bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Cardioprotective Potential of Cannabidiol and β-Adrenergic Stimulation Against Hypoxia/Reoxygenation Injury in Rat Atria and Ventricular Papillary Muscles
Authors: Pędzińska-Betiuk Anna , Gergs Ulrich , Weresa Jolanta , Remiszewski Patryk , Harasim-Symbor Ewa , Malinowska Barbara
Published: October 2024

Background: Hypoxia is one of the most significant pathogenic factors in cardiovascular diseases. Preclinical studies suggest that nonpsychoactive cannabidiol (CBD) and β-adrenoceptor stimulation might possess cardioprotective potential against ischemia-reperfusion injury. The current study evaluates the influence of hypoxia-reoxygenation (H/R) on the function of atria and ventricular papillary muscles in the presence of CBD and the nonselective β-adrenoceptor agonist isoprenaline (ISO). Methods: The concentration curves for ISO were constructed in the presence of CBD (1 µM) before or after H/R. In chronic experiments (CBD 10 mg/kg, 14 days), the left atria isolated from spontaneously hypertensive (SHR) and their normotensive control (WKY) rats were subjected to H/R following ISO administration. Results: Hypoxia decreased the rate and force of contractions in all compartments. The right atria were the most resistant to hypoxia regardless of prior β-adrenergic stimulation. Previous β-adrenergic stimulation improved recovery in isolated left atria and right (but not left) papillary muscles. Acute (but not chronic) CBD administration increased the effects of ISO in left atria and right (but not left) papillary muscles. Hypertension accelerates left atrial recovery during reoxygenation. Conclusions: H/R directly modifies the function of particular cardiac compartments in a manner dependent on cardiac region and β-adrenergic prestimulation. The moderate direct cardioprotective potential of CBD and β-adrenergic stimulation against H/R is dependent on the cardiac region, and it is less than in the whole heart with preserved coronary flow. In clinical terms, our research expands the existing knowledge about the impact of cannabidiol on cardiac ischemia, the world′s leading cause of death.
In Vivo Effects of a GHR Synthesis Inhibitor During Prolonged Treatment in Dogs
Authors: Timmermans Elpetra P. M. , Blankevoort Joëlle , Grinwis Guy C. M. , Mesu Sietske J. , Gehring Ronette , Delhanty Patric J. D. , Maas Peter E. M. , Strous Ger J. , Mol Jan A.
Published: October 2024

Background: The activation of the growth hormone receptor (GHR) is a major determinant of body growth. Defective GHR signaling, as seen in human Laron dwarfism, resulted in low plasma IGF-1 concentrations and limited growth, but also marked absence in the development of breast cancer and type 2 diabetes. In vitro, we identified a small molecule (C#1) that inhibits the translation of GHR mRNA to receptor protein. Methods: Before its application in humans as a potential anticancer drug, C#1 was tested in animals to evaluate whether it could be administered to achieve a plasma concentration in vivo that inhibits cell proliferation in vitro without causing unwanted toxicity. To evaluate the efficacy and toxicity of C#1, a group of six intact female Beagle dogs was treated daily each morning for 90 days with an oral solution of C#1 in Soiae oleum emulgatum at a dose of 0.1 mg/kg body weight. During treatment, dogs were closely monitored clinically, and blood samples were taken to measure plasma C#1 concentrations, complete blood counts (CBC), clinical chemistry, and endocrinology. At the end of the treatment, dogs were euthanized for gross and histopathological analysis. An additional group of six female Beagle dogs was included for statistical reasons and only evaluated for efficacy during treatment for 30 days. Results: Daily administration of C#1 resulted in a constant mean plasma concentration of approximately 50 nmol/L. In both groups, two out of six dogs developed decreased appetite and food refusal after 4–5 weeks, and occasionally diarrhea. No significant effects in CBC or routine clinical chemistry were seen. Plasma IGF-1 concentrations, used as biomarkers for defective GHR signaling, significantly decreased by 31% over time. As plasma growth hormone (GH) concentrations decreased by 51% as well, no proof of GHR dysfunction could be established. The measured 43% decrease in plasma acylated/non-acylated ghrelin ratios will also lower plasma GH concentrations by reducing activation of the GH secretagogue receptor (GHSR). C#1 did not directly inhibit the GHSR in vivo, as shown in vitro. There were no significant effects on glucose, lipid, or folate/homocysteine metabolism. Conclusions: It is concluded that with daily dosing of 0.1 mg C#1/kg body weight, the induction of toxic effects prevented further increases in dosage. Due to the concomitant decrease in both IGF-1 and GH, in vivo inhibition of GHR could not be confirmed. Since the concept of specific inhibition of GHR synthesis by small molecules remains a promising strategy, searching for compounds similar to C#1 with lower toxicity should be worthwhile.
Analysis of the Mechanisms Underlying the Specificity of the Variation Potential Induced by Different Stimuli
Authors: Mudrilov Maxim , Ladeynova Maria , Vetrova Yana , Vodeneev Vladimir
Published: October 2024

Plants are able to perceive diverse environmental factors and form an appropriate systemic functional response. Systemic responses are induced by stimulus-specific long-distance signals that carry information about the stimulus. Variation potential is proposed as a candidate for the role of such a signal. Here, we focus on the mechanisms that determine the specificity of the variation potential under the action of different local stimuli. Local stimuli such as heating, burning and wounding cause variation potential, the parameters of which differ depending on the type of stimulus. It was found that the stimulus-specific features of the hydraulic signal monitored by changes in leaf thickness and variation potential, such as a greater amplitude upon heating and burning and a significant amplitude decrement upon burning and wounding, were similar. The main features of these signals are the greater amplitude upon heating and burning, and a significant amplitude decrement upon burning and wounding. Together with the temporal correspondence of signal propagation, this evidence indicates a role for the hydraulic signal in the induction of stimulus-specific variation potential. Experiments using mechanosensitive channel inhibitors have demonstrated that the hydraulic signal contributes more to the induction of the variation potential in the case of rapidly growing stimuli, such as burning and wounding, than in the case of gradual heating. For thermal stimuli (gradual heating and burning), a greater contribution, compared to wounding, of the chemical signal related to reactive oxygen species to the induction of the variation potential was demonstrated. Thus, the specificity of the parameters of the variation potential is determined by the different contributions of hydraulic and chemical signals.
Geophysical Monitoring Technologies for the Entire Life Cycle of CO2 Geological Sequestration
Authors: Li Chenyang , Zhang Xiaoli
Published: October 2024

Geophysical monitoring of CO2 geological sequestration represents a critical technology for ensuring the long-term safe storage of CO2 while verifying its characteristics and dynamic changes. Currently, the primary geophysical monitoring methods employed in CO2 geological sequestration include seismic, fiber optic, and logging technologies. Among these methods, seismic monitoring techniques encompass high-resolution P-Cable three-dimensional seismic systems, delayed vertical seismic profiling technology, and four-dimensional distributed acoustic sensing (DAS). These methods are utilized to monitor interlayer strain induced by CO2 injection, thereby indirectly determining the injection volume, distribution range, and potential diffusion pathways of the CO2 plume. In contrast, fiber optic monitoring primarily involves distributed fiber optic sensing (DFOS), which can be further classified into distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This technology serves to complement seismic monitoring in observing interlayer strain resulting from CO2 injection. The logging techniques utilized for monitoring CO2 geological sequestration include neutron logging methods, such as thermal neutron imaging and pulsed neutron gamma-ray spectroscopy, which are primarily employed to assess the sequestration volume and state of CO2 plumes within a reservoir. Seismic monitoring technology provides a broader monitoring scale (ranging from dozens of meters to kilometers), while logging techniques operate at centimeter to meter scales; however, their results can be significantly affected by the heterogeneity of a reservoir.
Jewish Elements in the Ancient Chinese Christian Manuscript Yishen Lun (Discourse on God)
Authors: Tam David
Published: October 2024

This article identifies and analyzes four passages in the ancient Chinese Christian manuscript Yishen Lun (YSL) that exhibit distinct Jewish characteristics. The phrase “yizhong zuo shenghua” (lines 356–358) mirrors the Book of Acts’ theme of “sanctification of the Gentiles,” rooted in the Jewish dichotomous worldview, placing Jews, or Shihu Ren, at the center. The author’s use of this phrase distinguishes him from yizhong ren (Gentiles) and aligns him with Shihu Ren. In lines 256–263, YSL directly attributes messianic declarations to Jesus, a central issue in Jewish accusations of false Messiahship. In contrast, the Gospel accounts avoid making such direct accusations, as Jesus did not openly declare himself the Messiah. This distinction highlights YSL’s closer alignment with Jewish polemical traditions and legal concerns. Additionally, the use of “City of Judah” in lines 345–347 as an archaic designation for Jerusalem, predominantly found in Jewish traditions, contrasts with the more common “City of David” in other biblical texts. A philological analysis of lines 279–281 reveals imagery analogous to the synagogue parochet covering the Ark of the Scrolls. These four Jewish elements complement the one analyzed in the author’s earlier 2024 article, “The Parable of Wise and Foolish Builders in Yishen Lun and Rabbinic Literature.” That study concludes that the parable of wise and foolish builders in lines 146–156 of YSL aligns more closely with Jewish rabbinic traditions than the Gospel version. These new hermeneutical insights should provide interesting and fresh data for ongoing research into YSL.
Theology of Play in Omar Khayyam: Unacknowledged Parallels Between Hinduism, Persian Sufism, and Khayyam’s Quatrains
Authors: Yansori Ali
Published: October 2024

This paper examines the motif of divine play in Omar Khayyam’s quatrains within the broader context of Persian Sufi poetry. Given the strong association of “divine play” with Hinduism, a comparative approach is employed; to keep the focus on the main subject, this comparison is kept minimal. The comparison reveals three major themes common to both Sufism and Hinduism: God as the Player, God as the Playful Designer, and God as the Playful Dancer. To illustrate these themes, previously untranslated verses have been translated into English for the first time in this paper, highlighting a neglected aspect of Persian mysticism that scholars have often overlooked.
3D Modelling and Measuring Dam System of a Pellucid Tufa Lake Using UAV Digital Photogrammetry
Authors: Zhang Xianwei , Zhou Guiyun , He Jinchen , Lin Jiayuan
Published: October 2024

The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored.
Spatial Downscaling of Sea Surface Temperature Using Diffusion Model
Authors: Wang Shuo , Li Xiaoyan , Zhu Xueming , Li Jiandong , Guo Shaojing
Published: October 2024

In recent years, advancements in high-resolution digital twin platforms or artificial intelligence marine forecasting have led to the increased requirements of high-resolution oceanic data. However, existing sea surface temperature (SST) products from observations often fail to meet researchers’ resolution requirements. Deep learning models serve as practical techniques for improving the spatial resolution of SST data. In particular, diffusion models (DMs) have attracted widespread attention due to their ability to generate more vivid and realistic results than other neural networks. Despite DMs’ potential, their application in SST spatial downscaling remains largely unexplored. Hence we propose a novel DM-based spatial downscaling model, called DIFFDS, designed to obtain a high-resolution version of the input SST and to restore most of the meso scale processes. Experimental results indicate that DIFFDS is more effective and accurate than baseline neural networks, its downscaled high-resolution SST data are also visually comparable to the ground truth. The DIFFDS achieves an average root-mean-square error of 0.1074 °C and a peak signal-to-noise ratio of 50.48 dB in the 4× scale downscaling task, which shows its accuracy.
Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data
Authors: Sun Xiaoyu , Li Guiying , Wu Qinquan , Ruan Jingyi , Li Dengqiu , Lu Dengsheng
Published: October 2024

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.
Kernel Density Estimation for the Interpretation of Seismic Big Data in Tectonics Using QGIS: The Türkiye–Syria Earthquakes (2023)
Authors: Amador Luna David , Alonso-Chaves Francisco M. , Fernández Carlos
Published: October 2024

Numerous studies have utilized remote sensing techniques to analyze seismic data in active areas. Point density techniques, widely used in remote sensing, examine the spatial distribution of point clouds related to specific variables. Applying these techniques to complex tectonic settings, such as the East Anatolian Fault Zone, helps identify major active fractures using both surface and deep information. This study employed kernel density estimation (KDE) to compare two distinct point-cloud populations from the seismic event along the Türkiye–Syria border on 6 February 2023, providing insights into the main active orientations supporting the Global Tectonics framework. This study considered two populations of seismic foci point clouds containing over 40,000 events, recorded by the Turkish Disaster and Emergency Management Authority (AFAD) and Kandilli Observatory and Earthquake Research Institute (KOERI). These populations were divided into two datasets: crude and relocated-filtered. Kernel density analysis demonstrated that both datasets yielded similar geological interpretations. The high-density cores of both datasets perfectly matched, exhibiting identical structures consistent with geological knowledge. Areas with a minimal concentration of earthquakes at depth were also identified, separating different crustal strength levels.
Embedded IoT Design for Bioreactor Sensor Integration
Authors: Baicu Laurentiu Marius , Andrei Mihaela , Ifrim George Adrian , Dimitrievici Lucian Traian
Published: October 2024

This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system’s effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes.
Electrochemical Sensors and Biosensors for the Determination of Food Nutritional and Bioactive Compounds: Recent Advances
Authors: Brunetti Barbara
Published: October 2024

The significance of food nutrients and bioactive compounds in human health has driven the development of many methods for their determination in different matrices. Among these, electroanalysis has gained popularity due to its cost-effectiveness, rapidity, and, in many cases, portability and minimal sample treatment. This review highlights key advances in electrochemical sensors and biosensors from 2019 to the present. Given the variability and the challenges of managing food matrices, the focus is limited to methods that have been thoroughly assessed for their applicability to real samples. The technical characteristics and analytical performance of the proposed sensors are discussed, along with breakthrough features and future trends.
Motion Control System for USV Target Point Convergence
Authors: Zhou Jian , Zhang Hui , Liu Kai , Ma Linhan , Yang Yanxia , Fan Zhanchao
Published: October 2024

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV’s positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA).
Efficient Method for Photovoltaic Power Generation Forecasting Based on State Space Modeling and BiTCN
Authors: Dai Guowei , Luo Shuai , Chen Hu , Ji Yulong
Published: October 2024

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.
Remote Sensing and Assessment of Compound Groundwater Flooding Using an End-to-End Wireless Environmental Sensor Network and Data Model at a Coastal Cultural Heritage Site in Portsmouth, NH
Authors: Routhier Michael R. , Curran Benjamin R. , Carlson Cynthia H. , Goddard Taylor A.
Published: October 2024

The effects of climate change in the forms of rising sea levels and increased frequency of storms and storm surges are being noticed across many coastal communities around the United States. These increases are impacting the timing and frequency of tidal and rainfall influenced compound groundwater flooding events. These types of events can be exemplified by the recent and ongoing occurrence of groundwater flooding within building basements at the historic Strawbery Banke Museum (SBM) living history campus in Portsmouth, New Hampshire. Fresh water and saline groundwater intrusion within basements of historic structures can be destructive to foundations, mortar, joists, fasteners, and the overlaying wood structure. Although this is the case, there appears to be a dearth of research that examines the use of wireless streaming sensor networks to monitor and assess groundwater inundation within historic buildings in near-real time. Within the current study, we designed and deployed a three-sensor latitudinal network at the SBM. This network includes the deployment and remote monitoring of water level sensors in the basements of two historic structures 120 and 240 m from the river, as well as one sensor within the river itself. Groundwater salinity levels were also monitored within the basements of the two historic buildings. Assessments and model results from the recorded sensor data provided evidence of both terrestrial rainfall and tidal influences on the flooding at SBM. Understanding the sources of compound flooding within historic buildings can allow site managers to mitigate better and adapt to the effects of current and future flooding events. Data and results of this work are available via the project’s interactive webpage and through a public touchscreen kiosk interface developed for and deployed within the SBM Rowland Gallery’s “Water Has a Memory” exhibit.
Optimizing Low-Cost Gas Analysis with a 3D Printed Column and MiCS-6814 Sensor for Volatile Compound Detection
Authors: Skowronkova Nela , Adamek Martin , Zvonkova Magdalena , Matyas Jiri , Adamkova Anna , Dlabaja Stepan , Buran Martin , Sevcikova Veronika , Mlcek Jiri , Volek Zdenek , Cernekova Martina
Published: October 2024

This paper explores an application of 3D printing technology on the food industry. Since its inception in the 1980s, 3D printing has experienced a huge rise in popularity. This study uses cost-effective, flexible, and sustainable components that enable specific features of certain gas chromatography. This study aims to optimize the process of gas detection using a 3D printed separation column and the MiCS-6814 sensor. The principle of the entire device is based on the idea of utilizing a simple capillary chromatographic column manufactured by 3D printing for the separation of samples into components prior to their measurement using inexpensive chemiresistive sensors. An optimization of a system with a 3D printed PLA block containing a capillary, a mixing chamber, and a measuring chamber with a MiCS-6814 sensor was performed. The optimization distributed the sensor output signal in the time domain so that it was possible to distinguish the peak for the two most common alcohols, ethanol and methanol. The paper further describes some optimization types and their possibilities.
Research on Underwater Constant High-Voltage DC Switching Technology for MCSEM
Authors: Ren Zhibin , Wang Meng , Luo Xianhu , Wang Chentao , Chen Tailong
Published: October 2024

The marine controlled source electromagnetic (MCSEM) transmitter can transmit high currents near the seabed to detect the electrical structure of the seafloor. The use of three-phase alternating current (AC) transmission can lead to three-phase imbalance, which results in an excessive current in one phase’s power line and affects the safety of the tow cable. This paper proposes an MCSEM underwater constant high-voltage direct-current (DC) switching scheme that replaces AC transmission with DC transmission. This scheme can fundamentally avoid three-phase imbalance and the AC loss caused by inductance. After establishing a simulation model to analyze the effect of the scheme, the relevant hardware units were designed. The hardware unit mainly consists of three parts: a DC switching inverter unit, a filter unit, and a step-down rectification unit. The DC inverter unit controls six insulated gate bipolar transistor (IGBT) modules with sinusoidal pulse width modulation (SPWM) signals to convert DC to three-phase AC power; the filter unit filters out extra harmonic components; and the step-down rectification unit converts high-voltage three-phase AC to low-voltage DC. The scheme ultimately achieved an adjustable DC output of 48.3–73.4 V under a constant DC input voltage of 3000 V and effectively reduced the current on the cable. This scheme has the potential to replace the previous AC transmission, reducing the risk of tow cable burnout and enhancing the safety of MCSEM operations.
Circumferential Crack Detection in Ultra-High-Pressure Tubular Reactors with Pulsed Eddy Current Testing
Authors: Wang Yaxing , Sun Jie , Hu Huasheng , Hu Bo , Bin Weiqi , Shi Wen , Fu Yuewen
Published: October 2024

Ultra-high-pressure tubular reactors are crucial pieces of equipment for polyethylene production. Long-term operation under high temperature, high pressure, and other extremely harsh conditions can lead to various defects, with circumferential cracks posing a major safety risk. Detecting cracks is challenging, particularly when they are under a protective layer of a certain thickness. This study designed a pulsed eddy current differential probe to detect circumferential cracks in ultra-high-pressure tubular reactors, with the lift-off distance acting as a protective layer. Detection models for traditional cylindrical and semi-circular excitation differential probes were established using finite element simulations. Corresponding experiments under different lift-off conditions were carried out, and the model’s accuracy was verified by the consistency between the simulation results and experimental data. The distribution of the eddy current field under different conditions and the disturbances caused by cracks at various positions to the detection signal were then calculated in the simulations. The simulation results showed that the cracks significantly disturbed the eddy current field of the semi-circular excitation differential probe compared with that of the traditional cylindrical probe. The designed differential probe effectively detected circumferential cracks of specific lengths and depths using the difference in the voltage signals. The experimental results were in agreement with the simulation results, showing that the designed probe could effectively detect 20 mm-long circumferential cracks at a lift-off of 60 mm. The experimental results also show that the probe’s detection coverage area in the axial direction varied with the lift-off height. The probe design and findings are valuable for detecting cracks in ultra-high-pressure tubular reactors with protective layers.
Depth Video-Based Secondary Action Recognition in Vehicles via Convolutional Neural Network and Bidirectional Long Short-Term Memory with Spatial Enhanced Attention Mechanism
Authors: Shao Weirong , Bouazizi Mondher , Tomoaki Ohtuski
Published: October 2024

Secondary actions in vehicles are activities that drivers engage in while driving that are not directly related to the primary task of operating the vehicle. Secondary Action Recognition (SAR) in drivers is vital for enhancing road safety and minimizing accidents related to distracted driving. It also plays an important part in modern car driving systems such as Advanced Driving Assistance Systems (ADASs), as it helps identify distractions and predict the driver’s intent. Traditional methods of action recognition in vehicles mostly rely on RGB videos, which can be significantly impacted by external conditions such as low light levels. In this research, we introduce a novel method for SAR. Our approach utilizes depth-video data obtained from a depth sensor located in a vehicle. Our methodology leverages the Convolutional Neural Network (CNN), which is enhanced by the Spatial Enhanced Attention Mechanism (SEAM) and combined with Bidirectional Long Short-Term Memory (Bi-LSTM) networks. This method significantly enhances action recognition ability in depth videos by improving both the spatial and temporal aspects. We conduct experiments using K-fold cross validation, and the experimental results show that on the public benchmark dataset Drive&Act, our proposed method shows significant improvement in SAR compared to the state-of-the-art methods, reaching an accuracy of about 84% in SAR in depth videos.
Correction: Zeng et al. Research on Aviation Safety Prediction Based on Variable Selection and LSTM. Sensors 2023, 23, 41
Authors: Zeng Hang , Guo Jiansheng , Zhang Hongmei , Ren Bo , Wu Jiangnan
Published: October 2024

In the original publication [...]
Single-Input Multiple-Output (SIMO) Cascode Low-Noise Amplifier with Switchable Degeneration Inductor for Carrier Aggregation
Authors: Kim Min-Su
Published: October 2024

This paper presents a single-input multiple-output (SIMO) cascode low-noise amplifier with inductive degeneration for inter- and intra-band carrier aggregation. The proposed low-noise amplifier has two output ports for flexible operation in carrier aggregation combinations for band 30 and band 7. However, during inter- and intra-band operation, gain variation occurs depending on the output mode. To compensate for this, a switching circuit is proposed to adjust the degeneration inductor, optimizing gain performance for both modes. The switching operation can minimize the control for the dynamic range in the receiver system to support carrier aggregation. The designed low-noise amplifier was fabricated using a 65 nm CMOS process, occupying an area of 2.1 mm2. In inter-band operation, the small-signal gain was measured by 18.9 dB for band 30 and 18.6 dB for band 7, with the noise figures of 1.03 dB and 1.07 dB, respectively. For intra-band operation, the small-signal gain was 17.3 dB and 17.2 dB, with the noise figures of 1.3 dB and 1.41 dB. The IIP3 values were measured by −7.6 dBm and −6.7 dBm for inter-band, and −6.3 dBm and −6.2 dBm for intra-band. Power consumption was 8.04 mW and 7.68 mW in inter-band, and 17.04 mW and 17.64 mW in intra-band depending on the output configuration.
Low-Cost Device for Measuring Wastewater Flow Rate in Open Channels
Authors: Wotzka Daria , Zmarzły Dariusz
Published: October 2024

This research paper describes the development of a low-cost device for measuring wastewater flow rates in open channels, a significant advancement enabled by the evolution of microcomputers and processing techniques. A laboratory setup was constructed to validate the device’s accuracy against a standard flow measurement method, optimizing key parameters to achieve a linear relationship between detected and set flow rates, while considering hardware limitations and energy efficiency. The central focus of the research was developing a method to measure the velocity of contaminated fluid using ultrasonic signals, employing the cross-correlation method for signal delay analysis in a stochastic environment. This was complemented by a procedure to measure fluid levels, also based on ultrasonic signals. The device’s reliability was assessed through repeatability and uncertainty measurements, confirming its accuracy with an extended uncertainty not exceeding an average of 3.47% for flows above 40 L/min. The device has potential to provide valuable data on the operational dynamics of sanitary networks, crucial for developing and calibrating simulation models.
LFIR-YOLO: Lightweight Model for Infrared Vehicle and Pedestrian Detection
Authors: Wang Quan , Liu Fengyuan , Cao Yi , Ullah Farhan , Zhou Muxiong
Published: October 2024

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices.
Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction
Authors: Henry Fergal , Glavin Martin , Jones Edward , Parsi Ashkan
Published: October 2024

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information.
A Review: Laser Interference Lithography for Diffraction Gratings and Their Applications in Encoders and Spectrometers
Authors: Luo Linbin , Shan Shuonan , Li Xinghui
Published: October 2024

The unique diffractive properties of gratings have made them essential in a wide range of applications, including spectral analysis, precision measurement, optical data storage, laser technology, and biomedical imaging. With advancements in micro- and nanotechnologies, the demand for more precise and efficient grating fabrication has increased. This review discusses the latest advancements in grating manufacturing techniques, particularly highlighting laser interference lithography, which excels in sub-beam generation through wavefront and amplitude division. Techniques such as Lloyd’s mirror configurations produce stable interference fringe fields for grating patterning in a single exposure. Orthogonal and non-orthogonal, two-axis Lloyd’s mirror interferometers have advanced the fabrication of two-dimensional gratings and large-area gratings, respectively, while laser interference combined with concave lenses enables the creation of concave gratings. Grating interferometry, utilizing optical interference principles, allows for highly precise measurements of minute displacements at the nanometer to sub-nanometer scale. This review also examines the application of grating interferometry in high-precision, absolute, and multi-degree-of-freedom measurement systems. Progress in grating fabrication has significantly advanced spectrometer technology, with integrated structures such as concave gratings, Fresnel gratings, and grating–microlens arrays driving the miniaturization of spectrometers and expanding their use in compact analytical instruments.
Review of Photodetectors for Space Lidars
Authors: Sun Xiaoli
Published: October 2024

Photodetectors play a critical role in space lidars designed for scientific investigations from orbit around planetary bodies. The detectors must be highly sensitive due to the long range of measurements and tight constraints on the size, weight, and power of the instrument. The detectors must also be space radiation tolerant over multi-year mission lifetimes with no significant performance degradation. Early space lidars used diode-pumped Nd:YAG lasers with a single beam for range and atmospheric backscattering measurements at 1064 nm or its frequency harmonics. The photodetectors used were single-element photomultiplier tubes and infrared performance-enhanced silicon avalanche photodiodes. Space lidars have advanced to multiple beams for surface topographic mapping and active infrared spectroscopic measurements of atmospheric species and surface composition, which demand increased performance and new capabilities for lidar detectors. Higher sensitivity detectors are required so that multi-beam and multi-wavelength measurements can be performed without increasing the laser and instrument power. Pixelated photodetectors are needed so that a single detector assembly can be used for simultaneous multi-channel measurements. Photon-counting photodetectors are needed for active spectroscopy measurements from short-wave infrared to mid-wave infrared. HgCdTe avalanche photodiode arrays have emerged recently as a promising technology to fill these needs. This paper gives a review of the photodetectors used in past and present lidars and the development and outlook of HgCdTe APD arrays for future space lidars.
A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion
Authors: Liu Wenlong , Ren Lemei , Tian Guansan
Published: October 2024

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.
Design and Implementation of Digital Calibration Certificate for RFID Tag Storage
Authors: Kan Kan , Wang Shuaizhe , Liu Zilong , Xiong Xingchuang
Published: October 2024

The digital transformation of metrology is one of the most active activities in the field of metrology at present, where the digital calibration certificate (DCC) being developed is the topic receiving the most concern in the current digital transformation. In practical industrial applications, the issue of storage carrier for the DCC plays a crucial role in its promotion and implementation. To address this issue, a DCC meta-model called DCC-Lite schema has been designed along with a corresponding processing method. This solution involves compressing and segmenting the DCC to make it suitable for storage using RFID tags. These RFID tags are affixed to the instruments and accompany them throughout their usage. Additionally, the DCC-RFID processing system has been developed to validate the effectiveness of the DCC meta-model and its corresponding processing method within a wireless temperature acquisition system. Experimental results demonstrate that the system successfully reads and writes the DCC stored within the RFID tag group. Furthermore, it enables automated parsing of the DCC calibration data by the machine and real-time compensation of measurement data to reduce measurement errors.
Prototype of a New Head Grabber for Robotic Strawberry Harvesting with a Vision System
Authors: Sobol Zygmunt , Kurpaska Sławomir , Nawara Piotr , Pedryc Norbert , Basista Grzegorz , Tabor Janusz , Hebda Tomasz , Tomasik Marcin
Published: October 2024

This paper presents the design of a strawberry fruit head gripper unit, together with the concept of a control system for the operation of its mechanisms and vision system. The developed design consists of three specialised mechanisms: positioning, grasping, and cutting off of the fruit. A Finite Element Method (FEM) model was developed for the described design. Next, calculations were carried out, based on which the construction materials were selected. The key performance parameters of the functional model, built on the basis of the developed design concept, were verified under laboratory conditions. In tests carried out on the possible hematoma caused by exceeding the breaking stress induced by the pressure of the encompassing jaws on the fruit, it was found that none of the fruit tested suffered mechanical damage as a result of the sensor triggering force, and the average length of the trimmed stalk was approximately 14 mm. The designed head gripper, together with the proposed automation system, will contribute to improving harvesting precision, and this will favour a reduction in the quantitative and qualitative losses of the harvested crop. The experimental tests conducted under harvesting conditions showed a high efficiency of 95% in identifying ripe fruit, and the harvesting efficiency of the robotic arm was 90%.