bluuebunny/crossref_metadata_2025 huggingface dataset:
Mine Pit Wall Geological Mapping Using UAV-Based RGB Imaging and Unsupervised Learning
Authors: Yang Peng , Esmaeili Kamran , Goodfellow Sebastian , Ordóñez Calderón Juan Carlos
Published: March 2023

In surface mining operations, geological pit wall mapping is important since it provides significant information on the surficial geological features throughout the pit wall faces, thereby improving geological certainty and operational planning. Conventional pit wall geological mapping techniques generally rely on close visual observations and laboratory testing results, which can be both time- and labour-intensive and can expose the technical staff to different safety hazards on the ground. In this work, a case study was conducted by investigating the use of drone-acquired RGB images for pit wall mapping. High spatial resolution RGB image data were collected using a commercially available unmanned aerial vehicle (UAV) at two gold mines in Nevada, USA. Cluster maps were produced using unsupervised learning algorithms, including the implementation of convolutional autoencoders, to explore the use of unlabelled image data for pit wall geological mapping purposes. While the results are promising for simple geological settings, they deviate from human-labelled ground truth maps in more complex geological conditions. This indicates the need to further optimize and explore the algorithms to increase robustness for more complex geological cases.
An Operational Processing Framework for Spaceborne SAR Formations
Authors: Petrushevsky Naomi , Monti Guarnieri Andrea , Manzoni Marco , Prati Claudio , Tebaldini Stefano
Published: March 2023

The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by each sensor with a low pulse repetition frequency (PRF). This is usually performed by first merging the different channels in the wavenumber domain, followed by bulk focusing. In this paper, we reverse this paradigm by first upsampling and focusing each acquisition and then combining the focused images to form a high-resolution, unambiguous image. Such a procedure is suited to estimate and mitigate artifacts generated by incorrect positioning of the sensors. An efficient wave–number method is proposed to focus data by adequately coping with the orbit curvature. Two implementations are provided with different quality/efficiency. The image quality in phase preservation, resolution, sidelobes, and ambiguities suppression is evaluated by simulating both point and distributed scatterers. Finally, a demonstration of the capability to compensate for ambiguities due to a small across-track baseline between sensors is provided by simulating a realistic X-band multi-sensor acquisition starting from a stack of COSMO-SkyMed images.
Prediction of Sea Surface Temperature in the South China Sea Based on Deep Learning
Authors: Hao Peng , Li Shuang , Song Jinbao , Gao Yu
Published: March 2023

Sea surface temperature is an important physical parameter in marine research. Accurate prediction of sea surface temperature is important for coping with climate change, marine ecological protection, and marine economic development. In this study, the SST prediction performance of ConvLSTM and ST-ConvLSTM with different input lengths, prediction lengths, and hidden sizes is investigated. The experimental results show that: (1) The input length has an impact on the prediction results of SST, but it does not mean that the longer the input length, the better the prediction performance. ConvLSTM and ST-ConvLSTM have the best prediction performance when the input length is set to 1, and the prediction performance gradually decreases as the input length increases. (2) Prediction length affects SST prediction. As the prediction length increases, the prediction performance gradually decreases. When other parameters are kept constant and only the prediction length is changed, the ConvLSTM gets the best result when the prediction length is set to 2, and the ST-ConvLSTM gets the best result when the prediction length is set to 1. (3) The setting of the hidden size has a great influence on the prediction ability of the sea surface temperature, but the hidden size cannot be set blindly. For ST-ConvLSTM, although the prediction performance of SST is better when the hidden size is set to 128 than when it is set to 64, the consequent computational cost increases by about 50%, and the performance only improves by about 10%.
CRSNet: Cloud and Cloud Shadow Refinement Segmentation Networks for Remote Sensing Imagery
Authors: Zhang Chao , Weng Liguo , Ding Li , Xia Min , Lin Haifeng
Published: March 2023

Cloud detection is a critical task in remote sensing image tasks. Due to the influence of ground objects and other noises, the traditional detection methods are prone to miss or false detection and rough edge segmentation in the detection process. To avoid the defects of traditional methods, Cloud and Cloud Shadow Refinement Segmentation Networks are proposed in this paper. The network can correctly and efficiently detect smaller clouds and obtain finer edges. The model takes ResNet-18 as the backbone to extract features at different levels, and the Multi-scale Global Attention Module is used to strengthen the channel and spatial information to improve the accuracy of detection. The Strip Pyramid Channel Attention Module is used to learn spatial information at multiple scales to detect small clouds better. Finally, the high-dimensional feature and low-dimensional feature are fused by the Hierarchical Feature Aggregation Module, and the final segmentation effect is obtained by up-sampling layer by layer. The proposed model attains excellent results compared to methods with classic or special cloud segmentation tasks on Cloud and Cloud Shadow Dataset and the public dataset CSWV.
Terrestrial Laser Scanning for the Detection of Coastal Changes along Rauk Coasts of Gotland, Baltic Sea
Authors: Tyszkowski Sebastian , Zbucki Łukasz , Kaczmarek Halina , Duszyński Filip , Strzelecki Mateusz C.
Published: March 2023

Clusters of sea stacks, called rauks, are unique rocky landforms characteristic of Baltic Sea coasts. These limestone stacks raise interest due to their spectacular morphology and yet unexplained origin. This study presents the results of seasonal observations (2019–2020) of rauk coast changes carried out in one of the key rauk fields on Fårö Island, Sweden. The landforms developing within three test sites were examined. At Langhammars ‘classic’ field of rauks built from homogeneous limestone (1) and a shore platform devoid of rauks, underlain clearly separate limestone slabs were explored (2); and at Gamla Hamn, the analysis covered a non-typical rauk field built of densely laminated limestone (3). We applied terrestrial laser scanning (TLS) to obtain data needed in morphometric analyses and rauk surface change monitoring. We identified and determined contemporary erosional rates and dominant processes responsible for eroding and downwearing of rauk coasts. The observed changes were strongly associated with differences in local lithological and structural conditions—from a nearly complete absence within massive limestone rauks notches, to clear signs of erosion within limestone slabs eroded by waves, almost 1 m2 in size.
Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020
Authors: Feng Yaya , Huang Chunlin , Song Xiaoyu , Gu Juan
Published: March 2023

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda).
The Lipidome of the Gastrointestinal Tract in Lactating Holstein Cows
Authors: Jiang Qianming , Loor Juan J.
Published: March 2023

The lipidome is a key determinant of structural and functional characteristics of tissues, contributing to optimal gut function and efficiency of nutrient use in the gastrointestinal tract (GIT). Our objective was to study lipidomic profiles in different sections of the GIT in lactating dairy cows and to link them with biological functions. We studied the lipid species in ruminal papillae and epithelium from duodenum, jejunum, and ileum harvested after slaughter from five lactating Holstein cows. Extracted lipids were identified by LC/MS/MS and analyzed via Lipidsearch, Metaboanalyst 5.0, and lipid ontology (LION). Of 1259 lipid species identified across the GIT, 387, 565, 193, and 86 were neutral lipids, phospholipids, sphingolipids, and derivatized lipids, respectively. Among the 1223 lipid species common to the GIT, a PLS-DA analysis revealed similar profiles for jejunum and ileum and discriminated them from rumen and duodenum. The content of 12 out of 28 lipid classes differed (p < 0.05) among GIT sections. The average fatty acid chain length in lipid species spanned from 9 to 37 carbons, and the average degree of unsaturation ranged from 0 to 6. The term ‘membrane component’ from LION analysis differed markedly between the rumen and the small intestine. Future studies will help better understand what factors (function or cellular component) in a given section of the GIT are related to the different lipid species. This is the first description of the lipidome profiles across sections of the GIT in lactating dairy cows. The unique lipidome profiles uncovered distinct structural and functional properties across the bovine GIT, which may impact the efficiency of nutrient use.
Sieve Search Centroiding Algorithm for Star Sensors
Authors: Karaparambil Vivek Chandran , Manjarekar Narayan Suresh , Singru Pravin Madanrao
Published: March 2023

The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time.
3D Object Detection for Self-Driving Cars Using Video and LiDAR: An Ablation Study
Authors: Salmane Pascal Housam , Rivera Velázquez Josué Manuel , Khoudour Louahdi , Mai Nguyen Anh Minh , Duthon Pierre , Crouzil Alain , Pierre Guillaume Saint , Velastin Sergio A.
Published: March 2023

Methods based on 64-beam LiDAR can provide very precise 3D object detection. However, highly accurate LiDAR sensors are extremely costly: a 64-beam model can cost approximately USD 75,000. We previously proposed SLS–Fusion (sparse LiDAR and stereo fusion) to fuse low-cost four-beam LiDAR with stereo cameras that outperform most advanced stereo–LiDAR fusion methods. In this paper, and according to the number of LiDAR beams used, we analyzed how the stereo and LiDAR sensors contributed to the performance of the SLS–Fusion model for 3D object detection. Data coming from the stereo camera play a significant role in the fusion model. However, it is necessary to quantify this contribution and identify the variations in such a contribution with respect to the number of LiDAR beams used inside the model. Thus, to evaluate the roles of the parts of the SLS–Fusion network that represent LiDAR and stereo camera architectures, we propose dividing the model into two independent decoder networks. The results of this study show that—starting from four beams—increasing the number of LiDAR beams has no significant impact on the SLS–Fusion performance. The presented results can guide the design decisions by practitioners.
PTA-Det: Point Transformer Associating Point Cloud and Image for 3D Object Detection
Authors: Wan Rui , Zhao Tianyun , Zhao Wei
Published: March 2023

In autonomous driving, 3D object detection based on multi-modal data has become an indispensable perceptual approach when facing complex environments around the vehicle. During multi-modal detection, LiDAR and a camera are simultaneously applied for capturing and modeling. However, due to the intrinsic discrepancies between the LiDAR point and camera image, the fusion of the data for object detection encounters a series of problems, which results in most multi-modal detection methods performing worse than LiDAR-only methods. In this investigation, we propose a method named PTA-Det to improve the performance of multi-modal detection. Accompanied by PTA-Det, a Pseudo Point Cloud Generation Network is proposed, which can represent the textural and semantic features of keypoints in the image by pseudo points. Thereafter, through a transformer-based Point Fusion Transition (PFT) module, the features of LiDAR points and pseudo points from an image can be deeply fused under a unified point-based form. The combination of these modules can overcome the main obstacle of cross-modal feature fusion and achieves a complementary and discriminative representation for proposal generation. Extensive experiments on KITTI dataset support the effectiveness of PTA-Det, achieving a mAP (mean average precision) of 77.88% on the car category with relatively few LiDAR input points.
Preprocessing for Keypoint-Based Sign Language Translation without Glosses
Authors: Kim Youngmin , Baek Hyeongboo
Published: March 2023

While machine translation for spoken language has advanced significantly, research on sign language translation (SLT) for deaf individuals remains limited. Obtaining annotations, such as gloss, can be expensive and time-consuming. To address these challenges, we propose a new sign language video-processing method for SLT without gloss annotations. Our approach leverages the signer’s skeleton points to identify their movements and help build a robust model resilient to background noise. We also introduce a keypoint normalization process that preserves the signer’s movements while accounting for variations in body length. Furthermore, we propose a stochastic frame selection technique to prioritize frames to minimize video information loss. Based on the attention-based model, our approach demonstrates effectiveness through quantitative experiments on various metrics using German and Korean sign language datasets without glosses.
Attitude-Orbit Coupled Control of Gravitational Wave Detection Spacecraft with Communication Delays
Authors: Zhang Yu , Liu Yuan , Yang Jikun , Lu Zhenkun , Zhang Juzheng
Published: March 2023

In order to meet the position and attitude requirements of spacecrafts and test masses for gravitational-wave detection missions, the attitude-orbit coordination control of multiple spacecrafts and test masses is studied. A distributed coordination control law for spacecraft formation based on dual quaternion is proposed. By describing the relationship between spacecrafts and test masses in the desired states, the coordination control problem is converted into a consistent-tracking control problem in which each spacecraft or test mass tracks its desired states. An accurate attitude-orbit relative dynamics model of the spacecraft and the test masses is proposed based on dual quaternions. A cooperative feedback control law based on a consistency algorithm is designed to achieve the consistent attitude tracking of multiple rigid bodies (spacecraft and test mass) and maintain the specific formation configuration. Moreover, the communication delays of the system are taken into account. The distributed coordination control law ensures almost global asymptotic convergence of the relative position and attitude error in the presence of communication delays. The simulation results demonstrate the effectiveness of the proposed control method, which meets the formation-configuration requirements for gravitational-wave detection missions.
A Comprehensive Security Architecture for Information Management throughout the Lifecycle of IoT Products
Authors: Yousefnezhad Narges , Malhi Avleen , Keyriläinen Tuomas , Främling Kary
Published: March 2023

The Internet of things (IoT) is expected to have an impact on business and the world at large in a way comparable to the Internet itself. An IoT product is a physical product with an associated virtual counterpart connected to the internet with computational as well as communication capabilities. The possibility to collect information from internet-connected products and sensors gives unprecedented possibilities to improve and optimize product use and maintenance. Virtual counterpart and digital twin (DT) concepts have been proposed as a solution for providing the necessary information management throughout the whole product lifecycle, which we here call product lifecycle information management (PLIM). Security in these systems is imperative due to the multiple ways in which opponents can attack the system during the whole lifecycle of an IoT product. To address this need, the current study proposes a security architecture for the IoT, taking into particular consideration the requirements of PLIM. The security architecture has been designed for the Open Messaging Interface (O-MI) and Open Data Format (O-DF) standards for the IoT and product lifecycle management (PLM) but it is also applicable to other IoT and PLIM architectures. The proposed security architecture is capable of hindering unauthorized access to information and restricts access levels based on user roles and permissions. Based on our findings, the proposed security architecture is the first security model for PLIM to integrate and coordinate the IoT ecosystem, by dividing the security approaches into two domains: user client and product domain. The security architecture has been deployed in smart city use cases in three different European cities, Helsinki, Lyon, and Brussels, to validate the security metrics in the proposed approach. Our analysis shows that the proposed security architecture can easily integrate the security requirements of both clients and products providing solutions for them as demonstrated in the implemented use cases.
Experimental Study on the Azimuthal-Transmitting Transducer of Acoustic Logging
Authors: Lu Junqiang , Men Baiyong , Che Xiaohua
Published: March 2023

Azimuthal acoustic logging can survey the downhole formation more accurately, and the acoustic source is the crucial component of the downhole acoustic logging tool with azimuthal resolution characteristics. To realize downhole azimuthal detection, assembling multiple transmitting piezoelectric vibrators in the circumferential direction is necessary, and the performance of azimuthal-transmitting piezoelectric vibrators needs attention. However, effective heating test and matching methods are not yet developed for downhole multi-azimuth transmitting transducers. Therefore, this paper proposes an experimental method to comprehensively evaluate downhole azimuthal transmitters; furthermore, we analyze the azimuthal-transmitting piezoelectric vibrator parameters. This paper presents a heating test apparatus and studies the admittance and driving responses of the vibrator at different temperatures. The transmitting piezoelectric vibrators showing a good consistency in the heating test were selected, and an underwater acoustic experiment was performed. The main lobe angle of the radiation beam, horizontal directivity, and radiation energy of the azimuthal vibrators and azimuthal subarray are measured. The peak-to-peak amplitude radiated from the azimuthal vibrator and the static capacitance increase with an increase in temperature. The resonant frequency first increases and then decreases slightly with an increase in temperature. After cooling to room temperature, the parameters of the vibrator are consistent with those before heating. Hence, this experimental study can provide a foundation for the design and matching selection of azimuthal-transmitting piezoelectric vibrators.
Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario
Authors: Pan Ruoyu , Jie Lihua , Zhao Xinyu , Wang Honggang , Yang Jingfeng , Song Jiwei
Published: March 2023

V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the “stop–go” state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle’s active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency.
Distributed Consensus Kalman Filter Design with Dual Energy-Saving Strategy: Event-Triggered Schedule and Topological Transformation
Authors: Yang Chunxi , Li Gengen , Gao Guanbin , Shi Qinghua
Published: March 2023

In the distributed information fusion of wireless sensor networks (WSNs), the filtering accuracy is commonly negatively correlated with energy consumption. Therefore, a class of distributed consensus Kalman filters was designed to balance the contradiction between them in this paper. Firstly, an event-triggered schedule was designed based on historical data within a timeliness window. Furthermore, considering the relationship between energy consumption and communication distance, a topological transformation schedule with energy-saving is proposed. The energy-saving distributed consensus Kalman filter with a dual event-driven (or event-triggered) strategy is proposed by combining the above two schedules. The sufficient condition of stability for the filter is given by the second Lyapunov stability theory. Finally, the effectiveness of the proposed filter was verified by a simulation.
Characterization of the Response of Magnetron Sputtered In2O3−x Sensors to NO2
Authors: Panzardi Enza , Calisi Nicola , Enea Nicoleta , Fort Ada , Mugnaini Marco , Vignoli Valerio , Vinattieri Anna , Bruzzi Mara
Published: March 2023

The response of resistive In2O3−x sensing devices was investigated as a function of the NO2 concentration in different operative conditions. Sensing layers are 150 nm thick films manufactured by oxygen-free room temperature magnetron sputtering deposition. This technique allows for a facile and fast manufacturing process, at same time providing advantages in terms of gas sensing performances. The oxygen deficiency during growth provides high densities of oxygen vacancies, both on the surface, where they are favoring NO2 absorption reactions, and in the bulk, where they act as donors. This n-type doping allows for conveniently lowering the thin film resistivity, thus avoiding the sophisticated electronic readout required in the case of very high resistance sensing layers. The semiconductor layer was characterized in terms of morphology, composition and electronic properties. The sensor baseline resistance is in the order of kilohms and exhibits remarkable performances with respect to gas sensitivity. The sensor response to NO2 was studied experimentally both in oxygen-rich and oxygen-free atmospheres for different NO2 concentrations and working temperatures. Experimental tests revealed a response of 32%/ppm at 10 ppm NO2 and response times of approximately 2 min at an optimal working temperature of 200 °C. The obtained performance is in line with the requirements of a realistic application scenario, such as in plant condition monitoring.
Validity and Reliability of a Wearable Goniometer Sensor Controlled by a Mobile Application for Measuring Knee Flexion/Extension Angle during the Gait Cycle
Authors: Ishida Tomoya , Samukawa Mina
Published: March 2023

Knee kinematics during gait is an important assessment tool in health-promotion and clinical fields. This study aimed to determine the validity and reliability of a wearable goniometer sensor for measuring knee flexion angles throughout the gait cycle. Twenty-two and seventeen participants were enrolled in the validation and reliability study, respectively. The knee flexion angle during gait was assessed using a wearable goniometer sensor and a standard optical motion analysis system. The coefficient of multiple correlation (CMC) between the two measurement systems was 0.992 ± 0.008. Absolute error (AE) was 3.3 ± 1.5° (range: 1.3–6.2°) for the entire gait cycle. An acceptable AE (<5°) was observed during 0–65% and 87–100% of the gait cycle. Discrete analysis revealed a significant correlation between the two systems (R = 0.608–0.904, p ≤ 0.001). The CMC between the two measurement days with a 1-week interval was 0.988 ± 0.024, and the AE was 2.5 ± 1.2° (range: 1.1–4.5°). A good-to-acceptable AE (<5°) was observed throughout the gait cycle. These results indicate that the wearable goniometer sensor is useful for assessing knee flexion angle during the stance phase of the gait cycle.
MAX-DOAS Measurements of Tropospheric NO2 and HCHO Vertical Profiles at the Longfengshan Regional Background Station in Northeastern China
Authors: Liu Shuyin , Cheng Siyang , Ma Jianzhong , Xu Xiaobin , Lv Jinguang , Jin Junli , Guo Junrang , Yu Dajiang , Dai Xin
Published: March 2023

The vertical profiles of nitrogen dioxide (NO2) and formaldehyde (HCHO) in the troposphere at the Longfengshan (LFS) regional atmospheric background station (127°36′ E, 44°44′ N, 330.5 m above sea level) from 24 October 2020 to 13 October 2021 were retrieved from solar scattering spectra by multi-axis differential optical absorption spectroscopy (MAX-DOAS). We analyzed the temporal variations of NO2 and HCHO as well as the sensitivity of ozone (O3) production to the concentration ratio of HCHO to NO2. The largest NO2 volume mixing ratios (VMRs) occur in the near-surface layer for each month, with high values concentrated in the morning and evening. HCHO has an elevated layer around the altitude of 1.4 km consistently. The means ± standard deviations of vertical column densities (VCDs) and near-surface VMRs were 4.69 ± 3.72 ×1015 molecule·cm−2 and 1.22 ± 1.09 ppb for NO2, and they were 1.19 ± 8.35 × 1016 molecule·cm−2 and 2.41 ± 3.26 ppb for HCHO. The VCDs and near-surface VMRs for NO2 were high in the cold months and low in the warm months, while HCHO presented the opposite. The larger near-surface NO2 VMRs appeared in the condition associated with lower temperature and higher humidity, but this relationship was not found between HCHO and temperature. We also found the O3 production at the Longfengshan station was mainly in the NOx-limited regime. This is the first study presenting the vertical distributions of NO2 and HCHO in the regional background atmosphere of northeastern China, which are significant to enhancing the understanding of background atmospheric chemistry and regional ozone pollution processes.
Disposable Sensor Chips with Molecularly Imprinted Carbon Paste Electrodes for Monitoring Anti-Epileptic Drugs
Authors: Aaryashree  , Choudhary Ashish Kumar , Yoshimi Yasuo
Published: March 2023

Epilepsy is a neurological disorder that affects millions of people worldwide. Anti-epileptic drugs (AEDs) are critical for their management. However, the therapeutic window is narrow, and traditional laboratory-based therapeutic drug monitoring (TDM) methods can be time consuming and unsuitable for point-of-care testing. To address this issue, we developed a disposable sensor chip based on molecularly imprinted polymer-modified carbon paste electrodes (MIP-CPs) for the TDM of AEDs such as phenobarbital (PB), carbamazepine (CBZ), and levetiracetam (LEV). In this work, functional monomers (methacrylic acid) and crosslinking monomers (methylene bisacrylamide and ethylene glycol dimethacrylate) were copolymerized in the presence of the AED template and grafted on the graphite particles by simple radical photopolymerization. The grafted particles were mixed with silicon oil, dissolving ferrocene as a redox marker to make the MIP-carbon paste (CP). Disposable sensor chips were fabricated by packing the MIP-CP into the base made of poly (ethylene glycol terephthalate) (PET) film. The sensor’s sensitivity was determined using differential pulse voltammetry (DPV), carried out on a single sensor chip for each operation. Linearity was obtained from 0–60 μg/mL in PB and LEV and 0–12 μg/mL in CBZ, covering their respective therapeutic range. The time taken for each measurement was around 2 min. The experiment using whole bovine blood and bovine plasma indicated that the existence of species that interfered had a negligible effect on the test’s sensitivity. This disposable MIP sensor provides a promising approach for point-of-care testing and facilitating the management of epilepsy. Compared with existing tests, this sensor offers a faster and more accurate way to monitor AEDs, which is crucial for optimizing therapy and improving patient outcomes. Overall, the proposed disposable sensor chip based on MIP-CPs represents a significant advancement in AED monitoring, with the potential for rapid, accurate, and convenient point-of-care testing.
Semiconductor Gas Sensors for Detecting Chemical Warfare Agents and Their Simulants
Authors: Witkiewicz Zygfryd , Jasek Krzysztof , Grabka Michał
Published: March 2023

On-site detection of chemical warfare agents (CWAs) can be performed by various analytical techniques. Devices using well-established techniques such as ion mobility spectrometry, flame photometry, infrared and Raman spectroscopy or mass spectrometry (usually combined with gas chromatography) are quite complex and expensive to purchase and operate. For this reason, other solutions based on analytical techniques well suited to portable devices are still being sought. Analyzers based on simple semiconductor sensors may be a potential alternative to the currently used CWA field detectors. In sensors of this type, the conductivity of the semiconductor layer changes upon interaction with the analyte. Metal oxides (both in the form of polycrystalline powders and various nanostructures), organic semiconductors, carbon nanostructures, silicon and various composites that are a combination of these materials are used as a semiconductor material. The selectivity of a single oxide sensor can be adjusted to specific analytes within certain limits by using the appropriate semiconductor material and sensitizers. This review presents the current state of knowledge and achievements in the field of semiconductor sensors for CWA detection. The article describes the principles of operation of semiconductor sensors, discusses individual solutions used for CWA detection present in the scientific literature and makes a critical comparison of them. The prospects for the development and practical application of this analytical technique in CWA field analysis are also discussed.
An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors
Authors: Sharif Mhd Saeed , Raj Theeng Tamang Madhav , Fu Cynthia H. Y. , Baker Aaron , Alzahrani Ahmed Ibrahim , Alalwan Nasser
Published: March 2023

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.
Selective Light Measurement in the Control of Reference LED Sources
Authors: Gilewski Marian
Published: March 2023

This paper describes an original adaptive multispectral LED light source that utilizes miniature spectrometer to control its flux in real time. Current measurement of the flux spectrum is necessary in high-stability LED sources. In such cases, it is important the spectrometer work effectively with the system that controls the source and the whole system. Therefore, as important as flux stabilization is the integration of the integrating sphere-based design with the electronic module and power subsystem. Since the problem is interdisciplinary, the paper mainly focuses on presenting the solution of the flux measurement circuit. In particular, the proprietary way of operating the MEMS optical sensor as a real-time spectrometer was proposed. Then, the implementation of the sensor handling circuit, which determines the spectral measurements accuracy and thus the output flux quality, is described. Also presented is the custom method of coupling the analog part of the flux measurement path with the analog-to-digital conversion system and the control system based on the FPGA. The description of the conceptual solutions was supported by the results of simulation and laboratory tests at selected points of the measurement path. The presented concept allows to build adaptive LED light sources in the spectral range from 340 nm to 780 nm with adjustable spectrum and flux value, with electrical power up to 100 W, with adjustable flux value in the range of 100 dB, operating in constant current or pulsed mode.
Mycotoxins in Seafood: Occurrence, Recent Development of Analytical Techniques and Future Challenges
Authors: Smaoui Slim , D’Amore Teresa , Agriopoulou Sofia , Mousavi Khaneghah Amin
Published: March 2023

The co-occurrence of mycotoxigenic fungi and mycotoxins in aquatic food commodities has recently become a source of severe worldwide food insecurity since these toxicants may damage human health. The consumption of aquatic food itself represents a relatively novel and non-negligible source of mycotoxins. Mycotoxins in seafood lead to important human genotoxins, carcinogens, and immunosuppressors. Consequently, it is crucial to quantify and characterize these contaminants in aquatic food products subject to extensive consumption and develop new regulations. The present paper provides an overview of recent advancements in liquid chromatography and mass spectrometry and the coupling of these techniques for identifying and characterizing mycotoxins in various fresh, comestible, and treated marine products. The disposable data display that a multiplicity of fungal species and further mycotoxins have been detected in seafood, comprising aflatoxins, ochratoxins, fumonisins, deoxynivalenol, zearalenone, and trichothecenes. In addition, a wider and up-to-date overview of global occurrence surveys of mycotoxin occurrence in seafood in 2017–2022 is explored. In this regard, the predominant occurrence of enniatins has been documented in seafood products. Likewise, special attention has been given to current EU seafood legal and existing national regulations of mycotoxins in seafood. In this way, rigorous national and international guidelines are needed for palpable and effective measures in the future. Nevertheless, controlling mycotoxins in aquatic foods is an ambitious aim for scientists and industry stakeholders to ensure sustainable global food safety.
Childhood Adversity Moderates Change in Latent Patterns of Psychological Adjustment during the COVID-19 Pandemic: Results of a Survey of U.S. Adults
Authors: O’Connor Kelly E. , Tomlinson Camie A. , McDonald Shelby E. , Brown Samantha , Applebaum Jennifer W. , Murphy Jennifer L. , Matijczak Angela , Zsembik Barbara A. , Porges Stephen W.
Published: March 2023

Emerging evidence suggests that the consequences of childhood adversity impact later psychopathology by increasing individuals’ risk of experiencing difficulties in adjusting to stressful situations later in life. The goals of this study were to: (a) identify sociodemographic factors associated with subgroups of psychological adjustment prior to and after the onset of the COVID-19 pandemic and (b) examine whether and to what extent types of childhood adversity predict transition probabilities. Participants were recruited via multiple social media platforms and listservs. Data were collected via an internet-based survey. Our analyses reflect 1942 adults (M = 39.68 years); 39.8% reported experiencing at least one form of childhood adversity. Latent profile analyses (LPAs) and latent transition analyses (LTAs) were conducted to determine patterns of psychological adjustment and the effects of childhood adversity on transition probabilities over time. We identified five subgroups of psychological adjustment characterized by symptom severity level. Participants who were younger in age and those who endorsed marginalized identities exhibited poorer psychological adjustment during the pandemic. Childhood exposure to family and community violence and having basic needs met as a child (e.g., food, shelter) significantly moderated the relation between latent profile membership over time. Clinical and research implications are discussed.
The Effects of Kinesio Taping® on Muscle Interplay within the Lumbo–Pelvic–Hip Complex: A Randomized Placebo-Controlled Trial
Authors: Kiseljak Dalibor , Medved Vladimir
Published: March 2023

Coordination of muscle activity is determined by the recruitment order of agonists and synergists that results from their onset times. Motor recruitment deficits are possible. This study examined the acute and prolonged effects of three different techniques of the kinesio taping method in optimizing the intermuscular coordination within the lumbo–pelvic–hip complex. The sample consisted of 56 healthy participants of both genders, randomly divided into equal groups by kinesio taping muscle facilitation, muscle inhibition and functional correction technique, and placebo kinesio taping condition. The onsets of the ipsilateral and contralateral erector spinae muscles, in relation to the semitendinosus muscle of the tested leg, were measured using the surface electromyography, during the active performance of the prone hip extension test. Time span was also determined. Measurements were performed at baseline, 60 min, and 48 h post-intervention. For the control group, we did not find statistically significant differences in the onset between the measurement points (p > 0.05), while in the experimental groups, there was a significant delay in the onset of the contralateral erector spinae (p < 0.001) in the second and third measurement points. These results indicate that the kinesio taping method can optimize the intermuscular coordination, with the potential for primary injury prevention.
Quality Improvement of the Forging Process Using Pareto Analysis and 8D Methodology in Automotive Manufacturing: A Case Study
Authors: Lestyánszka Škůrková Katarína , Fidlerová Helena , Niciejewska Marta , Idzikowski Adam
Published: March 2023

A systematic approach to nonconformity management and continuous improvements are the key elements of the quality management system. The objective of this paper is to present quality improvement for a manufacturing company producing forgings through the combination of several statistical methods and quality control techniques. First, Pareto analysis was applied, followed by the 8D (eight disciplines) methodology using a structured eight-step approach to problem solving following the PDCA (plan-do-check-act) method. The aim was to identify the problem and discover the critical causes of failures in the management system that allowed the problem to occur, by modifying and combining appropriate quality management methods and tools. The paper introduces a case study considering the forging process in the production of gearboxes, where customer complaints were identified in the last year, demanding the need to eliminate failures. Using the mentioned methods, the root cause of the problem was identified and permanent corrective action was planned and implemented according to the recommendations of the 8D report, which made it possible to reduce the likelihood of a recurrence of this problem and increase customer satisfaction.
A Phylogenetic Regression Model for Studying Trait Evolution on Network
Authors: Jhwueng Dwueng-Chwuan
Published: March 2023

A phylogenetic regression model that incorporates the network structure allowing the reticulation event to study trait evolution is proposed. The parameter estimation is achieved through the maximum likelihood approach, where an algorithm is developed by taking a phylogenetic network in eNewick format as the input to build up the variance–covariance matrix. The model is applied to study the common sunflower, Helianthus annuus, by investigating its traits used to respond to drought conditions. Results show that our model provides acceptable estimates of the parameters, where most of the traits analyzed were found to have a significant correlation with drought tolerance.
Efficient Two-Stage Analysis for Complex Trait Association with Arbitrary Depth Sequencing Data
Authors: Xu Zheng , Yan Song , Yuan Shuai , Wu Cong , Chen Sixia , Guo Zifang , Li Yun
Published: March 2023

Sequencing-based genetic association analysis is typically performed by first generating genotype calls from sequence data and then performing association tests on the called genotypes. Standard approaches require accurate genotype calling (GC), which can be achieved either with high sequencing depth (typically available in a small number of individuals) or via computationally intensive multi-sample linkage disequilibrium (LD)-aware methods. We propose a computationally efficient two-stage combination approach for association analysis, in which single-nucleotide polymorphisms (SNPs) are screened in the first stage via a rapid maximum likelihood (ML)-based method on sequence data directly (without first calling genotypes), and then the selected SNPs are evaluated in the second stage by performing association tests on genotypes from multi-sample LD-aware calling. Extensive simulation- and real data-based studies show that the proposed two-stage approaches can save 80% of the computational costs and still obtain more than 90% of the power of the classical method to genotype all markers at various depths d≥2.