bluuebunny/crossref_metadata_2025 huggingface dataset:
A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction
Authors: Fan Zhengyang , Li Wanru , Chang Kuo-Chu
Published: January 2024

The accurate estimation of the remaining useful life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of the attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention-based hierarchical Transformer (STAR) framework. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrating hierarchical encoder–decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms the current state-of-the-art models for RUL prediction.
Full-Matrix Imaging in Fourier Domain towards Ultrasonic Inspection with Wide-Angle Oblique Incidence for Welded Structures
Authors: Chen Mu , Xu Xintao , Yang Keji , Wu Haiteng
Published: January 2024

The total focusing method (TFM) has been increasingly applied to weld inspection given its high image quality and defect sensitivity. Oblique incidence is widely used to steer the beam effectively, considering the defect orientation and structural complexity of welded structures. However, the conventional TFM based on the delay-and-sum (DAS) principle is time-consuming, especially for oblique incidence. In this paper, a fast full-matrix imaging algorithm in the Fourier domain is proposed to accelerate the TFM under the condition of oblique incidence. The algorithm adopts the Chebyshev polynomials of the second kind to directly expand the Fourier extrapolator with lateral sound velocity variation. The direct expansion maintains image accuracy and resolution in wide-angle situations, covering both small and large angles, making it highly suitable for weld inspection. Simulations prove that the third-order Chebyshev expansion is required to achieve image accuracy equivalent to the TFM with wide-angle incidence. Experiments verify the algorithm’s performance for weld flaws using the proposed method with the transverse wave and the full-skip mode. The depth deviation is within 0.53 mm, and the sizing error is below 15%. The imaging efficiency is improved by a factor of up to 8 compared to conventional TFM. We conclude that the proposed method is applicable to high-speed weld inspection with various oblique incidence angles.
Leveraging Google Earth Engine for a More Effective Grassland Management: A Decision Support Application Perspective
Authors: Parracciani Cecilia , Gigante Daniela , Bonini Federica , Grassi Anna , Morbidini Luciano , Pauselli Mariano , Valenti Bernardo , Lilli Emanuele , Antonielli Francesco , Vizzari Marco
Published: January 2024

Grasslands cover a substantial portion of the earth’s surface and agricultural land and is crucial for human well-being and livestock farming. Ranchers and grassland management authorities face challenges in effectively controlling herders’ grazing behavior and grassland utilization due to underdeveloped infrastructure and poor communication in pastoral areas. Cloud-based grazing management and decision support systems (DSS) are needed to address this issue, promote sustainable grassland use, and preserve their ecosystem services. These systems should enable rapid and large-scale grassland growth and utilization monitoring, providing a basis for decision-making in managing grazing and grassland areas. In this context, this study contributes to the objectives of the EU LIFE IMAGINE project, aiming to develop a Web-GIS app for conserving and monitoring Umbria’s grasslands and promoting more informed decisions for more sustainable livestock management. The app, called “Praterie” and developed in Google Earth Engine, utilizes historical Sentinel-2 satellite data and harmonic modeling of the EVI (Enhanced Vegetation Index) to estimate vegetation growth curves and maturity periods for the forthcoming vegetation cycle. The app is updated in quasi-real time and enables users to visualize estimates for the upcoming vegetation cycle, including the maximum greenness, the days remaining to the subsequent maturity period, the accuracy of the harmonic models, and the grassland greenness status in the previous 10 days. Even though future additional developments can improve the informative value of the Praterie app, this platform can contribute to optimizing livestock management and biodiversity conservation by providing timely and accurate data about grassland status and growth curves.
A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial Sensors
Authors: Wang Xiaotao , Li Jiacheng , Xu Guangfei , Wang Xingyu
Published: January 2024

The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian’s gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems.
A Deep Learning Approach to Lunar Rover Global Path Planning Using Environmental Constraints and the Rover Internal Resource Status
Authors: Tanaka Toshiki , Malki Heidar
Published: January 2024

This research proposes a novel approach to global path and resource planning for lunar rovers. The proposed method incorporates a range of constraints, including static, time-variant, and path-dependent factors related to environmental conditions and the rover’s internal resource status. These constraints are integrated into a grid map as a penalty function, and a reinforcement learning-based framework is employed to address the resource constrained shortest path problem (RCSP). Compared to existing approaches referenced in the literature, our proposed method enables the simultaneous consideration of a broader spectrum of constraints. This enhanced flexibility leads to improved path search optimality. To evaluate the performance of our approach, this research applied the proposed learning architecture to lunar rover path search problems, generated based on real lunar digital elevation data. The simulation results demonstrate that our architecture successfully identifies a rover path while consistently adhering to user-defined environmental and rover resource safety criteria across all positions and time epochs. Furthermore, the simulation results indicate that our approach surpasses conventional methods that solely rely on environmental constraints.
Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments
Authors: Huang Wenkuan , Li Yong , Tang Jinsong , Qian Linfang
Published: January 2024

With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outside the artillery itself. In this study, we creatively propose a fault diagnosis model based on an attention mechanism, the AdaBoost method and a wavelet noise reduction network to address the difficulty in obtaining high-quality motor signals in complex noisy interference environments. First, multiple fusion wavelet basis, soft thresholding, and index soft filter optimization were used to train multiple wavelet noise reduction networks that could recover sample signals under different noise conditions. Second, a convolutional neural network (CNN) classification module was added to construct end-to-end classification models that could correctly identify faults. The above basis classification models were then integrated into the AdaBoost method with an improved attention mechanism to develop a fault diagnosis model suitable for complex noisy environments. Finally, two experiments were conducted to validate the proposed method. Under motor signals with varying signal-to-noise ratios (SNRs) noises, the proposed method achieved an average accuracy of 92%, surpassing the conventional method by over 8.5%.
Effect of Light-Emitting Grid Panel on Indoor Aquaculture for Measuring Fish Growth
Authors: Huynh Nguyen Ngoc , Jun Myoungjae , Jeong Hieyong
Published: January 2024

This study is related to Smart Aqua Farm, which combines artificial intelligence (AI) and Internet of things (IoT) technology. This study aimed to monitor fish growth in indoor aquaculture while automatically measuring the average size and area in real time. Automatic fish size measurement technology is one of the essential elements for unmanned aquaculture. Under the condition of labor shortage, operators have much fatigue because they use a primitive method that samples the size and weight of fish just before fish shipment and measures them directly by humans. When this kind of process is automated, the operator’s fatigue can be significantly reduced. Above all, after measuring the fish growth, predicting the final fish shipment date is possible by estimating how much feed and time are required until the fish becomes the desired size. In this study, a video camera and a developed light-emitting grid panel were installed in indoor aquaculture to acquire images of fish, and the size measurement of a mock-up fish was implemented using the proposed method.
Temporal Super-Resolution Using a Multi-Channel Illumination Source
Authors: Cohen Khen , Mendlovic David , Raviv Dan
Published: January 2024

While sensing in high temporal resolution is necessary for a wide range of applications, it is still limited nowadays due to the camera sampling rate. In this work, we try to increase the temporal resolution beyond the Nyquist frequency, which is limited by the sensor’s sampling rate. This work establishes a novel approach to temporal super-resolution that uses the object-reflecting properties from an active illumination source to go beyond this limit. Following theoretical derivation and the development of signal-processing-based algorithms, we demonstrate how to increase the detected temporal spectral range by a factor of six and possibly even more. Our method is supported by simulations and experiments, and we demonstrate (via application) how we use our method to dramatically improve the accuracy of object motion estimation. We share our simulation code on GitHub.
Implantable pH Sensing System Using Vertically Stacked Silicon Nanowire Arrays and Body Channel Communication for Gastroesophageal Reflux Monitoring
Authors: Kim Changhee , Han Seungju , Kim Taehwan , Lee Sangmin
Published: January 2024

Silicon nanowires (SiNWs) are emerging as versatile components in the fabrication of sensors for implantable medical devices because of their exceptional electrical, optical, and mechanical properties. This paper presents a novel top-down fabrication method for vertically stacked SiNWs, eliminating the need for wet oxidation, wet etching, and nanolithography. The integration of these SiNWs into body channel communication (BCC) circuits was also explored. The fabricated SiNWs were confirmed to be capable of forming arrays with multiple layers and rows. The SiNW-based pH sensors demonstrated a robust response to pH changes, and when tested with BCC circuits, they showed that it was possible to quantize based on pH when transmitting data through the human body. This study successfully developed a novel method for SiNW fabrication and integration into BCC circuits, which could lead to improvements in the reliability and efficiency of implantable medical sensors. The findings demonstrate significant potential for bioelectronic applications and real-time biochemical monitoring.
Evaluating the Soil Quality Index Using Three Methods to Assess Soil Fertility
Authors: Chaudhry Hiba , Vasava Hiteshkumar Bhogilal , Chen Songchao , Saurette Daniel , Beri Anshu , Gillespie Adam , Biswas Asim
Published: January 2024

Soil health plays a crucial role in crop production, both in terms of quality and quantity, highlighting the importance of effective methods for preserving soil quality to ensure global food security. Soil quality indices (SQIs) have been widely utilized as comprehensive measures of soil function by integrating multiple physical, chemical, and biological soil properties. Traditional SQI analysis involves laborious and costly laboratory analyses, which limits its practicality. To overcome this limitation, our study explores the use of visible near-infrared (vis-NIR) spectroscopy as a rapid and non-destructive alternative for predicting soil properties and SQIs. This study specifically focused on seven soil indicators that contribute to soil fertility, including pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN). These properties play key roles in nutrient availability, pH regulation, and soil structure, influencing soil fertility and overall soil health. By utilizing vis-NIR spectroscopy, we were able to accurately predict the soil indicators with good accuracy using the Cubist model (R2 = 0.35–0.93), offering a cost-effective and environmentally friendly alternative to traditional laboratory analyses. Using the seven soil indicators, we looked at three different approaches for calculating and predicting the SQI, including: (1) measured SQI (SQI_m), which is derived from laboratory-measured soil properties; (2) predicted SQI (SQI_p), which is calculated using predicted soil properties from spectral data; and (3) direct prediction of SQI (SQI_dp), The findings demonstrated that SQI_dp exhibited a higher accuracy (R2 = 0.90) in predicting soil quality compared to SQI_p (R2 = 0.23).
Reliability of Xsens IMU-Based Lower Extremity Joint Angles during In-Field Running
Authors: Debertin Daniel , Wargel Anna , Mohr Maurice
Published: January 2024

The Xsens Link motion capture suit has become a popular tool in investigating 3D running kinematics based on wearable inertial measurement units outside of the laboratory. In this study, we investigated the reliability of Xsens-based lower extremity joint angles during unconstrained running on stable (asphalt) and unstable (woodchip) surfaces within and between five different testing days in a group of 17 recreational runners (8 female, 9 male). Specifically, we determined the within-day and between-day intraclass correlation coefficients (ICCs) and minimal detectable changes (MDCs) with respect to discrete ankle, knee, and hip joint angles. When comparing runs within the same day, the investigated Xsens-based joint angles generally showed good to excellent reliability (median ICCs > 0.9). Between-day reliability was generally lower than the within-day estimates: Initial hip, knee, and ankle angles in the sagittal plane showed good reliability (median ICCs > 0.88), while ankle and hip angles in the frontal plane showed only poor to moderate reliability (median ICCs 0.38–0.83). The results were largely unaffected by the surface. In conclusion, within-day adaptations in lower-extremity running kinematics can be captured with the Xsens Link system. Our data on between-day reliability suggest caution when trying to capture longitudinal adaptations, specifically for ankle and hip joint angles in the frontal plane.
Efficient and Accurate Analog Voltage Measurement Using a Direct Sensor-to-Digital Port Interface for Microcontrollers and Field-Programmable Gate Arrays
Authors: Grossi Marco
Published: January 2024

Portable sensor systems are usually based on microcontrollers and/or Field-Programmable Gate Arrays (FPGAs) that are interfaced with sensors by means of an Analog-to-Digital converter (ADC), either integrated in the computing device or external. An alternative solution is based on the direct connection of the sensors to the digital input port of the microcontroller or FPGA. This solution is particularly interesting in the case of devices not integrating an internal ADC or featuring a small number of ADC channels. In this paper, a technique is presented to directly interface sensors with analog voltage output to the digital input port of a microcontroller or FPGA. The proposed method requires only a few passive components and is based on the measurements of the duty cycle of a digital square-wave signal. This technique was investigated by means of circuit simulations using LTSpice and was implemented in a commercial low-cost FPGA device (Gowin GW1NR-9). The duty cycle of the square-wave signal features a good linear correlation with the analog voltage to be measured. Thus, a look-up table to map the analog voltage values to the measured duty cycle is not required with benefits in terms of memory occupation. The experimental results on the FPGA device have shown that the analog voltage can be measured with a maximum accuracy of 1.09 mV and a sampling rate of 9.75 Hz. The sampling rate can be increased to 31.35 Hz and 128.31 Hz with an accuracy of 1.61 mV and 2.68 mV, respectively.
Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images
Authors: Aldibaja Mohammad , Yanase Ryo , Suganuma Naoki
Published: January 2024

Lane graphs are very important for describing road semantics and enabling safe autonomous maneuvers using the localization and path-planning modules. These graphs are considered long-life details because of the rare changes occurring in road structures. On the other hand, the global position of the corresponding topological maps might be changed due to the necessity of updating or extending the maps using different positioning systems such as GNSS/INS-RTK (GIR), Dead-Reckoning (DR), or SLAM technologies. Therefore, the lane graphs should be transferred between maps accurately to describe the same semantics of lanes and landmarks. This paper proposes a unique transfer framework in the image domain based on the LiDAR intensity road surfaces, considering the challenging requirements of its implementation in critical road structures. The road surfaces in a target map are decomposed into directional sub-images with X, Y, and Yaw IDs in the global coordinate system. The XY IDs are used to detect the common areas with a reference map, whereas the Yaw IDs are utilized to reconstruct the vehicle trajectory in the reference map and determine the associated lane graphs. The directional sub-images are then matched to the reference sub-images, and the graphs are safely transferred accordingly. The experimental results have verified the robustness and reliability of the proposed framework to transfer lane graphs safely and accurately between maps, regardless of the complexity of road structures, driving scenarios, map generation methods, and map global accuracies.
Exploring Convolutional Neural Network Architectures for EEG Feature Extraction
Authors: Rakhmatulin Ildar , Dao Minh-Son , Nassibi Amir , Mandic Danilo
Published: January 2024

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention
Authors: Chen Hui , Gouin-Vallerand Charles , Bouchard Kévin , Gaboury Sébastien , Couture Mélanie , Bier Nathalie , Giroux Sylvain
Published: January 2024

Deep learning models have gained prominence in human activity recognition using ambient sensors, particularly for telemonitoring older adults’ daily activities in real-world scenarios. However, collecting large volumes of annotated sensor data presents a formidable challenge, given the time-consuming and costly nature of traditional manual annotation methods, especially for extensive projects. In response to this challenge, we propose a novel AttCLHAR model rooted in the self-supervised learning framework SimCLR and augmented with a self-attention mechanism. This model is designed for human activity recognition utilizing ambient sensor data, tailored explicitly for scenarios with limited or no annotations. AttCLHAR encompasses unsupervised pre-training and fine-tuning phases, sharing a common encoder module with two convolutional layers and a long short-term memory (LSTM) layer. The output is further connected to a self-attention layer, allowing the model to selectively focus on different input sequence segments. The incorporation of sharpness-aware minimization (SAM) aims to enhance model generalization by penalizing loss sharpness. The pre-training phase focuses on learning representative features from abundant unlabeled data, capturing both spatial and temporal dependencies in the sensor data. It facilitates the extraction of informative features for subsequent fine-tuning tasks. We extensively evaluated the AttCLHAR model using three CASAS smart home datasets (Aruba-1, Aruba-2, and Milan). We compared its performance against the SimCLR framework, SimCLR with SAM, and SimCLR with the self-attention layer. The experimental results demonstrate the superior performance of our approach, especially in semi-supervised and transfer learning scenarios. It outperforms existing models, marking a significant advancement in using self-supervised learning to extract valuable insights from unlabeled ambient sensor data in real-world environments.
Experimental Investigation for Monitoring Corrosion Using Plastic Optical Fiber Sensors
Authors: Hou Liang , Akutagawa Shinichi , Tomoshige Yuki , Kimura Takashi
Published: January 2024

The timely and cost-effective identification of the onset of corrosion and its progress would be critical for effectively maintaining structural integrity. Consequently, a series of fundamental experiments were conducted to capture the corrosion process on a steel plate using a new type of plastic optical fiber (POF) sensor. Electrolytic corrosion experiments were performed on a 5 mm thick steel plate immersed in an aqueous solution. The POF sensor installed on the upper side of the plate and directed downward detected the upward progression of the corrosion zone that formed on the underside of the plate. The results showed that the POF sensors could detect the onset of the upward-progressing corrosion front as it passed the 1 and 2 mm marks related to the thickness of the corroded zone. The POF sensors were designed to optically identify corrosion; therefore, the data obtained by these sensors could be processed using a newly developed graphic application software for smartphones and also identified by the naked eye. This method offered an easy and cost-effective solution for verifying the corrosion state of structural components.
Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks
Authors: Xing Na , Zhang Ye , Wang Yuehai , Zhou Yang
Published: January 2024

Unmanned Aerial Vehicles (UAVs) have critical applications in various real-world scenarios, including mapping unknown environments, military reconnaissance, and post-disaster search and rescue. In these scenarios where communication infrastructure is missing, UAVs will form an ad hoc network and perform tasks in a distributed manner. To efficiently carry out tasks, each UAV must acquire and share global status information and data from neighbors. Meanwhile, UAVs frequently operate in extreme conditions, including storms, lightning, and mountainous areas, which significantly degrade the quality of wireless communication. Additionally, the mobility of UAVs leads to dynamic changes in network topology. Therefore, we propose a method that utilizes graph neural networks (GNN) to learn cooperative data dissemination. This method leverages the network topology relationship and enables UAVs to learn a decision policy based on local data structure, ensuring that all UAVs can recover global information. We train the policy using reinforcement learning that enhances the effectiveness of each transmission. After repeated simulations, the results validate the effectiveness and generalization of the proposed method.
Research on the Algorithm of Position Correction for High-Speed Moving Express Packages Based on Traditional Vision and AI Vision
Authors: Dai Ning , Lu Zhehao , Chen Jingchao , Xu Kaixin , Hu Xudong , Yuan Yanhong
Published: January 2024

The rapid development of the logistics industry poses significant challenges to the sorting work within this sector. The fast and precise identification of moving express parcels holds immense significance for the performance of logistics sorting systems. This paper proposes a motion express parcel positioning algorithm that combines traditional vision and AI-based vision. In the traditional vision aspect, we employ a brightness-based traditional visual parcel detection algorithm. In the AI vision aspect, we introduce a Convolutional Block Attention Module (CBAM) and Focal-EIoU to enhance YOLOv5, improving the model’s recall rate and robustness. Additionally, we adopt an Optimal Transport Assignment (OTA) label assignment strategy to provide a training dataset based on global optimality for the model training phase. Our experimental results demonstrate that our modified AI model surpasses traditional algorithms in both parcel recognition accuracy and inference speed. The combined approach of traditional vision and AI vision in the motion express parcel positioning algorithm proves applicable for practical logistics sorting systems.
Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations
Authors: Islam Bashima , McElwain Nancy L. , Li Jialu , Davila Maria I. , Hu Yannan , Hu Kexin , Bodway Jordan M. , Dhekne Ashutosh , Roy Choudhury Romit , Hasegawa-Johnson Mark
Published: January 2024

Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.
Investigation into the Co-Phase Detection Methodology for Segmented Plane Mirrors Utilizing Grazing Incidence Interferometry
Authors: Liu Rengcong , Guo Jiang , Li Yibo
Published: January 2024

Segmented plane mirrors constitute a crucial component in the self-aligned detection process for large-aperture space optical imaging systems. Surface shape errors inherent in segmented plane mirrors primarily manifest as tilt errors and piston errors between sub-mirrors. While the detection and adjustment techniques for tilt errors are well-established, addressing piston errors poses a more formidable challenge. This study introduces a novel approach to achieve long-range, high-precision, and efficient co-phase detection of segmented plane mirrors by proposing a segmented plane mirror shape detection method based on grazing incidence interferometry. This method serves to broaden the detection range of piston errors, mitigate the issue of the 2π ambiguity resulting from piston errors in co-phase detection, and extend the detection capabilities of the interferometer. By manipulating the incident angle of the interferometer, both rough and precise adjustments of the segmented plane mirrors can be effectively executed.
SVS-VPR: A Semantic Visual and Spatial Information-Based Hierarchical Visual Place Recognition for Autonomous Navigation in Challenging Environmental Conditions
Authors: Arshad Saba , Park Tae-Hyoung
Published: January 2024

Robust visual place recognition (VPR) enables mobile robots to identify previously visited locations. For this purpose, the extracted visual information and place matching method plays a significant role. In this paper, we critically review the existing VPR methods and group them into three major categories based on visual information used, i.e., handcrafted features, deep features, and semantics. Focusing the benefits of convolutional neural networks (CNNs) and semantics, and limitations of existing research, we propose a robust appearance-based place recognition method, termed SVS-VPR, which is implemented as a hierarchical model consisting of two major components: global scene-based and local feature-based matching. The global scene semantics are extracted and compared with pre-visited images to filter the match candidates while reducing the search space and computational cost. The local feature-based matching involves the extraction of robust local features from CNN possessing invariant properties against environmental conditions and a place matching method utilizing semantic, visual, and spatial information. SVS-VPR is evaluated on publicly available benchmark datasets using true positive detection rate, recall at 100% precision, and area under the curve. Experimental findings demonstrate that SVS-VPR surpasses several state-of-the-art deep learning-based methods, boosting robustness against significant changes in viewpoint and appearance while maintaining efficient matching time performance.
An Improved Theory for Designing and Numerically Calibrating Circular Touch Mode Capacitive Pressure Sensors
Authors: He Xiao-Ting , Wang Xin , Li Fei-Yan , Sun Jun-Yi
Published: January 2024

The design, especially the numerical calibration, of a circular touch mode capacitive pressure sensor is highly dependent on the accuracy of the analytical solution of the contact problem between the circular conductive membrane and the rigid plate of the sensor. In this paper, the plate/membrane contact problem is reformulated using a more accurate in-plane equilibrium equation, and a new and more accurate analytical solution is presented. On this basis, the design and numerical calibration theory for circular touch mode capacitive pressure sensors has been greatly improved and perfected. The analytical relationships of pressure and capacitance are numerically calculated using the new and previous analytical solutions, and the gradually increasing difference between the two numerical calculation results with the gradual increase in the applied pressure is graphically shown. How to use analytical solutions and analytical relationships to design and numerically calibrate a circular touch mode capacitive pressure sensor with a specified pressure detecting range is illustrated in detail. The effect of changing design parameters on capacitance–pressure analytical relationships is comprehensively investigated; thus, the direction of changing design parameters to meet the required or desired range of pressure or capacitance is clarified.
Sci Reloaded: Introducing the New Aims and Scope
Authors: Abdin Ahmad Yaman , Jacob Claus
Published: January 2024

We are excited to share with you a crucial moment in the journey of Sci (ISSN 2413-4155) as we are announcing its new Aims and Scope [...]
Phytochemical Content and Anticancer Activity of Jamaican Dioscorea alata cv. White Yam Extracts
Authors: Wallace Kenroy , Wright Racquel , Williams-Longmore Melisa , Wright Sasha-Gay , Asemota Helen
Published: January 2024

Dioscorea spp. is known for its myriad medicinal properties. D. alata, specifically crude extracts, have displayed potent anticancer properties. However, the chemical constituents of these extracts have not been examined. The aim of this study is to determine the chemical composition and antioxidant characteristics of the active extracts from D. alata tuber. Chemoinformatic profiling of the Jamaican Dioscorea alata cultivar white yam tuber was generated by a sequential Soxhlet extraction of dried milled tuber, producing five crude extracts: hexane (E-1), diethyl ether (E-2), acetone (E-3), ethanol (E-4) and water (E-5). The analytes within the five extracts were dissolved in 0.1% DMSO and their anticancer activity was determined using DU145 prostate cancer cells. Both the acetone and the ethanolic extract were able to induce greater than 50% cell death at 50 µg/mL. The order of growth inhibition of the extracts in DU-145 cell is E3 (IC50, 10.81 µg/mL) > E-4 (IC50 24.17 µg/mL) > E-1 (IC50 > 100 µg/mL) ≥ E-2 (IC50 > 100 µg/mL) ≥ E-5 (IC50 > 100 µg/mL). Phytochemical screening of both E-3 and E-4 revealed the presence of all major classes of secondary metabolites except tannins. Resins were also absent in the E-3 extract. Phenolic quantification indicated that E-3 and E-4 possessed GAEs of 31 ± 1.1 and 72 ± 1.8 mg per g of sample, respectively. Inversely, E-3 displayed greater antioxidant capability with IC50 of 82.9 µg/mL compared to E-4 (166.9 µg/mL); however, neither was comparable to citric acid (33.6 µg/mL). The extract E-3 was further isolated by HPLC into 11 fractions. Fractions 4 and 5 possessed potent cell growth inhibitory effects. GCMs of fractions 4 and 5 showed they possessed numerous saturated fatty acids with pharmacological relevance. The presence of these compounds shows potential for exploitation of D. alata extracts for pharmacological purposes.
HILIC Separation Methods on Poly-Hydroxyl Stationary Phases for Determination of Common Saccharides with Evaporative Light-Scattering Detector and Rapid Determination of Isomaltulose in Protein-Rich Food Supplements
Authors: Crha Tomáš , Odedina Grace F. , Pazourek Jiří
Published: January 2024

This article highlights the fundamental aspects of hydrophilic interaction liquid chromatography (HILIC) on poly-hydroxyl stationary phases to analyze non-derivatized mono- and disaccharides, including commonly consumed carbohydrates like glucose, fructose, sucrose, and lactose. The evaporative light-scattering detector (ELSD) is utilized as an alternative to an MS detector, and the separation system’s selectivity allows the separation of anomers of monosaccharides. The study also includes a rapid method for determining isomaltulose (Palatinose), which was validated and applied to food supplement samples available in the Czech market, even those with high protein content. Additionally, isomaltulose was separated from sucrose in just 13 min.
Reconsidering Recognition in the Lives of Children and Young People in Care: Insights from the Mockingbird Family in South Australia
Authors: Patmisari Emi , McLaren Helen , Jones Michelle
Published: January 2024

This study explored the experiences of children and young people in the Mockingbird Family, South Australia, during implementation and roll-out. The study involved semi-structured interviews with a diverse group of 54 participants, including 21 children and young people, 12 foster carers, and 14 agency workers. Thematic analysis, with the application of Axel Honneth’s recognition theory, showed the Mockingbird Family model to validate the emotional, cognitive, and social support needs of children and young people. Through interconnected experience, the nurturing of care and the promotion of rights-based, holistic approaches were crucial for achieving social recognition, dignity, and developmental growth. The study indicates the potential benefits of the Mockingbird Family model for addressing the needs of children and young people in care. The research suggests that a community-based support model such as the Mockingbird Family should be considered in child welfare practices. However, further research is necessary to fully understand the model’s long-term effects and justify its integration into wider child welfare policies.
Law Programs, Ethno–Racial Relations Education, and Confronting Racism in the Brazilian Judiciary
Authors: Dos Santos Sales Augusto
Published: January 2024

This article focuses on the lack of full compliance with teaching ethno–racial relations education in Brazilian university undergraduate programs, particularly law programs. Teaching this topic was specified by the Conselho Nacional de Educação (CNE, National Education Council) in Resolution CNE/CP no. 01/2004. Although teaching ethno–racial relations education has not been a panacea for judicial sentencing based on racial criteria, we propose the working hypothesis that teaching it is a tool that can help catalyze a reduction in racist sentences by courts, for example, a defendant not fitting the stereotypical criminal pattern by being white or being assumed to belong to some criminal group for being black (preto) or brown (pardo). Through surveys at sixty-nine federal universities and documentary research into law program curricula, it was discovered that Resolution CNE/CP no. 01/2004 is not being fully or appropriately implemented at these institutions, a fact that may be enabling the continuance of race-based penal sentencing, which is illegal and extremely harmful to the black/brown Brazilian population. To prevent or minimize this problem, full compliance with Resolution CNE/CP no. 01/2004 is suggested.
The Efficiency of a Biological Reactor in a Domestic Wastewater Treatment Plant Operating Based on ABS (Acrylonitrile Butadiene Styrene) Material and Recycled PUR (Polyurethane) Foam
Authors: Chmielowski Krzysztof , Halecki Wiktor , Masłoń Adam , Bąk Łukasz , Kalenik Marek , Spychała Marcin , Bedla Dawid , Pytlowany Tomasz , Paśmionka Iwona B. , Sikora Jakub , Sionkowski Tomasz
Published: January 2024

The primary objective of this research was to assess the efficacy of a novel solution under conditions closely resembling those of real-world scenarios. Biological beds, or filters, hold significant potential for widespread implementation in individual households, particularly in areas with dispersed housing. The system’s aim was to improve the quality of wastewater treated in on-site domestic biological treatment plants. A pivotal aspect of the project involved developing a prototype research installation for conducting comprehensive testing. Our installation system consisted of several components designed to create a laboratory-scale model for domestic wastewater treatment. The model comprised four biological reactors filled with ABS material and secured by a PUR frame. Additionally, the tested model included a controller for wastewater dosing control, a septic tank as a reservoir, and four tanks for collecting purified wastewater. Through regression analysis using the Generalized Linear Model (GLM), a correlation between CODCr and TSS was revealed. This study presents the research findings concerning the development of a prototype installation that incorporates an advanced reactor or filter. The data derived from this research have the potential to contribute to the creation of products that enhance the performance and efficiency of household wastewater treatment systems.
Activated-Carbon-Doped Non-Solvent-Induced Phase-Inversion Membranes: A Comprehensive Study on Synthesis, Characterisation, and Performance Evaluation
Authors: Mompó-Curell Raúl , Biti Simbarashe , Iborra-Clar Alicia , Iborra-Clar María Isabel , Garcia-Castello Esperanza M. , Fernández-Martín Claudia
Published: January 2024

Wastewater treatment often enables discharge into natural water bodies, but for effective reuse, further treatment is essential. Membrane processes provide a precise solution yet face limitations due to fouling and organic material adsorption, impacting their performance. This study focuses on synthesising ultrafiltration membranes using non-solvent-induced phase separation. These membranes are produced from a Polyethersulfone/N,N′-dimethylacetanamide (PES/DMA) solution with varying concentrations of three commercial powdered activated carbons (ACs). The membranes undergo comprehensive analysis, revealing different behaviours based on AC type and concentration in the active layer. Among the membranes, Norit R with 0.5 wt.% concentration exhibits the highest polyethylene glycol (PEG) rejection, with an impressive rejection index (R) of 80.34% and permeability coefficient of 219.29 (L·m−2·h−1·bar−1). AC-enhanced membranes display superior selectivity compared to non-doped PES membranes. This work highlights the significant influence of AC textural properties, specifically specific surface area, total micropore volume, and average micropore width, on membrane performance, particularly the rejection index.
Heavy Metal Pollution in Sediments of the Yu River in a Polymetallic Ore Concentration Area: Temporal–Spatial Variation, Risk Assessment, and Sources Apportionment
Authors: Bai Heling , Liu Guannan , Chen Danli , Xing Zhengsong , Wang Yuhao , Wang Juan , Zhao Yuanyi
Published: January 2024

In a polymetallic ore concentration area, large-scale mining activities can dramatically increase heavy metal concentrations in river sediments, and their temporal–spatial variation and source apportionment are significant for understanding heavy metal migration in rivers and formulating management strategies for environmental protection and the mining industry. Sediment samples were collected along the Yu River, which flows through the Luanchan polymetallic ore concentration area in China, during high-water period (HWP), low-water period (LWP) and flat-water period (FWP) to assess the pollution level and identify the sources of Mo, Cr, W, Cu, Zn, As, Cd, Pb and Hg in the sediments. The findings revealed that Mo, Cd, W, Zn, Pb and Cu were the main pollutants, and Hg was extremely high at some specific locations. Sediments in the upstream region of the Yu River were more severely polluted by heavy metals and had greater ecological risk due to stronger mine exploration. Furthermore, consistent distribution patterns of various heavy metals during different seasons were not found. Some sharp decreases in heavy metal concentrations between adjacent sediments were observed; moreover, at some sites, heavy metal concentrations during LWP and FWP were lower than those during HWP. The results indicated that heavy metals in the Yu River mainly migrated in dissolved form. Mo, Cu, Pb and As for HWP, Mo and As for LWP and Mo, Cr and W for FWP mainly originated from Mo/W mines. Pb/Zn mines contributed to the amounts of W, Zn and Cd during HWP, Cu, Zn, Cd and Pb during LWP and Cu, Zn, Cd and Pb during FWP. Hg was mainly attributed to Au mines, and Cr was the geogenic element. The results could contribute to the sustainability of the mining industry and the formulation of science-based remediation and protection strategies for the rivers near mining areas.
Biochemical and Quality Parameter Changes of Wheat Grains during One-Year Storage under Different Storage Conditions
Authors: Strelec Ivica , Mrša Vladimir , Simović Dragana Šoronja , Petrović Jovana , Zahorec Jana , Budžaki Sandra
Published: January 2024

The selection of suitable storage conditions and monitoring of changes in the wheat grains using suitable parameters are of great importance for the sustainability of technological quality and utilization of the entire stored quantity of wheat grains without waste generation until the next harvest. Wheat grains of three varieties, stored for 12 months at three different conditions of environmental temperature and relative humidity (% RH): (1) 40 ± 1.06 °C; 45 ± 3% RH; (2) 4 ± 1.05 °C; 45 ± 4% RH, (3) 15 ± 8.51 °C; and 67 ± 4% RH, were compared for germination percentage, protein and advanced glycation end products content, oxidoreductive and proteolytic enzyme activity, wet gluten content and falling number. A decrease in the activity of guaiacol peroxidase and polyphenol oxidase, as well as an increase in the content of advanced glycosylation end products and the falling number, was observed in wheat grains during storage conditions at 40 ± 1.06 °C; 45 ± 4% RH. On the other hand, storage of wheat grains at lower temperatures resulted in much lower changes in examined parameters, among which advanced glycation end-product content, peroxidase activity and falling number values were the greatest. Based on the obtained results, it can be assumed that changes in guaiacol peroxidase activity and falling number might be used as indicators of improper wheat grain storage.
Global Change Impacts on Indigenous Sustainability in Sakha Republic: A Synthesis of Knowledge
Authors: Ksenofontov Stanislav Saas , Petrov Andrey N.
Published: January 2024

Indigenous understanding of sustainability is embedded in close relations to land and environment, Indigenous Knowledge systems, Indigenous epistemologies and ontologies, and Indigenous languages. However, the sustainability of Indigenous peoples’ livelihoods is significantly affected by various global change drivers. In the Arctic, Indigenous peoples’ livelihoods are impacted by environmental, social, and cultural changes, including climate change, environmental pollution, economic processes, and resource extraction. This paper aims to review and synthesize recent academic and gray literature on the sustainability of Indigenous communities in Sakha Republic, Northeast Siberia, Russia in the face of global change with a particular focus on land- and water-based traditional activities, native language, and the Indigenous Knowledge system.