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This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service. [ABSTRACT FROM AUTHOR]

Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads. Furthermore, we evaluate CPU management strategies such as CPU affinity and CPU isolation as potential solutions to these performance challenges. Our results highlight that the implementation of CPU affinity can significantly reduce throughput variability by up to 40%, decrease vBBU's NACK ratios, and reduce vBBU scheduling latency within the Linux RT-Kernel. Collectively, these findings underscore the potential of CPU management strategies to enhance vBBU performance in Cloud-RAN environments, enabling more efficient and stable network operations. The paper concludes with a discussion on the efficient realization of Cloud-RAN, elucidating the benefits of implementing proposed CPU affinity allocations. The demonstrated enhancements, including reduced scheduling latency and improved end-to-end throughput, affirm the practicality and efficacy of the proposed strategies for optimizing Cloud-RAN deployments. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system's architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load's power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model's performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications. [ABSTRACT FROM AUTHOR]

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system. [ABSTRACT FROM AUTHOR]

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs. [ABSTRACT FROM AUTHOR]

With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method. [ABSTRACT FROM AUTHOR]

This paper presents a visual compass method utilizing global features, specifically spherical moments. One of the primary challenges faced by photometric methods employing global features is the variation in the image caused by the appearance and disappearance of regions within the camera's field of view as it moves. Additionally, modeling the impact of translational motion on the values of global features poses a significant challenge, as it is dependent on scene depths, particularly for non-planar scenes. To address these issues, this paper combines the utilization of image masks to mitigate abrupt changes in global feature values and the application of neural networks to tackle the modeling challenge posed by translational motion. By employing masks at various locations within the image, multiple estimations of rotation corresponding to the motion of each selected region can be obtained. Our contribution lies in offering a rapid method for implementing numerous masks on the image with real-time inference speed, rendering it suitable for embedded robot applications. Extensive experiments have been conducted on both real-world and synthetic datasets generated using Blender. The results obtained validate the accuracy, robustness, and real-time performance of the proposed method compared to a state-of-the-art method. [ABSTRACT FROM AUTHOR]

Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues. [ABSTRACT FROM AUTHOR]

This paper describes the methodology of combining experimental measurements with mathematical–physics analyses in the investigation of flow in the aperture and nozzle. The aperture and nozzle separate the differentially pumped chamber from the specimen chamber in an environmental scanning electron microscope (ESEM). Experimental measurements are provided by temperature and pressure sensors that meet the demanding conditions of cryogenic temperature zones and low pressures. This aperture maintains the required pressure difference between the chambers. Since it separates the large pressure gradient, critical flow occurs on it and supersonic gas flow with the characteristic properties of critical flow in the state variables occurs behind it. As a primary electron beam passes through the differential pumped chamber and the given aperture, the aperture is equipped with a nozzle. The shape of the nozzle strongly influences the character of the supersonic flow. The course of state variables is also strongly influenced by this shape; thus, it affects the number of collisions the primary beam's electrons have with gas molecules, and so the resulting image. This paper describes experimental measurements made using sensors under laboratory conditions in a specially created experimental chamber. Then, validation using mathematical–physical analysis in the Ansys Fluent system is described. [ABSTRACT FROM AUTHOR]

This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 c m was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Film cooling technology is of great significance to enhance the performance of aero-engines and extend service life. With the increasing requirements for film cooling efficiency, researchers and engineers have carried out a lot of work on the precision and digital measurement of cooling holes. Based on the above, this paper outlines the importance and principles of film cooling technology and reviews the evolution of cooling holes. Also, this paper details the traditional measurement methods of the cooling hole used in current engineering scenarios with their limitations and categorizes digital measurement methods into five main types, including probing measurement technology, optical measurement technology, infrared imaging technology, computer tomography (CT) scanning technology, and composite measurement technology. The five types of methods and integrated automated measurement platforms are also analyzed. Finally, through a generalize and analysis of cooling hole measurement methods, this paper points out technical challenges and future trends, providing a reference and guidance for forward researches. [ABSTRACT FROM AUTHOR]

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery's health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm's responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm's accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings. [ABSTRACT FROM AUTHOR]

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter. [ABSTRACT FROM AUTHOR]

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution. [ABSTRACT FROM AUTHOR]

With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth's surface objects. However, the abundance of spectral information presents certain challenges for data processing, such as the "curse of dimensionality" leading to the "Hughes phenomenon", "strong correlation" due to high resolution, and "nonlinear characteristics" caused by varying surface reflectances. Consequently, dimensionality reduction of hyperspectral data emerges as a critical task. This paper begins by elucidating the principles and processes of hyperspectral image dimensionality reduction based on manifold theory and learning methods, in light of the nonlinear structures and features present in hyperspectral remote-sensing data, and formulates a dimensionality reduction process based on manifold learning. Subsequently, this study explores the capabilities of feature extraction and low-dimensional embedding for hyperspectral imagery using manifold learning approaches, including principal components analysis (PCA), multidimensional scaling (MDS), and linear discriminant analysis (LDA) for linear methods; and isometric mapping (Isomap), locally linear embedding (LLE), Laplacian eigenmaps (LE), Hessian locally linear embedding (HLLE), local tangent space alignment (LTSA), and maximum variance unfolding (MVU) for nonlinear methods, based on the Indian Pines hyperspectral dataset and Pavia University dataset. Furthermore, the paper investigates the optimal neighborhood computation time and overall algorithm runtime for feature extraction in hyperspectral imagery, varying by the choice of neighborhood k and intrinsic dimensionality d values across different manifold learning methods. Based on the outcomes of feature extraction, the study examines the classification experiments of various manifold learning methods, comparing and analyzing the variations in classification accuracy and Kappa coefficient with different selections of neighborhood k and intrinsic dimensionality d values. Building on this, the impact of selecting different bandwidths t for the Gaussian kernel in the LE method and different Lagrange multipliers λ for the MVU method on classification accuracy, given varying choices of neighborhood k and intrinsic dimensionality d, is explored. Through these experiments, the paper investigates the capability and effectiveness of different manifold learning methods in feature extraction and dimensionality reduction within hyperspectral imagery, as influenced by the selection of neighborhood k and intrinsic dimensionality d values, identifying the optimal neighborhood k and intrinsic dimensionality d value for each method. A comparison of classification accuracies reveals that the LTSA method yields superior classification results compared to other manifold learning approaches. The study demonstrates the advantages of manifold learning methods in processing hyperspectral image data, providing an experimental reference for subsequent research on hyperspectral image dimensionality reduction using manifold learning methods. [ABSTRACT FROM AUTHOR]

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model's accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 μ ε ), pressure (0.14 bar), and refractive index (6 × 10−5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/ μ ε , −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development. [ABSTRACT FROM AUTHOR]

There have been many studies on the significant correlation between the hydrogen peroxide content of different tissues or cells in the human body and the risk of disease, so the preparation of biosensors for detecting hydrogen peroxide concentration has been a hot topic for researchers. In this paper, palladium nanoparticles (PdNPs) and laser-induced graphene (LIG) were prepared by liquid-phase pulsed laser ablation and laser-induced technology, respectively. The complexes were prepared by stirring and used for the modification of screen-printed electrodes to develop a non-enzymatic hydrogen peroxide biosensor that is low cost and mass preparable. The PdNPs prepared with anhydrous ethanol as a solvent have a uniform particle size distribution. The LIG prepared by laser direct writing has good electrical conductivity, and its loose porous structure provides more adsorption sites. The electrochemical properties of the modified electrode were characterized by cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy. Compared with bare screen-printed electrodes, the modified electrodes are more sensitive for the detection of hydrogen peroxide. The sensor has a linear response range of 5 µM–0.9 mM and 0.9 mM–5 mM. The limit of detection is 0.37 µM. The above conclusions indicate that the hydrogen peroxide electrochemical biosensor prepared in this paper has great advantages and potential in electrochemical catalysis. [ABSTRACT FROM AUTHOR]

This paper presents a novel method of fabricating an enzymatic biosensor for breath analysis using chromatography paper as enzyme supporting layer and a liquid phase layer on top of screen printed carbon electrodes. We evaluated the performance with ethanol vapor being one of the breathing ingredients. The experimental results show that our sensor is able to measure the concentration of ethanol vapor within the range of 50 to 500 ppm. These results suggest the ability of detecting breath ethanol, and it can possibly be applied as a generic vapor biosensor to a wide range of diseases. [ABSTRACT FROM AUTHOR]

We report on the recent progress and development of research into cellulose-based electro-active paper for bending actuators, bioelectronics devices, and electromechanical transducers. The cellulose electro-active paper is characterized in terms of its biodegradability, chirality, ample chemically modifying capacity, light weight, actuation capability, and ability to form hybrid nanocomposites. The mechanical, electrical, and chemical characterizations of the cellulose-based electro-active paper and its hybrid composites such as blends or coatings with synthetic polymers, biopolymers, carbon nanotubes, chitosan, and metal oxides, are explained. In addition, the integration of cellulose electro-active paper is highlighted to form various functional devices including but not limited to bending actuators, flexible speaker, strain sensors, energy harvesting transducers, biosensors, chemical sensors and transistors for electronic applications. The frontiers in cellulose paper devices are reviewed together with the strategies and perspectives of cellulose electro-active paper and cellulose nanocomposite research and applications. [ABSTRACT FROM AUTHOR]

We describe the use of a paper-based probe impregnated with a vanadium-containing polyoxometalate anion, [PMo11VO40]5−, on screen-printed carbon electrodes for the electrochemical determination of chlorate. Cyclic voltammetry (CV) and chronocoulometry were used to characterize the ClO3− response in a pH = 2.5 solution of 100 mM sodium acetate. A linear CV current response was observed between 0.156 and 1.25 mg/mL with a detection limit of 0.083 mg/mL (S/N > 3). This performance was reproducible using [PMo11VO40]5−-impregnated filter paper stored under ambient conditions for as long as 8 months prior to use. At high concentration of chlorate, an additional catalytic cathodic peak was seen in the reverse scan of the CVs, which was digitally simulated using a simple model. For chronocoulometry, the charge measured after 5 min gave a linear response from 0.625 to 2.5 mg/mL with a detection limit of 0.31 mg/mL (S/N > 3). In addition, the slope of charge vs. time also gave a linear response. In this case the linear range was from 0.312 to 2.5 mg/mL with a detection limit of 0.15 mg/mL (S/N > 3). Simple assays were conducted using three types of soil, and recovery measurements reported. [ABSTRACT FROM AUTHOR]

In recent years paper-based microfluidic systems have emerged as versatile tools for developing sensors in different areas. In this work; we report a novel physical sensing principle for the characterization of liquids using a paper-based hygro-mechanical system (PB-HMS). The PB-HMS is formed by the interaction of liquid droplets and paper-based mini-structures such as cantilever beams. The proposed principle takes advantage of the hygroscopic properties of paper to produce hygro-mechanical motion. The dynamic response of the PB-HMS reveals information about the tested liquid that can be applied to characterize certain properties of liquids. A suggested method to characterize liquids by means of the proposed principle is introduced. The experimental results show the feasibility of such a method. It is expected that the proposed principle may be applied to sense properties of liquids in different applications where both disposability and portability are of extreme importance. [ABSTRACT FROM AUTHOR]

There is a great demand for human–machine interfaces (HMIs) in emerging electronics applications. However, commercially available plastic-based HMIs are primarily rigid, application-specific, and hard to recycle and dispose of due to their non-biodegradability. This results in electronic and plastic waste, potentially damaging the environment by ending up in landfills and water resources. This work presents a green, capacitive pressure-sensitive (CPS), touch sensor-based keypad as a disposable, wireless, and intelligent HMI to mitigate these problems. The CPS touch keypads were fabricated through a facile green fabrication process by direct writing of graphite-on-paper, using readily available materials such as paper and pencils, etc. The interdigitated capacitive (IDC) touch sensors were optimized by analyzing the number of electrode fingers, dimensions, and spacing between the electrode fingers. The CPS touch keypad was customized to wirelessly control a robotic arm's movements based on the touch input. A low-pressure touch allows slow-speed robotic arm movement for precision movements, and a high-pressure touch allows high-speed robotic arm movement to cover the large movements quickly. The green CPS touch keypad, as a disposable wireless HMI, has the potential to enforce a circular economy by mitigating electronic and plastic waste, which supports the vision of a sustainable and green world. [ABSTRACT FROM AUTHOR]

This work proposes a new compact triple-band triangular patch antenna for RF energy harvesting applications in IoT devices. It is realized on Teflon glass substrate with a thickness of 0.67 mm and a relative permittivity of 2.1. Four versions of this antenna have been designed and realized with inclinations of 0°, 30°, 60° and 90° to study the impact of the tilting on their characteristics (S11 parameter, radiation pattern, gain) and to explore the possibilities of their implementation in the architectures of electronic equipment according to the available space. The antenna is also realized on waterproof paper with a thickness of 0.1 mm and a relative permittivity of 1.4 for biomedical domain. All the antennas (vertical antenna, tilted antennas and antenna realized on waterproof paper) have a size of 39 × 9 mm2 and cover the 2.45 GHz and 5.2 GHz Wi-Fi bands and the 8.2 GHz band. A good agreement is obtained between measured and simulated results. Radiation patterns show that all the antennas are omnidirectional for 2.45 GHz and pseudo-omnidirectional for 5.2 GHz and 8.2 GHz with maximum measured gains of 2.6 dBi, 4.55 dBi and 6 dBi, respectively. The maximum measured radiation efficiencies for the three antenna configurations are, respectively, of 75%, 70% and 72%. The Specific Absorption Rate (SAR) for the antenna bound on the human body is of 1.1 W/kg, 0.71 W/kg and 0.45 W/kg, respectively, for the three frequencies 2.45 GHz, 5.2 GHz and 8.2 GHz. All these antennas are then applied to realize RF energy harvesting systems. These systems are designed, realized and tested for the frequency 2.45 GHz, −20 dBm input power and 2 kΩ resistance load. The maximum measured output DC power is of 7.68 µW with a maximum RF-to-DC conversion efficiency of 77%. [ABSTRACT FROM AUTHOR]

The high incidence of Diabetes Mellitus in low-income regions has promoted the development of low-cost alternatives to replace blood-based procedures. In this work, we present a bienzymatic paper-based sensor suitable for the naked-eye detection of glucose in saliva samples. The sensor was obtained by a stamping procedure and modified with chitosan to improve the colorimetric readout. The bienzymatic reaction of GOx-HRP coupled with 2,4,6-tribromo-3-hydroxy benzoic acid was applied for the detection of glucose within a range from 0 to 180 mgdL–1 in buffer and artificial saliva solutions. The visual readout was perceived by the naked eye and registered with an office scanner to evaluate the analytical performance. The results showed a limit of detection of 0.37 mgdL–1 (S/N = 3) with an R.S.D. of 1.69% and a linear range from 1 to 22.5 mgdL–1 with an R2 of 0.99235. The analysis of human saliva samples was performed without pre-processing, achieving recoveries from 92 to 114%. The naked-eye detection was evaluated under two different light settings, showing average recoveries of 108.58 and 90.65% for standard and low illumination. The proposed device showed potential for easy-to-use, sensitive, low-cost, fast, and device-free detection of salivary glucose suitable for untrained personnel operation and limited facilities. [ABSTRACT FROM AUTHOR]

Energy-efficient Buildings (EeB) are demanded in today’s constructions, fulfilling the requirements for green cities. Pre-fab buildings, which are modularly fully-built in factories, are a good example of this. Although this kind of building is quite new, the in situ inspection is documented using traditional tools, mainly based on paper annotations. Thus, the inspection process is not taking advantage of new technologies. In this paper, we present the preliminary results of the SIRAE project that aims to provide an Augmented Reality (AR) tool that can seamlessly aid in the regular processes of pre-fab building inspections to detect and eliminate the possible existing quality and energy efficiency deviations. In this regards, we show a description of the current inspection process and how an interactive tool can be designed and adapted to it. Our first results show the design and implementation of our tool, which is highly interactive and involves AR visualizations and 3D data-gathering, allowing the inspectors to quickly manage it without altering the way the inspection process is done. First trials on a real environment show that the tool is promising for massive inspection processes. [ABSTRACT FROM AUTHOR]

A fully integrated paper microfluidic electrochemical device equipped with three different cation permeable films is developed to determine blood ions (Cl-, Na+, K+, and Ca2+) at a time. These blood ions that are normally dissolved in the real human blood stream are essential for cell metabolisms and homeostasis in the human body. Abnormal concentration of blood ions causes many serious disorders. The optimized microfluidic device working without any external power source can directly and effectively separate human blood components, and subsequently detect a specific blood ion with minimized interference. The measured sensitivity to Cl-, K+, Na+, and Ca2+ are -47.71, 45.97, 51.06, and 19.46 in mV decade-1, respectively. Potentiometric responses of the microfluidic devices to blood serum samples are in the normal ranges of each cation, and comparable with responses from the commercial blood ion analyzer Abbott i-Stat. [ABSTRACT FROM AUTHOR]

Wireless passive neural recording systems integrate sensory electrophysiological interfaces with a backscattering-based telemetry system. Despite the circuit simplicity and miniaturization with this topology, the high electrode–tissue impedance creates a major barrier to achieving high signal sensitivity and low telemetry power. In this paper, buffered impedance is utilized to address this limitation. The resulting passive telemetry-based wireless neural recording is implemented with thin flexible packages. Thus, the paper reports neural recording implants and integrator systems with three improved features: (1) passive high impedance matching with a simple buffer circuit, (2) a bypass capacitor to route the high frequency and improve mixer performance, and (3) system packaging with an integrated, flexible, biocompatible patch to capture the neural signal. The patch consists of a U-slot dual-band patch antenna that receives the transmitted power from the interrogator and backscatters the modulated carrier power at a different frequency. When the incoming power was 5–10 dBm, the neurosensor could communicate with the interrogator at a maximum distance of 5 cm. A biosignal as low as 80 µV peak was detected at the receiver. [ABSTRACT FROM AUTHOR]

Power transformers are essential apparatuses used to transfer electrical energy from one voltage-level circuit to another. For reliable systems, preventive maintenance of the transformers is required to ensure good services of all mechanical, electrical, and insulation parts. Oil-immersed paper is most often used for transformer insulation. To ensure such good insulation performance and for assessing insulation conditions, advanced transformer sensing, monitoring, and effective assessment techniques are required. This paper introduces an effective technique for assessing the insulation conditions in power transformers, which are crucial for ensuring reliable energy transfer. The method utilizes advanced transformer sensing and monitoring, focusing on oil-immersed paper insulation commonly used in transformers. The technique employs dielectric response sensing, obtained from frequency-domain spectroscopy tests, to estimate degrees of polymerization (DP) and percentages of moisture content (PMCs) in the oil-immersed paper insulation. These parameters are well-known indicators of insulation performance. The approach is based on the weighted k-nearest neighbor regression, using a database of dielectric loss factors at low frequency and oil conductivities. To overcome limited data availability, linear interpolation and extrapolation techniques are applied to enlarge the database. Experimental verification and comparison with a previously developed method demonstrate the proposed technique's superiority in accuracy and complexity. The maximum deviations of DP and PMC in the validation cases are 6.2% and 18.7%, respectively. In addition, to evaluate the validity of our proposed method in the case of a real power transformer, a comparative analysis of the DP and PMC values determined by the proposed method with those obtained through a previously developed and complicated approach was performed. The predicted results indicate that the DP and PMC values of the oil-immersed insulation fall within the ranges of 800 to 1000 and 1.5 to 2.0, respectively, which agree with the results determined by the complicated approach and closely align with real conditions. By offering a reliable and advanced means of assessing insulation conditions, this technique contributes to the preventive maintenance and overall efficiency of power transformers. [ABSTRACT FROM AUTHOR]

Tuning the sensing properties of spiropyrans (SPs), which are one of the photochromic molecules useful for colorimetric sensing, is important for efficient analysis, but their synthetic modification is not always simple. Herein, we introduce an alkyne-functionalized SP, the modification of which would be easily achieved via Cu-catalyzed azide-alkyne cycloaddition ("click reaction"). The alkyne-SP was conjugated with a bis(triethylene glycol)-benzyl group (EG-BtSP) or a simple benzyl group (BtSP), forming a triazole linkage from the click reaction. The effects of auxiliary groups to SP were tested on metal-ion sensing and cyanide detection. We found that EG-BtSP was more Ca2+-sensitive than BtSP in acetonitrile, which were thoroughly examined by a continuous variation method (Job plot) and UV-VIS titrations, followed by non-linear regression analysis. Although both SPs showed similar, selective responses to cyanide in a water/acetonitrile co-solvent, only EG-BtSP showed a dramatic color change when fabricated on paper, highlighting the important contributions of the auxiliary groups. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the effect of humidity on paper substrates and propose a simple and low-cost method for their passivation using ZnO nanoparticles. To this end, we built paper-based microdevices based on an interdigitated electrode (IDE) configuration by means of a mask-less laser patterning method on simple commercial printing papers. Initial resistive measurements indicate that a paper substrate with a porous surface can be used as a cost-effective, sensitive and disposable humidity sensor in the 20% to 70% relative humidity (RH) range. Successive spin-coated layers of ZnO nanoparticles then, control the effect of humidity. Using this approach, the sensors become passive to relative humidity changes, paving the way to the development of ZnO-based gas sensors on paper substrates insensitive to humidity. [ABSTRACT FROM AUTHOR]

We report a battery-powered, paper-based electrochromic array for visualized electrochemical sensing. The paper-based sensing system consists of six parallel electrochemical cells, which are powered by an aluminum-air battery. Each single electrochemical cell uses a Prussian Blue spot electrodeposited on an indium-doped tin oxide thin film as the electrochromic indicator. Each electrochemical cell is preloaded with increasing amounts of analyte. The sample activates the battery for the sensing. Both the preloaded analyte and the analyte in the sample initiate the color change of Prussian Blue to Prussian White. With a reaction time of 60 s, the number of electrochemical cells with complete color changes is correlated to the concentration of analyte in the sample. As a proof-of-concept analyte, lactic acid was detected semi-quantitatively using the naked eye. [ABSTRACT FROM AUTHOR]

We report on the development of an electrochemical probe for the trace analysis of 2,4,6-trinitrotoluene (TNT) in soil samples. The probe is a combination of graphite electrodes, filter paper, with ethylene glycol and choline chloride as the solvent/electrolyte. Square wave chromatovoltammograms show the probes have a sensitivity for TNT of 0.75 nA/ng and a limit of detection of 100 ng. In addition, by taking advantage of the inherent paper chromatography step, TNT can be separated in both time and cathodic peak potential from 4-amino-dinitrotolene co-spotted on the probe or in soil samples with the presence of methyl parathion as a possible interferent. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for the three-dimensional (3D) image visualization of objects under photon-starved conditions using multiple observations and statistical estimation. To visualize 3D objects under these conditions, photon counting integral imaging was used, which can extract photons from 3D objects using the Poisson random process. However, this process may not reconstruct 3D images under severely photon-starved conditions due to a lack of photons. Therefore, to solve this problem, in this paper, we propose N-observation photon-counting integral imaging with statistical estimation. Since photons are extracted randomly using the Poisson distribution, increasing the samples of photons can improve the accuracy of photon extraction. In addition, by using a statistical estimation method, such as maximum likelihood estimation, 3D images can be reconstructed. To prove our proposed method, we implemented the optical experiment and calculated its performance metrics, which included the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), peak-to-correlation energy (PCE), and the peak sidelobe ratio (PSR). [ABSTRACT FROM AUTHOR]

Frequency-selective surfaces (FSS) are two-dimensional geometric structures made of conductive materials that selectively transmit or reflect electromagnetic waves. In this paper, flexible FSS made on textile and film substrates is presented and compared to show the effect of the texture associated with the type of substrate on the shielding properties. Three geometries of patterns of squares in the border, inversion of squares in the border, and circles with a border were used, and the patterns were made by the silver paste screen printing technique. Microscopic analysis (SEM and optical) was performed to determine the degree of substrate coverage and the actual geometry of the pattern. The resistance per square of the obtained patterns was about 50 mΩ/□. The shielding properties of FSS were simulated in Comsol Multiphysics 6.2 software and then measured by the antenna method. Selective textile filters were obtained, depending on the pattern used, with one or two modals with a transmission attenuation of about 15 dB. The paper analyzes the effect of the substrate and the screen printing technique used on the shielding properties of the flexible FSS. [ABSTRACT FROM AUTHOR]

Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evaluate the detector's performance. In this paper, we first notice that statistics of true positive detections rely on researchers' arbitrary selection of time tolerance between QRS detector output and the database reference. Next, we propose a multidimensional algorithm evaluation method and present its use on four example QRS detectors. The dimensions are (a) influence of detection temporal tolerance, tested for values between 8.33 and 164 ms; (b) noise immunity, tested with an ECG signal with an added muscular noise pattern and signal-to-noise ratio to the effect of "no added noise", 15, 7, 3 dB; and (c) influence of QRS morphology, tested on the six most frequently represented morphology types in the MIT-BIH Arrhythmia Database. The multidimensional evaluation, as proposed in this paper, allows an in-depth comparison of QRS detection algorithms removing the limitations of existing one-dimensional methods. The method enables the assessment of the QRS detection algorithms according to the medical device application area and corresponding requirements of temporal accuracy, immunity to noise, and QRS morphology types. The analysis shows also that, for some algorithms, adding muscular noise to the ECG signal improves algorithm accuracy results. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as "Smart Parking", "Environmental Behavior Analysis on Traffic Incidents", and "Emergency Management", we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity. [ABSTRACT FROM AUTHOR]

Semantic communication technology in the 6G wireless system focuses on semantic extraction in communication, that is, only the inherent meaning of the intention in the information. Existing technologies still have challenges in extracting emotional perception in the information, high compression rates, and privacy leakage due to knowledge sharing in communication. Large-scale generative-model technology could rapidly generate multimodal information according to user requirements. This paper proposes an approach that leverages large-scale generative models to create animated short films that are semantically and emotionally similar to real scenes and characters. The visual content of the data source is converted into text expression through semantic understanding technology; emotional clues from the data source media are added to the text form through reinforcement learning technology; and finally, a large-scale generative model is used to generate visual media, which is consistent with the semantics of the data source. This paper develops a semantic communication process with distinct modules and assesses the enhancements garnered from incorporating an emotion enhancement module. This approach facilitates the expedited generation of broad media forms and volumes according to the user's intention, thereby enabling the creation of generated multimodal media within applications in the metaverse and in intelligent driving systems. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the investigation of the metrological properties of phase-sensitive reflectometric measurement systems, with a particular focus on addressing the non-uniformity of responses along optical fibers. The authors highlight challenges associated with the stochastic distribution of Rayleigh reflectors in fiber optic systems and propose a methodology for assessing response non-uniformity using both cross-correlation algorithms and machine learning approaches, using chirped-reflectometry as an example. The experimental process involves simulating deformation impact by altering the light source's wavelength and utilizing a chirped-reflectometer to estimate response non-uniformity. This paper also includes a comparison of results obtained from cross-correlation and neural network-based algorithms, revealing that the latter offers more than 34% improvement in accuracy when measuring phase differences. In conclusion, the study demonstrates how this methodology effectively evaluates response non-uniformity along different sections of optical fibers. [ABSTRACT FROM AUTHOR]

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability analysis and design of the n-DOF (n-degree-of-freedom) mass-chain vibration systems containing both semi-active and passive mechanical controllers. Based on Lyapunov's stability theory, sufficient conditions are derived for the n-DOF vibration system containing a semi-active switched inerter and a passive mechanical network with the first-order admittance to be globally asymptotically stable. Furthermore, the optimization designs of a quarter-car vibration control system and a three-storey building vibration system are conducted together with the derived stability results, and the instability cases contradicting the stability conditions are presented for illustration. The optimization and simulation results show that the combination of semi-active and passive mechanical controllers in vibration systems can clearly enhance system performances in comparison with the conventional semi-active or passive control. The novelty of this paper is that the stability problem of a general n-DOF vibration system that simultaneously contains a semi-active controller and a first-order passive controller is investigated for the first time, where such a system combines the advantages of both semi-active and passive mechanical controllers. The investigations and results can provide an essential foundation for further exploring the stability problems of more general systems, and can be applied to the controller designs of many vibration systems in practice. [ABSTRACT FROM AUTHOR]

This paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. Controllers with different actuation formulations are presented and compared to study the path-tracking performance of the vehicle with different levels of actuation. The controllers are implemented in a high-fidelity simulation environment considering scenarios of vehicle handling limits. According to the simulation results, the vehicle achieves the best overall path-tracking performance with combined 4WS and TV, which illustrates that the over-actuation topology can enhance the path-tracking performance during conditions under the limits of handling. In addition, the performance of the over-actuation controller is further assessed with different sampling times as well as prediction horizons in order to investigate the effect of such parameters on the control performance, and its capability for real-time execution. In the end, the over-actuation control strategy is implemented on a target machine for real-time validation. The control formulation proposed in this paper is proven to be compatible with different levels of actuation, and it is also demonstrated in this work that it is possible to include the particular over-actuation formulation and specific nonlinear vehicle dynamics in real-time operation, with the sampling time and prediction time providing a compromise between path-tracking performance and computational time. [ABSTRACT FROM AUTHOR]

Imaging disdrometers are widely used in field campaigns to provide information on the shape of hydrometeors, together with the diameter and the fall velocity, which can be used to derive information on the shape–size relations of hydrometeors. However, due to their higher price compared to laser disdrometers, their use is limited to scientific research purposes. The 3D stereo (3DS) is a commercial imaging disdrometer recently made available by Thies Clima and on which there are currently no scientific studies in the literature. The most innovative feature of the 3DS is its ability in capturing images of the particles passing through the measurement volume, crucial to provide an accurate classification of hydrometeors based on information about their shape, especially in the case of solid precipitation. In this paper. the performance of the new device is analyzed by comparing 3DS with the Laser Precipitation Monitor (LPM) from the same manufacturer, which is a known laser disdrometer used in many research works. The data used in this paper were obtained from measurements of the two instruments carried out at the Casale Calore site in L'Aquila during the CORE-LAQ (Combined Observations of Radar Experiments in L'Aquila) campaign. The objective of the comparison analysis is to analyze the differences between the two disdrometers in terms of hydrometeor classification, number and falling speed of particles, precipitation intensity, and total cumulative precipitation on an event basis. As regards the classification of precipitation, the two instruments are in excellent agreement in identifying rain and snow; greater differences are observed in the case of particles in mixed phase (rain and snow) or frozen phase (hail). Due to the different measurement area of the two disdrometers, the 3DS generally detects more particles than the LPM. The performance differences also depend on the size of the hydrometeors and are more significant in the case of small particles, i.e., D < 1 mm. In the case of rain events, the two instruments are in agreement with respect to the terminal velocity in still air predicted by the Gunn and Kinzer model for drops with a diameter of less than 3 mm, while, for larger particles, terminal velocity is underestimated by both the disdrometers. The agreement between the two instruments in terms of total cumulative precipitation per event is very good. Regarding the 3DS ability to capture images of hydrometeors, the raw data provide, each minute, from one to four images of single particles and information on their size and type. Their number and coarse resolution make them suitable to support only qualitative analysis of the shape of precipitating particles. [ABSTRACT FROM AUTHOR]

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders. [ABSTRACT FROM AUTHOR]

This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user. This review gathers, in one place, a comprehensive collection of works that have been conducted thus far with respect to achieving the personalization or individualization of the amplification function of hearing aids. Furthermore, it underscores the impact that machine learning can have on enabling an improved and personalized hearing experience for hearing aid users. This paper concludes by stating the challenges and future research directions in this area. [ABSTRACT FROM AUTHOR]

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock. [ABSTRACT FROM AUTHOR]

In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach's superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for preload measurement of bolted connections, specifically tailored for offshore wind applications. The proposed method combines robotics, Phased Array Ultrasonic Testing (PAUT), nonlinear acoustoelasticity, and Finite Element Analysis (FEA). Acceptable defects, below a pre-defined size, are shown to have an impact on preload measurement, and therefore conducting simultaneous defect detection and preload measurement is discussed in this paper. The study demonstrates that even slight changes in the orientation of the ultrasonic transducer, the non-automated approach, can introduce a significant error of up to 140 MPa in bolt stress measurement and therefore a robotic approach is employed to achieve consistent and accurate measurements. Additionally, the study emphasises the significance of considering average preload for comparison with ultrasonic data, which is achieved through FEA simulations. The advantages of the proposed robotic PAUT method over single-element approaches are discussed, including the incorporation of nonlinearity, simultaneous defect detection and stress measurement, hardware and software adaptability, and notably, a substantial improvement in measurement accuracy. Based on the findings, the paper strongly recommends the adoption of the robotic PAUT approach for preload measurement, whilst acknowledging the required investment in hardware, software, and skilled personnel. [ABSTRACT FROM AUTHOR]