry in Indonesia and Asia.

Robotics-Driven Manufacturing of Cartilaginous Microtissues for Skeletal Tissue Engineering Applications.

Automated technologies are attractive for enhancing the robust manufacturing of tissue-engineered products for clinical translation. In this work, we present an automation strategy using a robotics platform for media changes, and imaging of cartilaginous microtissues cultured in static microwell platforms. We use an automated image analysis pipeline to extract microtissue displacements and morphological features as noninvasive quality attributes. As a result, empty microwells were identified with a 96% accuracy, and dice coefficient of 0.84 for segmentation. Design of experiment are used for the optimization of liquid handling parameters to minimize empty microwells during long-term differentiation protocols. We found no significant effect of aspiration or dispension speeds at and beyond manual speed. Instead, repeated media changes and time in culture were the driving force or microtissue displacements. As the ovine model is the preclinical model of choice for large skeletal defects, we used ovine periosteum-derived cells to form cartilage-intermediate microtissues. Increased expression of COL2A1 confirms chondrogenic differentiation and RUNX2 shows no osteogenic specification. Histological analysis shows an increased secretion of cartilaginous extracellular matrix and glycosaminoglycans in larger microtissues. Furthermore, microtissue-based implants are capable of forming mineralized tissues and bone after 4 weeks of ectopic implantation in nude mice. We demonstrate the development of an integrated bioprocess for culturing and manipulation of cartilaginous microtissues and anticipate the progressive substitution of manual operations with automated solutions for the manufacturing of microtissue-based living implants.

Building automation by intelligent control of its environment

The fast development of digital technologies has contributed to building industry in the adoption of sophisticated strategies of automation. These strategies are oriented to control of environmental variables in the different parts of a building provided with high standards of performance in terms of both power saving and human comfort aspects. Control strategies can be expressed by means of rules that could be processed by the inference motor of an expert system. The rules of automation in this system will allow for a better integration of devices and will facilitate their interaction with the elements that define the operation of an intelligent building. According to the distributed paradigm currently used in building automation, the expert system located in the supervision computer will carry out specific functions of cooperation and intelligent coordination, reserving control strategies of each building sector for local controllers. As it is shown in this article, the use of technologies based on artificial intelligence to model and control the behavior of building automation systems contributes to optimize its performance in terms of comfort, security and energy saving.

DBTL bioengineering cycle for part characterization and refactoring

The Design-Build-Test-Learn (DBTL) cycle is a crucial framework in Synthetic Biology for the development and optimization of biological systems. However, the manual nature of the cycle poses limitations in terms of time and labor. This paper focuses on the application of automation techniques to the DBTL cycle, specifically in the testing and characterization of standard bioparts, which are essential components of genetic circuits. By automating the testing process, throughput, reliability, and reproducibility can be significantly improved. This paper discusses the challenges associated with manual testing methods and explores various automation strategies and technologies that can address these challenges. High-throughput screening methods, laboratory robotics, and data analysis algorithms are key elements in the automation process. As a case study, we utilize the automated DBTL cycle to refactor a biosensor, aiming to enhance its performance. Integrating automation in the DBTL cycle offers numerous advantages, including increased efficiency, standardization, and quality control of bioparts. It also enables the exploration of larger design spaces and rapid prototyping of complex genetic systems. Here, we show the advantages of using the DBTL cycle to refactor a biosensor that presents improved performance and can be readily used in more complex circuits.