bluuebunny/crossref_metadata_2025 huggingface dataset:
Aging Behaviors of Phenol-Formaldehyde Resin Modified by Bio-Oil under Five Aging Conditions
Authors: Yu Yuxiang , Li Chao , Jiang Chenxin , Chang Jianmin , Shen Danni
Published: March 2022

The bio-oil phenol-formaldehyde (BPF) resin, prepared by using bio-oil as a substitute for phenol, has similar bonding strength but lower price to phenol-formaldehyde (PF) resin. As a common adhesive for outdoor wood, the aging performance of BPF resin is particularly important. The variations in mass, bonding strength, microstructure, atomic composition, and chemical structure of BPF resin under five aging conditions (heat treatment, water immersion, UV exposure, hydrothermal treatment, and weatherometer treatment) were characterized by scanning electron microscope, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy, respectively. Compared under five aging conditions, after aging 960 h, the mass loss of plywood and film was largest under hydrothermal treatment; the bonding strength of plywood, the surface roughness, and O/C ratio of the resin film changed most obviously under weatherometer treatment. FT-IR analysis showed that the decreased degree of peak intensity on CH2 and C–O–C characteristic peaks of BPF resin were weaker under water immersion, hydrothermal treatment, and weatherometer treatment than those of PF resin. The comparison of data between BPF and PF resins after aging 960 h showed that adding bio-oil could obviously weaken the aging effect of water but slightly enhance that of heat. The results could provide a basis for the aging resistance modification of BPF resin.
Facile Preparation of Loose P84 Copolyimide/GO Composite Membrane with Excellent Selectivity and Solvent Resistance
Authors: Han Runlin , Wu Kui , Xu Lingfeng
Published: March 2022

In this study, multilayer graphene oxide (GO) was used to prepare the functional layer of polyimide/GO composite membrane with polyimide (P84) used as the supporting layer. Chitosan added in the functional layer was utilized to adjust the selectivity of the composite membrane. The effects of GO and chitosan contents on membrane morphology and separation performance were investigated in detail. The composite membrane showed high rejection to Congo red and Methyl orange with high flux but low rejection to Na2SO4 and MgCl2 at 0.2 MPa and ambient temperature. The membrane exhibited excellent solvent resistance in N,N-dimethylacetamide (DMAc) after being crosslinked with 0.5 wt.% triethylene tetramine. The result means that a highly selective and solvent-resistant P84/GO composite membrane was prepared with the facile filtration preparation method.
Effect of Hybridization and Ply Waviness on the Flexural Strength of Polymer Composites: An Experimental and Numerical Study
Authors: Subadra Sharath P. , Griskevicius Paulius
Published: March 2022

The study aims to ascertain the influence of hybridisation and ply waviness on the flexural behaviour of polymer composites. Two different resin systems, namely epoxy and Poly(methyl methacrylate)-PMMA, were chosen for the study, wherein two batches of carbon/glass hybrid composites (CGHC) were fabricated with the two resin systems. In addition to CGHC samples, four other neat batches with waviness (glass/epoxy and glass/PMMA) were prepared to study the effect of out-of-plane ply waviness. Two sets were additionally made with in-plane waviness (angles ranging from 15–35°) with epoxy to further understand the effect of waviness on flexural behaviour. Thereafter, two more batches of samples with neither waviness nor hybrid architectures were tested to achieve a better understanding of hybridization and the presence of waviness. It was seen that the hybridization of polymer composites introduces a pseudo-ductile behaviour in brittle composites, which makes the failure more predictable. An energy-based model was implemented to quantify the ductility introduced by hybridization. The presence of in-plane waviness increased the flexural load but reduced the modulus considerably. The presence of out-of-plane waviness decreased the flexural properties of composites drastically, though the displacement rate was seen to increase considerably. From the comparison between epoxy and PMMA, it was seen that PMMA exhibited similar flexural properties vis-à-vis epoxy. PMMA is easy to re-cycle and thus could serve as an ideal replacement for epoxy resin. Finally, a numerical model was built based on an LS-DYNA commercial solver; the model predicted the flexural behaviour close to what was seen in the experiments. The model could be calibrated correctly by ascertaining the influence of failure strain in the longitudinal direction, which is fibre dependent, and the failure strain in the transverse direction, which is matrix dependent.
Electrospun PVA/CuONPs/Bitter Gourd Nanofibers with Improved Cytocompatibility and Antibacterial Properties: Application as Antibacterial Wound Dressing
Authors: Sarwar Muhammad Nauman , Ali Hina Ghulam , Ullah Sana , Yamashita Kentaro , Shahbaz Aiman , Nisar Umair , Hashmi Motahira , Kim Ick-Soo
Published: March 2022

Antibacterial and cyto-compatible tricomponent composite electrospun nanofibers comprised of polyvinyl alcohol (PVA), copper II oxide nanoparticles (CuONPs), and Momordica charantia (bitter gourd, MC) extract were examined for their potential application as an effective wound dressing. Metallic nanoparticles have a wide range of applications in biomedical engineering because of their excellent antibacterial properties; however, metallic NPs have some toxic effects as well. The green synthesis of nanoparticles is undergoing development with the goal of avoiding toxicity. The aim of adding Momordica charantia extract was to reduce the toxic effects of copper oxide nanoparticles as well as to impart antioxidant properties to electrospun nanofibers. Weight ratios of PVA and MC extract were kept constant while the concentration of copper oxide was optimized to obtain good antibacterial properties with reduced toxicity. Samples were characterized for their morphological properties, chemical interactions, crystalline structures, elemental analyses, antibacterial activity, cell adhesion, and toxicity. All samples were found to have uniform morphology without any bead formation, while an increase in diameters was observed as the CuO concentration was increased in nanofibers. All samples exhibited antibacterial properties; however, the sample with CuO concentration of 0.6% exhibited better antibacterial activity. It was also observed that nanofibrous mats exhibited excellent cytocompatibility with fibroblast (NIH3T3) cells. The mechanical properties of nanofibers were slightly improved due to the addition of nanoparticles. By considering the excellent results of nanofibrous mats, they can therefore be recommended for wound dressing applications.
Threonine-Based Stimuli-Responsive Nanoparticles with Aggregation-Induced Emission-Type Fixed Cores for Detection of Amines in Aqueous Solutions
Authors: Kataoka Keita , Nakabayashi Kazuhiro , Lo Chen-Tsyr , Mori Hideharu
Published: March 2022

Stimuli-responsive polymeric nanoparticles (NPs) exhibit reversible changes in the dispersion or aggregation state in response to external stimuli. In this context, we designed and synthesized core-shell NPs with threonine-containing weak polyelectrolyte shells and fluorescent cross-linked cores, which are applicable for the detection of pH changes and amine compounds in aqueous solution. Stable and uniform NP(dTh) and NP(Fl), consisting of fluorescent symmetric diphenyl dithiophene (dTh) and diphenyl fluorene (Fl) cross-linked cores, were prepared by site-selective Suzuki coupling reactions in self-assembled block copolymer. NP(Fl) with the Fl unit in the core showed a high fluorescence intensity in different solvents, which is regarded as an aggregation-induced emission-type NP showing strong emission in aggregated states in the cross-linked core. Unimodal NPs were observed in water at different pH values, and the diameter of NP(Fl) changed from 122 (pH = 2) to 220 nm (pH = 11). Furthermore, pH-dependent changes of the fluorescence peak positions and intensities were detected, which may be due to the core aggregation derived from the deprotonation of the threonine-based shell fragment. Specific interactions between the threonine-based shell of NP(Fl) and amine compounds (triethylamine and p-phenylenediamine) resulted in fluorescence quenching, suggesting the feasibility of fluorescent amine detection.
Modification of Covalent Triazine-Based Frameworks for Photocatalytic Hydrogen Generation
Authors: Xie Jijia , Fang Zhiping , Wang Hui
Published: March 2022

The conversion of solar energy and water to hydrogen via semiconductor photocatalysts is one of the efficient strategies to mitigate the energy and environmental crisis. Conjugated polymeric photocatalysts have advantages over their inorganic counterparts. Their molecular structures, band structures, and electronic properties are easily tunable through molecular engineering to extend their spectral response ranges, improve their quantum efficiencies, and enhance their hydrogen evolution rates. In particular, covalent triazine-based frameworks (CTFs) present a strong potential for solar-driven hydrogen generation due to their large continuous π-conjugated structure, high thermal and chemical stability, and efficient charge transfer and separation capability. Herein, synthesis strategies, functional optimization, and applications in the photocatalytic hydrogen evolution of CTFs since the first investigation are reviewed. Finally, the challenges of hydrogen generation for CTFs are summarized, and the direction of material modifications is proposed.
Education for Religious Pluralism in Islam: One Book or Series of Books, a Singular Message or Myriad Messages?
Authors: Agbaria Ayman
Published: March 2022

As the literature on the philosophical and theological aspects of religious pluralism concerning Islamic education is indeed scarce and sporadic, this article discusses the complexity of religious pluralism in the Quranic discourse, while highlighting the tension between inclusive and exclusive interpretations. In doing so, the article reflects on the notion of religious pluralism, arguing that this notion should not be approached as a truth to be verified, but as a hypothesis to be tested. Additionally, the article presents various educational implications as to how to encourage religious pluralism in Islamic education. Specifically, I will advocate for a new type of education: education for religion.
Transcending History: (Re)Building Longchang Monastery of Mount Baohua in the Seventeenth Century
Authors: Zhou Zhenru
Published: March 2022

This paper analyzes the roles architectural renovation played in the revival of Longchang Monastery of Mount Baohua (Jiangsu), a major Chinese monastery of the Vinaya School and an ordination center in Late Imperial China. Based on temple gazetteers, monastic memoirs, and modern documentation of monastic architecture and life by Prip-Møller, the author reveals the formation of a spatial system that centered at the threefold ordination rituals. It took the entire seventeenth century for the system to take form under the supervision of a Chan monk-architect Miaofeng and three successive Vinaya abbots, Sanmei, Jianyue, and Ding’an. The spatial practices, comprising a series of reconstructions, reorientations, redesigns, re-demarcations, and refurbishments, have not only reconciled fractures and defects in the monastic architecture but also built a history for the rising institute. This article examines the construction of and the narratives around three centers of the Monastery, namely, the Open-Air Platform Unit where Miaofeng erected a copper hall, the Main Courtyard where Sanmei reoriented the monastic layout to follow the Vinaya tradition, the Ordination Platform Unit where Jianyue rebuilt a stone ordination platform, and again the Open-Air Platform Unit that Ding’an had refurbished and reunited with the later centers. The forces that have driven this seemingly non-progressive history, as the author argues, are not only the consistent efforts to counteract the natural course of material decay, but also the ambition of making a living history without beginning or end.
Developing a Deep Learning-Based Detector of Magnetic, Ne, Te and TEC Anomalies from Swarm Satellites: The Case of Mw 7.1 2021 Japan Earthquake
Authors: Akhoondzadeh Mehdi , De Santis Angelo , Marchetti Dedalo , Wang Ting
Published: March 2022

Since the appearance and evolution of earthquake ionospheric precursors are expected to show a nonlinear and complex behaviour, the use of nonlinear predictor models seems more appropriate. This paper proposes a new approach based on deep learning as a powerful tool for extracting the nonlinear patterns from a time series of ionospheric precursors. A Long Short-Term Memory (LSTM) network as a type of Recurrent Neural Network (RNN) was used to investigate 52 six-month time series, deduced from the three Swarm satellite (Alpha (A), Bravo (B) and Charlie (C)) measurements, including electron density (Ne), electron temperature (Te), magnetic scalar and vector (X, Y, Z) components, Slant and Vertical Total Electron Content (STEC and VTEC), for day and night periods around the time and location of a seismic event. This new approach was tested on a strong Mw = 7.1 earthquake in Japan on 13 February 2021, at 14:07:50 UTC by comparing the results with two implemented methods, i.e., Median and LSTM methods. Furthermore, clear anomalies are seen by a voting classification method 1, 6, 8, 13, 31 and 32 days before the earthquake. A comparison with atmospheric data investigation is further provided, supporting the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism as a suitable theory to explain the alteration of upper geolayers in the earthquake preparation phase. In other words, using multi-method and multi-precursor analysis applied to 52 time series and also to the orbit-by-orbit investigation, the observed anomalies on the previous day and up to 32 days before the event in normal solar and quiet geomagnetic conditions could be considered as a striking hint of the forthcoming Japan earthquake.
An Approach for Predicting Global Ionospheric TEC Using Machine Learning
Authors: Tang Jun , Li Yinjian , Yang Dengpan , Ding Mingfei
Published: March 2022

Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE’S 1-Day (C1PG) and CODE’S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days.
Pattern Recognition Approach and LiDAR for the Analysis and Mapping of Archaeological Looting: Application to an Etruscan Site
Authors: Danese Maria , Gioia Dario , Vitale Valentino , Abate Nicodemo , Amodio Antonio Minervino , Lasaponara Rosa , Masini Nicola
Published: March 2022

Illegal archaeological excavations, generally denoted as looting, is one of the most important damage factors to cultural heritage, as it upsets the human occupation stratigraphy of sites of archaeological interest. Looting identification and monitoring are not an easy task. A consolidated instrument used for the detection of archaeological features in general, and more specifically for the study of looting is remote sensing. Nevertheless, passive optical remote sensing is quite ineffective in dense vegetated areas. For these type of areas, in recent decades, LiDAR data and its derivatives have become an essential tool as they provide fundamental information that can be critical not only for the identification of unknown archaeological remains, but also for monitoring issues. Actually, LiDAR can suitably reveal grave robber devastation, even if, surprisingly, up today LiDAR has been generally unused for the identification of looting phenomenon. Consequently, this paper deals with an approach devised ad hoc for LiDAR data to detect looting. With this aim, some spatial visualization techniques and the geomorphon automatic landform extraction were exploited to enhance and extract features linked to the grave robber devastation. For this paper, the Etruscan site of San Giovenale (Northern Lazio, Italy) was selected as a test area as it is densely vegetated and was deeply plundered throughout the 20th century. Exploiting the LiDAR penetration capability, the prediction ability of the devised approach is highly satisfactory with a high rate of success, varying from 85–95%.
Mapping Heat-Health Vulnerability Based on Remote Sensing: A Case Study in Karachi
Authors: Wu Xilin , Liu Qingsheng , Huang Chong , Li He
Published: March 2022

As a result of global climate change, the frequency and intensity of heat waves have increased significantly. According to the World Meteorological Organization (WMO), extreme temperatures in southwestern Pakistan have exceeded 54 °C in successive years. The identification and assessment of heat-health vulnerability (HHV) are important for controlling heat-related diseases and mortality. At present, heat waves have many definitions. To better describe the heat wave mortality risk, we redefine the heat wave by regarding the most frequent temperature (MFT) as the minimum temperature threshold for HHV for the first time. In addition, different indicators that serve as relevant evaluation factors of exposure, sensitivity and adaptability are selected to conduct a kilometre-level HHV assessment. The hesitant analytic hierarchy process (H-AHP) method is used to evaluate each index weight. Finally, we incorporate the weights into the data layers to establish the final HHV assessment model. The vulnerability in the study area is divided into five levels, high, middle-high, medium, middle-low and low, with proportions of 3.06%, 46.55%, 41.85%, 8.53% and 0%, respectively. Health facilities and urbanization were found to provide advantages for vulnerability reduction. Our study improved the resolution to describe the spatial heterogeneity of HHV, which provided a reference for more detailed model construction. It can help local government formulate more targeted control measures to reduce morbidity and mortality during heat waves.
Observation System Design and Analysis for a New Staring Earth Radiation Budget Radiometer Based on the Lagrange L1 Point of the Earth–Moon System
Authors: Zhang Hanyuan , Ye Xin , Zhu Ping , Fang Wei , Wang Yuwei
Published: March 2022

The Earth’s radiation budget (ERB), measured at the top of the atmosphere (TOP), quantifies the radiation imbalance between the Earth–atmosphere system and space. The ERB’s measurement accuracy depends on the observation system’s design and calibration technology. Fitting regional scanning data from polar satellite payloads is the most common way to obtain the global radiation budget. Additionally, the radiometers are calibrated by onboard stable radiation sources. We propose a new ERB radiometer operating at the Lagrange L1 point of the Earth–Moon system, which has the characteristic of observing the Earth and Moon on a hemispheric spatial scale. Hence, earth-integrated radiation can be measured directly. Furthermore, the Moon is used as a natural source for on-orbit calibration, and the attenuation of the instrument is monitored and corrected through periodic observations. This paper presents the concept of the radiometer and focuses on the design and analysis of the radiometer’s observation systems based on optical design and stray radiation suppression. The results show that the system has good imaging quality. The external stray radiation suppression reached an order of 10−8, and the thermal stray radiation can be eliminated by temperature control and compensation. The radiometer will provide long-term integrated ERB data.
Bagged Tree Model to Retrieve Planetary Boundary Layer Heights by Integrating Lidar Backscatter Profiles and Meteorological Parameters
Authors: Wei Wang , Pan Ya’ni , Feng Huihui , Chen Biyan
Published: March 2022

The planetary boundary layer (PBL) is the part of the troposphere in which the soil’s influence is noticeable. It plays an important role in the fields of air pollution, meteorology, weather forecasting, and climate. Continuous observation of lidar makes obtaining the day–night PBL height (PBLH) with a high temporal resolution possible. A high-precision PBLH retrieval method is the key to achieving this goal. In this study, we propose a new method based on a bagged tree model to retrieve the PBLH from micro-lidar backscatter profiles. With the radiosonde measurements taken as the true reference, lidar features (the ten maximum slopes identified by the maximum gradient method) and four meteorological parameters (atmospheric pressure, temperature, relative humidity, and wind speed) serve as characteristic variables. The PBLH retrieval model is evaluated using a 10-fold cross-validation (CV) method and then compared with the four traditional methods (i.e., maximum gradient, maximum standard deviation, wavelet covariance, and the ideal profile method). The correlation coefficient (R) between the retrieved PBLHs and the radiosonde measurements is 0.89, which is much bigger than the R (0.2–0.48) from the four traditional methods. Moreover, the root mean square error and mean absolute error for the retrieved PBLH are 0.3 km and 0.2 km, respectively, which are lower than those of the four traditional methods (0.5~0.6 km for RMSE and 0.4–0.5 for MAE). Cases with different conditions show that this new method is almost undisturbed by cloud and suspended/thick aerosol layers. It can also be used to retrieve shallow PBL in cases in which using traditional methods would be difficult. Long-term analysis of averaged PBLHs retrieved by the proposed model from 2013 to 2016 shows that the hourly PBLH rises at sunrise and sets at sunset, and that the monthly PBLH in summer is higher than that in winter. The results suggest that the proposed method is better than the four traditional methods and available for use in conditions such as existing cloud layers and multiple-layers.
Design and Depth Control of a Buoyancy-Driven Profiling Float
Authors: Bai Yulin , Hu Rui , Bi Yuanbo , Liu Chunhu , Zeng Zheng , Lian Lian
Published: March 2022

This paper presents the design and fabrication of a profiling float primarily used for thermocline observations and tracking, with an emphasis on depth control performance. The proposed float consists of a frame-type electronic chamber and a variable buoyancy system (VBS) actuator chamber. Components or sensors can be added or removed according to specific requirements. All components were off the shelf, which lowered the cost of the float. In addition, a segment PD control method is introduced. Simulink results showed that there was no need to change any parameter when carrying out tasks at different depths. This method is superior to the traditional PD control and sliding mode control (SMC). In the process of diving, the speed could be well controlled to less than 0.2 m/s. We completed depth determination and control method validation in Qiandao Lake. The final results were consistent with the simulation results, and the maximum depth retention error was less than 0.3 m. Field tests also demonstrated that the prototype float can be used for thermocline observations in the upper layer of seawater or lake water.
An Edge Computing and Ambient Data Capture System for Clinical and Home Environments
Authors: Suresha Pradyumna Byappanahalli , Hegde Chaitra , Jiang Zifan , Clifford Gari D.
Published: March 2022

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.
An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation
Authors: Roy Avirup , Dutta Hrishikesh , Griffith Henry , Biswas Subir
Published: March 2022

A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module. In this paper, a detailed characterization of sip detection is performed to understand the accuracy-complexity tradeoffs by developing and implementing a variety of different ML models with varying complexities. The maximum energy consumed by the entire framework is around 119 mJ during a maximum computation time of 300 μs. The energy consumption and computation times of the proposed framework is suitable for implementation in low-power embedded hardware that can be incorporated in consumer grade water bottles.
Liquid Metal Based Nano-Composites for Printable Stretchable Electronics
Authors: Xu Dan , Cao Jinwei , Liu Fei , Zou Shengbo , Lei Wenjuan , Wu Yuanzhao , Liu Yiwei , Shang Jie , Li Run-Wei
Published: March 2022

Liquid metal (LM) has attracted prominent attention for stretchable and elastic electronics applications due to its exceptional fluidity and conductivity at room temperature. Despite progress in this field, a great disparity remains between material fabrication and practical applications on account of the high surface tension and unavoidable oxidation of LM. Here, the composition and nanolization of liquid metal can be envisioned as effective solutions to the processibility–performance dilemma caused by high surface tension. This review aims to summarize the strategies for the fabrication, processing, and application of LM-based nano-composites. The intrinsic mechanism and superiority of the composition method will further extend the capabilities of printable ink. Recent applications of LM-based nano-composites in printing are also provided to guide the large-scale production of stretchable electronics.
Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia
Authors: Góngora Alonso Susel , Marques Gonçalo , Agarwal Deevyankar , De la Torre Díez Isabel , Franco-Martín Manuel
Published: March 2022

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.
Nonlinear Dynamics of a Star-Shaped Structure and Variable Configuration of Elastic Elements for Energy Harvesting Applications
Authors: Margielewicz Jerzy , Gąska Damian , Litak Grzegorz , Wolszczak Piotr , Trigona Carlo
Published: March 2022

The subject of the model research contained in this paper is a new design solution of the energy harvesting system with a star-shaped structure of elastic elements and variable configuration. Numerical experiments focused mainly on the assessment of the configuration of elastic elements in the context of energy harvesting efficiency. The results of computer simulations were limited to zero initial conditions as it is the natural position of the static equilibrium. The article compares the energy efficiency for the selected range of the dimensionless excitation frequency. For this purpose, four cases of elastic element configurations were compared. The results are visualized based on the diagram of RMS voltage induced on piezoelectric electrodes, bifurcation diagrams, Lyapunov exponents, and Poincaré maps, showing the impact of individual solutions on the efficiency of energy harvesting. The results of the simulations show that the harvester’s efficiency ranges from 4 V to 20 V depending on the configuration and the frequency range of the excitation, but the design allows for a smooth adjustment to the given conditions.
Highly Sensitive Detection of the Insecticide Azamethiphos by Tris(2,2′-bipyridine)ruthenium(II) Electrogenerated Chemiluminescence
Authors: Barkae Tesfaye Hailemariam , Zeid Abdallah M. , Xu Guobao
Published: March 2022

Azamethiphos (AZA) is an insecticide and neurotoxic agent that causes the inhibition of acetylcholinesterase (AChE). AChE is a vital enzyme for neurotransmission because it metabolizes acetylcholine neurotransmitter at the synaptic cleft and terminates synaptic transmission. It is worth mentioning that organophosphates and carbamates inhibit AChE. These AChE inhibitors bind to the active site of the enzyme and inactivate it, leading to paralysis and death. Herein, for the first time, we develop a sensitive, low-cost, and rapid electrogenerated chemiluminescence (ECL) system for the detection of AZA. The designed ECL sensor was applied for the highly sensitive detection of AZA with a wide dynamic range (from 0.1 μM to 1000 μM) and low detection limit of 0.07 μM (S/N = 3). The practical utility of the sensor demonstrates high recoveries (96–102%) in real samples of lake water and wastewater.
Non-Destructive Measurement of Acetic Acid and Its Distribution in a Photovoltaic Module during Damp Heat Testing Using pH-Sensitive Fluorescent Dye Sensors
Authors: Nagasaki Hideaki , Asaka Takashi , Iwami Kentaro , Umeda Norihiro , Yamamoto Chizuko , Hara Yukiko , Masuda Atsushi
Published: March 2022

An optical pH sensor that enables the non-destructive measurement of acetic acid and its distribution in a photovoltaic module during damp heat (DH) testing is reported. The sensor was fabricated by impregnating a solution of a pH-sensitive fluorescent dye into a fluororesin membrane filter, which was then dried. While conducting the DH test, fluorescence spectra from 20 pH sensors were periodically recorded and converted into pH values using a predetermined calibration curve. As a result, we succeeded in measuring changes in pH with a DH test time of up to 2000 h, and it was possible to obtain information on the pH distribution in the module. We also confirmed no change in pH in a module with a silicone encapsulant free from acetic acid, and revealed that the sensor that we developed does not respond to moisture and heat, but only to acetic acid.
Hierarchical Human-Inspired Control Strategies for Prosthetic Hands
Authors: Gentile Cosimo , Cordella Francesca , Zollo Loredana
Published: March 2022

The abilities of the human hand have always fascinated people, and many studies have been devoted to describing and understanding a mechanism so perfect and important for human activities. Hand loss can significantly affect the level of autonomy and the capability of performing the activities of daily life. Although the technological improvements have led to the development of mechanically advanced commercial prostheses, the control strategies are rather simple (proportional or on/off control). The use of these commercial systems is unnatural and not intuitive, and therefore frequently abandoned by amputees. The components of an active prosthetic hand are the mechatronic device, the decoding system of human biological signals into gestures and the control law that translates all the inputs into desired movements. The real challenge is the development of a control law replacing human hand functions. This paper presents a literature review of the control strategies of prosthetics hands with a multiple-layer or hierarchical structure, and points out the main critical aspects of the current solutions, in terms of human’s functions replicated with the prosthetic device. The paper finally provides several suggestions for designing a control strategy able to mimic the functions of the human hand.
Design of a Gough–Stewart Platform Based on Visual Servoing Controller
Authors: Zhu Minglei , Huang Cong , Song Shijie , Gong Dawei
Published: March 2022

Designing a robot with the best accuracy is always an attractive research direction in the robotics community. In order to create a Gough–Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough–Stewart platform: leg-direction-based visual servoing, line-based visual servoing, and image moment visual servoing. Depending on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough–Stewart platform for each type of controller. Then, we perform co-simulations on the three optimized Gough–Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps get both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.
Multiple Cycle Slip Detection Algorithm for a Single Frequency Receiver
Authors: Yoon Young-Min , Lee Byung-Seok , Heo Moon-Beom
Published: March 2022

A satellite navigation system makes it simple to find and navigate to a specific position. Although a carrier measurement is required to establish a precise position due to the characteristics of the carrier observation, it is difficult to determine a robust position in a poor signal reception environment such as urban areas. Various studies are being carried out to overcome this problem, with cycle slips being the most important factor. With only a single frequency, it is very challenging to detect cycle slips in multiple satellite channels at the same time. A geometry-based technique is proposed in this study as a technical solution for detecting simultaneous cycle slips for multiple channels utilizing only a single-frequency receiver. The method could detect a half-wavelength size of cycle slip for each channel through the geometry information.
Ocean-Bottom Laser Seismograph
Authors: Dolgikh Grigory , Dolgikh Stanislav , Plotnikov Aleksandr
Published: March 2022

This paper describes an ocean-bottom laser seismograph, based on the modified laser meter of hydrosphere pressure variations, and designed to record vertical bottom displacements at the place of its location. Its measuring accuracy is about 1 nm, limited by the stability of the laser emission, which can be improved by using more advanced lasers. The purpose of this instrument is to measure the displacements of the seabed’s upper layer in the low-frequency sonic and infrasonic ranges. Theoretically, it can operate in the frequency range from 0 (conditionally) to 1000 Hz; the upper limit is determined by the operating speed of the digital registration system. We demonstrated the capabilities of the ocean-bottom laser seismograph while registering vertical bottom displacements caused by sea wind waves and lower frequency processes—seiches, i.e., eigenoscillations of the bay in which the instrument was installed. Comparison of experimental data of the bottom laser seismograph with the data of the laser hydrosphere pressure variations meter and the velocimeter—installed in close proximity—shows good efficiency of the instrument.
IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism
Authors: Popescu Aura-Loredana , Popescu Nirvana , Dobre Ciprian , Apostol Elena-Simona , Popescu Decebal
Published: March 2022

In the context in which it was demonstrated that humanoid robots are efficient in helping children diagnosed with autism in exploring their affective state, this paper underlines and proves the efficiency of a previously developed machine learning-based mobile application called PandaSays, which was improved and integrated with an Alpha 1 Pro robot, and discusses performance evaluations using deep convolutional neural networks and residual neural networks. The model trained with MobileNet convolutional neural network had an accuracy of 56.25%, performing better than ResNet50 and VGG16. A strategy for commanding the Alpha 1 Pro robot without its native application was also established and a robot module was developed that includes the communication protocols with the application PandaSays. The output of the machine learning algorithm involved in PandaSays is sent to the humanoid robot to execute some actions as singing, dancing, and so on. Alpha 1 Pro has its own programming language—Blockly—and, in order to give the robot specific commands, Bluetooth programming is used, with the help of a Raspberry Pi. Therefore, the robot motions can be controlled based on the corresponding protocols. The tests have proved the robustness of the whole solution.
Integrated IoT-Based Secure and Efficient Key Management Framework Using Hashgraphs for Autonomous Vehicles to Ensure Road Safety
Authors: Jha Sudan , Jha Nishant , Prashar Deepak , Ahmad Sultan , Alouffi Bader , Alharbi Abdullah
Published: March 2022

Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner. This centralized network is vulnerable to cyber-attacks which can cause data loss, resulting in road accidents. Thus, to prevent the vehicular network from being attacked and to prevent the privacy of the data, key management is used. However, key management alone over a centralized network is not effective in ensuring data integrity in a vehicular network. To resolve this issue, various studies have introduced a blockchain-based approach and enabled key management over a decentralized network. This technique is also found effective in ensuring the privacy of all the stakeholders involved in a vehicular network. Furthermore, a blockchain-based key management system can also help in storing a large amount of data over a distributed network, which can encourage a faster exchange of information between vehicles in a network. However, there are certain limitations of blockchain technology that may affect the efficient working of autonomous vehicles. Most of the existing blockchain-based systems are implemented over Ethereum or Bitcoin. The transaction-processing capability of these blockchains is in the range of 5 to 20 transactions per second, whereas hashgraphs are capable of processing thousands of transactions per second as the data are processed exponentially. Furthermore, a hashgraph prevents the user from altering the order of the transactions being processed, and they do not need high computational powers to operate, which may help in reducing the overall cost of the system. Due to the advantages offered by a hashgraph, an advanced key management framework based on a hashgraph for secure communication between the vehicles is suggested in this paper. The framework is developed using the concept of Leaving of Vehicles based on a Logical Key Hierarchy (LKH) and Batch Rekeying. The system is tested and compared with other closely related systems on the basis of the transaction compilation time and change in traffic rates.
Multiscale Sensing of Bone-Implant Loosening for Multifunctional Smart Bone Implants: Using Capacitive Technologies for Precision Controllability
Authors: Peres Inês , Rolo Pedro , Ferreira Jorge A. F. , Pinto Susana C. , Marques Paula A. A. P. , Ramos António , Soares dos Santos Marco P.
Published: March 2022

The world population growth and average life expectancy rise have increased the number of people suffering from non-communicable diseases, namely osteoarthritis, a disorder that causes a significant increase in the years lived with disability. Many people who suffer from osteoarthritis undergo replacement surgery. Despite the relatively high success rate, around 10% of patients require revision surgeries, mostly because existing implant technologies lack sensing devices capable of monitoring the bone–implant interface. Among the several monitoring methodologies already proposed as substitutes for traditional imaging methods, cosurface capacitive sensing systems hold the potential to monitor the bone–implant fixation states, a mandatory capability for long-term implant survival. A multifaceted study is offered here, which covers research on the following points: (1) the ability of a cosurface capacitor network to effectively monitor bone loosening in extended peri-implant regions and according to different stimulation frequencies; (2) the ability of these capacitive architectures to provide effective sensing in interfaces with hydroxyapatite-based layers; (3) the ability to control the operation of cosurface capacitive networks using extracorporeal informatic systems. In vitro tests were performed using a web-based network sensor composed of striped and interdigitated capacitive sensors. Hydroxyapatite-based layers have a minor effect on determining the fixation states; the effective operation of a sensor network-based solution communicating through a web server hosted on Raspberry Pi was shown. Previous studies highlight the inability of current bone–implant fixation monitoring methods to significantly reduce the number of revision surgeries, as well as promising results of capacitive sensing systems to monitor micro-scale and macro-scale bone–interface states. In this study, we found that extracorporeal informatic systems enable continuous patient monitoring using cosurface capacitive networks with or without hydroxyapatite-based layers. Findings presented here represent significant advancements toward the design of future multifunctional smart implants.
Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
Authors: Li Man , Zhou Huachun , Qin Yajuan
Published: March 2022

5G technologies provide ubiquitous connectivity. However, 5G security is a particularly important issue. Moreover, because public datasets are outdated, we need to create a self-generated dataset on the virtual platform. Therefore, we propose a two-stage intelligent detection model to enable 5G networks to withstand security issues and threats. Finally, we define malicious traffic detection capability metrics. We apply the self-generated dataset and metrics to thoroughly evaluate the proposed mechanism. We compare our proposed method with benchmark statistics and neural network algorithms. The experimental results show that the two-stage intelligent detection model can distinguish between benign and abnormal traffic and classify 21 kinds of DDoS. Our analysis also shows that the proposed approach outperforms all the compared approaches in terms of detection rate, malicious traffic detection capability, and response time.
Fast Constant-Time Modular Inversion over Fp Resistant to Simple Power Analysis Attacks for IoT Applications
Authors: Sghaier Anissa , Zeghid Medien , Massoud Chiraz , Ahmed Hassan Yousif , Chehri Abdellah , Machhout Mohsen
Published: March 2022

The advent of the Internet of Things (IoT) has enabled millions of potential new uses for consumers and businesses. However, with these new uses emerge some of the more pronounced risks in the connected object domain. Finite fields play a crucial role in many public-key cryptographic algorithms (PKCs), which are used extensively for the security and privacy of IoT devices, consumer electronic equipment, and software systems. Given that inversion is the most sensitive and costly finite field arithmetic operation in PKCs, this paper proposes a new, fast, constant-time inverter over prime fields Fp based on the traditional Binary Extended Euclidean (BEE) algorithm. A modified BEE algorithm (MBEEA) resistant to simple power analysis attacks (SPA) is presented, and the design performance area-delay over Fp is explored. Furthermore, the BEE algorithm, modular addition, and subtraction are revisited to optimize and balance the MBEEA signal flow and resource utilization efficiency. The proposed MBEEA architecture was implemented and tested on Xilinx FPGA Virtex #5, #6, and #7 devices. Our implementation over Fp (length of p = 256 bits) with 2035 slices achieved one modular inversion in only 1.12 μs on Virtex-7. Finally, we conducted a thorough comparison and performance analysis to demonstrate that the proposed design outperforms the competing designs, i.e., has a lower area-delay product (ADP) than the reported inverters.