bluuebunny/crossref_metadata_2025 huggingface dataset:
Rheological Assessment of Oil-Xanthan Emulsions in Terms of Complex, Storage, and Loss Moduli
Authors: Ghannam Mamdouh Taha , Selim Mohamed Y. E. , Zekri Abdulrazag Y. , Esmail Nabil
Published: January 2023

This experimental assessment was carried out to study the viscoelastic performance of crude oil-xanthan emulsions employing a RheoStress RS100 rheometer. Crude oil with a concentration range of 0–75% by volume was used to prepare the oil-gum emulsions. Two xanthan gums of Sigma and Kelzan were added in the emulsions with concentration ranges of 0–104 ppm. The linear viscoelastic ranges of all the tested oil-gum emulsions were found in the range of 0.1–10 Pa. Thus, the experimental tests were completed within the linear viscoelastic range of 1 Pa. The complex modulus increased gradually and steadily with frequency and gum concentration for all the examined emulsions. The addition of crude oil into the lighter xanthan concentration of <103 ppm provided almost the same behavior as the xanthan solution, whereas the presence of crude oil within the higher xanthan concentrations significantly stimulated the measured values of the complex modulus. For lower gum concentrations of up to 1000 ppm, oil concentration displayed no effect on both the storage and loss moduli, whereas for gum concentrations higher than 1000 ppm, both moduli increased gradually with crude oil concentration.
Encapsulation of D-Limonene into O/W Nanoemulsions for Enhanced Stability
Authors: Sohan Md Sohanur Rahman , Elshamy Samar , Lara-Valderrama Grace , Changwatchai Teetach , Khadizatul Kubra , Kobayashi Isao , Nakajima Mitsutoshi , Neves Marcos A.
Published: January 2023

The present study aimed to investigate the physical stability in terms of (droplet size, pH, and ionic strength) and chemical stability in terms of (retention) of D-limonene (LM) in the nanoemulsions after emulsification as well as after storing them for 30 days under different temperatures (5 °C, 25 °C, and 50 °C). LM is a cyclic monoterpene and a major component extracted from citrus fruits. The modification of disperse phase with soybean oil (SB) and a nonionic emulsifier (Tween 80) was adequate to prepare stable LM-loaded nanoemulsions. LM blended with SB-loaded nanoemulsions were stable against droplet growth over pH (3–9) and ionic strength (0–500 mM NaCl). Regarding long-term storage, the prepared nanoemulsions demonstrated excellent physical stability with droplet size ranging from 120–130 nm during 30 days of storage at both 5 °C and 25 °C; however, oiling off started in the emulsions, which were stored at 50 °C from day 10. On the other hand, the retention of LM in the emulsions was significantly impacted by storage temperature. Nanoemulsions stored at 5 °C had the highest retention of 91%, while nanoemulsions stored at 25 °C had the lowest retention of 82%.
The Dhāraṇī Coffin from the Nongso Tomb and the Cult of Shattering Hell during the Koryŏ Dynasty
Authors: Lee Seunghye
Published: January 2023

Inscriptions of Buddhist mantras and dhāraṇīs were interred in Chinese tombs on behalf of the deceased from the Tang dynasty onward as the fear of postmortem judgment and punishment in the ten courts of hell became firmly rooted. This notion of the afterlife and the practice of enclosing dhāraṇī inscriptions in tombs seem to have been received by Korean Buddhists by the beginning of the Koryŏ (918–1392). This paper, through a close examination of a wooden inner coffin excavated in 2014 from a tomb in Nongso-myŏn, Ullim-ri, Sunch’ang-gun, North Chŏlla Province of Korea, sheds new light on the use of dhāraṇī on funerary objects and structures during the Koryŏ. The coffin bears, on its lacquered exterior walls, inscriptions of the Mantra of the Six-Syllable King of Great Clarities (Oṃ maṇi padme hūṃ) and the Mantra for Shattering Hell (Oṃ karadeya svāhā). While defining the dhāraṇī coffin from Nongso Tomb as one form of funerary dhāraṇī, this paper situates it in the historical trajectory of such practice by comparing it with Liao (916–1125) precedents. The dhāraṇī coffins from Koryŏ and Liao tombs attest that the Buddhists made use of mantras and dhāraṇīs promising salvation of the deceased from hell during their lifetime and beyond. This study shows that two different kinds of dhāraṇīs, which are similarly named and believed to have the power of shattering hell, were practiced by Chinese and Korean Buddhists. It also proposes that the Mantra for Shattering Hell (Oṃ karadeya svāhā) may have been transmitted to the Korean peninsula as part of the food bestowal ritual before its incorporation into the funerary context.
Defeat and Glory: Social Media, Neoliberalism and the Transnational Tragedy of a Divinized Baba
Authors: Parciack Ronie
Published: January 2023

This essay addresses the intersection between the Facebook, Instagram, Twitter, Tik-Tok and Pinterest social media platforms and a contemporary religious leader/teacher who exploited them to rise from subalternity to the status of a deified celebrity. It examines his underprivileged disciples and followers and rival formal and informal levels, within Indian Sufi circles. Employing a combined perspective of ethnography, media studies and textual analysis, I discuss the transformations engendered by this social media celebrity and the impact of neo-liberalism on religious teacher–disciple (peeri–mureedi) relations. I show that this transformation involved a commodification of peeri–mureedi relations, leading to a neoliberal morphing of religious practices into marketable products. In so doing, I provide a critical reading of Mazzarella’s social media as “re-enlightened” or “inclusive capitalism” that gives voice, agency and new economic possibilities to capitalism’s most marginal subjects, who aspire to break the grip of what I term the “economies of despair”.
The Nature of Religious and Spiritual Needs in Palliative Care Patients, Carers, and Families and How They Can Be Addressed from a Specialist Spiritual Care Perspective
Authors: Bradford Kate L.
Published: January 2023

This paper is written from the perspective of a specialist religious and spiritual care practitioner who practises in major referral hospitals in Sydney, Australia. In these hospitals, religious care and ward-based spiritual care chaplaincy services are offered in tandem. The perspective offered is based on the author’s knowledge, research, and experience in how people make the necessary religious or spiritual adjustments to their deep view of reality when faced with severe or chronic pain or terminal illness. Religious and/or Spiritual Care (R/SC) are interventions that scaffold people through a process of sense-making which helps them find meaning in their new reality. These R/SC adjustments concern conscious and unconscious beliefs about ultimate meaning, morality, justice, transcendence, and relationships within themselves, and with others and the supernatural. Palliative care practitioners described the importance of spiritual care and integrated spiritual care principles into their biopsychosocial-spiritual model of care in the 1960s. As palliative care practitioners have increasingly clarified their discipline as a distinct discipline in medicine, religious and spiritual care practitioners have struggled to define their place. These concerns merit a fresh evaluation of the religious and spiritual needs of patients, carers, and families of those suffering chronic pain and terminal illness and further clarification of the specialist discipline of religious and spiritual care.
North-African Jewish People in Paris: Multiple Identities—Ethnic-Religious, National and Transnational
Authors: Lev-Ari Lilach
Published: January 2023

The purpose of this study is to compare native-born and immigrant Jewish people from North African roots who reside in greater Paris regarding their multiple identities: ethnic-religious, as Jewish people; national, as French citizens; and transnational, as migrants and ‘citizens of the world’. This study employed the correlative quantitative method using survey questionnaires (N = 145) combined with qualitative semi-structured interviews. The main results indicate that both groups have strong Jewish and religious identities. However, while immigrants had fewer opportunities for upward mobility and were more committed to national integration, the younger second-generation have higher socio-economic status and more choices regarding their identities in contemporary France. In conclusion, even among people of the same North African origin, there are inter-generational differences in several dimensions of identity and identification which stem from being native-born or from their experience as immigrants. Different social and political circumstances offer different integration opportunities and thus, over the years, dynamically construct identities among North African Jewish people as minorities. Nonetheless, the Jewish community in Paris is not passive; it has its own strength, cohesiveness, vitality and resilience which are expressed not only in economic but also in social and religious prosperity of Jewish organizations shared by both the native-born and immigrants, who can be considered a ‘privileged’ minority.
Simulated and Real Robotic Reach, Grasp, and Pick-and-Place Using Combined Reinforcement Learning and Traditional Controls
Authors: Lobbezoo Andrew , Kwon Hyock-Ju
Published: January 2023

The majority of robots in factories today are operated with conventional control strategies that require individual programming on a task-by-task basis, with no margin for error. As an alternative to the rudimentary operation planning and task-programming techniques, machine learning has shown significant promise for higher-level task planning, with the development of reinforcement learning (RL)-based control strategies. This paper reviews the implementation of combined traditional and RL control for simulated and real environments to validate the RL approach for standard industrial tasks such as reach, grasp, and pick-and-place. The goal of this research is to bring intelligence to robotic control so that robotic operations can be completed without precisely defining the environment, constraints, and the action plan. The results from this approach provide optimistic preliminary data on the application of RL to real-world robotics.
Blind Hyperspectral Image Denoising with Degradation Information Learning
Authors: Wei Xing , Xiao Jiahua , Gong Yihong
Published: January 2023

Although existing hyperspectral image (HSI) denoising methods have exhibited promising performance in synthetic noise removal, they are seriously restricted in real-world scenarios with complicated noises. The major reason is that model-based methods largely rely on the noise type assumption and parameter setting, and learning-based methods perform poorly in generalizability due to the scarcity of real-world clean–noisy data pairs. To overcome this long-standing challenge, we propose a novel denoising method with degradation information learning (termed DIBD), which attempts to approximate the joint distribution of the clean–noisy HSI pairs in a Bayesian framework. Specifically, our framework learns the mappings of noisy-to-clean and clean-to-noisy in a priority dual regression scheme. We develop more comprehensive auxiliary information to simplify the joint distribution approximation process instead of only estimating noise intensity. Our method can leverage both labeled synthetic and unlabeled real data for learning. Extensive experiments show that the proposed DIBD achieves state-of-the-art performance on synthetic datasets and has better generalization to real-world HSIs. The source code will be available to the public.
Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China
Authors: Chen Li , Zhang Nannan , Zhao Tongyang , Zhang Hao , Chang Jinyu , Tao Jintao , Chi Yujin
Published: January 2023

Lithium (Li) resources are widely used in many strategic emerging fields; recently, several large-scale to super-large-scale pegmatite-type lithium deposits have been discovered in Dahongliutan, NW China. However, the natural environmental conditions in the Dahongliutan area are extremely harsh; hence, manpower in field exploration is difficult to achieve. Efficient and rapid methods for identifying Li-rich pegmatites, based on hyperspectral remote sensing technology, have great potential for promoting the discovery of lithium resources. Ground spectral research is the cornerstone of regional hyperspectral imaging (HSI) for geological mapping. Direct observation and analysis by the naked eye are part of a process that is mainly dependent upon abundant experience and knowledge from experts. Machine learning (ML) technology has the advantages of automatic feature extraction and relationship characterization. Therefore, identifying the spectral features of Li-rich pegmatite via ML can accurately and efficiently distinguish the spectral characteristics of Li-rich pegmatites and Li-poor pegmatites, enabling further excavation to identify the strongest predictors of Li-pegmatite and laying a foundation for the accurate extraction of Li-rich pegmatites in the West Kunlun region using HSI. The spectral characteristics of pegmatite in the visible near-infrared and shortwave infrared (VNIR–SWIR) spectra were observed and analyzed. Li-rich pegmatite was identified based on the diagnostic spectral waveform characteristic parameters of the local wavelength range. The results demonstrated that the pegmatite ML recognition model was based on spectral characteristic parameters of the local wavelength range, with good model explicability, and the area under the curve (AUC) calculated for the model is 0.843. A recognition model based on full-range spectrum data achieved a higher precision, and the AUC value was up to 0.977. The evaluation of the Gini coefficient presented the strongest predictors, which were used to map the spatial distribution lithology, based on GF-5, in Akesayi and the 509 mines, producing encouraging lithological mapping results (Kappa > 0.9, OA > 94%).
Combined GRACE and GPS to Analyze the Seasonal Variation of Surface Vertical Deformation in Greenland and Its Influence
Authors: Bian Yankai , Li Zhen , Huang Zhiquan , He Bing , Shi Liangliang , Miao Song
Published: January 2023

The geophysical effects are the main factor that causes the nonlinear motion of the station, and a comprehensive analysis of the relationship between the GRACE seasonal load deformation and the GPS station coordinates is helpful to study the physical mechanism that causes the nonlinear motion of the station. Aiming at the continuous GPS coordinate time series in Greenland, this paper comprehensively analyzes the correlation between GRACE seasonal load deformation and GPS station coordinates. First, in order to improve the accuracy of GPS station coordinates, the principle component analysis (PCA) method was used to eliminate the common mode error (CME) of the station coordinates. The results show that this method effectively reduces the uncertainty of the station coordinates time series. Secondly, when extracting seasonal signals, it is found that the singular spectrum (SSA) method can effectively obtain the time-varying part of seasonal signals, and its extraction effect is better than that of the least square fitting (LSF) method. Finally, the seasonal relationship between GRACE load deformation and GPS station coordinates is analyzed from the aspects of time series change, correlation, and WRMS reduction. It is found that there are differences in the amplitude and phase parts of the time series. The mean value of correlation is 0.73, the maximum reduction of WRMS is 55.20% (QAQ1 station), and the minimum is −22.69% (KMJP station), indicating that most stations mainly exhibit seasonal load deformation, while individual stations cannot effectively reflect. In addition, the influence of GRACE seasonal load deformation on the station coordinate parameters is quantitatively analyzed. The results show that the best noise model of the station is mainly WN + FN, which effectively reduces the velocity uncertainty of the station coordinate, and weakens the seasonal term oscillation.
Inversion of Regional Groundwater Storage Changes Based on the Fusion of GNSS and GRACE Data: A Case Study of Shaanxi–Gansu–Ningxia
Authors: Li Wanqiu , Zhang Chuanyin , Wang Wei , Guo Jinyun , Shen Yingchun , Wang Zhiwei , Bi Jingxue , Guo Qiuying , Zhong Yulong , Li Wei , Zhu Chengcheng , Xu Pengfei
Published: January 2023

This paper aims to address the limitations of the distribution number and uniformity of Continuously Operating Reference Stations (CORS) and their impact on the reliability of inverting regional groundwater storage (GWS) based on Green’s function method and using global navigation satellite system (GNSS) data. A fusion method on the inversion of regional GWS changes from GNSS and the Gravity Recovery and Climate Experiment (GRACE) was proposed in this paper. Taking the Shaanxi–Gansu–Ningxia (SGN) region as an example, the in situ groundwater level data from ten CORS stations and eight wells were used for test analyses. In this paper, an atmospheric pressure model from the European Centre for Medium-Range Weather Forecasts (ECMWF), a global land data assimilation system (GLDAS), a WaterGAP global hydrology model (WGHM), and mean sea level anomaly (MSLA) data were used to quantitatively monitor the influence of vertical deformation caused by non-tidal environmental load. After deducing these loading deformations from the filtered time series of non-linear monthly geodetic height from the GNSS, the GWS changes in the SGN region from 2011 to 2014 were inverted. Meanwhile, the change in surface water storage from the GLDAS and WGHM models were removed from the terrestrial water storage (TWS) changes derived from GRACE. On this basis, the remove–restore theory in the Earth’s gravity field was introduced to both fuse the inversion results and obtain the regional GWS changes based on the fusion method. The results showed the following: (1) The local characteristics from the fusion results were more prominent than those of GRACE on the spatial scale, such as in the southwest and northeast in the study area. In addition, the fusion results were more uniform than those from GNSS, especially for the sparse and missing areas in which CORS stations were located, and the local effect was weakened. (2) On the time scale, compared with GRACE, the trends in GWS changes obtained from the fusion method and from GNSS inversion were roughly the same as the in situ groundwater level changes. (3) For the in situ groundwater wells “6105010031” and “6101260010”, the correlation coefficients of the fusion result were 0.53 and 0.56, respectively. The accuracy of the fusion method was slightly higher than that from GNSS, which indicates that the fusion method may be more effective for areas where CORS stations are missing or sparsely distributed. The methods in this paper can provide significant reference material for hydrodynamic research, sustainable management of water resources, and the dynamic maintenance of height data.
Hybrid Attention-Based Encoder–Decoder Fully Convolutional Network for PolSAR Image Classification
Authors: Fang Zheng , Zhang Gong , Dai Qijun , Xue Biao , Wang Peng
Published: January 2023

Recently, methods based on convolutional neural networks (CNNs) achieve superior performance in polarimetric synthetic aperture radar (PolSAR) image classification. However, the current CNN-based classifiers follow patch-based frameworks, which need input images to be divided into overlapping patches. Consequently, these classification approaches have the drawback of requiring repeated calculations and only relying on local information. In addition, the receptive field size in conventional CNN-based methods is fixed, which limits the potential to extract features. In this paper, a hybrid attention-based encoder–decoder fully convolutional network (HA-EDNet) is presented for PolSAR classification. Unlike traditional CNN-based approaches, the encoder–decoder fully convolutional network (EDNet) can use an arbitrary-size image as input without dividing. Then, the output is the whole image classification result. Meanwhile, the self-attention module is used to establish global spatial dependence and extract context characteristics, which can improve the performance of classification. Moreover, an attention-based selective kernel module (SK module) is included in the network. In the module, softmax attention is employed to fuse several branches with different receptive field sizes. Consequently, the module can capture features with different scales and further boost classification accuracy. The experiment results demonstrate that the HA-EDNet achieves superior performance compared to CNN-based and traditional fully convolutional network methods.
Evaluation of Anti-Burst Performance in Mining Roadway Support System
Authors: Zhang Rupei , Gong Siyuan , Dou Linming , Cai Wu , Li Xuwei , Li Hui , Tian Xinyuan , Ding Xiaomin , Niu Jiasheng
Published: January 2023

The hazardous effect of a mine earthquake on a roadway is not only related to its energy scale but also to its distance from the roadway. In this study, a signal attenuation model and a disaster-causing model were established to evaluate the mine earthquake effects based on peak particle velocity (PPV) data recorded for 37221-1 upper roadway of the Dongxia Coal Mine, China. The characteristic of dynamic loads due to mine earthquake propagation to roadway surfaces was researched, and critical PPV values were identified using FLAC3D numerical simulation, which can be used to evaluate the roadway anti-burst performance under the existing support system. The results show that the support system is able to resist a mine earthquake with energy below 2.33 × 103 J; however, considering the energy accumulation volume of surrounding rocks and the range of source fracture, the maximum resistible mine earthquake energy can be up to 7.09 × 106 J when the roadway is 50 m away from the source. The validity and applicability of the disaster-causing models was verified by two rockburst cases that occurred during the excavation of the working face.
Vocal Tract Resonance Detection at Low Frequencies: Improving Physical and Transducer Configurations
Authors: Thilakan Jithin , B.T. Balamurali , P.M. Sarun , Chen Jer-Ming
Published: January 2023

Broadband excitation introduced at the speaker’s lips and the evaluation of its corresponding relative acoustic impedance spectrum allow for fast, accurate and non-invasive estimations of vocal tract resonances during speech and singing. However, due to radiation impedance interactions at the lips at low frequencies, it is challenging to make reliable measurements of resonances lower than 500 Hz due to poor signal to noise ratios, limiting investigations of the first vocal tract resonance using such a method. In this paper, various physical configurations which may optimize the acoustic coupling between transducers and the vocal tract are investigated and the practical arrangement which yields the optimal vocal tract resonance detection sensitivity at low frequencies is identified. To support the investigation, two quantitative analysis methods are proposed to facilitate comparison of the sensitivity and quality of resonances identified. Accordingly, the optimal configuration identified has better acoustic coupling and low-frequency response compared with existing arrangements and is shown to reliably detect resonances down to 350 Hz (and possibly lower), thereby allowing the first resonance of a wide range of vowel articulations to be estimated with confidence.
Blockchain—Internet of Things Applications: Opportunities and Challenges for Industry 4.0 and Society 5.0
Authors: Tyagi Amit Kumar , Dananjayan Sathian , Agarwal Deepshikha , Thariq Ahmed Hasmath Farhana
Published: January 2023

Today, blockchain is becoming more popular in academia and industry because it is a distributed, decentralised technology which is changing many industries in terms of security, building trust, etc. A few blockchain applications are banking, insurance, logistics, transportation, etc. Many insurance companies have been thinking about how blockchain could help them be more efficient. There is still a lot of hype about this immutable technology, even though it has not been utilised to its full potential. Insurers have to decide whether or not to use blockchain, just like many other businesses do. This technology keeps a distributed ledger on each blockchain node, making it more secure and transparent. The blockchain network can operate smart contracts and convince others to agree, so criminals cannot make mistakes. On another side, the Internet of Things (IoT) might make a real-time application work faster through its automation. With the integration of blockchain and IoT, there will always be a problem with technology regarding IoT devices and mining the blockchain. This paper gives a real-time view of blockchain—IoT-based applications for Industry 4.0 and Society 5.0. The last few sections discuss essential topics such as open issues, challenges, and research opportunities for future researchers to expand research in blockchain—IoT-based applications.
Bio-Tailored Sensing at the Nanoscale: Biochemical Aspects and Applications
Authors: Fata Francesca , Gabriele Federica , Angelucci Francesco , Ippoliti Rodolfo , Di Leandro Luana , Giansanti Francesco , Ardini Matteo
Published: January 2023

The demonstration of the first enzyme-based electrode to detect glucose, published in 1967 by S. J. Updike and G. P. Hicks, kicked off huge efforts in building sensors where biomolecules are exploited as native or modified to achieve new or improved sensing performances. In this growing area, bionanotechnology has become prominent in demonstrating how nanomaterials can be tailored into responsive nanostructures using biomolecules and integrated into sensors to detect different analytes, e.g., biomarkers, antibiotics, toxins and organic compounds as well as whole cells and microorganisms with very high sensitivity. Accounting for the natural affinity between biomolecules and almost every type of nanomaterials and taking advantage of well-known crosslinking strategies to stabilize the resulting hybrid nanostructures, biosensors with broad applications and with unprecedented low detection limits have been realized. This review depicts a comprehensive collection of the most recent biochemical and biophysical strategies for building hybrid devices based on bioconjugated nanomaterials and their applications in label-free detection for diagnostics, food and environmental analysis.
An Adaptive Sampling Framework for Life Cycle Degradation Monitoring
Authors: Yin Yuhua , Liu Zhiliang , Zhang Junhao , Zio Enrico , Zuo Mingjian
Published: January 2023

Data redundancy and data loss are relevant issues in condition monitoring. Sampling strategies for segment intervals can address these at the source, but do not receive the attention they deserve. Currently, the sampling methods in relevant research lack sufficient adaptability to the condition. In this paper, an adaptive sampling framework of segment intervals is proposed, based on the summary and improvement of existing problems. The framework is implemented to monitor mechanical degradation, and experiments are implemented on simulation data and real datasets. Subsequently, the distributions of the samples collected by different sampling strategies are visually presented through a color map, and five metrics are designed to assess the sampling results. The intuitive and numerical results show the superiority of the proposed method in comparison to existing methods, and the results are closely related to data status and degradation indicators. The smaller the data fluctuation and the more stable the degradation trend, the better the result. Furthermore, the results of the objective physical indicators are obviously better than those of the feature indicators. By addressing existing problems, the proposed framework opens up a new idea of predictive sampling, which significantly improves the degradation monitoring.
FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices
Authors: Alam Mahbub Ul , Rahmani Rahim
Published: January 2023

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.
Offline Calibration for Infant Gaze and Head Tracking across a Wide Horizontal Visual Field
Authors: Capparini Chiara , To Michelle P. S. , Dardenne Clément , Reid Vincent M.
Published: January 2023

Most well-established eye-tracking research paradigms adopt remote systems, which typically feature regular flat screens of limited width. Limitations of current eye-tracking methods over a wide area include calibration, the significant loss of data due to head movements, and the reduction of data quality over the course of an experimental session. Here, we introduced a novel method of tracking gaze and head movements that combines the possibility of investigating a wide field of view and an offline calibration procedure to enhance the accuracy of measurements. A 4-camera Smart Eye Pro system was adapted for infant research to detect gaze movements across 126° of the horizontal meridian. To accurately track this visual area, an online system calibration was combined with a new offline gaze calibration procedure. Results revealed that the proposed system successfully tracked infants’ head and gaze beyond the average screen size. The implementation of an offline calibration procedure improved the validity and spatial accuracy of measures by correcting a systematic top-right error (1.38° mean horizontal error and 1.46° mean vertical error). This approach could be critical for deriving accurate physiological measures from the eye and represents a substantial methodological advance for tracking looking behaviour across both central and peripheral regions. The offline calibration is particularly useful for work with developing populations, such as infants, and for people who may have difficulties in following instructions.
State of the Art on Developments of (Bio)Sensors and Analytical Methods for Rifamycin Antibiotics Determination
Authors: Noor Hassan , David Iulia Gabriela , Jinga Maria Lorena , Popa Dana Elena , Buleandra Mihaela , Iorgulescu Emilia Elena , Ciobanu Adela Magdalena
Published: January 2023

This review summarizes the literature data reported from 2000 up to the present on the development of various electrochemical (voltammetric, amperometric, potentiometric and photoelectrochemical), optical (UV-Vis and IR) and luminescence (chemiluminescence and fluorescence) methods and the corresponding sensors for rifamycin antibiotics analysis. The discussion is focused mainly on the foremost compound of this class of macrocyclic drugs, namely rifampicin (RIF), which is a first-line antituberculosis agent derived from rifampicin SV (RSV). RIF and RSV also have excellent therapeutic action in the treatment of other bacterial infectious diseases. Due to the side-effects (e.g., prevalence of drug-resistant bacteria, hepatotoxicity) of long-term RIF intake, drug monitoring in patients is of real importance in establishing the optimum RIF dose, and therefore, reliable, rapid and simple methods of analysis are required. Based on the studies published on this topic in the last two decades, the sensing principles, some examples of sensors preparation procedures, as well as the performance characteristics (linear range, limits of detection and quantification) of analytical methods for RIF determination, are compared and correlated, critically emphasizing their benefits and limitations. Examples of spectrometric and electrochemical investigations of RIF interaction with biologically important molecules are also presented.
Evaluation of the Cell Concentration in Suspensions of Human Leukocytes by Ultrasound Imaging: The Influence of Size Dispersion and Cell Type
Authors: Elvira Luis , Fernández Alba , León Lucía , Ibáñez Alberto , Parrilla Montserrat , Martínez Óscar , Jiménez Javier
Published: January 2023

This work focuses on the use of ultrasound imaging to evaluate the cell concentration of dilute leukocyte suspensions in the range of 10–3000 cells/µL. First, numerical simulations were used to study the influence of the size dispersion and the leukocyte type on the performance of the concentration estimation algorithms, which were developed in previous works assuming single-sized scatterers. From this analysis, corrections to the mentioned algorithms were proposed and then the performance of these corrections was evaluated from experiments. For this, ultrasound images were captured from suspensions of lymphocytes, granulocytes, and their mixtures. These images were obtained using a 20 MHz single-channel scanning system. Results confirmed that concentration estimates provided by conventional algorithms were affected by the size dispersion of cells, leading to a remarkable underestimation of results. The proposed correction to compensate for cell size dispersion obtained from simulations improved the concentration estimation of these algorithms, for the cell suspensions tested, approaching the results to the reference optical characterization. Moreover, it was shown that these models provided a total leukocyte concentration from the ultrasound images which was independent of the relative populations of different white blood cell types.
Robust Control Based on Adaptive Neural Network for the Process of Steady Formation of Continuous Contact Force in Unmanned Aerial Manipulator
Authors: Fang Qian , Mao Pengjun , Shen Lirui , Wang Jun
Published: January 2023

Contact force control for Unmanned Aerial Manipulators (UAMs) is a challenging issue today. This paper designs a new method to stabilize the UAM system during the formation of contact force with the target. Firstly, the dynamic model of the contact process between the UAM and the target is derived. Then, a non-singular global fast terminal sliding mode controller (NGFTSMC) is proposed to guarantee that the contact process is completed within a finite time. Moreover, to compensate for system uncertainties and external disturbances, the equivalent part of the controller is estimated by an adaptive radial basis function neural network (RBFNN). Finally, the Lyapunov theory is applied to validate the global stability of the closed-loop system and derive the adaptive law for the neural network weight matrix online updating. Simulation and experimental results demonstrate that the proposed method can stably form a continuous contact force and reduce the chattering with good robustness.
Dosimetric Study of Heat-Treated Calcium–Aluminum–Silicon Borate Dosimeter for Diagnostic Radiology Applications
Authors: Algain Ibrahim , Arib Mehenna , Al-Said Said A. Farha , Donya Hossam
Published: January 2023

The production of thermoluminescence (TL) dosimeters fabricated from B2O3-CaF2-Al2O3-SiO2 doped with Cu and Pr for use in diagnostic radiology is the main goal of this research. The TL samples were synthesized via the melt-quench technique processed by melting the mixture at 1200 °C for 1 h, and, after cooling, the sample thus created was divided into two samples and retreated by heating for 2 h (referred to as TLV30) and for 15 h (referred to as TLV17). SEM and EDS analyses were performed on the TL samples to confirm the preparation process and to investigate the effects of irradiation dosimetry on the TL samples. Furthermore, the TL samples were irradiated with γ-rays using a 450 Ci 137Cs irradiator and variable X-ray beams (5–70 mGy). Two important diagnostic radiology applications were considered: CT (6–24 mGy) and mammography (2.72–10.8 mGy). Important dosimetric properties, such as the glow curves, reproducibility, dose–response linearity, energy dependence, minimum dose detectability and fading, were investigated for the synthetized samples (TLV17 and TLV30), the results of which were compared with the Harshaw TLD-100. The TLV17 dosimeter showed higher sensitivity than TLV30 in all applied irradiation procedures. The dose–response linearity coefficients of determination R2 for TLV17 were higher than TLD-100 and TLV30 in some applications and were almost equal in others. The reproducibility results of TLV17, TLV30 and TLD-100 were less than 5%, which is acceptable. On the other hand, the results of the fading investigations showed that, in general, TLV17 showed less fading than TLV30. Both samples showed a significant decrease in this regard after the first day, and then the signal variation became essentially stable though with a slight decrease until the eighth day. Therefore, it is recommended to read the TL dosimeters after 24 h, as with TLD-100. The SEM images confirmed the existence of crystallization, whilst the EDS spectra confirmed the presence of the elements used for preparation. Furthermore, we noticed that TLV17 had grown dense crystals that were larger in size compared to those of TLV30, which explains the higher sensitivity in TLV17. Overall, despite the fading, TLV17 showed greater radiation sensitivity and dose–response linearity compared with TLD-100. The synthetized TL samples showed their suitability for use as dosimeters in diagnostic radiology radiation dosimetry.
Random Matrix Transformation and Its Application in Image Hiding
Authors: Wang Jijun , Tan Fun Soo , Yuan Yi
Published: January 2023

Image coding technology has become an indispensable technology in the field of modern information. With the vigorous development of the big data era, information security has received more attention. Image steganography is an important method of image encoding and hiding, and how to protect information security with this technology is worth studying. Using a basis of mathematical modeling, this paper makes innovations not only in improving the theoretical system of kernel function but also in constructing a random matrix to establish an information-hiding scheme. By using the random matrix as the reference matrix for secret-information steganography, due to the characteristics of the random matrix, the secret information set to be retrieved is very small, reducing the modification range of the steganography image and improving the steganography image quality and efficiency. This scheme can maintain the steganography image quality with a PSNR of 49.95 dB and steganography of 1.5 bits per pixel and can ensure that the steganography efficiency is improved by reducing the steganography set. In order to adapt to different steganography requirements and improve the steganography ability of the steganography schemes, this paper also proposes an adaptive large-capacity information-hiding scheme based on the random matrix. In this scheme, a method of expanding the random matrix is proposed, which can generate a corresponding random matrix according to different steganography capacity requirements to achieve the corresponding secret-information steganography. Two schemes are demonstrated through simulation experiments as well as an analysis of the steganography efficiency, steganography image quality, and steganography capacity and security. The experimental results show that the latter two schemes are better than the first two in terms of steganography capacity and steganography image quality.
Application of Neural Network in Predicting H2S from an Acid Gas Removal Unit (AGRU) with Different Compositions of Solvents
Authors: Hakimi Mohd , Omar Madiah Binti , Ibrahim Rosdiazli
Published: January 2023

The gas sweetening process removes hydrogen sulfide (H2S) in an acid gas removal unit (AGRU) to meet the gas sales’ specification, known as sweet gas. Monitoring the concentration of H2S in sweet gas is crucial to avoid operational and environmental issues. This study shows the capability of artificial neural networks (ANN) to predict the concentration of H2S in sweet gas. The concentration of N-methyldiethanolamine (MDEA) and Piperazine (PZ), temperature and pressure as inputs, and the concentration of H2S in sweet gas as outputs have been used to create the ANN network. Two distinct backpropagation techniques with various transfer functions and numbers of neurons were used to train the ANN models. Multiple linear regression (MLR) was used to compare the outcomes of the ANN models. The models’ performance was assessed using the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate that ANN trained by the Levenberg–Marquardt technique, equipped with a logistic sigmoid (logsig) transfer function with three neurons achieved the highest R2 (0.966) and the lowest MAE (0.066) and RMSE (0.122) values. The findings suggested that ANN can be a reliable and accurate prediction method in predicting the concentration of H2S in sweet gas.
Low Cost Magnetic Field Control for Disabled People
Authors: Acosta Daniel , Fariña Bibiana , Toledo Jonay , Sanchez Leopoldo Acosta
Published: January 2023

Our research presents a cost-effective navigation system for electric wheelchairs that utilizes the tongue as a human–machine interface (HMI) for disabled individuals. The user controls the movement of the wheelchair by wearing a small neodymium magnet on their tongue, which is held in place by a suction pad. The system uses low-cost electronics and sensors, including two electronic compasses, to detect the position of the magnet in the mouth. One compass estimates the magnet’s position while the other is used as a reference to compensate for static magnetic fields. A microcontroller processes the data using a computational algorithm that takes the mathematical formulations of the magnetic fields as input in real time. The system has been tested using real data to control an electric wheelchair, and it has been shown that a trained user can effectively use tongue movements as an interface for the wheelchair or a computer.
A Novel Adaptive Time-Frequency Filtering Approach to Enhance the Ultrasonic Inspection of Stainless Steel Structures
Authors: Wu Biao , Yang Haitao , Huang Yong , Zhou Wensong , Liu Xiaohui
Published: January 2023

Ultrasonic nondestructive testing (NDT) provides a valuable insight into the integrity of stainless steel structures, but the noise caused by the scattering of stainless steel microstructure often limits the effectiveness of inspection. This work presents a novel adaptive filtering approach to enhance the signal-to-noise ratio (SNR) of a measured ultrasonic signal from the inspection of a stainless steel component, enabling the detection of hidden flaws under strong noise. After the spectral modeling of the noisy ultrasonic NDT signal, the difference between the spectral characteristics of a flaw echo and that of grain noise is highlighted, and a reference spectrum model to estimate the frequency spectrum of the echo reflected by any possible flaw is developed. Then, the signal is segmented and the similarity between the spectra of data segments and the reference spectra is evaluated quantitatively by the spectral similarity index (SSI). Based on this index, an adaptive time-frequency filtering scheme is proposed. Each data segment is processed by the filtering to suppress the energy of noise. The processed data segments are recombined to generate the de-noised signal after multiplying weighting coefficients, which again is determined by the SSI. The performance of the proposed method for SNR enhancement is evaluated by both the simulated and experimental signal and the effectiveness has been successfully demonstrated.
Analysis of the Underwater Radiated Noise Generated by Hull Vibrations of the Ships
Authors: Javier Rodrigo F. , Jaime Ramis , Pedro Poveda , Jesus Carbajo , Enrique Segovia
Published: January 2023

Shipping traffic is recognised as the main man-noise source of the anthropogenic noise generated in the marine environment. The underwater acoustic pollution is increased due to the increment of the human activity at seas supposing a threat for marine habitats. The ship as acoustic source must be understood and controlled to manage the maritime areas both in time and space to reduce the impact of noise in marine fauna. Shipping noise is mainly composed of flow noise, propeller noise and machinery noise. This research is focused on the analysis and estimation of the underwater radiated noise generated by the vibrations of the onboard machinery or structure-borne noise based on the calculation of the transfer function. This function relates the acceleration levels of the vibrations of the hull’s panels and the radiated noise by them using the radiation efficiency. Different analytical methods to estimate the radiation efficiency are presented and compared with data collected at sea. The measurements are performed acquiring simultaneously acceleration and acoustic levels by means on accelerometers installed on the hull’s panels at different positions and hydrophones deployed close to the bow, middle and stern of the ship. The analysis of the transmission of the vibrations along the ships is performed using the data from different locations of the hydrophones. The quality of the measurements is analysed using the coherence function through the spectral correlation between the measurement of vibrations and acoustic levels. On the other hand, signal-to-noise ratio is computed to verify the strength of the noise sources. The computed transfer function is used to predict the underwater radiated noise from vibrations showing differences less than 2 dB re to 1 μPa2.