Detected Research Articles

Gas Pipe Leakage Inspection Robot with Real Time Monitoring System

In this Review paper the study focusses on Pipe inspection robots,robots are advanced devices engineered to traverse the interiors of pipelines, assessing conditions and identifying issues like leaks, corrosion, or blockages in industries such as oil and gas, water supply, and sewage. Equipped with high-resolution cameras, sensors, and diagnostic tools, these robots offer detailed, real-time data to support preventive maintenance and reduce manual inspection challenges. Recent developments focus on improving robot agility, compactness, and durability, enabling them to handle varied pipe diameters and complex pathways. Enhanced with wireless connectivity and artificial intelligence, some models can now perform autonomous navigation and defect detection, significantly boosting efficiency and safety. This review examines current innovations, operational hurdles, and future advancements in pipe inspection robotics, with a particular emphasis on smarter navigation systems and sensor technology to enhance inspection accuracy and dependability.

Quantifying head and withers movement asymmetry in sound and naturally forelimb lame horses trotting on a circle on hard and soft surfaces.

Reliable lameness thresholds for vertical head displacement for trotting on a circular path and on different surfaces have yet to be defined. Withers movement in lame horses could help improve the diagnostic accuracy of sensor technology. To define head movement lameness threshold ranges and describe the relationship between head and withers movement during trotting under different circumstances in sound and forelimb lame horses. Retrospective analysis of clinical data and comparison with control group. Fifty-five sound and thirty-four naturally lame horses were trotted under field conditions on a hard surface on a straight line and on both directions on soft and hard surfaces. Movement asymmetry was analysed by measuring differences in displacement minima for head and withers using an inertial measurement unit sensor-based gait analysis system. Receiver operating characteristic (ROC) analysis was performed to define threshold ranges. Relationships between head and withers data were identified by calculating the correlation with linear regression analysis. Estimated lameness threshold values for vertical head movement asymmetry ranged from 11.5 to 12.5 mm. On both surfaces, a head movement asymmetry threshold range from 24.5 to 26.5 mm could be found for the circle. Lame horses showed significant correlations between head and withers movement on hard ground straight line (R2 0.714, p < 0.001) and inner leg lame horses on the lunge (R2 0.915, p < 0.001). Different sources of lameness were not distinguished. Radius and speed during lungeing could not be perfectly controlled. Inclusion in the sound group was based on a subjective examination by one clinician and in the lame group two clinicians subjectively determined inner and outer leg lameness creating an extra amount of uncertainty. Lameness thresholds for quantifying head movement asymmetry on the lunge were proposed. A significant correlation was observed between head and withers movement asymmetry under several conditions.

Optically accessible, 3D-printed flow chamber with integrated sensors for the monitoring of oral multispecies biofilm growth in vitro

The formation of pathogenic multispecies biofilms in the human oral cavity can lead to implant-associated infections, which may ultimately result in implant failure. These infections are neither easily detected nor readily treated. Due to high complexity of oral biofilms, detailed mechanisms of the bacterial dysbiotic shift are not yet even fully understood. In order to study oral biofilms in more detail and develop prevention strategies to fight implant-associated infections, in vitro biofilm models are sorely needed. In this study, we adapted an in vitro biofilm flow chamber model to include miniaturized transparent 3D-printed flow chambers with integrated optical pH sensors – thereby enabling the microscopic evaluation of biofilm growth as well as the monitoring of acidification in close proximity. Two different 3D printing materials were initially characterized with respect to their biocompatibility and surface topography. The functionality of the optically accessible miniaturized flow chambers was then tested using five-species biofilms (featuring the species Streptococcus oralis, Veillonella dispar, Actinomyces naeslundii, Fusobacterium nucleatum, and Porphyromonas gingivalis) and compared to biofilm growth on titanium specimens in the established flow chamber model. As confirmed by live/dead staining and fluorescence in situ hybridization via confocal laser scanning microscopy, the flow chamber setup proved to be suitable for growing reproducible oral biofilms under flow conditions while continuously monitoring biofilm pH. Therefore, the system is suitable for future research use with respect to biofilm dysbiosis and also has great potential for further parallelization and adaptation to achieve higher throughput as well as include additional optical sensors or sample materials.

Characteristic spatial and frequency distribution of mutations in SCN1A

BackgroundSCN1A is the most well-recognized and commonly mutated gene related to epilepsy. This study analyzed the characteristic spatial and frequency distributions of SCN1A mutations, aiming to provide important insight into the mutagenesis etiopathology of SCN1A-associated epilepsy.MethodsEpilepsy-associated SCN1A variants were retrieved from the SCN1A mutation database, the HGMD database, and literature reviews. The base substitutions, mutation frequencies in CpG dinucleotides, and spatial distributions of mutations in terms of exons and structural domains were analyzed.ResultsA total of 2621 SCN1A variants were identified in 5106 unrelated cases. The most common type was missense mutation, followed by frameshift mutations and splice site mutations. Among the missense mutations, transitions within CpG dinucleotides were much more recurrently identified than transitions within non-CpG dinucleotides, and the most common type was the G > A transition. Among the nonsense mutations, the most predominant type of single-base substitution was the C > T transition, among which 75.3% (235/312) were within CpG sites. The most common “hotspot” codons for missense mutations were codons 101, 946, and 1783; while for nonsense mutations it was codon 712. One-base deletion or insertion was the most common type of frameshift mutation, causing protein truncation. The three most common frameshift mutations were c.5536_5539delAAAC, c.4554dupA, and c.5010_5013delGTTT. Splice mutations were the most frequently identified in exon 4 with a hotspot site c.602 + 1G > A. The spatial distribution of missense mutations showed that exons 22 and 4 had the highest mutation density (111 and 84 mutations per 100 bp, respectively), and exon 12 had the lowest mutation density, with 4 mutations per 100 bp. Further distribution analysis of the protein domains revealed that missense mutations were more common in the pore region and voltage sensor (231 mutations per 100 amino acids, respectively), and the protein truncation mutations were distributed evenly among the domains.ConclusionsSCN1A mutations tend to cluster at distinct sites, depending on the characteristic CpG dinucleotides, exons, and functional domains. Higher mutation density in particular regions, such as exon 22 and exon 4, offers promising targets for therapeutic genetic interventions.

Custom chromatic confocal microscope for surface profilometry

In this work, a surface profiling system is introduced, employing a custom-designed chromatic confocal (CC) distance sensor. Today’s commercial sensors based on the CC principle have a major drawback of effectively coupling light from the illumination source into the optical imaging system, mainly due to etendue mismatch. Consequently, a substantial amount of light is lost, significantly reducing the overall SNR of the system. This limitation restricts its applicability primarily to highly reflective surfaces. To address this issue, an etendue-matched optical system is proposed, that incorporates an innovative superluminescent light source. Through this approach, the system achieves 4 orders of magnitudes higher illumination intensity concentrated within the focus spot compared to existing devices in the current market.

Broadband cavity-enhanced optical flux monitoring

This work describes a new type of sensor for growth process monitoring named broadband cavity-enhanced optical flux monitoring sensor (BBCE-OFM). Like existing optical flux monitoring (OFM) solutions, it relies on absorption spectroscopy. However, the implementation of an optical cavity reduces the measurement uncertainty, enabling efficient operation even at very low growth rates. Using the BBCE-OFM sensor mounted in our solid-source oxide molecular beam epitaxy reactor, we achieved an uncertainty of ±2% on the measurement of Sr and Ti growth rates in SrTiO3 at around 1 Ml/min, to be compared to the ±16% obtained in the same conditions using a conventional OFM setup. Furthermore, our sensor architecture, based on an echelle monochromator and LEDs replacing the hollow cathode lamps used in standard OFM sensors, is more robust against drift.

High Q-factor multi-Fano resonances in all-dielectric metasurface for high-performance optical switching and sensors

High Q-factor multi-Fano resonances in all-dielectric metasurface for high-performance optical switching and sensors

Abraded optical fibre-based dynamic range force sensor for tissue palpation

Tactile information acquired through palpation plays a crucial role in relation to surface characterisation and tissue differentiation - an essential clinical requirement during surgery. In the case of Minimally Invasive Surgery, access is restricted, and tactile feedback available to surgeons is therefore reduced. This paper presents a novel stiffness controllable, dynamic force range sensor that can provide remote haptic feedback. The sensor has an abraded optical fibre integrated into a silicone dome. Forces applied to the dome change the curvature of the optical fibres, resulting in light attenuation. By changing the pressure within the dome and thereby adjusting the sensor’s stiffness, we are able to modify the force measurement range. Results from our experimental study demonstrate that increasing the pressure inside the dome increases the force range whilst decreasing force sensitivity. We show that the maximum force measured by our sensor prototype at 20 mm/min was 5.02 N, 6.70 N and 8.83 N for the applied pressures of 0 psi (0 kPa), 0.5 psi (3.45 kPa) and 1 psi (6.9 kPa), respectively. The sensor has also been tested to estimate the stiffness of 13 phantoms of different elastic moduli. Results show the elastic modulus sensing range of the proposed sensor to be from 8.58 to 165.32 kPa.

The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology

This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring.

Bioinspired cooperation in a heterogeneous robot swarm using ferrofluid artificial pheromones for uncontrolled environments

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.

Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture

The integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) and machine learning (ML) has fundamentally transformed precision agriculture by enhancing efficiency, sustainability, and data-driven decision making. In this paper, we present a comprehensive overview of the integration of multispectral, hyperspectral, and thermal sensors mounted on drones with AI-driven algorithms to transform modern farms. Such technologies support crop health monitoring in real time, resource management, and automated decision making, thus improving productivity with considerably reduced resource consumption. However, limitations include high costs of operation, limited UAV battery life, and the need for highly trained operators. The novelty of this study lies in the thorough analysis and comparison of all UAV-AI integration research, along with an overview of existing related works and an analysis of the gaps. Furthermore, practical solutions to technological challenges are summarized to provide insights into precision agriculture. This paper also discusses the barriers to UAV adoption and suggests practical solutions to overcome existing limitations. Finally, this paper outlines future research directions, which will discuss advances in sensor technology, energy-efficient AI models, and how these aspects influence ethical considerations regarding the use of UAVs in agricultural research.

Ultra‐Low Power Photosynaptic Devices Based on Persistent Photoconductivity‐Modulated SnO2

AbstractRealizing visual perception performance using robust metal oxide devices is highly desirable for brain‐inspired neuromorphic computing, visual prosthetics, and artificial intelligence. SnO2, a representative transparent semiconductor, has attracted considerable attention for its potential in ultraviolet photodetectors and photosynaptic devices operating in solar‐blind spectral ranges. This problem has been circumvented by lowering the electron concentration and reducing the persistent photoconductivity (PPC), one of the essential properties of photosynaptic devices. In this case, the electron concentration and PPC properties in SnO2 must be controlled independently. This paper reports the successful control of PPC in SnO2 by controllable addition of Zn and Sr ions, which act as acceptors to annihilate the charge carriers and as a reductant to form oxygen vacancies, respectively. Zn and Sr‐added SnO2 exhibits superior synaptic performance with low energy consumption, successfully imitating the biological synapses without a gate electrode. As a result, the PPC remains at about 40% even after 20 seconds of turning off the ultraviolet light, and energy consumption is reduced to 1–10 fJ, similar to energy consumption with biological synapses. A novel optical sensor that can receive analog signals is demonstrated using a photosynaptic device with Zn and Sr‐added SnO2 thin films.

Development of a Chemical Sensor Device for Monitoring Hazardous Gases Generated in the Semiconductor Manufacturing Process

The semiconductor industry plays a crucial role in various fields but also contributes to environmental degradation. Throughout the semiconductor chip manufacturing process, hazardous gases are released at each stage, despite stringent treatment procedures. These gases can be categorized into four groups: acidic and alkaline gases, volatile organic compounds, flammable and corrosive gases, and greenhouse gases. To meet stricter emission standards, further advancements in gas sensor technology are essential. This review examines recent research on monitoring these gases, highlighting the capabilities and limitations of existing sensor technologies. Additionally, the paper discusses current challenges in gas sensing research and proposes future directions for improving technologies.

Fire Fighter Robot Using IOT and Mobile Application

In today's fast-paced world, fire hazards pose significant risks to life, property, and the environment. Traditional fire detection systems often rely on human intervention, leading to delays in response time and exacerbating the damage caused by fire incidents. Our project, the IoT-Based Automatic Fire Detection and Extinguisher Robot, addresses this challenge by utilizing a combination of modern sensor technology, robotics, and IoT to provide an autonomous and real-time solution. The robot is equipped with flame and temperature sensors that allow it to detect fires at an early stage. Once a fire is detected, the robot activates its movement mechanism using DC motors to navigate toward the fire while the integrated servo-controlled water pump precisely sprays water to extinguish the fire. In addition to automatic firefighting, the robot features obstacle detection using ultrasonic sensors, ensuring safe navigation in complex environments. The system includes Bluetooth and Wi-Fi communication, allowing for remote control via an Android mobile app. This app provides both manual and automatic control options and sends real-time fire alerts when the robot detects a fire. This autonomous system minimizes human intervention, speeds up response times, and reduces fire damage effectively. The robot is powered by an ESP32 microcontroller, which processes data from the sensors and controls the motors and water pump. The rescue pod feature allows the robot to perform additional operations like transporting sensitive materials during emergencies. The system's IoT functionality enables real-time alerts and remote monitoring, enhancing its capability as a scalable and cost-effective solution for residential, commercial, and industrial fire safety.

Observation of the Goos-Hänchen shift in monolayer WSe2 for an arbitrary linearly polarized incident light beam using weak measurement

We report, to our knowledge, the first experimental investigation of the spatial Goos-Hänchen (GH) shift at an absorbing material interface comprised of monolayer (ML) tungsten di-selenide (WSe2) on a SiO2/Si substrate under a total internal reflection (TIR) condition. The critical angle for this design is drastically shifted to 23.31°, compared to the glass-air interface, which was at 41.3°. Utilizing the weak value amplification (WVA) approach, the behavior of spatial GH shifts at this interface with various regulating parameters such as angle of incidence, polarization angle, and post-selection angle has systematically been studied. At critical incidence, the greatest shift of approximately 116 µm exceeds the maximum limit of beam shift w0/2, where w0 is the beam waist (180 µm). A generic theoretical model compatible with polarization-dependent studies is also established that has demonstrated excellent agreement with experimental results. Moreover, this work established three distinct features that allow us to readily tweak the value of spatial GH shifts. The observation of a controllable spatial GH shift at the ML WSe2-SiO2/Si configuration has potential implications for optical sensors, optical differential operation, and other photonic manipulations.

Dual effects of mefenamic acid on the IKs molecular complex.

Mutations in both KCNQ1 and KCNE1, which together form the cardiac IKs current, are associated with inherited conditions such as long and short QT syndromes. Mefenamic acid, a non-steroidal anti-inflammatory drug, is an IKs potentiator and may be utilised as an archetype to design therapeutically useful IKs agonists. However, here we show that mefenamic acid can also act as an IKs inhibitor, and our data reveal its dual effects on KCNQ1/KCNE1 channels. Effects of mefenamic acid on wild type (WT) and mutant KCNQ1/KCNE1 channels expressed in tsA201 cells were studied using whole cell patch clamp. Molecular dynamics simulations were used to determine trajectory clustering. Mefenamic acid inhibits WT IKs at high concentrations while preserving some attributes of current potentiation. Inhibitory actions of mefenamic acid are unmasked at lower drug concentrations by KCNE1 and KCNQ1 mutations in the mefenamic acid binding pocket, at the extracellular end of KCNE1 and in the KCNQ1 S6 helix. Mefenamic acid does not inhibit KCNQ1 in the absence of KCNE1 but inhibits IKs current in a concentration-dependent manner in the mutant channels. Inhibition involves modulation of pore kinetics and/or voltage sensor domain-pore coupling in WT and in the KCNE1 E43C mutant. This work highlights the importance of structural motifs at the extracellular inter-subunit interface of KCNQ1 and KCNE1 channels, and their interactions, in determining the nature of drug effects on the IKs channel complex and has important implications for treating patients with specific long QT mutations.

Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes

The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems. Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex geometries. These processes are undergoing a major transformation with the advent of digital technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques for the digitalization of AM processes within a digital twin concept. The main contribution of this study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process data handling, optimization/control, and their integration into the real-time assessment of AM processes. Therefore, a novel combination of efficient data-solver technology and an architecturally designed neural network (NN) module is developed for transient manufacturing processes with high heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process.

Automatic Waste Segregation System

The Automatic Waste Segregation System aims to enhance waste management practices by efficiently sorting waste into three categories: wet waste, dry waste, and metallic waste. Utilizing advanced sensor technologies, this system leverages an LM393 IC moisture sensor to detect the moisture content in waste, effectively identifying wet organic materials such as food scraps. Additionally, an infrared (IR) sensor is employed to distinguish dry waste, including paper, plastics, and textiles, based on the reflective properties of the materials. To further enhance the sorting process, a metal detector is integrated to identify and segregate metallic waste, ensuring that recyclables are collected accurately. The automation of waste segregation not only reduces the manual labor involved in traditional waste sorting methods but also increases the accuracy and efficiency of the process. This system is designed to operate autonomously, with real-time monitoring and feedback mechanisms that allow for seamless integration into existing waste management infrastructures. The collected data on waste types can be utilized for further analysis, enabling municipalities and organizations to develop more effective waste disposal and recycling strategies. By implementing this Automatic Waste Segregation System, we aim to address the growing challenges of waste management, reduce landfill dependency, and promote recycling and resource recovery. This innovative approach aligns with global sustainability goals, contributing to cleaner environments and enhanced waste resource management. The project has the potential to significantly improve waste processing efficiency, reduce environmental impact, and foster community awareness regarding the importance of proper waste segregation. Key Words: Arduino Mega, IR Sensor, DC Motor, Rotary Indexer, Container, etc.

An optical vibration sensor enabled by coupling mechanoluminescence with photostimulable phosphor

An optical vibration sensor enabled by coupling mechanoluminescence with photostimulable phosphor

An FBG-based method for load measurement of a cylindrical rolling element bearing

Abstract Contact forces between raceways and rolling elements of a bearing stand as crucial operational aspects defining the bearing’s performance. While monitoring bearing load through load cells or strain gauges on the shaft or housing is feasible, it may not precisely reflect the distributed load transmitted by the rollers directly to the raceways. This paper introduces a method to calculate these contact forces, relying on a linear assumption correlating the loads to the measured strains on the outer race of a bearing. In our research, we conducted both static and dynamic tests on cylindrical roller bearings through simulations and experiments. We designed an experimental test rig to conduct both static and dynamic experiments and utilized FBG optical fiber sensors for contact force measurement in bearings due to their multiple advantages, including high sensitivity, resistance to corrosion and magnetic interference, and ease of installation on the bearing outer race due to their shape and size. Our findings indicate a consistent linear relationship between contact forces and strains measured on the bearing’s outer race. Furthermore, we calculated the linear coefficient K values from three test groups: static tests under simulation study, static tests under experimental study, and dynamic tests under experimental study. The K values obtained from static tests (simulation and experimental studies) and dynamic tests (experimental study) align consistently. Following this, we calculated contact forces in both static and dynamic experiments by multiplying the measured strains with K. Our calculations resulted in an error percentage of less than 2% for static tests and below 5% for dynamic tests, highlighting the accuracy of our approach in determining these contact forces.