bluuebunny/crossref_metadata_2025 huggingface dataset:
Flexible and Robust Triboelectric Nanogenerators with Chemically Prepared Metal Electrodes and a Plastic Contact Interface Based on Low-Cost Pressure-Sensitive Adhesive
Authors: Wang Shuai-Chen , Zhang Binbin , Kang Lijing , Liang Cunman , Chen Dongdong , Liu Guoqiang , Guo Xuyun
Published: February 2023

Triboelectric nanogenerators (TENGs) are devices that can harvest energy from mechanical motions; such devices can be used to power wearable sensors and various low-power electronics. To increase the lifetime of the device, scientists mainly use the method of making TENG in a hard skeleton to simplify the complex possible relative movements between two triboelectric parts. However, the hard skeletons cannot be embedded in soft and lightweight clothing. To make matters worse, the materials used in the garments must be able to withstand high mechanical forces when worn, such as the pressure of more than 100 KPa exerted by body pressure or everyday knocks. Notably, the TENGs are usually made of fragile materials, such as vacuum-evaporated metal electrodes and nano-sized coatings, on the contact interface; these electrodes and coatings often chip or wear off under the action of external loads. In this work, we succeeded in creating a thin, light-weight, but extremely robust garment-integrated triboelectric nanogenerator (G-TENG) that can be embedded in clothing and pass the water wash test. First, we chemically deposited a durable electrode with flexible properties for G-TENG using a novel technique called polymer-assisted metal deposition (PAMD). The as-formed metal electrodes are firmly bonded to the plastic substrate by a sub-10 nm adhesive polymer brush and can withstand a pressure of 22.5 MPa and a tear force of 0.7 MPa. We then removed the traditionally used fragile nanoparticle materials and the non-durable poly-dimethylsiloxane (PDMS) layer at the triboelectric interface, and then used a cost-effective, durable and slightly flowable pressure-sensitive adhesive to form a plastic contact interface. Such a soft plastic interface can ensure full contact of the triboelectric materials, which is excellent in complex environments and ultimately improves the power generation efficiency of the devices. The as-formed low-cost energy harvesting device could become an industry standard for future smart clothing.
Sol-Gel Dipping Devices for H2S Visualization
Authors: Strianese Maria , Ferrara Giovanni , Vykhovanets Viktoriia , Blal Naym , Guarnieri Daniela , Landi Alessandro , Lamberti Marina , Peluso Andrea , Pellecchia Claudio
Published: February 2023

In this contribution we report the synthesis and full characterization, via a combination of different spectroscopies (e.g., 1H NMR, UV-vis, fluorescence, MALDI), of a new family of fluorescent zinc complexes with extended π-conjugated systems, with the final aim of setting up higher performance H2S sensing devices. Immobilization of the systems into a polymeric matrix for use in a solid-state portable device was also explored. The results provided proof-of-principle that the title complexes could be successfully implemented in a fast, simple and cost-effective H2S sensing device.
Cyber Attacker Profiling for Risk Analysis Based on Machine Learning
Authors: Kotenko Igor , Fedorchenko Elena , Novikova Evgenia , Jha Ashish
Published: February 2023

The notion of the attacker profile is often used in risk analysis tasks such as cyber attack forecasting, security incident investigations and security decision support. The attacker profile is a set of attributes characterising an attacker and their behaviour. This paper analyzes the research in the area of attacker modelling and presents the analysis results as a classification of attacker models, attributes and risk analysis techniques that are used to construct the attacker models. The authors introduce a formal two-level attacker model that consists of high-level attributes calculated using low-level attributes that are in turn calculated on the basis of the raw security data. To specify the low-level attributes, the authors performed a series of experiments with datasets of attacks. Firstly, the requirements of the datasets for the experiments were specified in order to select the appropriate datasets, and, afterwards, the applicability of the attributes formed on the basis of such nominal parameters as bash commands and event logs to calculate high-level attributes was evaluated. The results allow us to conclude that attack team profiles can be differentiated using nominal parameters such as bash history logs. At the same time, accurate attacker profiling requires the extension of the low-level attributes list.
Forest Sound Classification Dataset: FSC22
Authors: Bandara Meelan , Jayasundara Roshinie , Ariyarathne Isuru , Meedeniya Dulani , Perera Charith
Published: February 2023

The study of environmental sound classification (ESC) has become popular over the years due to the intricate nature of environmental sounds and the evolution of deep learning (DL) techniques. Forest ESC is one use case of ESC, which has been widely experimented with recently to identify illegal activities inside a forest. However, at present, there is a limitation of public datasets specific to all the possible sounds in a forest environment. Most of the existing experiments have been done using generic environment sound datasets such as ESC-50, U8K, and FSD50K. Importantly, in DL-based sound classification, the lack of quality data can cause misguided information, and the predictions obtained remain questionable. Hence, there is a requirement for a well-defined benchmark forest environment sound dataset. This paper proposes FSC22, which fills the gap of a benchmark dataset for forest environmental sound classification. It includes 2025 sound clips under 27 acoustic classes, which contain possible sounds in a forest environment. We discuss the procedure of dataset preparation and validate it through different baseline sound classification models. Additionally, it provides an analysis of the new dataset compared to other available datasets. Therefore, this dataset can be used by researchers and developers who are working on forest observatory tasks.
Breaking of Wavelength-Dependence in Holographic Wavefront Sensors Using Spatial-Spectral Filtering
Authors: Stsepuro Nikita , Kovalev Michael , Zlokazov Evgenii , Kudryashov Sergey
Published: February 2023

Nowadays, wavefront sensors are widely used to control the shape of the wavefront and detect aberrations of the complex field amplitude in various fields of physics. However, almost all of the existing wavefront sensors work only with quasi-monochromatic radiation. Some of the methods and approaches applied to work with polychromatic radiation impose certain restrictions. However, the contemporary methods of computer and digital holography allow implementing a holographic wavefront sensor that operates with polychromatic radiation. This paper presents a study related to the analysis and evaluation of the error in the operation of holographic wavefront sensors with such radiation.
High-Spatial-Resolution NDVI Reconstruction with GA-ANN
Authors: Zhao Yanhong , Hou Peng , Jiang Jinbao , Zhao Jiajun , Chen Yan , Zhai Jun
Published: February 2023

The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm–artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space–time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction’s NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution.
Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases
Authors: Pawlicki Łukasz , Fotyga Alicja , Rewieński Jakub , Groth Mateusz , Kulas Łukasz , Fotyga Grzegorz
Published: February 2023

This paper proposes an idea of Wireless Body Area Networks (WBANs) based on Bluetooth Low-Energy (BLE) standards to recognize and alarm a gesture of touching the face, and in effect, to prevent self-inoculation of respiratory viral diseases, such as COVID-19 or influenza A, B, or C. The proposed network comprises wireless modules placed in bracelets and a necklace. It relies on the received signal strength indicator (RSSI) measurements between the bracelet and necklace modules. The measured signal is cleared of noise using the exponential moving average (EMA). Next, we use a classification algorithm based on a Least-Squares Support Vector Machine (LSSVM) in order to detect facial touches. When the results of the classification indicate that the hand is moving toward the face, an alarm is sent through the neck module and the vibrator embedded in the wrist module is switched on. Based on the performed tests, it can be concluded that the proposed solution is characterized by high accuracy and reliability. It should be useful, especially for individuals who are regularly exposed to the risk of respiratory infections.
Wireless Passive Sensor Technology through Electrically Conductive Media over an Acoustic Channel
Authors: Schaechtle Thomas , Aftab Taimur , Reindl Leonhard M. , Rupitsch Stefan J.
Published: February 2023

Hydrogen-based technologies provide a potential route to more climate-friendly mobility in the automotive and aviation industries. High-pressure tanks consisting of carbon-fiber-reinforced polymers (CFRPs) are exploited for the storage of compressed hydrogen and have to be monitored for safe and long-term operation. Since neither wired sensors nor wireless radio technology can be used inside these tanks, acoustic communication through the hull of the tank has been the subject of research in recent years. In this paper, we present for the first time a passive wireless sensor technology exploiting an ultrasonic communication channel through an electrically conductive transmission medium with an analog resonant sensor featuring a high quality factor. The instrumentation system comprised a readout unit outside and a passive sensor node inside the tank, coupled with geometrically opposing electromechanical transducers. The readout unit wirelessly excited a resonant sensor, whose temperature-dependent resonance frequency was extracted from the backscattered signal. This paper provides a description of the underlying passive sensor technology and characterizes the electric impedances and acoustic transmission as an electrical 2-Port to design a functional measurement setup. We demonstrated a wireless temperature measurement through a 10 mm CFRP plate in its full operable temperature range from −40 to 110 °C with a resolution of less than 1 mK.
A Structure-Based Iterative Closest Point Using Anderson Acceleration for Point Clouds with Low Overlap
Authors: Zeng Chao , Chen Xiaomei , Zhang Yongtian , Gao Kun
Published: February 2023

The traditional point-cloud registration algorithms require large overlap between scans, which imposes strict constrains on data acquisition. To facilitate registration, the user has to strategically position or move the scanner to ensure proper overlap. In this work, we design a method of feature extraction based on high-level information to establish structure correspondences and an optimization problem. And we rewrite it as a fixed-point problem and apply the Lie algebra to parameterize the transform matrix. To speed up convergence, we introduce Anderson acceleration, an approach enhanced by heuristics. Our model attends to the structural features of the region of overlap instead of the correspondence between points. The experimental results show the proposed ICP method is robust, has a high accuracy of registration on point clouds with low overlap on a laser datasets, and achieves a computational time that is competitive with that of prevalent methods.
Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
Authors: Lian Xing , Zhao Erwei , Zheng Wei , Peng Xiaodong , Li Ang , Zhen Zheng , Wen Yan
Published: February 2023

Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in complex scenes with multiple background edges and noise. Therefore, this study proposes a weighted sparse hyperspectral anomaly detection method. First, using the idea of matrix decomposition in mathematics, the original hyperspectral data matrix is reconstructed into three sub-matrices with low rank, small sparsity and representing noise, respectively. Second, to suppress the noise interference in the complex background, we employed the low-rank, background image as a reference, built a local spectral and spatial dictionary through the sliding window strategy, reconstructed the HSI pixels of the original data, and extracted the sparse coefficient. We proposed the sparse coefficient divergence evaluation index (SCDI) as a weighting factor to weight the sparse anomaly map to obtain a significant anomaly map to suppress the background edge, noise, and other residues caused by decomposition, and enhance the abnormal target. Finally, abnormal pixels are segmented based on the adaptive threshold. The experimental results demonstrate that, on a real-scene hyperspectral dataset with a complicated background, the proposed method outperforms the existing representative algorithms in terms of detection performance.
Investigating Hydroclimatic Variables Trends on the Natural Lakes of Western Greece Using Earth Observation Data
Authors: Gourgouletis Nikolaos , Baltas Evangelos
Published: February 2023

Expected global climate change is allegedly becoming more intense, and the impacts on water resources are being tracked in various hydroclimatic regimes. The present research investigates a hydrologically important area of Greece, where four natural lakes are concentrated. It aims to quantify any potential long-term trends in lake water area, precipitation, and temperature timeseries. Water area timeseries spanning four decades are estimated by the mNDWI from Landsat satellite imagery and used as an index of each lake’s water storage. Precipitation and temperature measurements are obtained from the open access datasets Hydroscope and ERA5-Land, respectively. All of the timeseries were tested seasonally and annually with the Pettitt and Mann–Kendal tests for statistically significant breakpoints and trends detection. No timeseries analysis resulted in a statistically significant (at 0.05 or 0.1 levels) annual or seasonal trend. The hydroclimatic regime over the past forty years in western Greece is found to have been relatively stable. Land use was also assessed to have been relatively unchanging, converging to the overall stability of the local water regime. However, the findings of this research should not be interpreted as a reassurance against climate change, but as a call to further research for the detailed regional and local assessment of climate change and hydroclimatic variability with acknowledged statistical approaches.
An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream
Authors: Fatlawi Hayder K. , Kiss Attila
Published: February 2023

Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk’s samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generating synthetic points for oversampling rather than the actual data points. The evaluation of the proposed models using the Siena EEG dataset showed their ability to improve the performance of several adaptive classifiers. The best results have been obtained using Adaptive Random Forest in which Sensitivity reached 96.83% and Precision reached 99.96%.
High Latitude Ionospheric Gradient Observation Results from a Multi-Scale Network
Authors: Sokolova Nadezda , Morrison Aiden , Jacobsen Knut Stanley
Published: February 2023

In this article, a cluster comprised of eight Continuously Operating Reference Station (CORS) receivers surrounding five supplemental test stations located on much shorter baselines is used to form a composite multi-scale network for the purpose of isolating, extracting, and analyzing ionospheric spatial gradient phenomena. The purpose of this investigation is to characterize the levels of spatial decorrelation between the stations in the cluster during the periods with increased ionospheric activity. The location of the selected receiver cluster is at the auroral zone at night-time (cluster centered at about 69.5° N, 19° E) known to frequently have increased ionospheric activity and observe smaller size of high-density irregularities. As typical CORS networks are relatively sparse, there is a possibility that spatially small-scale ionospheric delay gradients might not be observed by the network/closest receiver cluster but might affect the user, resulting in residual errors affecting system accuracy and integrity. The article presents high level statistical observations based on several hundred manually validated ionospheric spatial gradient events along with low level analysis of specific events with notable temporal/spatial characteristics.
How Accurately Can Wearable Sensors Assess Low Back Disorder Risks during Material Handling? Exploring the Fundamental Capabilities and Limitations of Different Sensor Signals
Authors: Nurse Cameron A. , Elstub Laura Jade , Volgyesi Peter , Zelik Karl E.
Published: February 2023

Low back disorders (LBDs) are a leading occupational health issue. Wearable sensors, such as inertial measurement units (IMUs) and/or pressure insoles, could automate and enhance the ergonomic assessment of LBD risks during material handling. However, much remains unknown about which sensor signals to use and how accurately sensors can estimate injury risk. The objective of this study was to address two open questions: (1) How accurately can we estimate LBD risk when combining trunk motion and under-the-foot force data (simulating a trunk IMU and pressure insoles used together)? (2) How much greater is this risk assessment accuracy than using only trunk motion (simulating a trunk IMU alone)? We developed a data-driven simulation using randomized lifting tasks, machine learning algorithms, and a validated ergonomic assessment tool. We found that trunk motion-based estimates of LBD risk were not strongly correlated (r range: 0.20–0.56) with ground truth LBD risk, but adding under-the-foot force data yielded strongly correlated LBD risk estimates (r range: 0.93–0.98). These results raise questions about the adequacy of a single IMU for LBD risk assessment during material handling but suggest that combining an IMU on the trunk and pressure insoles with trained algorithms may be able to accurately assess risks.
Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking
Authors: Haghi Mostafa , Ershadi Arman , Deserno Thomas M.
Published: February 2023

The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer’s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.
Test–Retest and Between–Device Reliability of Vmaxpro IMU at Hip and Ankle for Vertical Jump Measurement
Authors: Villalon-Gasch Lamberto , Jimenez-Olmedo Jose M. , Olaya-Cuartero Javier , Pueo Basilio
Published: February 2023

The ability to generate force in the lower body can be considered a performance factor in sports. This study aims to analyze the test–retest and between-device reliability related to the location on the body of the inertial measurement unit Vmaxpro for the estimation of vertical jump. Eleven highly trained female athletes performed 220 countermovement jumps (CMJ). Data were simultaneously captured by two Vmaxpro units located between L4 and L5 vertebrae (hip method) and on top of the tibial malleolus (ankle method). Intrasession reliability was higher for ankle (ICC = 0.96; CCC = 0.93; SEM = 1.0 cm; CV = 4.64%) than hip (ICC = 0.91; CCC = 0.92; SEM = 3.4 cm; CV = 5.13%). In addition, sensitivity was higher for ankle (SWC = 0.28) than for the hip method (SWC = 0.40). The noise of the measurement (SEM) was higher than the worthwhile change (SWC), indicating lack of ability to detect meaningful changes. The agreement between methods was moderate (rs = 0.84; ICC = 0.77; CCC = 0.25; SEM = 1.47 cm). Significant differences were detected between methods (−8.5 cm, p < 0.05, ES = 2.2). In conclusion, the location of the device affects the measurement by underestimating CMJ on ankle. Despite the acceptable consistency of the instrument, the results of the reliability analysis reveal a significant magnitude of both random and systematic error. As such, the Vmaxpro should not be considered a reliable instrument for measuring CMJ.
Improvement of Acoustic Models Fused with Lip Visual Information for Low-Resource Speech
Authors: Yu Chongchong , Yu Jiaqi , Qian Zhaopeng , Tan Yuchen
Published: February 2023

Endangered language generally has low-resource characteristics, as an immaterial cultural resource that cannot be renewed. Automatic speech recognition (ASR) is an effective means to protect this language. However, for low-resource language, native speakers are few and labeled corpora are insufficient. ASR, thus, suffers deficiencies including high speaker dependence and over fitting, which greatly harms the accuracy of recognition. To tackle the deficiencies, the paper puts forward an approach of audiovisual speech recognition (AVSR) based on LSTM-Transformer. The approach introduces visual modality information including lip movements to reduce the dependence of acoustic models on speakers and the quantity of data. Specifically, the new approach, through the fusion of audio and visual information, enhances the expression of speakers’ feature space, thus achieving the speaker adaptation that is difficult in a single modality. The approach also includes experiments on speaker dependence and evaluates to what extent audiovisual fusion is dependent on speakers. Experimental results show that the CER of AVSR is 16.9% lower than those of traditional models (optimal performance scenario), and 11.8% lower than that for lip reading. The accuracy for recognizing phonemes, especially finals, improves substantially. For recognizing initials, the accuracy improves for affricates and fricatives where the lip movements are obvious and deteriorates for stops where the lip movements are not obvious. In AVSR, the generalization onto different speakers is also better than in a single modality and the CER can drop by as much as 17.2%. Therefore, AVSR is of great significance in studying the protection and preservation of endangered languages through AI.
Portable TDLAS Sensor for Online Monitoring of CO2 and H2O Using a Miniaturized Multi-Pass Cell
Authors: Gu Mingsi , Chen Jiajin , Zhang Yiping , Tan Tu , Wang Guishi , Liu Kun , Gao Xiaoming , Mei Jiaoxu
Published: February 2023

We designed a tunable diode laser absorption spectroscopy (TDLAS) sensor for the online monitoring of CO2 and H2O concentrations. It comprised a small self-design multi-pass cell, home-made laser drive circuits, and a data acquisition circuit. The optical and electrical parts and the gas circuit were integrated into a portable carrying case (height = 134 mm, length = 388 mm, and width = 290 mm). A TDLAS drive module (size: 90 mm × 45 mm) was designed to realize the function of laser current and temperature control with a temperature control accuracy of ±1.4 mK and a current control accuracy of ±0.5 μA, and signal acquisition and demodulation. The weight and power consumption of the TDLAS system were only 5 kg and 10 W, respectively. Distributed feedback lasers (2004 nm and 1392 nm) were employed to target CO2 and H2O absorption lines, respectively. According to Allan analysis, the detection limits of CO2 and H2O were 0.13 ppm and 3.7 ppm at an average time of 18 s and 35 s, respectively. The system response time was approximately 10 s. Sensor performance was verified by measuring atmospheric CO2 and H2O concentrations for 240 h. Experimental results were compared with those obtained using a commercial instrument LI-7500, which uses non-dispersive infrared technology. Measurements of the developed gas analyzer were in good agreement with those of the commercial instrument, and its accuracy was comparable. Therefore, the TDLAS sensor has strong application prospects in atmospheric CO2 and H2O concentration detection and ecological soil flux monitoring.
Deep Learning Technique-Enabled Web Application Firewall for the Detection of Web Attacks
Authors: Dawadi Babu , Adhikari Bibek , Srivastava Devesh
Published: February 2023

New techniques and tactics are being used to gain unauthorized access to the web that harm, steal, and destroy information. Protecting the system from many threats such as DDoS, SQL injection, cross-site scripting, etc., is always a challenging issue. This research work makes a comparative analysis between normal HTTP traffic and attack traffic that identifies attack-indicating parameters and features. Different features of standard datasets ISCX, CISC, and CICDDoS were analyzed and attack and normal traffic were compared by taking different parameters into consideration. A layered architecture model for DDoS, XSS, and SQL injection attack detection was developed using a dataset collected from the simulation environment. In the long short-term memory (LSTM)-based layered architecture, the first layer was the DDoS detection model designed with an accuracy of 97.57% and the second was the XSS and SQL injection layer with an obtained accuracy of 89.34%. The higher rate of HTTP traffic was investigated first and filtered out, and then passed to the second layer. The web application firewall (WAF) adds an extra layer of security to the web application by providing application-level filtering that cannot be achieved by the traditional network firewall system.
Variable-Sensitivity Force Sensor Based on Structural Modification
Authors: Takashima Kazuto , Ota Kengo , Cho Hiroki
Published: February 2023

Force sensors are used in a wide variety of fields. They require different measurement ranges and sensitivities depending on the operating environment because there is generally a trade-off between measurement range and sensitivity. In this study, we developed a variable-sensitivity, variable-measurement-range force sensor that utilizes structural modification, namely changes in the distance between the force application point and the detection area, and changes in the cross-sectional area. The use of shape-memory materials allows the sensor structure to be easily changed and fixed by controlling the temperature. First, we describe the theory of the proposed sensor. Then, we present prototypes and the experimental methods used to verify the performance of the sensor. We fabricated the prototypes by attaching two strain gauges to two sides of a shape-memory alloy and shape-memory polymer plates. Experiments on the prototypes show that the relationship between the applied force and the detected strain can be changed by bending the plate. This allows the sensitivity and measurement range of the sensor to be changed.
LPWAN Cyber Security Risk Analysis: Building a Secure IQRF Solution
Authors: Bouzidi Mohammed , Amro Ahmed , Dalveren Yaser , Alaya Cheikh Faouzi , Derawi Mohammad
Published: February 2023

Low-power wide area network (LPWAN) technologies such as IQRF are becoming increasingly popular for a variety of Internet of Things (IoT) applications, including smart cities, industrial control, and home automation. However, LPWANs are vulnerable to cyber attacks that can disrupt the normal operation of the network or compromise sensitive information. Therefore, analyzing cybersecurity risks before deploying an LPWAN is essential, as it helps identify potential vulnerabilities and threats as well as allowing for proactive measures to be taken to secure the network and protect against potential attacks. In this paper, a security risk analysis of IQRF technology is conducted utilizing the failure mode effects analysis (FMEA) method. The results of this study indicate that the highest risk corresponds to four failure modes, namely compromised end nodes, a compromised coordinator, a compromised gateway and a compromised communication between nodes. Moreover, through this methodology, a qualitative risk evaluation is performed to identify potential security threats in the IQRF network and propose countermeasures to mitigate the risk of cyber attacks on IQRF networks.
Laboratory Radiometric Calibration Technique of an Imaging System with Pixel-Level Adaptive Gain
Authors: Li Ze , Wei Jun , Huang Xiaoxian , Xu Feifei
Published: February 2023

In a routine optical remote sensor, there is a contradiction between the two requirements of high radiation sensitivity and high dynamic range. Such a problem can be solved by adopting pixel-level adaptive-gain technology, which is carried out by integrating multilevel integrating capacitors into photodetector pixels and multiple nondestructive read-outs of the target charge with a single exposure. There are four gains for any one pixel: high gain (HG), medium gain (MG), low gain (LG), and ultralow gain (ULG). This study analyzes the requirements for laboratory radiometric calibration, and we designed a laboratory calibration scheme for the distinctive imaging method of pixel-level adaptive gain. We obtained calibration coefficients for general application using one gain output, and the switching points of dynamic range and the proportional conversion relationship between adjacent gains as the adaptive-gain output. With these results, on-orbit quantification applications of spectrometers adopting pixel-level automatic gain adaptation technology are guaranteed.
An IoT-Based GeoData Production System Deployed in a Hospital
Authors: Samama Nel , Patarot Alexandre
Published: February 2023

Navigation in large hospitals remains a challenge, especially for patients, visitors and, in some cases, for staff, but in particular it is notable in the case of tracking ambulatory equipment. Current techniques generally seek to reproduce what outdoor navigation systems provide, i.e., “good” accuracy. In many cases, especially in hospitals, reliability is much more important than accuracy. We show that it is possible to realize a simple, reliable system with a low accuracy, but which perfectly fulfills the task assigned in the particular case of tracking stretchers. Optimizing the use of hospital equipment requires the knowledge of its movement. The possibility to access equipment location in real time as well as on the knowledge of the time necessary to move it between two locations allows to predict or to estimate the load and possibly to scale the necessary number of stretchers, and thus the availability of the stretcher bearers. In this paper, an approach of the real-time location of these devices is proposed, and it is called “symbolic”. The principle is described, as well as the practical implementation and the data that can be retrieved. In the second part, an analysis of the results obtained is provided in two directions: the location of stretchers and the determination of travel times. The methodology followed is described, and it is shown that a correct positioning rate of 90% is reached, which is slightly lower than expected, explained by the chosen practical implementation. Moreover, the average error on the determination of travel times is approximately ten seconds on 2 to 7 min trips. The “reliability” (the terminology of which is discussed at the end of the paper) of the results is related to the simplicity of the approach.
A YOLOX-Based Automatic Monitoring Approach of Broken Wires in Prestressed Concrete Cylinder Pipe Using Fiber-Optic Distributed Acoustic Sensors
Authors: Ma Baolong , Gao Ruizhen , Zhang Jingjun , Zhu Xinmin
Published: February 2023

Wire breakage is a major factor in the failure of prestressed concrete cylinder pipes (PCCP). In the presented work, an automatic monitoring approach of broken wires in PCCP using fiber-optic distributed acoustic sensors (DAS) is investigated. The study designs a 1:1 prototype wire break monitoring experiment using a DN4000 mm PCCP buried underground in a simulated test environment. The test combines the collected wire break signals with the previously collected noise signals in the operating pipe and transforms them into a spectrogram as the wire break signal dataset. A deep learning-based target detection algorithm is developed to detect the occurrence of wire break events by extracting the spectrogram image features of wire break signals in the dataset. The results show that the recall, precision, F1 score, and false detection rate of the pruned model reach 100%, 100%, 1, and 0%, respectively; the video detection frame rate reaches 35 fps and the model size is only 732 KB. It can be seen that this method greatly simplifies the model without loss of precision, providing an effective method for the identification of PCCP wire break signals, while the lightweight model is more conducive to the embedded deployment of a PCCP wire break monitoring system.
Accurate 3D to 2D Object Distance Estimation from the Mapped Point Cloud Data
Authors: Usmankhujaev Saidrasul , Baydadaev Shokhrukh , Kwon Jang Woo
Published: February 2023

Distance estimation is one of the oldest and most challenging tasks in computer vision using only a monocular camera. This can be challenging owing to the presence of occlusions, noise, and variations in the lighting, texture, and shape of objects. Additionally, the motion of the camera and objects in the scene can affect the accuracy of the distance estimation. Various techniques have been proposed to overcome these challenges, including stereo matching, structured light, depth from focus, depth from defocus, depth from motion, and time of flight. The addition of information from a high-resolution 3D view of the surroundings simplifies the distance calculation. This paper describes a novel distance estimation method that operates with converted point cloud data. The proposed method is a reliable map-based bird’s eye view (BEV) that calculates the distance to the detected objects. Using the help of the Euler-region proposal network (E-RPN) model, a LiDAR-to-image-based method for metric distance estimation with 3D bounding box projections onto the image was proposed. We demonstrate that despite the general difficulty of the BEV representation in understanding features related to the height coordinate, it is possible to extract all parameters characterizing the bounding boxes of the objects, including their height and elevation. Finally, we applied the triangulation method to calculate the accurate distance to the objects and statistically proved that our methodology is one of the best in terms of accuracy and robustness.
The Developmental Course of Parental Time Investments in Children from Infancy to Late Adolescence
Authors: Wikle Jocelyn , Cullen Clara
Published: February 2023

This study evaluated parent-child time from a child’s infancy to age 18 for mothers and fathers. Parent-child time remains a key input in child development. The theory on intergenerational transfers from parents to children posits that mother-child time and father-child time may adjust as children grow. This study used the nationally representative American Time Use Survey (2003–2019; N = 148,576) to study children ages 0–18 in a pooled cross-sectional sample. Using least squares regression, the study traced out parent-child contact, playing time, quality time, and one-on-one time, and tested differences between mothers and fathers. Mothers and fathers have provided substantial time investments in children of all ages. When children were young, mothers spent more time with children compared to fathers, highlighting a need for more nuanced discussions about differences in parenting between mothers and fathers. One-on-one time remained stable through late childhood and adolescence as parents prioritized focused interactions as children aged.
Estimating the Impact of Digital Nomads’ Sustainable Responsibility on Entrepreneurial Self-Efficacy
Authors: Mourato Inês , Dias Álvaro , Pereira Leandro
Published: February 2023

Digital nomads live outside of the classical organizational borders and can be seen as ‘contemporary entrepreneurs’ who bring disruptive business models into different industries, giving value to different working cultures and different types of capital. Because they are operating out of their home country, their social responsibility as entrepreneurs may have different implications. This study aims to explore the outcomes of digital nomads’ social responsibility in terms of self-efficacy and innovation. To test the hypothesis model, structural equation modeling (SEM) was used to analyze survey data. The results show that tourism firms should always have in mind their social responsibility levels to attract this target niche and should pay attention to the fact that they are mostly solo travelers, so they value the sense of community of a place and its legal, bureaucratic, and flexible terms of living. Findings also show that social and environmental concerns are more associated to social self-efficacy than to self-efficacy. The results complement existing research by helping tourism businesses and destination managers to understand the implications of the digital nomads’ social responsibility.
The Impact of Work-Related Barriers on Job Satisfaction of Practitioners Working with Migrants
Authors: Brendel Hannah , Sbaa Maha Yomn , Zappala Salvatore , Puzzo Gabriele , Pietrantoni Luca
Published: February 2023

The work environment of practitioners working with migrants may be very demanding as they are frequently exposed to the sad narratives of such a vulnerable population, the lack of professional support, or the frequent change of policies towards refugees and asylum seekers. Little research has been conducted to explore the job satisfaction of practitioners working with migrants and the organizational characteristics that can hinder or promote such satisfaction. The present study investigated the relationship between work-related barriers (i.e., intra-organizational, legal, and interaction-related barriers) and job satisfaction of practitioners working with migrants, also testing if perceived organizational efficacy is mediating this relation. This study was part of a larger European funded project, and participants were 428 First-Line Practitioners working with migrants in various sectors (e.g., social and health services, immigration and asylum services, or border guards) and working in several European countries. Data were collected through an online survey in the period between October and December 2020. Results showed that intra-organizational and legal barriers had a negative impact on job satisfaction, while interaction-related barriers did not have any. Perceived organizational efficacy mediated the relationship between two work-related barriers (intra-organizational and interaction-related barriers) and job satisfaction. These findings suggest that organizations working with migrants should focus on addressing intra-organizational and legal barriers, and on implementing actions aimed at building employees’ collective efficacy beliefs to improve their job satisfaction.
Repurposing Hazelnut Waste Products for a Sustainable Economy: A Metabolomic Analysis of Cuticles and Shells to Highlight Their Antioxidant Potential and Inhibitory Activity against Verocytotoxic Escherichia coli
Authors: Frazzini Sara , Zuorro Antonio , Panseri Sara , Pavlovic Radmila , Sgoifo Rossi Carlo Angelo , Rossi Luciana
Published: February 2023

This study evaluated the functional components of hazelnut waste products (cuticles and shells) for potential use in the circular economy. HPLC-HRMS and ABTS assay were used to determine the molecules of waste products with functional properties and antioxidant capacity, respectively. The antioxidant capacity of hazelnut cuticles and shells was tested using two different methods of extraction (EtOH 50/50 and acetone 40/70) by ABTS radical cation decolorization assay. The growth inhibition effect of different extracts against porcine O138 E. coli F18+ was evaluated by the microdilution bacterial growth method and confirmed with the determination of minimal inhibitory concentration (MIC). The results showed that acetone extraction produced a higher yield of metabolites compared to ethanol extraction, and the cuticles exhibited higher antioxidant and antimicrobial potential. The acetone/water extraction led to the identification of 738 signals compared to 453 detected after EtOH/water extraction. Through metabolomic analysis, it was found that the cuticles presented a much higher quantitative and qualitative polyphenolic profile than did the shells. In addition, the ABTS assay revealed that the cuticles had a higher antioxidant capacity than did the shells. Thus, we believe that hazelnut-derived waste products show a high potential for use in a circular and sustainable economy.
Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF
Authors: Darwish Hiba H. , Al-Quraan Ayman
Published: February 2023

Estimating wind energy at a specific wind site depends on how well the real wind data in that area can be represented using an appropriate distribution function. In fact, wind sites differ in the extent to which their wind data can be represented from one region to another, despite the widespread use of the Weibull function in representing the wind speed in various wind locations in the world. In this study, a new probability distribution model (normal PDF) was tested to implement wind speed at several wind locations in Jordan. The results show high compatibility between this model and the wind resources in Jordan. Therefore, this model was used to estimate the values of the wind energy and the extracted energy of wind turbines compared to those obtained by the Weibull PDF. Several artificial intelligence techniques were used (GA, BFOA, SA, and a neuro-fuzzy method) to estimate and predict the parameters of both the normal and Weibull PDFs that were reflected in conjunction with the actual observed data of wind probabilities. Afterward, the goodness of fit was decided with the aid of two performance indicators (RMSE and MAE). Surprisingly, in this study, the normal probability distribution function (PDF) outstripped the Weibull PDF, and interestingly, BFOA and SA were the most accurate methods. In the last stage, machine learning was used to classify and predict the error level between the actual probability and the estimated probability based on the trained and tested data of the PDF parameters. The proposed novel methodology aims to predict the most accurate parameters, as the subsequent energy calculation phases of wind depend on the proper selection of these parameters. Hence, 24 classifier algorithms were used in this study. The medium tree classifier shows the best performance from the accuracy and training time points of view, while the ensemble-boosted trees classifier shows poor performance regarding providing correct predictions.