Innovative Technologies Research Articles

Literature Synthesis of Emerging Last-Mile Delivery Technologies and their Applications to Rural Areas: Drones, Autonomous Delivery Vehicles, and Truck-Drones

Rural areas have the distinct opportunity to become trendsetters of innovation and technology, molding and optimizing delivery advances to operate in a way that meets their needs. Drones, autonomous delivery vehicles (ADVs), and truck-drones have the potential to improve rural delivery services significantly. However, rural environments have considerably different characteristics than urban or suburban areas. Therefore, delivery services must be evaluated specifically for the rural use case because the outcome of rural drone delivery is likely different from urban. This study conducted a systematic literature review about the current state-of-the-art drone, ADV, and truck-drone package delivery in rural areas (excluding medical or healthcare delivery). Drones can significantly reduce rural delivery emissions and costs while increasing efficiency. ADVs have the potential to greatly improve rural delivery efficiency. The truck-drone solution is one of the best mode choices for rural delivery because the truck can travel longer distances while the drone can cross challenging geography without delay. The literature review identified a general need for more drone, ADV, and truck-drone delivery research focusing on rural locations. There also is a need for research about the specific economic, environmental, and societal impacts of drone, ADV, and truck-drone delivery in rural areas. There is a significant gap in the literature on realistic rural delivery modeling and gaps about attitudes and perceptions of drone, ADV, and truck-drone delivery services in rural areas. With additional research, innovative delivery modes and technologies can become more cost-effective, accessible, and efficient in rural areas.

Advancing maintenance, monitoring, and control techniques for the optimization of power electronic systems

This paper explores the innovative methodologies and technologies in predictive maintenance, real-time monitoring, fault detection, and advanced control strategies for power electronic devices. Initially, we delve into data acquisition and preprocessing techniques crucial for ensuring the quality and reliability of data used in predictive models. These models, leveraging machine learning and time-series analysis, predict the remaining useful life of devices, guiding proactive maintenance strategies. Furthermore, we discuss the significance of risk assessment and decision support systems in prioritizing maintenance tasks and allocating resources efficiently. The narrative then shifts to real-time monitoring and fault detection, emphasizing the role of sensor integration, data fusion, anomaly detection, and diagnostics in maintaining system integrity. Condition-based maintenance strategies, underscored by real-time data analytics, are presented as a means to optimize maintenance activities and enhance operational performance. The paper concludes with a detailed examination of advanced control strategies, including model predictive control, reinforcement learning, and distributed control and optimization techniques, highlighting their potential to improve system efficiency, reliability, and adaptability. Through comprehensive research and analysis, this work aims to provide valuable insights into the development of sophisticated maintenance and control mechanisms for power electronic systems, ultimately contributing to their longevity and operational efficacy.

Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials

Vibroacoustic metamaterials represent an innovative technology developed for broadband vibration reduction. They consist of an array of local resonators and are able to reduce vibrations over a wide frequency range, commonly referred to as a stop band. Vibroacoustic metamaterials may be a promising strategy to reduce out-of-plane vibrations of thin-walled, disk-shaped structures, such as saw blades. However, their behavior in rotating systems has not yet been fully understood. In this study, a vibroacoustic metamaterial integrated into a circular disk for the reduction of out-of-plane vibrations is experimentally investigated in the rotating and non-rotating state. Derived from the predominant frequency range of noise emitted by saw blades, a vibroacoustic metamaterial with a numerically predicted stop band in the frequency range from 2000 Hz to 3000 Hz, suitable for integration into a circular disk, is designed. The resonators of the metamaterial are realized by cutting slots into the disk using a waterjet cutting machine. To experimentally examine the structural dynamic behavior, the disk is excited by an impulse hammer and observed by a stationary optical velocity sensor on a rotor dynamics test stand. The results of the rotating and the non-rotating state are compared. The measurements are carried out at two different radii and at speeds up to 3000 rpm. A distinct stop band characteristic is shown in the desired frequency range from 2000 Hz to 3000 Hz in the rotating and non-rotating state. No significant shift of the stop band frequency range was observed during rotation. However, adjacent modes were observed to propagate into the stop band frequency range. This work contributes to a better understanding of the behavior of vibroacoustic metamaterials in the rotating state and enables future applications of vibroacoustic metamaterials for vibration reduction in rotating, disk-shaped structures such as saw blades, brake disks or gears.

Application of Additive Manufacturing in the Automobile Industry: A Mini Review

The automobile industry is recognized as one of the most influential sectors shaping global economies, societies, and individual lifestyles. Therefore, fierce competition among different companies is continuously undergoing, and special attention is focused on innovations to improve competitiveness. In the past several years, additive manufacturing (AM) has emerged as an innovative technology in applications in the automobile industry with significant advantages over traditional techniques. As a result, increasing efforts have been paid to combining AM technology with the development of the automobile industry. Currently, many automobile players are optimizing their industrial layout by incorporating innovative AM techniques, and meanwhile, a lot of research progress has been achieved in order to meet the market demand. This article aims at presenting a timely review to conclude the recent advances in the application of AM techniques in the automobile industry, focusing on the available AM techniques, printable materials, and industry applications, based on which the advantages and disadvantages of each technique and material system are discussed in order to reveal the current application situation. The current research gaps and challenges are also outlined to indicate future research opportunities. Hopefully, this work can be useful to related researchers as well as game players in the industry of this field.

Fano resonance in one-dimensional quasiperiodic topological phononic crystals towards a stable and high-performance sensing tool

Phononic crystals (PnCs) emerge as an innovative sensor technology, especially for high-performance sensing applications. This study strives to advance this field by developing new designs of PnC structures that exhibit stability in the face of construction imperfections and deformations, focusing on the evolution of topological PnCs (TPnCs). These designs could be promising to overcome the problem of instability involved in most of the theoretical PnC sensors when they emerge in experimental verification. In particular, the fabrication process of any design could collide with some fluctuations in controlling the size of each component. Thus, Fano resonance is introduced through a one-dimensional (1D) quasiperiodic TPnC. To the best of the author’s knowledge, this study is the first to observe Fano modes in liquid cavities through 1D PnCs. Various quasiperiodic PnC designs are employed to detect the temperature of alcohols (specifically propanol) across an extensive temperature range (160–240 °C). The effects of many geometrical parameters on the sensor stability, such as material thicknesses, are studied. Numerical findings demonstrated that the designed quasiperiodic topological PnCs based on Fibonacci sequence of the second order proved superior performance. This sensing tool provides sensitivity, quality factor and figure-of-merit values of 104,533.33 Hz/°C, 223.69 and 0.5221 (/°C), respectively, through temperature detection of propanol in the range of 160–240 °C.

A Comprehensive Analysis of Fall Risk and Prevention

Fall prevention is a crucial component of healthcare, particularly for older adults and individuals with specific medical conditions. Understanding the significance of fall prevention and recognizing the various risk factors associated with falls are essential in developing effective strategies to mitigate this public health concern. This comprehensive review aims to explore the multidimensional aspects of fall prevention, from assessing individual fall risks to implementing interventions and evaluating the efficacy of fall prevention programs. By delving into the latest trends, innovative technologies, and collaborative approaches in fall prevention, this article seeks to provide valuable insights for healthcare professionals, caregivers, and policymakers in enhancing the safety and well-being of individuals at risk of falls.

Modern approaches to the training of pediatricians and assessment of their professional competences: domestic and American experience

In the article, issues related to the effective training of students/pediatricians and assessment of their competencies are discussed, taking into account the specifics of pediatric care in modern conditions. Nowadays, pediatricians are using a number of innovative technologies and provide daily assistance to a larger number of patients than before, with much less time spent on professional communication. To a certain extent, this reduces the desire of pediatricians to take care of children, because under such conditions, the art of communicating with a patient and his fa­mily members is lost. The article provides recommendations on the experience of American educational institutions regarding the effective elimination of communication problems between students/ pediatric residents and a patient, members of his family, and colleagues. It describes methods that help learn the principles of cultural and effective professional communication in order to improve understanding of the psychology of a child and his family members in the context of the family medicine. Further, information is provided regarding the key approaches to the formation of the main components of professionalism in pediatric practice at the undergraduate and postgraduate stages of the training of pediatricians, collegiality, resolving issues of conflict of interests in the context of cooperation with representatives of the pharmaceutical industry.

Innovative technologies in thoracolumbar and lumbar spine surgery failing to reach standard of care: state-of-art review.

To evaluate previously popular technologies in the field of spine surgery, and to better understand their advantages and limitations to the current standards of care. Spine surgery is an ever-evolving field that serves to resolve various spinal pathologies in patients of all ages. While there are established treatments for various conditions, such as lumbar spinal stenosis, idiopathic scoliosis, and degenerative lumbar disease, there is always further research and development in these areas to produce innovative technologies that can lead to better outcomes. As this process progresses, we must remind ourselves of previously tried and tested inventions and their outcomes that have fallen short of becoming a standard to ensure we are able to learn lessons from the past. A thorough literature review was conducted with the aim of compiling literature of previously utilized technologies in spine surgery. Biomedical databases were utilized to gather relevant articles including PubMed, MEDLINE, and EMBASE. Emphasis was placed on gathering articles with technologies or therapeutics aimed at treating common spinal pathologies including lumbar spinal stenosis (LSS), adolescent idiopathic scoliosis (AIS), and other degenerative lumbar spine diseases. The keywords used were: "failed technologies", "historical technologies", "spine surgery", "spinal stenosis", "adolescent idiopathic scoliosis", and "degenerative lumbar spine disease". A total of 47 articles were gathered after initial review. Different technologies pertaining to spine surgery were identified and critically evaluated. Some of these technologies included X-STOP, Vertiflex, Vertebral Body Stapling, and Dynesys. These technologies were evaluated for their strengths and limitations across their spinal pathology applications. While each type of technology had their benefits, the data tended to be mixed with various limitations across studies. These technologies have been trialed in the field of spine surgery across various spinal pathologies, but still prove of limited efficacy and shortcomings to the current standards of care.

The Effect of Innovation and Information Technology on Financial Resilience

The Effect of Innovation and Information Technology on Financial Resilience

Application of Platelet-rich Fibrin as a Method of Treatment for Medication-related Osteonecrosis of the Jaws in Bulgaria

Lately, medication-related osteonecrosis of the jaw (MRONJ) has emerged as a common disease due to the increasing use of antiresorptive drugs (intravenous bisphosphonates) for the treatment of oncological and osteoporotic diseases, which at an advanced stage significantly affects the quality of life of patients. Therefore, new therapies that accelerate the recovery of musculoskeletal tissue have become preferred in the treatment of such diseases in recent years. An innovative approach, which supports the healing processes is the application of autologous platelet concentrates known as platelet-rich plasma (PRP) and platelet-rich fibrin (PRF). Platelets are unique blood elements responsible for hemostasis. Activated platelets secrete a large amount of growth factors that initiate regeneration in soft tissues and bones. PRP is plasma with many more platelets than are usually found in the blood. The concentration of platelets and therefore the concentration of growth factors may be 5–10 times higher (or richer) than usual. PRF is a biological material, which has the ability to release growth factors over time. Growth factors stimulate stem cells, which improve collagen and elastin in the skin. Platelets in PRF are thought to survive better than those in PRP and thus contribute to faster recovery and formation of new tissue. Fibrin is a biological scaffold that forms when an injury occurs anywhere in the body. Platelets normally circulate in the blood and bind to the fibrin scaffold. Thus connected, they are activated and release growth factors, which are small “granules”. These growth factors begin the process of wound healing by creating new skin cells, collagen and blood vessels. The present study is related to the treatment of MRONJ with PRF. It is aimed at researching the attitudes of oral and maxillofacial surgeons in Bulgaria regarding the application of innovative medical technology such as PRF in the treatment of MRONJ. An author's questionnaire was developed with the help of which the survey was carried out. The analysis of the data shows that the interest in PRF in Bulgaria is great, but unfortunately it is rarely applied. However, respondents believe that this is a promising and innovative method in various aspects of surgery.

A Comprehensive Survey of Predictive Maintenance Techniques for Aircraft Engines Utilizing the C-MAPSS Dataset

The application of deep learning and sophisticated machine learning techniques is driving the rapid advancement of aircraft engine prognostics and predictive maintenance. Remain ing Useful Life (RUL) of aviation engines has been the subject of numerous studies aimed at improving prediction accuracy and efficacy to improve aviation safety and maintenance plans. Innovative approaches and technologies are demonstrated by these projects, which use a variety of methodologies and datasets, including C-MAPSS and N-CMAPSS. Combining feature engi neering, ensemble learning, and deep learning models such as Restricted Boltzmann Machines (RBMs), Long Short-Term Mem ory (LSTM) networks, Convolutional Neural Networks (CNNs), and Deep Bidirectional Recurrent Neural Networks (DBRNNs) is one prominent method. Features are chosen and models are optimized using a variety of methods, including Genetic Algorithms, Recursive Feature Elimination, Lasso, and Feature Importances. In prognostic modeling, the research emphasize the need of interpretability, model adaptability, and measuring uncertainty. Additionally, in order to pinpoint important features and improve model transparency, the study investigates the use of explainable AI techniques such aggregated feature importances with cross-validation (AFICv) and Shapley additive explanation (SHAP). In order to capture prediction uncertainties, the inte gration of Gaussian Processes (GPs) and Bayesian Deep Neural Networks (DNNs) is also investigated. This provides insights into uncertainty-aware prognosis and predictive analytics for industrial assets. The development and publication of datasets such as the N-CMAPSS dataset also makes it possible to conduct more comprehensive and realistic assessments of prognostic models under real-world flight conditions, providing useful tools for benchmarking and improving machine learning algorithms in predictive maintenance. All things considered, these research projects highlight current developments and the possibility of combining cutting edge technology to improve system reliability, improve predictive maintenance techniques, and guarantee safer airline operations. Key Words: Aircraft engine, Prognostics, Predictive main tenance, Machine learning, Deep learning, Remaining Useful Life (RUL), Aviation safety, Feature engineering, Ensemble learning, Uncertainty quantification, Explainable AI, Bayesian Deep Neural Networks (DNNs), Gaussian Processes (GPs), N CMAPSS dataset, Real flight conditions, Industrial assets, System reliability, Benchmarking

Numerical Investigation on Mooring Line Configurations of a Semisubmersible Fish Farm for Global Performance

The growth of the aquaculture industry has led to the development of innovative fish farming technologies, such as semisubmersible fish farms which offer significant advantages in terms of scalability and environmental sustainability. The study utilizes a new workflow based on an open-source finite element solver, Code_Aster, to calculate the hydrodynamic dynamic response of a fish farm. The mooring line configuration of a semisubmersible fish farm plays a crucial role in ensuring its stability and overall performance. This study presents a comprehensive numerical investigation aimed at evaluating the influence of different mooring line configurations (i.e., 4 × 1, 4 × 2, and 4 × 3) on the global performance of a semisubmersible fish farm under regular waves and irregular waves conditions. Increasing the number of mooring lines can reduce the mean and extreme tension in individual mooring lines and suppress the horizontal motions, but bring neglectable effects on the rotational responses. The findings from this research provide valuable insights into the optimal mooring line configuration for the global performance of semisubmersible fish farms.

Route optimization of battery electric vehicles using dynamic charging on electrified roads

The Electric Road System (ERS) is an innovative technology enabling electric vehicles (EVs) to charge while in motion, offering increased EV range and cost-effective battery solutions. This advancement is particularly valuable for logistics companies seeking to enhance their EV adoption. To provide long-distance delivery routes for heavy-duty EVs through ERSs, we present a first-time attempt to solve the Electric Vehicle Travelling Salesman Problem with Dynamic Charging, where the EV can be charged while driving on the electrified road segments. We formulate this problem by considering path choices between consecutive customers and propose a network reduction heuristic (NRH) to solve large-size instances. We test our model and NRH on grid-like synthetic instances and real-world data developed from the UK road network. Our results demonstrate the effectiveness of the NRH and highlight the potential of ERSs in improving logistics activities and decarbonising road transport. Our experiments revealed that although the typical driving range of most EVs falls short of 400 kilometres, with the widespread deployment of ERS, EVs can traverse distances exceeding 2000 kilometres without requiring recharging stops. Our experiments also revealed that establishing infrastructure on approximately 30 % to 40 % of all roadways is adequate to ensure reliable access for EVs.

СТРАТЕГИЧЕСКИЕ ПРИОРИТЕТЫ И ПЕРСПЕКТИВНЫЕ НАПРАВЛЕНИЯ РАЗВИТИЯ ЮЖНОГО АГРОПРОМЫШЛЕННОГО ТЕРРИТОРИАЛЬНОГО КЛАСТЕРА КРАСНОЯРСКОГО КРАЯ

The purpose of the study is to analyze the priority areas and prospects for the development of the agro-industrial territorial cluster of the Southern macro-district of the Krasnoyarsk Region to increase competitiveness and ensure sustainable development of the region’s agricultural sector. The study was conducted on materials from the Krasnoyarsk Region according to the Federal State Statistics Service, accounting and financial statements of enterprises participating in the cluster. Research used theoretical and practical methods, including the study of scientific literature, statistical data analysis and strategic analysis methods. Based on the results of the study, it was established that, according to their production potential, the enterprises of the Southern Cluster can provide up to 65 % of the region’s needs for flour, up to 21 % for cereals, and up to 12 % for dairy products. The current reserves for growth in flour production volumes are 40 thousand tons, cereals – 5, mixed feed – 3, dairy products – 30–40 thousand tons, soft drinks – 1,7 million decaliters. To solve strategic development problems, cluster participants need to focus on such areas as the development of intra-cluster interaction and the formation of an effective organizational structure, the development of new products, penetration into new market segments, including exports, as well as the development of competencies for launching innovative projects. This process should be carried out through the implementation of joint projects on import substitution, the organization of joint logistics, as well as taking into account the development and implementation of innovative production technologies, the development of new products, expanding the number of cluster participants, training highly qualified personnel within the cluster, and lobbying for common interests at various sites.

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

Tourists’ willingness to adopt government-led smart tourism platforms: A mixed-methods study

The diversity of demands in the mass tourism market together with the complexity of innovative technologies makes promoting smart tourism a challenge. In recent years, government-led smart tourism platforms have become a representative product of smart tourism in China, but tourists do not seem ready to embrace innovative products. The current research adopted a mixed-methods approach by employing a decision-making trial and evaluation laboratory method followed by a survey-based study to explore key factors driving Chinese tourists’ adoption of government-led smart tourism platforms. The results revealed that the elements influencing tourists’ willingness to adopt are complex and include tourists’ innovativeness, performance expectancy, perceived cost, perceived risk, and perceived playfulness. Among them, tourists’ innovativeness showed a positive impact on tourists’ willingness to adopt through the mediation effects of performance expectancy. Meanwhile, the positive effect of performance expectancy on their willingness to adopt can be mediated by perceived playfulness. The findings enhance our understanding of which factors impact tourists’ adoption theoretically thereby practically helping the government provide better tourism service experiences in both online and offline contexts.

Analysis of the Teslas Sustainable Supply Chain Management

Tesla, a prominent company in electric vehicles and renewable energy technology, prioritizes environmental sustainability, social responsibility, and economic rewards in its supply chain management alongside cost and efficiency. The paper uncovers Tesla's methods in resource usage and conservation, environmental impact assessment, social responsibility management, and economic benefit management through literature analysis and case analysis. The study determined that Tesla has made notable advancements in environmental performance throughout its supply chain by implementing innovative technologies and management strategies, including battery technology improvements, automated production processes, supplier responsibility evaluations, and life cycle assessments (LCA). Tesla encounters obstacles including worldwide chip shortages and variations in raw material costs, affecting the stability and efficiency of its supply chain. The paper suggests that governments, enterprises, and industries collaborate to enhance supply chain sustainability. This can be achieved by implementing policies, fostering technological innovation, and utilizing sharing platforms to support the global sustainable development goals.

CUTTING-EDGE BIOFUEL TECHNOLOGIES FOR ENVIRONMENTAL MANAGEMENT AND SUSTAINABILITY

Researchers focus on finding Renewable Energy Sources (RES) and green energy for a sustainable future owing to ecological sustainability standards, growing power demands, depletion of traditional energy resources, and ecological damage caused by global warming and climate change. Bioenergy is a very viable option as it may be used for various energy needs via appropriate conversion methods. This analysis focuses on the promise and problems of producing biofuels using different feedstocks and advancements in process technology. The use of biofuels, such as biodiesel, ethanol, bio-oil, syngas, Fischer-Tropsch Hydrogen, and methane (CH4), generated from agricultural crop left-overs, micro- and macroalgae, and other biological waste via thermo-bio-chemical procedures, has been determined to be an environmentally beneficial option for energy production. Developing and implementing biofuels in industry and transportation significantly reduces reliance on petroleum and coal. The literature study indicated that biofuels derived from crops and microalgae have the potential to be the most effective and appealing method. The area of biofuels has made significant advancements via genetic engineering, which has opened up new possibilities for large-scale production for commercial use. However, the process of producing biofuels on a big scale remains difficult. Therefore, it is crucial to address this issue by converting biomass into biofuels using innovative technology to meet present and future energy demands.

First multicenter experience of pulmonary vein and posterior wall isolation via a novel focal 9mm lattice tip catheter in a real-world scenario

Abstract Background Pulsed field ablation (PFA) is an innovative technology recently adopted for the treatment of atrial fibrillation (AF). Preclinical and clinical studies have reported a remarkable safety profile, as a result of its tissue specific effect targeting cardiomyocytes and sparing adjacent tissues. The AfferaTM system is a focal 9-mm lattice tip catheter that has recently received regulatory approval. No data currently exist on the feasibility of this technology for first-time pulmonary vein isolation (PVI) and posterior wall isolation (PWI) in a real-world scenario. Objective To evaluate the safety and efficacy of the focal 9-mm lattice tip catheter Sphere-9 (Affera Inc) in a real-world population undergoing PVI and PWI. Methods Consecutive AF patients undergoing first-time PVI plus PWI with the AfferaTM system in two different centres between August and November 2023 were prospectively included. PFA was always used to ablate the posterior left atrium, whereas anterior applications were performed using either radiofrequency energy (RF/PF strategy) or PFA (PF/PF strategy) based on operator's discretion. Primary efficacy endpoint was acute procedural success (successful PVI with evidence of bidirectional block at the end of the procedure). Primary safety endpoint included major technology-related complications occurring within 7 days post-ablation. Secondary endpoints were first pass isolation, acute reconnection, overall complications, procedural, dwelling, and fluoroscopy time. Results Thirty consecutive patients [mean age: 68±6.8 years; 61.7% males; CHA2DS2-VASc Score 2.0 (1.5 – 3.5)] were included. Among them, 66.7% (n=20) had non-paroxysmal AF. PVI was performed with an RF/PF strategy or PF/PF one in 10 (33.3%) and 20 (66.7%) patients, respectively. Posterior wall isolation (PWI) was performed in 30 (100%) cases. The primary endpoint was achieved in 100% of patients for both PVI and PWI with average procedural time of 95 min (77-120 min), dwelling time of 78 min (55-92 min) and fluoroscopy time of 6 min (5-10.5 min) (Figure 1). No technology-related complications were observed. First-pass isolation occurred in 100% of PVI and PWI, with no acute reconnection for PW and 1 case of right PV reconnection in the RF/PF strategy group. Conclusions AF ablation with the focal 9-mm lattice tip catheter Sphere-9 confirmed high efficacy and safety in PVI + PW in a real-world population.Figure 1

Recommendations on Robotic Hepato-Pancreato-Biliary Surgery. The Paris Jury-Based Consensus Conference.

To establish the first consensus guidelines on the safety and indications of robotics in Hepato-Pancreatic-Biliary (HPB) surgery. The secondary aim was to identify priorities for future research. HPB robotic surgery is reaching the IDEAL 2b exploration phase for innovative technology. An objective assessment endorsed by the HPB community is timely and needed. The ROBOT4HPB conference developed consensus guidelines using the Zurich-Danish model. An impartial and multidisciplinary jury produced unbiased guidelines based on the work of ten expert panels answering predefined key questions and considering the best-quality evidence retrieved after a systematic review. The recommendations conformed with the GRADE and SIGN50 methodologies. Fifty-four experts from 20 countries considered 285 studies, and the conference included an audience of 220 attendees. The jury (n=10) produced recommendations or statements covering five sections of robotic HPB surgery: technology, training and expertise, outcome assessment, and liver and pancreatic procedures. The recommendations supported the feasibility of robotics for most HPB procedures and its potential value in extending minimally invasive indications, emphasizing however the importance of expertise to ensure safety. The concept of expertise was defined broadly, encompassing requirements for credentialing HPB robotics at a given center. The jury prioritized relevant questions for future trials and emphasized the need for prospective registries, including validated outcome metrics for the forthcoming assessment of HPB robotics. The ROBOT4HPB consensus represents a collaborative and multidisciplinary initiative, defining state-of-the-art expertise in HPB robotics procedures. It produced the first guidelines to encourage their safe use and promotion.