bluuebunny/crossref_metadata_2025 huggingface dataset:
Functional Separators for Long-Life and Safe Li Metal Batteries: A Minireview
Authors: Li Yanyan , Zhao Yu , Yang Yong , Wang Zhijie , Yang Qin , Deng Jiaojiao
Published: October 2022

Lithium (Li) metal batteries (LMBs) have received extensive research attention in recent years because of their high energy density. However, uncontrollable Li dendrite growth deteriorates the battery life and brings about severe safety hazards. The rational design of battery separators is an effective approach to regulate uniform Li metal deposition towards boosted cycle life and safety of LMBs. Herein, we review the recent research progress concerning this issue, including mechanically strengthened separator fabrication, functional separator construction towards regulated Li ion deposition, and flame-retardant separator design. Moreover, the key issues and prospects of optimal design of separators are clarified for future development. This minireview is expected to bring new insight into developing advanced separators for long-life and safe LMBs.
Effects of Ion Cyclotron Frequencies on Human Resistance and Reactance in 31 Healthy Subjects
Authors: Liguori Aldo , Brizhik Larissa , Liguori Stefano , Silli Laura , Bangrazi Sergio , Petti Filomena , Pinti Medardo , Pistelli Maria Ilaria , Giuliani Livio
Published: October 2022

In order to test the theory of Brizhik et al. about the dynamic allocation of acupuncture meridians on human body and their role as hydric paths for solitons, we tested the effect of both acupuncture and exposure to extremely low frequencies (ELF) tuned with suitable ion cyclotron frequencies. The similarity of the effects, inducing variations of body impedance measured in well-known acupuncture points, up to the interchangeability and the synergy of the two treatments, the mechanic and the electromagnetic ones, turns to be evidence of the validity of the theory. Resistance and reactance variations have been detected in a group of 31 healthy volunteers before and after stimulation with a standard sequence of cyclotron frequencies, emitted from an innovative electromagnetic therapy (EMT) device. These variations were then compared with the variations produced by the well-known percutaneous stimuli of mechanical and piezoelectric nature, and, in particular in this work, acupuncture. Our results show that the observed variations can be considered as significant in both groups: cyclotron and acupuncture. The greater variations brought about by the cyclotron treatment stand out clearly.
Finding Mountains with Music: Growth and Spiritual Transcendence in a U.S. Prison
Authors: Rhodd Anthony R. , Cohen Mary L.
Published: October 2022

Resulting in pervasive feelings of despair, the culture of incarceration in the U.S. relies on punitive correctional strategies such as solitary confinement to control the behavior of incarcerated individuals. Inevitably, correctional culture which focuses primarily on punishment is dysfunctional, rife with gang violence, drug use, suicide, and violence perpetuated by and against staff. Our dialogic essay is voiced by (a) a currently incarcerated, Native American person who has survived solitary confinement and the spiritual drain of castigating correctional culture; and (b) a music educator who founded a prison choir for both non-incarcerated and incarcerated individuals in an effort to erode and transform some of the revengeful structures of US incarceration. We draw from Indigenous educator, language specialist, and member of the Lil’wat First Nation, Dr. Lorna Williams’ research on Indigenous Knowledge in our efforts to understand the relationships among group singing, spirituality, and our experiences in the Oakdale prison choir. Our dialogue charts a search for spiritual healing in the unsympathetic atmosphere of prison and offers an experience-based account of ways in which group singing can function as a medium of spiritual healing and growth in environments of conflict.
Breaking the Silence: An Islamic Legal Approach to Facilitating Reporting and Testimony by Muslim Victims and Witnesses of Sexual Crimes
Authors: Lowe Julie
Published: October 2022

Many Muslims hold the doctrine of the legal schools (madhāhib) in high esteem. As such, the schools’ approaches to rape and sexual assault may impact the behaviour of Muslim victims and witnesses. Through an examination of the legal rules that regulate rape and sexual assault in fiqh and fatwā works associated with the four Sunni schools and Ibn Ḥazm, I aim to determine whether the relevant rules may interfere with the willingness of Muslim victims and witnesses to report or testify to sexual crimes. I argue that although the jurists’ prosecution of sexual assault as a discretionary offence (taʿzīr) is compatible with reporting and testimony, their prosecution of rape as coerced illicit intercourse (zinā), usurpation (ghaṣb), or banditry (ḥirāba) silences victims and witnesses. Further, rules related to financial compensation do not encourage victims to come forward. Reclassifying rape as a discretionary offence would better promote reporting and testimony by victims and witnesses. The central role of ijtihād in creating the historical rules on rape, the jurists’ intent behind those rules, and modern knowledge regarding the reality of rape support this reclassification. Fully resolving issues related to civil compensation is difficult without broader reforms of Islamic tort law.
The New Testament and Workplace Mobbing: Structuring of Victims’ Experiences
Authors: Vveinhardt Jolita , Deikus Mykolas
Published: October 2022

Both practitioners and researchers confirm the utility of spiritual assistance for victims of violence, but the opportunities for religious spiritual assistance for persons who have experienced workplace mobbing have remained little explored in recent years. Although it is acknowledged that biblical narratives can help to structure personal experiences in coping with the traumatic consequences of violence, the main problem is the indefiniteness of the systematic application of specific texts in the process of assisting victims of workplace mobbing. In order to fill this gap, the analysis of the literature on workplace mobbing was performed and based on the identified essential features of the phenomenon, the types of response to violent behaviour in the Gospel of Luke were distinguished. Links between workplace mobbing and the gospel as well as guidelines for their practical application are discussed.
Towards a Society of Stable Nones: Lifelong Non-Denominationalism as the Prevailing Pattern in East Germany
Authors: Müller Olaf , Porada Chiara
Published: October 2022

The increase in the number of people who do not belong to any religion (“nones”) has long been observed in many societies in the Northern hemisphere. This process is already well advanced in East Germany, where the proportion of “stable nones”, i.e., those who grew up without any ties to religion from childhood onwards, has become particularly large and will most likely form the majority in the near future. Given the sociocultural relevance of this group, it is worth taking a closer look at them. In this paper, we examine how far they differ in terms of their familial religious conditions of origin and their present-day religiosity from the nones who still grew up in a religious-denominational tradition (“leavers”), and also from those who still have ties to a denomination (“affiliates”). Finally, we discuss the consequences for the future development of the religious field that arise from the fact that the group of “stable nones” will constitute the majority.
A New Seismic Inversion Scheme Using Fluid Dispersion Attribute for Direct Gas Identification in Tight Sandstone Reservoirs
Authors: Guo Zhiqi , Zhao Danyu , Liu Cai
Published: October 2022

Sufficient gas accumulation is an essential factor that controls the effective development of tight sandstone gas reservoirs that are generally characterized by low porosity and permeability. Seismic methods are important for predicting potential gas areas in tight sandstones. However, the complex relationships between rock physical properties and gas saturation make gas enrichment estimation with seismic methods challenging. Nonetheless, seismic velocity dispersion using a wave-induced fluid flow mechanism can enable gas identification by utilizing the associated dispersion attributes. This paper proposes a method for improved gas identification using a new fluid dispersion attribute obtained by incorporating the decoupled fluid-solid seismic amplitude variation with offset representation into the frequency-dependent inversion scheme. Numerical analyses and synthetic data tests confirmed the enhanced sensitivity of the fluid dispersion attribute to gas saturation compared to the conventionally used compressional wave velocity dispersion attribute. Field data applications further validated the ability of the proposed fluid dispersion attribute to improve gas prediction in tight sandstone reservoirs. The results of the measurements enable rational interpretation of the geological significance of assessments of reservoir properties from gas-producing wellbores. The proposed fluid dispersion attribute is a reliable indicator for gas prediction and represents a useful tool for characterizing tight sandstone reservoirs.
Drone-Borne Electromagnetic (DR-EM) Surveying in The Netherlands: Lab and Field Validation Results
Authors: Karaoulis Marios , Ritsema Ipo , Bremmer Chris , De Kleine Marco , Oude Essink Gualbert , Ahlrichs Edvard
Published: October 2022

In the past decade, drones have become available and affordable for civil applications, including mapping and monitoring the Earth with geophysical sensors. In 2017 and 2019, the feasibility of executing frequency domain electromagnetic (FDEM) surveys using an off-the-shelf drone was investigated at Deltares Institute. This paper reports firstly the preparatory tests executed to determine the optimal instrumental configuration, flight path, data processing and inversion schemes and secondly the three field validation tests executed to demonstrate the feasibility of the drone-borne electromagnetic survey in real-scale applications. At several test sites, the optimal configuration of the drone and electromagnetic instruments, such as the mounting device and distance of the electromagnetic (EM) sensor with respect to the drone, the flight altitude, the coil separation and frequency of the EM source, efficiency and safety, and the assemblage of instrument and drone data were investigated. This has resulted in a robust method to acquire accurate and repeatable in-phase, quadrature and apparent resistivity data, and a workflow for data correction, processing and inversion scheme was developed. During those tests, three EM instruments were tested. The drone-borne electromagnetic (DR-EM) system has the ability and efficacy to fly over inaccessible areas and surface water. Compared to helicopter-borne electromagnetic surveys, the spatial resolution is much higher, which allows very detailed 3D mapping of subsurface targets, and the survey costs are relatively low. Repeated drone-borne electromagnetic (DR-EM) surveys allow low-cost monitoring of local changes in water saturation and salinity.
Interannual, Seasonal, and Monthly Variability of Sea Surface Temperature Fronts in Offshore China from 1982–2021
Authors: Zhao Linhong , Yang Dingtian , Zhong Rong , Yin Xiaoqing
Published: October 2022

The offshore China (OC) region is a significant sea area in the Western Pacific and many researchers have been interested in the distribution of sea surface temperature (SST) fronts in this area. In this study, the Cayula and Cornillon single image edge detection algorithm was used to detect SST fronts using the Daily Optimum Interpolation Sea Surface Temperature data from 1982 to 2021. The results revealed that there are eighteen SST fronts in OC— three in the Bohai Sea, seven in the Yellow Sea, two in the East China Sea, five in the South China Sea, one in the Pacific Ocean east of Taiwan province, China—and among them a new front was detected in the Yellow Sea and named the Yellow Sea Ring Front. The frequency of most fronts showed a tendency of initially increasing and then decreasing from January to September, followed by a trend of growing steadily from October to December. The frequency of a few fronts showed a decreasing tendency from January to September and an increasing tendency from October to December. The frequency of most fronts is highest in winter and lowest in summer. In spring and autumn, the frequency of most fronts is lower than that in winter and higher than that in summer. The annual average frontal probability of five-ninths of the fronts showed an upward trend, and the annual average frontal probability value of one-third of the fronts showed a downward trend. The rest of the fronts showed a stable trend. The results of this paper also showed that the Liaodong Bay Front and the Bohai-Laizhou Bay Front did not form a complete front, as previously reported. In addition, the frontal probability of the Bohai Front to the north of 39° N was in the tendency of decreasing.
Haiti Earthquake (Mw 7.2): Magnetospheric–Ionospheric–Lithospheric Coupling during and after the Main Shock on 14 August 2021
Authors: D’Angelo Giulia , Piersanti Mirko , Battiston Roberto , Bertello Igor , Carbone Vincenzo , Cicone Antonio , Diego Piero , Papini Emanuele , Parmentier Alexandra , Picozza Piergiorgio , Plainaki Christina , Recchiuti Dario , Sparvoli Roberta , Ubertini Pietro
Published: October 2022

In the last few decades, the efforts of the scientific community to search earthquake signatures in the atmospheric, ionospheric and magnetospheric media have grown rapidly. The increasing amount of good quality data from both ground stations and satellites has allowed for the detections of anomalies with high statistical significance such as ionospheric plasma density perturbations and/or atmospheric temperature and pressure changes. However, the identification of a causal link between the observed anomalies and their possible seismic trigger has so far been prevented by difficulties in the identification of confounders (such as solar and atmospheric activity) and the lack of a global analytical lithospheric–atmospheric–magnetospheric model able to explain (and possibly forecast) any anomalous signal. In order to overcome these problems, we have performed a multi-instrument analysis of a low-latitude seismic event by using high-quality data from both ground bases and satellites and preserving their statistical significance. An earthquake (Mw = 7.2) occurred in the Caribbean region on 14 August 2021 under both solar quiet and fair weather conditions, thus proving an optimal case study to reconstruct the link between the lithosphere, atmosphere, ionosphere, and magnetosphere. The good match between the observations and novel magnetospheric–ionospheric–lithospheric coupling (M.I.L.C.) modeling of the event confirmed that the fault break generated an atmospheric gravity wave that was able to mechanically perturb the ionospheric plasma density, in turn triggering a variation in the magnetospheric field line resonance frequency.
A Novel Error Criterion of Fundamental Matrix Based on Principal Component Analysis
Authors: Bian Yuxia , Fang Shuhong , Zhou Ye , Wu Xiaojuan , Zhen Yan , Chu Yongbin
Published: October 2022

Estimating the fundamental matrix (FM) using the known corresponding points is a key step for three-dimensional (3D) scene reconstruction, and its uncertainty directly affects camera calibration and point-cloud calculation. The symmetric epipolar distance is the most popular error criterion for estimating FM error, but it depends on the accuracy, number, and distribution of known corresponding points and is biased. This study mainly focuses on the error quantitative criterion of FM itself. First, the calculated FM process is reviewed with the known corresponding points. Matrix differential theory is then used to derive the covariance equation of FMs in detail. Subsequently, the principal component analysis method is followed to construct the scalar function as a novel error criterion to measure FM error. Finally, three experiments with different types of stereo images are performed to verify the rationality of the proposed method. Experiments found that the scalar function had approximately 90% correlation degree with the Manhattan norm, and greater than 80% with the epipolar geometric distance. Consequently, the proposed method is also appropriate for estimating FM error, in which the error ellipse or normal distribution curve is the reasonable error boundary of FM. When the error criterion value of this method falls into a normal distribution curve or an error ellipse, its corresponding FM is considered to have less error and be credible. Otherwise, it may be necessary to recalculate an FM to reconstruct 3D models.
Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest
Authors: Brodić Nenad , Cvijetinović Željko , Milenković Milutin , Kovačević Jovan , Stančić Nikola , Mitrović Momir , Mihajlović Dragan
Published: October 2022

Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km × 4 km. The classification model’s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (κ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a κ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.
Weakly Supervised Object Detection for Remote Sensing Images: A Survey
Authors: Fasana Corrado , Pasini Samuele , Milani Federico , Fraternali Piero
Published: October 2022

The rapid development of remote sensing technologies and the availability of many satellite and aerial sensors have boosted the collection of large volumes of high-resolution images, promoting progress in a wide range of applications. As a consequence, Object detection (OD) in aerial images has gained much interest in the last few years. However, the development of object detectors requires a massive amount of carefully labeled data. Since annotating datasets is very time-consuming and may require expert knowledge, a consistent number of weakly supervised object localization (WSOL) and detection (WSOD) methods have been developed. These approaches exploit only coarse-grained metadata, typically whole image labels, to train object detectors. However, many challenges remain open due to the missing location information in the training process of WSOD approaches and to the complexity of remote sensing images. Furthermore, methods studied for natural images may not be directly applicable to remote sensing images (RSI) and may require carefully designed adaptations. This work provides a comprehensive survey of the recent achievements of remote sensing weakly supervised object detection (RSWSOD). An analysis of the challenges related to RSWSOD is presented, the advanced techniques developed to improve WSOD are summarized, the available benchmarking datasets are described and a discussion of future directions of RSWSOD research is provided.
Influence of Stratospheric Ozone Changes on Stratospheric Temperature Trends in Recent Decades
Authors: Zhou Lingyu , Xia Yan , Zhao Chuanfeng
Published: October 2022

Associated with the recovery of stratospheric ozone, stratospheric cooling has decelerated since the late 1990s. This study investigates the contribution of ozone changes to the long-term stratospheric temperature trends in recent decades using satellite observations and model simulations. Observational analysis shows that total column ozone experienced little depletion in the Northern Hemisphere (NH) and weak recovery in the Southern Hemisphere (SH) in the period 1998–2020. It is found that the cease of stratospheric ozone depletion has reduced the stratospheric cooling from 1998 onwards, especially in the summer hemisphere. The correlation analysis indicates that the lower-stratospheric temperature is primarily regulated by ozone changes. The ozone recovery in the SH is associated with the weak warming in the lower stratosphere in the period 1998–2020 in summer. The impact of ozone changes is further isolated in the ozone-only experiments from CMIP6. We find that ozone depletion results in significant cooling in the summer hemisphere in the period 1979–1997, especially in the upper and lower stratosphere, while ozone recovery leads to significant warming in the summer hemisphere in the period 1998–2020 in the upper stratosphere. Our results also suggest that the wave-mean flow interactions associated with stratospheric ozone variations may play an important role in regulating the strength of polar vortex in winter.
A Change Detection Method Based on Multi-Scale Adaptive Convolution Kernel Network and Multimodal Conditional Random Field for Multi-Temporal Multispectral Images
Authors: Feng Shou , Fan Yuanze , Tang Yingjie , Cheng Hao , Zhao Chunhui , Zhu Yaoxuan , Cheng Chunhua
Published: October 2022

Multispectral image change detection is an important application in the field of remote sensing. Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. The multi-scale adaptive kernel network (MSAK-Net) extends the encoding path of the U-Net, and designs a weight-sharing bilateral encoding path, which simultaneously extracts independent features of bi-temporal multispectral images without introducing additional parameters. A selective convolution kernel block (SCKB) that can adaptively assign weights is designed and embedded in the encoding path of MSAK-Net to extract multi-scale features in images. MSAK-Net retains the skip connections in the U-Net, and embeds an upsampling module (UM) based on the attention mechanism in the decoding path, which can give the feature map a better expression of change information in both the channel dimension and the spatial dimension. Finally, the multimodal conditional random field (MCRF) is used to smooth the detection results of the MSAK-Net. Experimental results on two public multispectral datasets indicate the effectiveness and robustness of the proposed method when compared with other state-of-the-art methods.
An Enhanced Architecture to Resolve Public-Key Cryptographic Issues in the Internet of Things (IoT), Employing Quantum Computing Supremacy
Authors: Shamshad Shuhab , Riaz Farina , Riaz Rabia , Rizvi Sanam Shahla , Abdulla Shahab
Published: October 2022

The Internet of Things (IoT) strongly influences the world economy; this emphasizes the importance of securing all four aspects of the IoT model: sensors, networks, cloud, and applications. Considering the significant value of public-key cryptography threats on IoT system confidentiality, it is vital to secure it. One of the potential candidates to assist in securing public key cryptography in IoT is quantum computing. Although the notion of IoT and quantum computing convergence is not new, it has been referenced in various works of literature and covered by many scholars. Quantum computing eliminates most of the challenges in IoT. This research provides a comprehensive introduction to the Internet of Things and quantum computing before moving on to public-key cryptography difficulties that may be encountered across the convergence of quantum computing and IoT. An enhanced architecture is then proposed for resolving these public-key cryptography challenges using SimuloQron to implement the BB84 protocol for quantum key distribution (QKD) and one-time pad (OTP). The proposed model prevents eavesdroppers from performing destructive operations in the communication channel and cyber side by preserving its state and protecting the public key using quantum cryptography and the BB84 protocol. A modified version is introduced for this IoT situation. A traditional cryptographic mechanism called “one-time pad” (OTP) is employed in hybrid management.
Hyperspectral Image Data and Waveband Indexing Methods to Estimate Nutrient Concentration on Lettuce (Lactuca sativa L.) Cultivars
Authors: Eshkabilov Sulaymon , Stenger John , Knutson Elizabeth N. , Küçüktopcu Erdem , Simsek Halis , Lee Chiwon W.
Published: October 2022

Lettuce is an important vegetable in the human diet and is commonly consumed for salad. It is a source of vitamin A, which plays a vital role in human health. Improvements in lettuce production will be needed to ensure a stable and economically available supply in the future. The influence of nitrogen (N), phosphorus (P), and potassium (K) compounds on the growth dynamics of four hydroponically grown lettuce (Lactuca sativa L.) cultivars (Black Seeded Simpson, Parris Island, Rex RZ, and Tacitus) in tubs and in a nutrient film technique (NFT) system were studied. Hyperspectral images (HSI) were captured at plant harvest. Models developed from the HSI data were used to estimate nutrient levels of leaf tissues by employing principal component analysis (PCA), partial least squares regression (PLSR), multivariate regression, and variable importance projection (VIP) methods. The optimal wavebands were found in six regions, including 390.57–438.02, 497–550, 551–600, 681.34–774, 802–821, and 822–838 nm for tub-grown lettuces and four regions, namely 390.57–438.02, 497–550, 551–600, and 681.34–774 nm for NFT-system-grown lettuces. These fitted models’ levels showed high accuracy (R2=0.85−0.99) in estimating the growth dynamics of the studied lettuce cultivars in terms of nutrient content. HSI data of the lettuce leaves and applied N solutions demonstrated a direct positive correlation with an accuracy of 0.82–0.99 for blue and green regions in 400–575 nm wavebands. The results proved that, in most of the tested multivariate regression models, HSI data of freshly cut leaves correlated well with laboratory-measured data.
Non-Contact VITAL Signs Monitoring of a Patient Lying on Surgical Bed Using Beamforming FMCW Radar
Authors: Lim Sungmook , Jang Gwang Soo , Song Wonyoung , Kim Baek-hyun , Kim Dong Hyun
Published: October 2022

Respiration and heartrates are important information for surgery. When the vital signs of the patient lying prone are monitored using radar installed on the back of the surgical bed, the surgeon’s movements reduce the accuracy of these monitored vital signs. This study proposes a method for enhancing the monitored vital sign accuracies of a patient lying on a surgical bed using a 60 GHz frequency modulated continuous wave (FMCW) radar system with beamforming. The vital sign accuracies were enhanced by applying a fast Fourier transform (FFT) for range and beamforming which suppress the noise generated at different ranges and angles from the patient’s position. The experiment was performed for a patient lying on a surgical bed with or without surgeon. Comparing a continuous-wave (CW) Doppler radar, the FMCW radar with beamforming improved almost 22 dB of signal-to-interference and noise ratio (SINR) for vital signals. More than 90% accuracy of monitoring respiration and heartrates was achieved even though the surgeon was located next to the patient as an interferer. It was analyzed using a proposed vital signal model included in the radar IF equation.
Co-Learning Computational and Design Thinking Using Educational Robotics: A Case of Primary School Learners in Namibia
Authors: Shipepe Annastasia , Uwu-Khaeb Lannie , De Villiers Carmen , Jormanainen Ilkka , Sutinen Erkki
Published: October 2022

In a two-day educational robotics workshop in a Namibian primary boarding school, learners with no programming skills managed to apply both computational and design thinking skills with the aid of educational robotics. Educational robotics has proved to be an area which enhances learning both computational thinking and design thinking. An educational robotics (ER) workshop focusing on Arduino robotics technologies was conducted with primary school learners at Nakayale Private Academy. Observation methods through watching, listening and video recordings were used to observe and analyze how the learners were interacting throughout the workshop. Based on the results, it was concluded that this approach could be applied in classrooms to enable the primary school learners apply computational and design thinking in preparation of becoming the producers and not only the consumers of the 4IR technologies.
Transient Thermal Analysis Model of Damaged Bearing Considering Thermo-Solid Coupling Effect
Authors: Sun Yali , Zhang Chong , Zhao Xing , Liu Xiaodong , Lu Chang , Fei Jiyou
Published: October 2022

As one of the important parameters of bearing operation, temperature is a key metric to diagnose the state of service of a bearing. However, there are still some shortcomings in the study of the temperature variation law for damaged bearings. In this paper, according to the structural characteristics of bearings, the influence law of thermal-solid coupling effect on bearing structure is considered, and a novel transient temperature analysis model of damaged bearings is established. First, a quasi-static analysis of the bearing is performed to obtain the variation laws of the key parameters of the bearing under thermal expansion. Then, the load variation law of the bearing under the condition of damage is discussed, and the heat generation and heat transfer of the damaged bearing during operation are studied. Based on the thermal grid method, a transient temperature analysis model of the damaged bearing is developed. Finally, the model is tested experimentally and the influence of the rotate speed and load on the bearing temperature variation is analyzed. The results show that the established model can effectively predict the temperature variation and thermal equilibrium temperature of damaged bearings.
Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach Based on Azure Kinect
Authors: Amprimo Gianluca , Masi Giulia , Priano Lorenzo , Azzaro Corrado , Galli Federica , Pettiti Giuseppe , Mauro Alessandro , Ferraris Claudia
Published: October 2022

Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.
Adaptation of IoT with Blockchain in Food Supply Chain Management: An Analysis-Based Review in Development, Benefits and Potential Applications
Authors: Kaur Amanpreet , Singh Gurpreet , Kukreja Vinay , Sharma Sparsh , Singh Saurabh , Yoon Byungun
Published: October 2022

In today’s scenario, blockchain technology is an emerging area and promising technology in the field of the food supply chain industry (FSCI). A literature survey comprising an analytical review of blockchain technology with the Internet of things (IoT) for food supply chain management (FSCM) is presented to better understand the associated research benefits, issues, and challenges. At present, with the concept of farm-to-fork gaining increasing popularity, food safety and quality certification are of critical concern. Blockchain technology provides the traceability of food supply from the source, i.e., the seeding factories, to the customer’s table. The main idea of this paper is to identify blockchain technology with the Internet of things (IoT) devices to investigate the food conditions and various issues faced by transporters while supplying fresh food. Blockchain provides applications such as smart contracts to monitor, observe, and manage all transactions and communications among stakeholders. IoT technology provides approaches for verifying all transactions; these transactions are recorded and then stored in a centralized database system. Thus, IoT enables a safe and cost-effective FSCM system for stakeholders. In this paper, we contribute to the awareness of blockchain applications that are relevant to the food supply chain (FSC), and we present an analysis of the literature on relevant blockchain applications which has been conducted concerning various parameters. The observations in the present survey are also relevant to the application of blockchain technology with IoT in other areas.
A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis
Authors: Li Zhifeng , Song Yaqin , Li Runchen , Gu Sen , Fan Xuze
Published: October 2022

Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.
A Survey and Ontology of Blockchain Consensus Algorithms for Resource-Constrained IoT Systems
Authors: Khan Misbah , den Hartog Frank , Hu Jiankun
Published: October 2022

The basic properties of blockchain, such as decentralization, security, and immutability, show promising potential for IoT applications. The main feature—decentralization of blockchain technology—depends on the consensus. However, consensus algorithms are mostly designed to work in extensive computational and communication environments for network security and immutability, which is not desirable for resource-restricted IoT applications. Many solutions are proposed to address this issue with modified consensus algorithms based on the legacy consensus, such as the PoW, PoS, and BFT, and new non-linear data structures, such as DAG. A systematic classification and analysis of various techniques in the field will be beneficial for both researchers and industrial practitioners. Most existing relevant surveys provide classifications intuitively based on the domain knowledge, which are infeasible to reveal the intrinsic and complicated relationships among the relevant basic concepts and techniques. In this paper, a powerful tool of systematic knowledge classification and explanation is introduced to structure the survey on blockchain consensus algorithms for resource-constrained IoT systems. More specifically, an ontology was developed for a consensus algorithm apropos of IoT adaptability. The developed ontology is subdivided into two parts—CONB and CONIoT—representing the classification of generic consensus algorithms and the ones that are particularly proposed for IoT, respectively. Guided by this ontology, an in depth discussion and analysis are provided on the major consensus algorithms and their IoT compliance based on design and implementation targets. Open research challenges and future research directions are provided.
Intelligent Manufacturing Technology in the Steel Industry of China: A Review
Authors: Zhou Dongdong , Xu Ke , Lv Zhimin , Yang Jianhong , Li Min , He Fei , Xu Gang
Published: October 2022

Intelligent manufacturing, defined as the integration of manufacturing with modern information technologies such as 5G, digitalization, networking, and intelligence, has grown in popularity as a means of boosting the productivity, intelligence, and flexibility of traditional manufacturing processes. The steel industry is a necessary support for modern life and economic development, and the Chinese steel industry’s capacity has expanded to roughly half of global production. However, the Chinese steel industry is now confronted with high labor costs, massive carbon emissions, a low level of intelligence, low production efficiency, and unstable quality control. Therefore, China’s steel industry has launched several large-scale intelligent manufacturing initiatives to improve production efficiency, product quality, manual labor intensity, and employee working conditions. Unfortunately, there is no comprehensive overview of intelligent manufacturing in China’s steel industry. We began this research by summarizing the construction goals and overall framework for intelligent manufacturing of the steel industry in China. Following that, we offered a brief review of intelligent manufacturing for China’s steel industry, as well as descriptions of two typical intelligent manufacturing models. Finally, some major technologies employed for intelligent production in China’s steel industry were introduced. This research not only helps to comprehend the development model, essential technologies, and construction techniques of intelligent manufacturing in China’s steel industry, but it also provides vital inspiration for the manufacturing industry’s digital and intelligence updates and quality improvement.
Addressing Power Issues in Biologging: An Audio/Inertial Recorder Case Study
Authors: Miquel Jonathan , Latorre Laurent , Chamaillé-Jammes Simon
Published: October 2022

In the past decades, biologging, i.e., the development and deployment of animal-borne loggers, has revolutionized ecology. Despite recent advances, power consumption and battery size however remain central issues and limiting factors, constraining the quantity of data that can be collected and the size of the animals that can be studied. Here, we present strategies to achieve ultra-low power in biologging, using the design of a lightweight audio-inertial logger as an example. It is designed with low-power MEMS sensors, and a firmware based on a small embedded RTOS. Both methodologies for power reduction and experimental results are detailed. With an average power consumption reduced to 5.3 mW, combined with a battery of 1800 mAh, the logger provides 900 h of continuous 8 kHz audio, 50 Hz accelerometer and 10 Hz magnetometer data.
Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition
Authors: Xefteris Vasileios-Rafail , Tsanousa Athina , Georgakopoulou Nefeli , Diplaris Sotiris , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: October 2022

Emotion recognition is a key attribute for realizing advances in human–computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.
Development of Online Tool Wear-Out Detection System Using Silver–Polyester Thick Film Sensor for Low-Duty Cycle Machining Operations
Authors: Rakkiyannan Jegadeeshwaran , Jakkamputi Lakshmipathi , Thangamuthu Mohanraj , Patange Abhishek D. , Gnanasekaran Sakthivel
Published: October 2022

This paper deals with the design and development of a silver–polyester thick film sensor and associated system for the wear-out detection of single-point cutting tools for low-duty cycle machining operations. Conventional means of wear-out detection use dynamometers, accelerometers, microphones, acoustic emission sensors, thermal infrared cameras, and machine vision systems that detect tool wear during the process. Direct measurements with optical instruments are accurate but affect the machining process. In this study, the use of a thick film sensor to detect wear-out for aa real-time low-duty machining operation was proposed to eliminate the limitations of the current methods. The proposed sensor monitors the tool condition accurately as the wear acts directly on the sensor, which makes the system simple and more reliable. The effect of tool temperature on the sensor during the machining operation was also studied to determine the displacement/deformation of tracing and the polymer substrate at different service temperatures. The proposed tool wear detection system with the silver–polyester thick film sensor mounted directly on the cutting tool tip proved to be highly capable of detecting the tool wear with good reliability.
The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient
Authors: Wu Yanyan , Li Qian
Published: October 2022

The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method; the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.
Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II
Authors: Elsayed Yasser , Gabbar Hossam A.
Published: October 2022

Fiber Bragg grating (FBG) technology has shown a mutation in developing fiber optic-based sensors because of their tiny size, high dielectric strength, distributed sensing, and immunity to high voltage and magnetic field interference. Therefore, FBG sensors significantly improve performance and accuracy in the world of measurements. The reflectivity and bandwidth are the main parameters that can dramatically affect the sensing performance and accuracy. Each industrial application has its requirements regarding the reflectivity and bandwidth of the reflected wavelength. Optimizing such problems with multi-objective functions that might t with each other based on applications’ needs is a big challenge. Therefore, this paper presents an optimization method based on the nondominated sorting genetic algorithm II (NSGA-II), aiming at determining the optimum grating parameters to suit applications’ needs. To sum up, the optimization process aims to convert industrial applications’ requirements, including bandwidth and reflectivity, into the manufacturing setting of FBG sensors, including grating length and modulation refractive index. The method has been implemented using MATLAB and validated with other research work in the literature. Results proved the capability of the new way to determine the optimum grating parameters for fulfilling application requirements.