Smart textiles are a wide category of products that embed ultra-technical fibers to implement sensing or actuating functions: electrodes and sensors able to measure bio-signals and detect several parameters - such as body temperature and motion, heart rate, electrocardiogram (ECG), electrical brain activity, electrical muscle activity, breathing rate, SpO₂, and blood pressure - and actuators to produce thermal or mechanical actions.

These smart components are integrated into textile fiber, clothes, and elastic bands and support various applications in several fields, including healthcare, security, military, and fashion.

One very recent technology is the fabrication of fibers or layers based on carbon allotrope materials such as graphene, reduced graphene oxide (rGO), carbon nanotubes (CNTs), and carbon black (CB).

This report aims to provide a patent landscape analysis on carbon allotrope-based textile electrodes and biosensors to measure biosignals and detect several parameters such as body temperature and motion, heart rate, electrocardiogram (ECG), electrical brain activity, electrical muscle activity, breathing rate, SpO₂, and blood pressure explore the newest technological advancement and the exploitation areas. Espacenet, a free-of-charge patent database provided by the EPO (European Patent Office) and containing data on more than 140 million patent publications from over 100 countries, was used as the reference database. The patent search was carried out by combining precise keywords and classification symbols. Both classification schemes (IPC – International Patent Classification and CPC – Cooperative Patent Classification) were used. As a result of this study, a total of 227 patent documents were found between 2004 and 2023. The first patent application claiming a fabric electrode arrangement with carbon black as conductive material was filed in 2004 by Philips. 2021 was the year with the highest patent filings, with 36 documents. The United States was ranked first with 126 patent documents. Carbon nanotubes and graphene are the most patented carbon allotrope materials, while body temperature, motion, and heart rate measurements are the main disclosed applications.

We also analyzed the Orbit database obtaining 288 items (vs 227) with only 238 still active records (148 granted and 90 pending): the first application by Philips on an electrode arrangement is confirmed (but filed in 2002), and the patents distribution shows a peak in the period 2016-2020 (146 records available), while today it seems to be stable or even decreasing (“only” 52 record in the half period January 2021-June 2023).

This outcome suggests that this material and related technology has reached its maximum exploitation or has not demonstrated a disruptive output.

Antimicrobial resistance (AMR), which poses a significant and swiftly growing threat to public health, is exacerbated by inappropriate antimicrobial prescribing practices. To promote targeted and appropriate antimicrobial usage and slow the development of AMR, the World Health Organization has identified the need to develop affordable, sensitive, and rapid point-of-care biosensors to be used as diagnostic tools. Towards this goal, a novel transducer has been developed that, when coupled with a biorecognition element, could serve as the basis for a new range of targeted biosensors. This transducer is designed for use in an electrochemical cell and is manufactured using an inexpensive approach that is suited to high-volume production. More specifically, the transducer consists of a nano- and microfiber mesh made through electrospinning from a customized polymer blend that is intrinsically conductive. The mesh is bound to gold inter-digitated electrodes (IDEs) with a treatment step that increases stability of the polymer blend in aqueous media. The efficacy of these transducers in a sensing environment was evaluated by using the transducers as sensors to distinguish between different concentrations of PBS in DI water. The transducers were placed in different concentration ratios and Electrochemical Impedance Spectroscopy (EIS) was performed over the range of 100 Hz to 1 MHz. The data was normalized with respect to the maximum value observed and characteristic curves were generated from the data. Inspection of the data at specific frequencies found a direct link between the impedance observed and the concentration ratio of the electrochemical cell, thus proving that these transducers are suited to biosensing applications.

Additive manufacturing, commonly known as 3D printing, has significantly advanced part fabrication in various industries. Despite its numerous benefits, including reduced lead times and complex geometries, a few obstacles still prevent widespread adoption. Current research efforts have predominantly focused on in-situ monitoring and investigating the mechanical properties of 3D printed materials, with limited attention given to the sources of skewness in the fabricated products. To address this gap, our study aims to explore the factors contributing to skewness in 3D-printed objects. Specifically, we examine the influence of the belt and carriage wheel conditions within the 3D printer on the shape of the fabricated products, resulting from potential distortions in the orientation of the print head carriage during the printing process. A comprehensive analysis was employed, utilizing One-Way ANOVA, Tukey, Fisher Least Significant Difference Method, and Friedman Rank test, to establish statistically significant evidence supporting the notion that the mechanical components, namely the belt, and wheel, have a substantial impact on the orientation of the print head, consequently leading to skewness in the final 3D printed products. Through rigorous analysis and experimentation, the findings of this study indicate that the condition of the belt and the wheel play a pivotal role in influencing skewness. This conclusion was derived from both parametric and non-parametric statistical analyses, which further underscore the significance of these factors. Overall, these results shed light on the key contributors to skewness in 3D printed products, providing valuable insights for improving such components' dynamic reliability and structural integrity. Further research and development in this area can help in devising strategies and interventions to mitigate skewness and enhance the overall quality of additively manufactured structures in real-time.

The world’s population is ageing, and older people are more likely to have chronic illnesses with the need for ongoing monitoring and medical support. This paper proposes a health monitoring and alarm system for elderly people based on LoRa communication technology. By leveraging Long-Range (LoRa) technology and connecting to the ThingsSpeak Network, the system incorporates various sensors to capture real-time vital signs consisting of heart rate and body temperature of elderly individuals. This enables family members, medical experts, and authorized entities to access the health status of the system users and attend to their immediate needs. The research indicates the viability of leveraging the LoRa gateway and other support infrastructures, such as the Internet of Things (IoT), to deploy sensor networks for elderly monitoring. The IoT infrastructure is involved in data storage and transmission to The ThingsSpeak Network for monitoring users' critical metrics. The system utilizes two Lora WAN radio modules for long-range wireless communication, an ESP32 Wireless Module for the IoT feature, a microcontroller, temperature and pulse rate sensors. Raw data from the temperature and pulse rate sensors are sent to the receiver LoRa board and Thingspeak IoT platform from which a medical personnel can monitor. Additionally, the system has an alarm feature that, in the event of any unusual readings, alerts caretakers or medical experts. The LoRa communication technology provides long-range and low-power wireless communication, making it suitable for healthcare applications with various added advantages to the system such as continuous monitoring of vital signs, timely intervention to prevent emergencies, cost-effectiveness and easily integrated into existing healthcare systems.

Algal Organic Matter (AOM) is derived from dissolved organic matter composition of the algal species observed. In this study, excitation-emission fluorescence spectroscopy has been used to determine the AOM and pigment characteristics of Chlorella sp. in varying algal biomass concentrations. The AOM and pigment characteristics were observed at 400-600 nm and 600-800 nm fluorescence emission, respectively with excitation spectrum of 300-450 nm. F680/450 is computed based on the ratio between the chlorophyll-a at 680 nm and dissolve organic matter contribution at 450 nm. F680/450 showed positive correlation with algal biomass (R² = 94.45), estimated chlorophyll-a pigment (R² = 87.12) of Chlorella sp. This study is a good reference for algal growth monitoring in biomass estimation and production in natural waters.

The introduction of robotic systems in medical surgery has achieved the goal of decreasing procedures invasiveness positively impacting the patient’s prognosis by reducing the incisions size, surgical infections, and hospitalization time. Nowadays robotic surgery is used as an integral part of urology, gynaecology, abdominal and cardiac interventions. Despite its adoption in several surgical specialties, robotic technology remains limited in microsurgery. These limitations arise from the fact that most commercial medical robots achieve millimetre precision levels, being unsuitable for manipulating fine structures with dimensions <0.8mm such as blood vessels.

In this paper, we present the development of a robotic system providing sub-millimetre motion resolution for the potential manipulation of fine structures. The design is based on a linear delta robotic geometry consisting of three linear actuators, three pairs of parallel legs and twelve spherical joints, enabling high stability, low inertia, and high motion precision all required for microsurgical tasks. The motion, resolution and repeatability of the developed system were experimentally tested.

Our results showed that the proposed design motion resolution is 3.37 ±0.3μm in both the X and Y axes and 1.32 ±0.2μm in the Z axis considering individual steps. We tested the system considering hexagon and serpentine trajectories with dimensions similar to those found in blood vessels, we found that performing the same trajectory for five times showed a displacement between these. For the hexagon path, the migration occurs in its lower section, with a displacement error of 9 ±1.35μm. Similarly, for the serpentine path, an error of 40 ±2.13μm occurs between adjacent lines. Though the addition of displacement and angle compensation these errors were reduced by 39.6% and 85.9%, for the hexagon and serpentine trajectories respectively. These results demonstrate the potential use of the here presented robotic system for its application in neurosurgery, plastic, and breast cancer surgery.

Prognostic studies of industrial systems essentially focus on health deterioration analysis that has recently been oriented toward data analytics and learning systems. In general, real degradation phenomena suffer from complex drifted data in which degradation patterns are hidden and change over time. Accordingly, such a process requires a well-structured processing and extraction mechanism to reveal such patterns, which facilitates the transition to other model reconstruction and investigation tasks. In this context, to provide additional simplicity of data processing in the field, a complete software package is designed and grouped into a single function that is fully automated and does not require human intervention. The package named ProgMachina (i.e., prognostic machine) provides a featured list of processed features from a life cycle that passed through denoising, filtering, outlier removal, and scaling process to ensure data significance in terms of degradation. The package allows using a time window with a specific overlap to ensure that the scanning process of all possible degradation patterns is properly done. Additionally, an exponential function is used to identify a corresponding health index of degraded signals. Data visualization and many previous experiments on machines show the effectiveness of such a methodology in terms of obtained prediction accuracy. The package is designed with a MATLAB library and made available online to be exploited in similar fields

Smart cities are the backbone of any digital economy. This concept entails the end-to-end integration of IT technologies within a smart city, including tracking passenger traffic, monitoring transport and crime rates, managing household expenses such as heating, water, and electricity, facilitating interaction between citizens and government, providing automatic payroll and taxes, paying for health insurance, interacting with assistants and chatbots, and much more. All these achievements are aimed at improving the quality of life and reliability for ordinary citizens.

Successful implementation of these processes requires a robust IT infrastructure that includes computers, sensors, and controllers. This infrastructure generates huge amounts of data that require processing, storage, and analysis. Artificial intelligence algorithms play a pivotal role in addressing these challenges, encompassing pattern recognition, voice recognition, chatbots, and intelligent assistants. These sophisticated mechanisms enable the analysis and processing of the acquired information.

In this context, we are introducing a novel blockchain-based Delta platform. This platform enables the creation of digital twins of smart cities, accurately simulating all processes and relationships within them. The Delta platform implements a hierarchical approach to data storage and processing in which multi-blockchains, in combination with other big data stores, serve as the underlying information stores. The work of intelligent systems inside smart cities is described using smart contracts executed within multi-blockchain structures, thereby ensuring efficient and secure functionality. To obtain new knowledge and predictions, we use a specially developed learning theory for intelligent systems based on the requirements of maximum specificity presented in the works of Carl Hempel.

Assessing the degree of implementation of Industry 4.0 technology in companies in a region is crucial to understand their technological progress and identify areas for improvement. In this paper, the assessment is carried out using indirect methods, such as data analysis through web scraping and the examination of publicly available information.

Web scraping involves collecting data from various online sources, such as company websites, industry platforms and relevant online databases. By extracting information related to technology adoption, utilisation and integration, valuable knowledge can be obtained on the level of implementation of Industry 4.0 technology in companies in the region.

Publicly available information, including industry reports, case studies and white papers, is also analysed to provide additional context and validation for the assessment. This information helps identify trends, best practices and benchmarks in the implementation of Industry 4.0 technology.

Through the analysis of web-based data and publicly available information, a complete picture of the 4.0 technology landscape in the region is obtained. Policy makers can use this information to develop initiatives and policies that promote the widespread adoption of these technologies. Companies can benchmark their technological progress, identify areas for improvement, and make informed decisions regarding technology investments. Researchers can analyze the collected data to understand the impact of Industry 4.0 technologies on regional economies and industries.

In summary, assessing the level of deployment of Industry 4.0 technology in companies in a region through indirect methods, such as web scraping and analysis of publicly available information, provides valuable information on the technological progress and growth potential of Industry 4.0 technology in companies. By leveraging online data sources, it is possible to track the adoption of 4.0 technologies in industries and thus design policies that foster innovation and contribute to regional economic progress.

Due to the exponential growth in the number of Internet of Things (IoT) devices that generate and collect data over the network, security presents significant challenges. It is crucial to ensure the integrity and security of IoT devices as well as to address issues such as interoperability and trust in data sources. Traceability and the ability to audit events and transactions play a fundamental role in achieving these goals.

In the proposed paper, we present a novel architecture together with its implementation as a proof-of-concept of a traceability and auditing IoT system based on a Distributed Ledger Technology (DLT). To demonstrate the applicability of the proposed solution, a smart contract-based system for occupational risk prevention (ORP) has been developed to monitor oxygen concentration in confined spaces that exist in ships and shipyards.

The system has been devised for the operators that weld inside the ships of the Spanish shipbuilding company Navantia, which is one of the largest shipbuilders in the world. Specifically, the IOTA network has been used, which benefits the system through its decentralized, secure, and scalable data structure. In addition, the integration of smart contracts allows for establishing predefined rules and conditions, ensuring the execution of logic in a reliable and automated manner. By combining these technologies, it is possible to develop a transparent, dependable and tamperproof system for IoT traceability and auditing. To demonstrate the viability of the system, it has been tested in one of the shipyards that Navantia owns in Ferrol, showing good performance and reliability.