Advanced Automation Technologies Research Articles

Understanding of the Maritime Future Mentality; Safe E-navigation and Safe Maritime Surface Communication

Developing and changing technology affects all sectors globally. Although it primarily affects information systems digitally, it affects all sectors indirectly. Maritime transport, the most important transportation mode in the world, is affected by technological progress as seafarers, ships, and ports. When used for its intended purpose, the technology employs intelligent and rational solutions based on the logic of identifying previous errors and developing predictions accordingly. Maritime transportation is the movement of ships between ports safely and without harming the environment. The sea is a dynamic surface not previously exposed to fixed effects and is affected by meteorological and environmental conditions. As the international maritime authorities keep pace with technological advancements, they have embraced the e-navigation concept, a digital revolution that is set to transform the industry. This shift to Electronic Navigation requires all operations to be digital, making transmission easier and more efficient. It also mandates uninterrupted and high-quality digital communication with ships' land facilities during the entire voyage. ECDIS, one of the advanced automation technology products used for e-navigation, and the vector map ENC it uses are of great importance. ENC maps are produced with specific standards. S-100, which is described as the latest and most advanced standard, provides sailors with good opportunities for safe navigation and communication. The study has been prepared to explain e-navigation types of equipment, their standards, and how they communicate according to cyber security.

Drilling Automation Serves as Engine of Change for Risk Prevention Offshore

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35137, “Drilling Automation: An Engine of Change for Risk Prevention Offshore,” by Brennan Goodkey, SPE, Andrew Fabo, and Francois Le Buhan, SLB, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ This paper delves into the evolving landscape of drilling automation, emphasizing the imperative for these systems to go beyond novelty and deliver quantifiable financial value. The complete paper presents an examination of several offshore drilling operations that exemplify the successful implementation of a closed-loop drilling-automation solution and the positive effect these technologies can have on the bottom line. Introduction In the dynamic landscape of drilling technologies, the conventional discourse on rig mechanization, robotics, and drilling automation is no longer a new topic, having nearly a decade of active discussion, development, and deployment. However, these systems, despite their longstanding presence, are still working toward substantiating their anticipated value. To navigate this challenge successfully, a clear understanding of where and how exactly the deployed solution will deliver value is required. The criteria of success inevitably will vary based on the customer, region, and objective. One of the clearest examples is obvious in the dichotomy between the priorities of high-volume drilling in onshore operations compared with offshore drilling. While the overarching goal of automation remains consistent—maintain safety while maximizing production and minimizing operational expenditures—the path to delivery in these spaces looks quite different. Offshore drilling introduces a myriad of complexities and must prioritize not only efficiency but also risk reduction and the minimization of nonproductive time. The complete paper examines the collaborative journey of several oil and gas organizations to deploy and improve a drilling-automation system aimed at the priorities and challenges of offshore drilling. Definitions The authors offer the following definitions to delineate scope as they describe an integrated system that combines domain drilling automation, rig-floor mechanization, and directional-drilling automation into a cohesive and comprehensive solution: - Domain Drilling Automation—This refers to the process of leveraging sensor data to interpret downhole conditions and react with the appropriate domain response, mimicking an experienced driller’s expertise with high accuracy and precision. - Rig-Floor Mechanization—This is the implementation of automated technologies and robotic systems on the rig floor to limit manual human involvement in tasks situated within the hazardous red zone. - Directional-Drilling Automation—This term refers to the use of automated systems and technologies to control and navigate precisely the trajectory of a drilling borehole, enhancing accuracy and efficiency in achieving specific wellbore paths and target zones. - Planning Drilling Automation—The use of advanced technologies and automated systems during office-based well planning is leveraged to optimize resources, enabling precise and efficient decision-making for the entire drilling process. - Real-Time-Monitoring Drilling Automation—The use of advanced automation technologies can monitor and analyze drilling operations continuously in real time, providing instantaneous feedback and adjustments to optimize performance, efficiency, and safety.

Advanced test automation techniques for DevOps: Bridging the gap between test-driven development and continuous deployment in agile environments

As software development continues to change, it becomes more important to incorporate modern test automation approaches in DevOps to support efficient deployment. This work investigates TDD and CD in Agile to critically appraise advanced automation technologies and practices to fill existing deficits. This research demonstrates that the brighter Test Automation Framework approaches could improve code quality and deployment time, along with the culture of embracing best practices. The results can help practitioners who want to make their DevOps practices even more efficient and focused on quickly delivering high-quality products.

Smart Poultry Farm Automation

Our case-study paper entitled "Smart Poultry Farm Automation" aims to enhance the efficiency and productivity of poultry farming through the application of advanced automation technologies. Poultry farming plays a crucial role in meeting the growing global demand for poultry products. However, traditional farming methods often face challenges related to labor intensity, resource management, and monitoring. This project proposes the design and implementation of a comprehensive automation system for poultry farms. The system will integrate sensors, actuators, and IoT (Internet of Things) technologies to automate various tasks such as environmental control, feeding, watering, and health monitoring of poultry. Real-time data collection and analysis will enable farmers to optimize farm conditions, improve bird health, and minimize resource wastage. Key components of the proposed system include environmental sensors for monitoring temperature, humidity, and ventilation; automated feeders and waterers controlled by IoT devices; and health monitoring systems utilizing image processing or sensor data for early disease detection. The system will be designed to provide farmers with remote access and control through a user-friendly interface, allowing them to monitor farm operations and receive alerts or notifications. The expected outcomes of this project include increased productivity, reduced operational costs, improved animal welfare, and better decision-making for farmers. By leveraging automation and IoT technologies, the proposed system will contribute to the modernization and sustainability of poultry farming practices

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.

Revolutionizing Power Systems Through Electrical Automation: Efficiency, Reliability, and Sustainability

This paper explores the transformative role of electrical automation technologies in the modernization and optimization of power systems. With a comprehensive analysis spanning power generation, distribution, and grid management, we delineate the multifaceted impacts of automation on enhancing operational efficiency, ensuring reliability, and promoting environmental sustainability. Through the lens of operational case studies and theoretical frameworks, we identify key insights into how electrical automation contributes to the streamlining of processes, reduction of human error, and integration of renewable energy sources. Moreover, we delve into the future trajectory of electrical automation, highlighting the potential integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms, the expansion of smart grid capabilities, and the paramount importance of cybersecurity in the digital era of power systems. Our discussion synthesizes these perspectives to underscore the pivotal role of electrical automation in driving the evolution of power systems towards greater adaptability, efficiency, and resilience. This paper posits that the future of power systems hinges on the strategic implementation of advanced automation technologies, guided by an overarching commitment to innovation, security, and sustainability.

AI in supply chain optimization: A comparative review of USA and African Trends

The integration of Artificial Intelligence (AI) in supply chain management has emerged as a critical driver of efficiency and competitiveness in global markets. This paper provides a comparative review of AI trends in supply chain optimization between the United States and African regions, shedding light on the unique challenges and opportunities faced by each. In the United States, AI adoption in supply chain optimization has been robust, with a focus on predictive analytics, machine learning, and advanced automation technologies. American companies leverage AI to enhance demand forecasting, optimize inventory management, and streamline logistics processes. The integration of AI-driven solutions has allowed U.S. businesses to achieve higher accuracy in demand predictions, reduce lead times, and minimize operational costs. Furthermore, the use of AI algorithms in route optimization has significantly improved delivery efficiency, leading to enhanced customer satisfaction. Contrastingly, African countries are experiencing a more gradual but steadily increasing adoption of AI in supply chain optimization. Limited access to advanced technology infrastructure, coupled with resource constraints, has posed challenges for African businesses. However, innovative approaches are being explored, such as the use of mobile technologies and cloud-based solutions to overcome infrastructure limitations. AI applications in African supply chains focus on improving visibility, minimizing waste, and ensuring timely delivery. The continent's diverse supply chain landscape, encompassing agriculture, mining, and manufacturing, presents a unique set of challenges that AI aims to address. Both the United States and African nations recognize the potential of AI to transform supply chain management. While the U.S. is at the forefront of AI implementation, Africa is forging ahead with tailored solutions that align with its specific context. Collaboration and knowledge exchange between these regions could pave the way for a globalized approach to AI in supply chain optimization. This review underscores the importance of understanding regional nuances in adopting AI technologies, fostering collaboration for mutual benefit, and advancing the global evolution of AI-driven supply chain management.

A new cooperative control solution of subway BAS: an improved fuzzy PID control algorithm

The building automation system (BAS) of a subway is the core component of monitoring and managing urban rail transit systems. For the current problems such as low control efficiency, insufficient accuracy, and poor stability of metro BAS, this article proposes a cooperative control framework based on an improved fuzzy proportional-integral-derivative (PID) algorithm. Firstly, the concept of an integrated supervisory control system (ISCS) for subways is introduced by summarizing the previously implemented engineering construction and combining it with advanced automation technology. The system’s overall design under the ISCS framework is also improved by integrating it with the fire alarm system (FAS) with the BAS as the core unit of the reliance. Then, an improved seeker optimization algorithm (ISOA) is employed to optimize the parameters of the fuzzy PID control algorithm to achieve a coordinated control of the system based on considering the time lag problem. Finally, the accuracy, efficiency, and stability of the coordinated control response of the BAS under the ISCS framework are tested experimentally. The results suggest that the proposed cooperative control solution of BAS employing the improved fuzzy PID algorithm has good control accuracy and response efficiency and can also ensure the BAS’s higher stability in the coordinated control process, which thus greatly improves the automation level of the subway and provides a safer and more reliable high-performance for the ISCS of the subway in the urban rail transportation industry.

Advancing energy efficiency: Harnessing machine learning for smart grid management

The concept of Smart Grids (SG) has emerged as a solution to address challenges in traditional power systems, including resource inefficiency, reliability issues, and instability. Since its inception in the early 21st century, Smart Grid technology has undergone significant development, integrating advanced information communication and automation technologies with conventional power infrastructure. This integration enhances efficiency, reliability, and sustainability, while enabling the integration of renewable energy sources and optimizing energy distribution and consumption. Machine learning algorithms play a pivotal role in the development of Smart Grids, facilitating energy consumption prediction, optimization, anomaly detection, and fault diagnosis. This paper explores methodologies for developing and improving machine learning algorithms for efficient energy consumption prediction and management within Smart Grids. It discusses the application of deep learning techniques, reinforcement learning, and integration with the Internet of Things (IoT) to enhance energy management systems. The study highlights the potential impact of deep convolutional neural networks (CNNs) on energy consumption regulation and emphasizes the need for further research to address challenges associated with model complexity and data requirements in Smart Grid contexts.

IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks

Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured.

Towards regenerative regional development in responsible value chains: an agentic response to recent crises

ABSTRACT In this paper, we empirically and theoretically present regenerative regional development in responsible value chains as an alternative to the prevailing traditional, neoliberal economic rationale of globalization. We develop the argument on the back of a longitudinal in-depth case study on actors’ engagement in the recurring crises in the maritime industry in Sunnmøre/Norway. The alternative perspective is an agentic response from the business community in the wake of recent crises. It builds on advanced manufacturing capabilities, automation and precision technologies, which promise local economic regeneration while reducing the reliance on low-cost labour, substantially cuts emissions through reduced long-haul transport, use of green energy and more energy-efficient production processes. To succeed, however, it calls for policies that promote the building of local capabilities and penalize practices causing environmental and social harm in global value chains, making it possible to move towards responsible and shorter value chains.

BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction

The construction industry is slow to adopt new technologies. The implementation of digital technologies and remote operations using robots were considered farfetched affairs and unbelievable approaches. However, the effect of COVID-19 on clients and construction companies put high pressure on construction managers to seek digital solutions and justified the need for remote operating or distant controlling technologies. This paper aims to investigate the state of play in construction technology implementation and presents a roadmap for developing and implementing required technologies for the construction industry. The COVID-19 disruption required new methods of working safely and remotely and coincided with the advent of advanced automation and autonomous technologies. This paper aims to identify gaps and 11 disruptive technologies that may lead to upheaval and transformation of the construction sector, perhaps in this decade. A road map for technology implementation can be helpful in developing business strategies at the organizational level as a theoretical measure, and it can facilitate the technology implementation process at the industry level as a practical measure. The roadmap can be used as a framework for policymakers to set industry or company strategies for the next 10 years (2030).

Analysis of the enterprise's external environment as the main stage of strategy development

Introduction. The external environment is a source that feeds the organization with the resources necessary to maintain its internal potential at the appropriate level. The organization is in a state of constant exchange with the external environment, thereby ensuring the possibility of survival. But the resources of the environment are unlimited. And many other organizations in the same environment claim them. Therefore, there is always a possibility that the organization will be able to obtain the necessary resources from the external environment. This can weaken its potential and lead to many negative consequences for the organization. The task of strategic management is to ensure such interaction of the organization with the environment, which allowed it to maintain its potential at the level necessary to achieve its goals and thus would give it the opportunity to survive in the long term. Method. The article considers two methods of analyzing the external environment of the enterprise: SWOT analysis, which divides factors into strengths and weaknesses, opportunities and threats, and PEST analysis, which monitors and evaluates political (stability of the political situation, tax policy, level of state influence on the industry , control over quality standards), economic (inflation rate, population income level, production volumes of natural and mineral bottled water, prices for raw materials and energy sources, investments in the industry), social (requirements for product quality, tastes and preferences of the population, population size) and technological factors (technology development in the industry, scientific and technical potential of the country, research and development costs) over which the company has no influence. Results. The study was carried out on the example of Tsarychansky Zavod Minvody LLC. In the process of analyzing the enterprise, the external environment was studied based on six groups of factors: economic, political, market, technological, international status and social behavior. The company's strengths have been identified, which are also competitive advantages: safe product packaging; growth in office consumption of bottled water; a wide range of mineral water; high quality of the product; average price level; use of advanced release automation technologies; Increasing the volume of water sales; increasing the product line; water comes from a well; water has important elements for the human body; water is used for medicinal purposes; the plant produces treated water using the reverse osmosis method; the enterprise is the owner of the main resources for production; wide target audience; delivery of water to offices; the presence of a branded store near enterprises.

Preface

After successfully held for six consecutive years, the 2021 7th International Conference onMechanical Engineering, Materials and Automation Technology (MMEAT 2021) was successfully held in Dali, China during June 18-20, 2021. MMEAT 2021 is to bring together innovative academics and industrial experts in the field of materials, mechanical engineering and automation technology to a common forum. The primary goal of the conference is to promote research and developmental activities in Mechanical Engineering, Materials and Automation Technology and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in materials, mechanical engineering and automation technology and related areas.MMEAT 2021 invited distinguished speakers to deliver plenary speeches and invited talks. More than 100 attendees participated in this conference. MMEAT 2021 mainly covers topics on Mechanical Engineering, Materials and Automation Technology and, etc.The conference model was divided into two sessions, including oral presentations and keynote speeches. In the first part, some scholars, whose submissions were selected as the excellent papers, were given 15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In this conference, we have invited Pro.Hui-Mi Hsu, from National Dong Hwa University and A.Prof.Wenbo Liu, from School of Mechanical Engineering, Sichuan University, China to serve as our Conference Co-Chairman.We were honored to invite 5 sophisticated keynote speakers to deliver keynote speeches during the conference. Ramesh Singh, from University of Malaya, Faculty of Engineering, Kuala Lumpur, Malaysia, delivered a wonderful speech on Effectiveness of Manganese Oxide in Supressing the Low-Temperature Degradation (LTD) of Tetragonal Zirconia. The next keynote speaker, Prof. Peizhou LI, from Shandong University, School of Chemistry and Chemical Engineering. His research interests mainly focus on the design and construction of novel functional porous materials (porous silica, porous carbon, MOFs and COFs) and their applications in energy and environmental fields. Prof.Fan Jiang, from School of Mechanics & Electrical Engineering, Guangzhou University, Guangzhou University, shared a speech on his specialty related to Numerical simulation of two-phase flow in pipe. Prof.Xinwu, from Sun Yat-sen University. He delivered a speech on Modulation of the structure and properties of 2D materials and vdW heterostructures by ion beam irradiation. The finale keynote speaker, Prof Muhammad Zubair Iqbal, from Zhejiang Sci-Tech University.We are glad to share with you that we received lots of submissions from the conference and we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: New Materials and Advanced Materials, Mechanical Engineering, Control, Automation and Information Technology and other related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard.We would like to thank the organization staff, the members of the program committees and reviewers. They have worked very hard in reviewing papers and making valuable suggestions for the authors to improve their work. We also would like to express our gratitude to the external reviewers, for providing extra help in the review process, and the authors for contributing their research result to the conference.Lastly, we would like to warmly thank all the authors who, with their presentations and papers, generously contributed to the lively exchange of scientific information that is so vital to the endurance of scientific conferences of this kind.List of Committee member are available in this pdf.

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

This paper studies the planning of conflict-free and efficient crossings of antagonistic vehicles’ movements at lightless intersections. A fully automated infrastructure environment is considered, where all vehicles that enter the intersection area are connected and automated (CAVs), i.e., they are equipped with advanced communication and automation technologies. In such a futuristic environment, traffic lights that regulate the right-of-way of different traffic streams are obsolete because of vehicle communication capabilities. The connectivity is utilized to derive vehicle trajectories such that a safe and efficient crossing of lightless intersections is possible. So far, published studies lack the application to complex intersection layouts. To fill this gap, we introduce a control method for CAV pairing allowing for the safe, collision-free crossing of the intersecting area and optimize traffic conditions, i.e., total delays of the system. Simulation results demonstrate the feasibility and applicability of the presented approach, given that all the technical specifications (e.g., communications, velocity actuators) are present. Finally, we conduct a sensitivity analysis for the algorithm’s main parameters, which provides practical insights for the studied experimental scenarios and other existing algorithms in the literature that tackle this problem.

Low-Cost Air Quality Sensing towards Smart Homes

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

Application of Automation Technology in Automobile Machinery Manufacturing

China’s science and technology have become increasingly mature in today’s society, and various fields of society will actively apply relevant advanced technologies to reshape the original operating model during the development process. In particular, the application range of automation technology has become more extensive, because automation technology itself has more advantages and cannot be replaced by many other traditional technologies. It can fundamentally improve the work efficiency of some manufacturing industries. Thanks to the application of high-precision automation technology, all work can be carried out continuously in accordance with established computer programs. In this way, the number of errors caused by human work is actually reduced, and the pass rate of the product can also take this opportunity to be significantly improved. When performing various tasks, workers in the mechanical manufacturing field will actively apply the most suitable automation technology according to their actual conditions, and organically integrate their valuable work experience with brand-new automation technology. This allows all work to be of higher quality in the implementation process. In the automobile production industry, since many parts are manufactured on the assembly line, if the advanced automation technology is actively applied, the production time of parts can be saved, and the assembly and processing work of the staff will be carried out in an orderly manner. In this way, the profits of the automobile manufacturing industry can be effectively improved and unnecessary cost input can be reduced.

A NOVEL LOW-DOSE COMBINATION DRUG THERAPY FOR ENDOMETRIOSIS THAT INCREASES PROGESTERONE RECEPTOR TO ENHANCE PROGESTIN EFFICACY

Endometriosis is an estrogen-dependent disease that is a leading cause of pelvic pain and infertility worldwide. While ectopic endometrial tissue should theoretically regress with progesterone treatment, resistance to progesterone is common, limiting the effectiveness of hormonal therapies to treat established disease and to prevent post-surgical disease recurrence. We sought to apply a quality-by-design (QbD) approach to develop a novel sensitizing therapy for endometriosis by targeting progesterone receptor (PR) expression. Our group has previously successfully utilized a QbD approach to develop a novel combination of clinical compounds to induce estrogen receptor alpha (ESR1) in hormone-positive breast cancer cells to sensitize them to tamoxifen therapy; similar to endometriosis, many types of breast cancer are resistant to hormonal therapies. This QbD approach leverages advanced automation technology and predictive mathematical modeling to investigate the additive and synergistic effects of combinatorial drug treatment on gene expression in immortalized cell lines. We selected 12 different compounds that are either known to be involved in sex hormone signaling, used in the treatment of endometrioid ovarian cancer (arising from endometriosis), and/or currently used or under investigation for the treatment of endometriosis. Using our custom-design QbD platform, we measured the relative effects of these compounds, each at concentrations well below their efficacious dose, on a variety of genes in 12Z cells (an immortalized endometriotic cell line). We identified five drugs (belinostat, celecoxib, everolimus, bentamapimod, and paclitaxel) that in combination have a synergistic effect on PR expression. In vitro studies confirmed elevated PR expression in both RNA and protein. After three days of drug treatment on 12Z cells compared to controls, combinatorial drug treatment resulted in a 8.8 fold increase in PR expression levels with no change in ESR1 levels. This work offers a novel combinatorial drug therapy and proof-of-concept for endometriosis treatment. It also provides new insight into the mechanisms by which known endometriosis drugs reduce disease burden. Efforts are underway to utilize a mouse model of endometriosis to determine whether this drug cocktail can enhance PR expression and thereby efficacy of progestin therapy. Future work will involve determining the precise molecular pathways by which these drugs act to enhance PR expression.

Cooperative control strategies to stabilise the freeway mixed traffic stability and improve traffic throughput in an intelligent roadside system environment

Previous studies used an adaptive time gap strategy to stabilise the head-to-tail string stability by connected and automated vehicles (CAVs). However, this strategy can bring an adverse effect on traffic throughput. Moreover, at a lower market penetration rate of CAVs, it is also difficult to implement a larger adaptive time gap to dampen the unstable traffic conditions. To this end, this study proposes cooperative control strategies to improve the mixed traffic throughput and stabilise the head-to-tail string stability in an intelligent road system (IRS) environment. The mixed traffic was composed of autonomous vehicles (AVs) with string instability/stability, and CAVs with low penetration. The feasible stable control parameters of AVs and CAVs are computed and implemented into the cooperative control strategies. The results show that the cooperative control strategies have better performances in improving traffic throughput and stabilising the overall head-to-tail string stability. Moreover, analytical solutions and simulation experiments are conducted to verify the effectiveness of the proposed control strategies subject to exogenous disturbances. The cooperative control strategies can be potentially applied to alleviate the mixed traffic problems, with the advanced connection and automation technologies in an IRS environment.

A preliminary study on a tooth preparation robot

ABSTRACT This is a study of a robot-controlled ultrashort pulse laser system used for tooth preparation to overcome drawbacks of traditional manual methods, thus improving its quality, precision, and clinical efficacy. Based on the international advanced digital technologies, automation technologies, and medical laser technologies, the entire tooth preparation process was completed autonomously using the robot-controlled ultrashort pulse laser. In the study, 15 extracted human first molars were prepared automatically by the robot system. And the result accuracy was evaluated using software. Compared with the CAD data of standard tooth preparation, the overall average error, preparation depth of the natural tooth preparation, and convergence angle were 0.05–0.17 mm, 2.097 ± 0.022 mm with an error of 0.097 ± 0.022 mm, and 6.98 ± 0.35° with an error of 0.98 ± 0.35°, respectively. The feasibility and accuracy of the robot-controlled femtosecond laser technique for tooth preparation were confirmed.