Real-time Information Research Articles

Research on the Application of the Internet of Things in Optimizing the Allocation of Ideological and Political Education Resources Using Machine Learning

This paper evaluates the application of the Internet of Things (IoT) in designing smart teaching systems by optimizing ideological and political education (IPE) resources using machine learning (ML). To increase educational reform’s efficacy, we also examine the issues with conventional IPE instruction. This study further develops a cutting-edge, smart framework for teaching ideological and political concepts in the educational setting and changes teaching according to real-life scenarios. Comprising the perception, network, and application stages, the IPE platform is a three-layer IoT architecture. The IoT and internet connections are used by the technology to receive effective IPE instructional activity information in real time. After that, the data are sent over the internet to the data center, where it is used as the initial information for applications, analysis of information, and modeling training. We are able to collect and acquire IPE teaching activity data sets on instructive and educational developments in courses by using IoT devices. The principal component analysis (PCA) approach is used to remove interface characteristics, date, and time elements from the data to increase the evaluation’s correctness. To remove noisy information from the data, the z-score normalization method is used. In addition, this paper suggests a novel efficient Cat Boost method inspired by hunter–prey optimization (AHPO-ECB) for evaluating IPE performance. In this case, the AHPO approach is employed to reduce the misprediction rate of the CB. The suggested model is then used to analyze the predicted performance using a Python program. The results of the experiments indicate that, in comparison to other models currently in use, the suggested AHPO-ECB model performs at the highest level when assessing IPE teaching performance.

The Challenges of Intergovernmental Organizations in Combating Transnational Crime: An Indonesian Perspective

Human trafficking is a pressing global issue, especially in the Southeast Asian region, where Indonesia plays a role as a country of origin, transit and destination. This study aims to identify the cooperation between the Indonesian Police and ASEANAPOL using descriptive qualitative research methods. This research also highlights the role of ASEANAPOL through 4 strategies of handling Indonesian Police in collaboration with ASEANAPOL which include capacity building through training (ATCM), international meetings (AMMTC and SOMTC), electronic-based real-time information exchange (System e-ADS), and strengthening cross-border investigation mechanisms through the Joint Coordination System (JCS) with border guarding and dispatching personnel such as Attache POLRI (ATPOL) and POLRI Technical Staff (STAFNISPOL). While this research focuses on preventive responses to human trafficking, there are limitations that need to be considered, including aspects of prevention and prosecution involving victim identification and protection. Future research is recommended to explore all aspects, including prevention and prosecution measures, to develop ASEANAPOL’s organisational framework in addressing transnational crimes.

A Self-Adaptive Escape Route Planning Model Based on Dynamic Wildfire Information

Background: Escape routes are important measures for firefighters to ensure their own safety, providing predetermined paths to safe areas. Their establishment needs to consider numerous factors, such as the timeliness and safety of the routes. Aims: Optimize the path planning method previously studied by our team to ensure the dynamic nature, timeliness, and safety of the routes. Methods: (1) Propose a comprehensive safety index that encompasses both spatial and temporal safety indices, providing a more holistic approach to route safety. (2) Introduce spatial adaptive factors and spatial safety windows corresponding to the spatial safety index within the comprehensive safety index. (3) Present a new concept, the “observation cycle”, as a standard for the frequency of updating wildfire spread information, thereby addressing the issue of a lack of real-time input information. Based on this, we propose a reliable dynamic update rule for its updating. Results: Compared to the unoptimized model, the final optimized model’s planned escape routes offer impressive dynamic performance, effectively guarding against sudden changes in wildfire conditions, enhancing route safety, and ensuring timeliness. Conclusions: This research ensures that firefighters can effectively guard against the threats posed by sudden changes in wildfire conditions when escaping in wildfire environments, while also guaranteeing timeliness and safety.

Augmented Reality Enhanced Image Processing Systems for Interactive Worker Training in Industrial Automation

This paper explores the integration of augmented reality (AR) with image processing systems to enhance interactive worker training in industrial automation environments. The proposed system leverages AR technology to overlay real-time visual information, such as instructional graphics and step-by-step guidance, onto physical workspaces, improving the learning experience for workers. By incorporating advanced image processing techniques, the system ensures precise recognition of industrial tools, components and machinery, facilitating context aware training that adapts to the real-time actions of the trainee. This approach aims to accelerate the onboarding process, reduce human error, and increase efficiency in manufacturing settings, providing workers with immersive, hands-on learning opportunities while enhancing safety and operational effectiveness. The paper discusses the design, implementation and potential benefits of AR – enhanced image processing systems in industrial training programs, with a focus on scalability and practical deployment in diverse industrial environments. Keywords— Augmented Reality (AR), Image Processing, algorithms, Interactive Training, Industrial automation.

Optimal control of grid-connected overvoltage of distributed photovoltaic power generation based on cluster division

To address the overvoltage problem caused by the reverse flow of current when a high proportion of distributed photovoltaic (PV) is connected to the distribution network, this paper proposes a grid-connected voltage regulation control strategy based on the cluster division of the Distributed Model Predictive Control (DMPC) algorithm. Firstly, the overvoltage responsibility of each node is calculated using the Shapley value method. This is combined with k-means clustering to achieve effective cluster division, enabling dynamic adjustment of the active and reactive power of photovoltaic power generation units to stabilize regional voltage. Secondly, a group grid-connected voltage control strategy is introduced. This strategy controls the active and reactive power outputs by integrating real-time power output and voltage information from PV generating units in the region with the DMPC algorithm, ensuring overall voltage stability of the grid-connected system. Finally, actual overvoltage data from a 10 kV distribution line in the Dingxi power grid, Gansu Province, is used to verify that under the proposed control strategy, PV grid-connected overvoltage nodes are maintained within 1.06 p.u. The control effect is improved by a margin of 0.05 compared to traditional control methods. This demonstrates the effectiveness of the grouped grid-connected voltage regulation control strategy, achieving smoother voltage regulation performance in distributed PV grid-connected systems.

Google Trends Assessment of Keywords Related to Smoking and Smoking Cessation During the COVID-19 Pandemic in 4 European Countries: Retrospective Analysis

Background Smoking is a modifiable risk factor for SARS-CoV-2 infection. Evidence of smoking behavior during the pandemic is ambiguous. Most investigations report an increase in smoking. In this context, Google Trends data monitor real-time public information–seeking behavior and are therefore useful to characterize smoking-related interest over the trajectory of the pandemic. Objective This study aimed to use Google Trends data to evaluate the effect of the pandemic on public interest in smoking-related topics with a focus on lockdowns, vaccination campaigns, and incidence. Methods The weekly relative search volume was retrieved from Google Trends for England, Germany, Italy, and Spain from December 31, 2017, to April 18, 2021. Data were collected for keywords concerning consumption, cessation, and treatment. The relative search volume before and during the pandemic was compared, and general trends were evaluated using the Wilcoxon rank-sum test. Short-term changes and hereby temporal clusters linked to lockdowns or vaccination campaigns were addressed by the flexible spatial scan statistics proposed by Takahashi and colleagues. Subsequently, the numbers of clusters after the onset of the pandemic were compared by chi-square test. Results Country-wise minor differences were observed while 3 overarching trends prevailed. First, regarding cessation, the statistical comparison revealed a significant decline in interest for 58% (7/12) of related keywords, and fewer clusters were present during the pandemic. Second, concerning consumption, significantly reduced relative search volume was observed for 58% (7/12) of keywords, while treatment-related keywords exhibited heterogeneous trends. Third, substantial clusters of increased interest were sparsely linked to lockdowns, vaccination campaigns, or incidence. Conclusions This study reports a substantial decline in overall relative search volume and clusters for cessation interest. These results underline the importance of intensifying cessation aid during times of crisis. Lockdowns, vaccination, and incidence had less impact on information-seeking behavior. Other public measures that positively affect smoking behavior remain to be determined.

Efficiency of Police Cooperation in Criminal Investigations

Police cooperation is essential not only to investigate crime, but also to prevent it. Sharing information and best practices between law enforcement agencies allows early identification of threats, which can lead to prompt and effective interventions. At the same time, working together can reduce legislative or procedural disparities between states, which helps to strengthen joint efforts to fight crime. In addition to legal and operational challenges, police cooperation also raises ethical, privacy and data protection issues. In the digital age, the exchange of information between police agencies must comply with strict data protection standards in line with national and international legislation, such as the General Data Protection Regulation (GDPR) in the European Union. Thus, police cooperation is an indispensable tool in criminal investigations, offering the possibility to go beyond the geographical and legal limits imposed by national borders. However, its effectiveness depends on a clear legislative framework, mutual trust between partners and the adoption of modern technologies to facilitate real-time communication and information exchange. Police cooperation is an essential element in the fight against contemporary crime, but its effectiveness depends on resolving the legislative and operational problems that sometimes prevent effective inter-agency cooperation. Technological development and adaptation to new forms of crime also remain major challenges in this area.

Automatic detection and classification of beluga whale calls in the St. Lawrence estuary.

The endangered beluga whale (Delphinapterus leucas) of the St. Lawrence Estuary (SLEB) faces threats from a variety of anthropogenic factors. Since belugas are a highly social and vocal species, passive acoustic monitoring has the potential to deliver, in a non-invasive and continuous way, real-time information on SLEB spatiotemporal habitat use, which is crucial for their monitoring and conservation. In this study, we introduce an automatic pipeline to analyze continuous passive acoustic data and provide standard and accurate estimations of SLEB acoustic presence and vocal activity. An object detector extracted vocalizations of beluga whales from an acoustic recording of beluga vocal activity. Then, two deep learning classifiers discriminated between high-frequency call types (40-120 kHz) and the presence of low-frequency components (0-20 kHz), respectively. Different algorithms were tested for each step and their main combinations were compared in time and performance. We focused our work on a high residency area, Baie Sainte-Marguerite (BSM), used for socialization and feeding by SLEB. Overall, this project showed that accurate continuous analysis of SLEB vocal activity at BSM could provide valuable information to estimate habitat use, link beluga behavior and acoustic activity within and between herds, and quantify beluga presence and abundance.

Survey of IoT and AI applications: future challenges and opportunities in agriculture

The internet of things (IoT) connects physical objects through sensors, software, and communication technologies, enabling efficient data collection and sharing. This interconnection promotes automation, real-time monitoring, and improved decision-making across various sectors. In agriculture, the integration of IoT with artificial intelligence (AI) is revolutionizing resource management by providing farmers with real-time information on crop health, climate conditions, and soil quality. This paper explores how IoT and AI are transforming traditional agricultural practices to enhance both efficiency and sustainability. Through an in-depth analysis of existing literature and practical applications in the sector, this study identifies significant advancements in crop management, reduction of losses, and resource optimization. Additionally, it highlights persistent challenges such as data security and interoperability. The aim is to address these challenges and propose innovative solutions to optimize agricultural processes. The results indicate that while IoT and AI offer substantial benefits, further advancements and solutions are needed to fully leverage these technologies for sustainable agricultural development.

Intelligent Monitoring System for Roller-Compacted Earth-Rock Dam Construction Process

Abstract The conventional quality control method of earth-rock dam is insufficient to meet the requirements of intelligent and fine management of dam filling construction in the new stage. With the expansion of construction scale and the overall improvement in information technology level, the traditional compaction quality control methods no longer meet the demands of modern mechanized rapid construction or provide real-time compaction information for engineering contractors. In response to the current conventional design and construction schemes for earth-rock dams, an intelligent real-time monitoring system established in this paper for compaction construction of earth-rock dams. This system utilizes advanced Beidou high-precision positioning and navigation technology, vibration-sensing IoT technology, real-time processing of massive construction information, and BIM technology. It achieves real-time intelligent monitoring of key compaction processes in dam construction and refined management of unit engineering. The intelligent system established provides crucial technical support for the safe and reliable operation of water conservancy and hydropower projects. Simultaneously, it enables real-time, remote, and intelligent monitoring of dam construction processes, significantly enhancing the technological content and informatization level of water conservancy engineering construction.

Information provision and financial incentives in Catalonia's public primary care (2010–2019): an interrupted time series analysis

The relative efficacy of information provision versus financial incentives in improving primary care quality remains a critical, unresolved question. We investigated these two strategies in Catalonia's public primary care system from 2010 to 2019: an innovative online platform providing real-time quality indicator information and targeted economic incentives for achieving indicator goals. We conducted a comprehensive interrupted time series regression analysis on data from 272 primary care practices (5,628,080 patients). This analysis used linear regression models with Newey-West standard errors, and a sensitivity analysis including logit transformations to address ceiling effects. We evaluated 1) immediate post-intervention changes (step changes) in indicator results and inter-practice variability (coefficient of variation, CV), and 2) shifts in pre-intervention trends (slopes). We scrutinized 39 indicators after rigorous quality control: 23 novel (12 informed, 11 incentivized) and 16 derived from existing incentivized indicators. Robustness checks included 14 consistently incentivized and 10 non-intervened indicators. Overall, we assessed 63 indicators: 18 control, 13 follow-up, 9 quaternary prevention, 7 treatment, 7 diagnosis, 6 screening and 3 vaccination indicators. Informed indicators showed positive impacts in 75% (9/12) of cases, and incentivized indicators in 64% (7/11) of cases. Incentivized indicators displayed improvements in annual trends ranging from 6.66 to 1.25 percentage points, with step changes up to 8.87 percentage points. Information led to step changes ranging from 19.67 to 1.07 percentage points, along with trend improvements between 1.09 and 0.34 percentage points annually. Both interventions were associated with step reductions in variability (up to-0.18 CV reduction) and significant trend improvements. Derived indicators showed limited improvements in results or variability (31%, 5/16), with minor step increases up to 2.22 percentage points. Our findings reveal that information provision alone can match or even surpass the impact of financial incentives in improving care quality and reducing practice variability. This challenges conventional wisdom and offers a cost-effective, scalable approach to primary care quality enhancement, with far-reaching implications for global health policy. European Union, Horizon Europe.

Real-time monitoring of multiparameter in 1,3-propanediol production process with near-infrared spectroscopy

1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocess control based on real-time information of feedstock and main products is crucial for reducing the cost of production. However, rapid quantification of 1,3-PDO and glycerol remains challenging due to their highly similar molecular structures. In this study, the feasibility of near-infrared (NIR) spectroscopy to monitor 1,3-PDO, glycerol, acetate, and butyrate concentrations in the fermentation process using strain Clostridium pasteurianum was evaluated. NIR spectra were acquired through at-line measurement involving sampling and ex-situ analysis or on-line measurement with a fiber optic probe immersed in fermentation broth, integrated with Partial Least Squares (PLS) regression to establish calibration models on a laboratory-scale and pilot-scale. The best PLS regressions of 1,3-PDO, glycerol, acetate, and butyrate with two measurement approaches provided excellent performance, with the root-mean-squared errors of prediction (RMSEP) of 1.656g/L, 1.502g/L, 0.746g/L, and 0.557g/L in at-line measurement and 1.113g/L, 1.581g/L, 0.415g/L, and 0.526g/L in on-line measurement. The cross-scale application performance of at-line measurement was evaluated by an external fermentation trial and an acceptable result was achieved. At-line measurement technique represents a superior choice for the optimization of fermentation process since the robustness across varying fermentation scales and its applicability in multiple bioreactors. Thus, a calibration model developed for one bioreactor is likely to be used in other bioreactors, which enables the reduction of modeling costs. On-line measurement technique, owing to its automated operation and frequent data acquisition, enables real-time monitoring and precise control of the fermentation process, thereby reducing cost and improving production efficiency.

Autonomous vehicle diagnostics and support: a framework for API-Driven microservices

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient for managing the complex, real-time data generated by AVs, necessitating a shift toward microservices architecture. By leveraging API-driven microservices, the proposed framework ensures modularity, scalability, and real-time communication between various vehicle components and external support systems. The framework is designed to support continuous vehicle monitoring, predictive maintenance, fault detection, and real-time decision-making, all while minimizing latency. APIs play a critical role in enabling interaction between different microservices, allowing for seamless data exchange across diverse platforms. This interoperability facilitates integration with third-party services, such as cloud-based diagnostics, real-time traffic information, and software updates. Additionally, the microservices architecture ensures fault isolation, meaning that failures in one service do not compromise the entire system. The framework also emphasizes the importance of cybersecurity and data privacy. Given the sensitive nature of AV data, API security protocols such as OAuth2 and TLS encryption are incorporated to safeguard communication between microservices and external systems. Furthermore, the design is scalable, allowing for the integration of future AV technologies and third-party service enhancements without disrupting existing functionalities. In conclusion, API-driven microservices provide an efficient, flexible, and secure solution for autonomous vehicle diagnostics and support. This framework has the potential to improve AV reliability, reduce downtime, and enhance safety by enabling real-time, data-driven decision-making and proactive maintenance. Future work may explore the integration of machine learning algorithms to further optimize diagnostics and predictive maintenance capabilities.

Advancing Cybersecurity Governance: Adaptive Resilience and Strategic Third-Party Risk Management in Financial Services

As cyberattacks increase, banks and financial firms face new challenges. This research aims to improve cybersecurity by integrating adaptive resilience and strategic third-party risk management into current systems. This study uses thorough qualitative research methods to assess existing frameworks, identifying shortcomings that put companies at more risk. Findings reveal that firms using adaptive resilience - where governance policies adjust with real-time threat information and post-incident evaluation after incidents are much better at preventing and addressing cyber attacks. This paper stresses the importance of including third-party risk management in a comprehensive governance framework, as weaknesses in these relationships often lead to breaches. This study contributes critical insights and a strategic framework for financial institutions to bolster their defenses against sophisticated cyber threats, ensuring sustained resilience and security.

Intelligent Vehicle Accident Detection System

Road traffic accidents in Nigeria represent a significant public safety concern, with thousands of lives lost annually. The Intelligent Vehicle Accident Detection System (IVADS) addresses this issue by providing a system that detects accidents automatically and instantly notifies emergency responders with real-time location information. IVADS integrates multiple sensors, GPS, and GSM technologies to achieve accurate accident detection and rapid communication. This paper provides a comprehensive review of the system’s design, implementation, testing results, and potential implications for road safety improvements in Nigeria. This work builds upon existing research on accident detection, using innovative sensor integration and machine learning for precise detection.

Slashing the surplus – how prosumers with smart metering respond to regulatory restrictions on self-consumption in Croatia

With the diffusion of prosumerism, where households act both as producers and consumers of energy, policy makers must strike a balance between encouraging microgeneration and regulating this new prosumer segment on the energy market. However, effective policy implementation depends on prosumers’ behavioural reactions. This paper provides evidence on the interplay between digital real-time information and regulation of self-consumption for rooftop photovoltaics (PV) in Croatia. Croatian households that produce more annual electricity than they consume are automatically re-classified as renewable traders, which means additional administrative duties and less favorable tax treatment. This creates perverse incentives to reduce PV generation or increase energy consumption by year-end. We document the behavioural reactions to this policy design, indicating that energy production and consumption are highly elastic regarding regulatory incentives, but only if these incentives are made transparent and accessible with timely information. We collected two survey waves (n = 54 and n = 80) and smart meter data (n = 39), which illustrate the behavioural reaction before and after year-end. According to the survey wave before year-end, almost half of the participants considered curtailing their PV output. According to the smart meter data, a sizable share did indeed take action by shutting down PV production or by powering additional devices to reduce the surplus near year-end. In a second survey wave in the new year, prosumers provide ex-post insights on the specific measures taken to reduce surplus. We discuss research insights regarding the transparency and control offered by metering feedback, and how this can influence household behaviour within regulatory frameworks.

Key to Safety: A Multi-Functional Smart Keychain for Real-Time Women\'s Security

Abstract: The increasing need for women's safety in public areas has provided the base for innovation in creating compact, efficient personal security tools. This paper explores an intelligent keychain device tailored and designed with a combination of various defensive and alerting technologies with applications specifically for women's safety. The input involves GPS and GSM modules integrated with the A9G board. It has a LiPo battery, which is fitted with a push button. A preset alarm would immediately be sent to contacts in case of emergency. Other hardware installed was the taser for personal defense and a buzzer, to ensure a sufficiently audible alarm. The system is programmed with information through a USB to TTL converter. The methodology used here was to couple hardware with software, monitor locations, and communicate real-time information to the emergency contacts. The GPS/ GSM modules are selected to allow for global coverage, and the push button allows easy usage in emergencies, so taser modules with a further number of added security features. It is designed to be compact yet cost-effective, consumer-friendly, and also available with a reduction in power consumption based on an efficient selection of components.

FPGA-Based Design for Digital Speedometer to Detect Speed Limit of Vehicles

This paper presents a proposed design for a digital speedometer using Field Programmable Gate Array (FPGA) technology. The speedometer is a crucial safety feature in vehicles, providing real-time information on the vehicle's speed in kilometres per hour (km/h). The objective of this design is to assist drivers in monitoring and controlling their velocity at regular intervals to ensure safe driving. The hardware platform chosen for implementation is the Altera DE2 Board with Cyclone IV E and the hardware description language used is Verilog HDL. The digital speedometer receives a square wave pulse as an input signal from a hall sensor, which is emitted based on the vehicle's speed. The designed system accurately detects and counts these pulses to convert them into the corresponding speed of the vehicle. This digital speedometer is specifically intended for vehicles with engine capacities (cc) ranging from 1000cc to 1300cc. It can detect speeds within the range of 0km/h to 255km/h. The speed value is displayed on a seven-segment display with precision. Additionally, if the vehicle's speed exceeds the expressway's speed limit of 110km/h, a red LED indicator turns on to alert the driver to control their speed.

Consensus Report on the Use of Continuous Glucose Monitoring in Chronic Kidney Disease and Diabetes.

This report represents the conclusions of 15 experts in nephrology and endocrinology, based on their knowledge of key studies and evidence in the field, on the role of continuous glucose monitors (CGMs) in patients with diabetes and chronic kidney disease (CKD), including those receiving dialysis. The experts discussed issues related to CGM accuracy, indications, education, clinical outcomes, quality of life, research gaps, and barriers to dissemination. Three main goals of management for patients with CKD and diabetes were identified: (1) greater use of CGMs for better glycemic monitoring and management, (2) further research evaluating the accuracy, feasibility, outcomes, and potential value of CGMs in patients with end-stage kidney disease (ESKD) on hemodialysis, and (3) equitable access to CGM technology for patients with CKD. The experts also developed 15 conclusions regarding the use of CGMs in this population related to CGMs' unique delivery of both real-time information that can guide monitoring and management of glycemia and continuous and predictive data in this population, which is at higher risk for hypoglycemia and hyperglycemia. The group noted three major clinical gaps: (1) CGMs are not routinely prescribed for patients with diabetes and CKD; (2) CGMs are not approved by the United States Food and Drug Administration (FDA) for patients with diabetes who are on dialysis; and (3) CGMs are not routinely available to all of those who need them because of structural barriers in the health care system. These gaps can be improved with greater stakeholder collaboration, education, and awareness brought to the use of CGM technology in CKD.

New Instant Digital Pathology for EUS/EBUS Samples: The Last Advance in Bedside Diagnostics for Lung Carcinoma

Background Ex vivo fluorescence laser scanning microscopes (FCMs) allow digital tissue imaging directly from fresh, unfixed specimens without the need for conventional histological slide preparation. To date, no data have been reported on the use of FCMs in the endoscopy suite for immediate evaluation of endoscopic ultrasound (EUS)/endobronchial ultrasound (EBUS) fine needle aspiration/biopsy (FNA-B) specimens of lung lesions and/or mediastinal lymph nodes. Objectives The aim of this study was to evaluate the performance of the FCM Vivascope 2500 (Vivascope, Munich, Germany) in providing real-time adequacy assessment and diagnostic information on the digital images of fresh unprocessed EUS/EBUS FNA-B specimens and to compare it with the corresponding final histological sections of formalin-fixed and paraffin-embedded cell blocks. Methods and Results Thirty-two patients (50% male; 71 ± 8 years old) were enrolled between May 2023 and June 2024. In 28/32 (87.5%) patients, samples were defined as adequate at Vivascope evaluation, and in 20/28 (71.4%) patients, samples were classified as malignant. At final cytohistological evaluation, 87.5% of specimens were defined as adequate and 20/28 (71.4%) were diagnosed as malignant. There was perfect agreement between the Vivascope assessment of adequacy and the final cytohistological assessment on the same specimen (k Cohen 1). From a diagnostic point of view, perfect agreement was found between the two techniques in the identification of malignant neoplasms (k Cohen 1). Conclusion The use of FCM could provide rapid information on both the adequacy and malignancy of the sample obtained during EBUS tissue acquisition (EBUS-TA), with minimal or no preparation and without destroying or losing the tissue. This technique allows for obtaining representative material in EBUS/EUS for lung cancer staging and is expected to change the turnaround time from biopsy sampling to diagnostic characterization of the tumor, ultimately improving patient management both at diagnosis and at restaging in follow up.