bluuebunny/crossref_metadata_2025 huggingface dataset:
The Kids Are Alright (?). Infants’ Development and COVID-19 Pandemic: A Cross-Sectional Study
Authors: Ferrari Eleonora , Palandri Lucia , Lucaccioni Laura , Talucci Giovanna , Passini Erica , Trevisani Viola , Righi Elena
Published: June 2022

Objectives: The study aimed to assess and compare the global development in six-month-old infants before and during the pandemic restrictive social distancing measures.

Methods: This cross-sectional nested study involved infants assessed through the Griffiths Scales of Child Development (GSCD) between September 2019 and April 2021. Infants were classified in a pre-COVID or a COVID group, considering the evaluation date and the restrictive measures in place. GSCD subscales and General Development Scores (GDS) were calculated and compared.

Results: One hundred and four healthy term-born infants were evaluated. GDS in the COVID group (n:70; median: 94; IQR: 90–100) appeared significantly lower than in the pre-COVID group (n:34; median: 98; IQR: 97–103; p < 0.001). Language and personal-social-emotional subareas scores appeared the most affected. A decreasing trend of GDS along with the severity of restriction was observed.

Conclusion: A reduction in infant development scores was observed during pandemic social distancing. Further studies are needed to systematize these findings and to address effective public health policies for infants and families during long-term forced isolation periods.
Measuring Resilience and Fatality Rate During the First Wave of COVID-19 Pandemic in Northern Italy: A Note
Authors: Guccio Calogero
Published: June 2022

Background: This Policy Brief aims to contribute to the debate on the resilience of the healthcare systems during the pandemic by discussing whether mortality indicators are appropriate for assessing resilience or whether other statistics should be employed.

Evidence: During the first wave of the COVID-19, much emphasis was placed on case-fatality rates to offer a preliminary assessment of the resilience of healthcare systems. However, these statistics are often biased and do not consider the real figure of the population that has been infected.

Policy Options and Recommendations: Comparing data obtained with different approaches based on statistical inference and large-scale serological survey, the brief highlights, that great care must be taken when using case-fatality data, which in the absence of careful analysis, can lead to erroneous conclusions.

Conclusion: Using case-fatality rate gives us no sounding information about the real capability of healthcare systems to save lives during the pandemic. However, even in the absence of detailed epidemiological data new advancements in statistical methods can be useful to provide a more sounding evaluation of the resilience of the healthcare systems.
A New Trial to Measure ABO Antibodies Using Complement-Dependent Cytotoxicity
Authors: Youk Hee-Jeong , Ryu Ho-yoon , Seo Suk Won , Kim Jin Seok , Chung Yousun , Kim Hyungsuk , Hwang Sang-Hyun , Oh Heung-Bum , Min Won-Ki , Ko Dae-Hyun
Published: June 2022

Background and objectives: The ABO antibody (Ab) titration tests are used in monitoring in ABO-incompatible (ABOi) solid organ transplantation (SOT). However, currently developed ABO Ab tests show Ab binding reactions. This study attempted to measure ABO Ab level using complement-dependent cytotoxicity (CDC). Materials and methods: We studied 93 blood group O serum samples from patients who underwent ABOi SOT from January 2019 to May 2021. Patients’ sera were incubated with A1 or B cells and added to a human complement solution. Supernatants were collected after centrifugation, and free hemoglobin (Hb) was measured by spectrophotometry. We converted plasma Hb value to hemolysis (%), which were compared with ABO Ab titer. Results: We found a mild correlation between hemolysis and ABO Ab titers. In simple regression analysis, the correlation coefficients were within 0.3660–0.4968 (p < 0.0001) before transplantation. In multiple linear regression analysis, anti-A hemolysis (%) was higher in immunoglobulin M (IgM) (β = 12.9) than in immunoglobulin G (IgG) (β = −3.4) (R2 = 0.5216). Anti-B hemolysis was higher in IgM (β = 8.7) than in IgG (β = 0.0) (R2 = 0.5114). There was a large variation in hemolysis within the same Ab titer. Conclusions: CDC can be used in a new trial for ABO Ab measurement. Furthermore, IgM rather than IgG seems to play a significant role in vivo activity, consistent with previous knowledge. Thus, this study may help in the development of the ABO Ab titration supplement test for post-transplant treatment policy establishment and pre-transplant desensitization.
Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation
Authors: Bouteraa Yassine , Ben Abdallah Ismail , Alnowaiser Khaled , Islam Md Rasedul , Ibrahim Atef , Gebali Fayez
Published: June 2022

In this study, we present an IoT-based robot for wrist rehabilitation with a new protocol for determining the state of injured muscles as well as providing dynamic model parameters. In this model, the torque produced by the robot and the torque provided by the patient are determined and updated taking into consideration the constraints of fatigue. Indeed, in the proposed control architecture based on the EMG signal extraction, a fuzzy classifier was designed and implemented to estimate muscle fatigue. Based on this estimation, the patient’s torque is updated during the rehabilitation session. The first step of this protocol consists of calculating the subject-related parameters. This concerns axis offset, inertial parameters, passive stiffness, and passive damping. The second step is to determine the remaining component of the wrist model, including the interaction torque. The subject must perform the desired movements providing the torque necessary to move the robot in the desired direction. In this case, the robot applies a resistive torque to calculate the torque produced by the patient. After that, the protocol considers the patient and the robot as active and all exercises are performed accordingly. The developed robotics-based solution, including the proposed protocol, was tested on three subjects and showed promising results.
Human Milk Oligosaccharides and Lactose Differentially Affect Infant Gut Microbiota and Intestinal Barrier In Vitro
Authors: Natividad Jane Mea , Marsaux Benoît , Rodenas Clara Lucia Garcia , Rytz Andreas , Vandevijver Gies , Marzorati Massimo , Van den Abbeele Pieter , Calatayud Marta , Rochat Florence
Published: June 2022

Background: The infant gut microbiota establishes during a critical window of opportunity when metabolic and immune functions are highly susceptible to environmental changes, such as diet. Human milk oligosaccharides (HMOs) for instance are suggested to be beneficial for infant health and gut microbiota. Infant formulas supplemented with the HMOs 2′-fucosyllactose (2′-FL) and lacto-N-neotetraose (LNnT) reduce infant morbidity and medication use and promote beneficial bacteria in the infant gut ecosystem. To further improve infant formula and achieve closer proximity to human milk composition, more complex HMO mixtures could be added. However, we currently lack knowledge about their effects on infants’ gut ecosystems. Method: We assessed the effect of lactose, 2′-FL, 2′-FL + LNnT, and a mixture of six HMOs (HMO6: consisting of 2′-FL, LNnT, difucosyllactose, lacto-N-tetraose, 3′- and 6′-sialyllactose) on infant gut microbiota and intestinal barrier integrity using a combination of in vitro models to mimic the microbial ecosystem (baby M-SHIME®) and the intestinal epithelium (Caco-2/HT29-MTX co-culture). Results: All the tested products had bifidogenic potential and increased SCFA levels; however, only the HMOs’ fermented media protected against inflammatory intestinal barrier disruption. 2′-FL/LNnT and HMO6 promoted the highest diversification of OTUs within the Bifidobactericeae family, whereas beneficial butyrate-producers were specifically enriched by HMO6. Conclusion: These results suggest that increased complexity in HMO mixture composition may benefit the infant gut ecosystem, promoting different bifidobacterial communities and protecting the gut barrier against pro-inflammatory imbalances.
The Effects of Eggs in a Plant-Based Diet on Oxidative Stress and Inflammation in Metabolic Syndrome
Authors: Thomas Minu S. , Huang Lindsey , Garcia Chelsea , Sakaki Junichi R. , Blesso Christopher N. , Chun Ock K. , Fernandez Maria Luz
Published: June 2022

We recently reported that the inclusion of whole eggs in plant-based diets (PBD) increased plasma choline, lutein, and zeaxanthin in individuals with metabolic syndrome (MetS). The objective of the current study was to evaluate whether this dietary pattern would protect against oxidative stress and low-grade inflammation, two common characteristics of MetS. We recruited 24 men and women with MetS, who, after following a PBD for 2 weeks (baseline), were randomly allocated to consume either two whole eggs with 70 g of spinach/day (EGG) or the equivalent amount of egg substitute with spinach (SUB) as breakfast for 4 weeks. After a 3-week washout, they were allocated to the alternate breakfast. We measured biomarkers of oxidation and inflammation at baseline and at the end of each intervention. Tumor necrosis factor-alpha, interleukin-6, monocyte protein attractant-1, liver enzymes, and C-reactive protein, as well as total antioxidant capacity, paraoxonase-1 (PON-1) activity, and other biomarkers of oxidation were not different at the end of EGG or SUB or when compared to baseline. However, plasma malondialdehyde (MDA) concentrations were lower (p < 0.05) during the EGG and baseline compared to SUB. In addition, the increases in dietary lutein and zeaxanthin previously observed had a strong positive correlation with PON-1 activity (r = 0.522, p < 0.01) only during the EGG period, whereas plasma zeaxanthin was negatively correlated with MDA (r = −0.437, p < 0.01). The number of participants with MetS was reduced from 24 during screening to 21, 13, and 17 during the BL, EGG, and SUB periods, respectively, indicating that eggs were more effective in reversing the characteristics of MetS. These data suggest that adding eggs to a PBD does not detrimentally affect inflammation or oxidative stress; on the contrary, eggs seem to provide additional protection against the biomarkers that define MetS.
Review on Starter Pellets: Inert and Functional Cores
Authors: Kállai-Szabó Nikolett , Lengyel Miléna , Farkas Dóra , Barna Ádám Tibor , Fleck Christian , Basa Bálint , Antal István
Published: June 2022

A significant proportion of pharmaceuticals are now considered multiparticulate systems. Modified-release drug delivery formulations can be designed with engineering precision, and patient-centric dosing can be accomplished relatively easily using multi-unit systems. In many cases, Multiple-Unit Pellet Systems (MUPS) are formulated on the basis of a neutral excipient core which may carry the layered drug surrounded also by functional coating. In the present summary, commonly used starter pellets are presented. The manuscript describes the main properties of the various nuclei related to their micro- and macrostructure. In the case of layered pellets formed based on different inert pellet cores, the drug release mechanism can be expected in detail. Finally, the authors would like to prove the industrial significance of inert cores by presenting some of the commercially available formulations.
Formulation and Optimal Design of Dioscorea bulbifera and Honey-Loaded Gantrez®/Xyloglucan Hydrogel as Wound Healing Patches
Authors: Eakwaropas Pattaranut , Ngawhirunpat Tanasait , Rojanarata Theerasak , Patrojanasophon Prasopchai , Opanasopit Praneet , Nuntharatanapong Nopparat
Published: June 2022

Hydrogel patches are some of the most effective dressings for wound healing. In this study, the Gantrez® S-97 (Gan)/xyloglucan (XG) hydrogel patches were formulated by using a full central composite design (CCD). The optimized hydrogel patches consisted of 17.78% w/w of Gan and 0.1% w/w of XG. Honey and D. bulbifera extract were loaded in the Gan/XG hydrogel patches. The physical properties of the hydrogel patches, including water content, water absorption, rate of water vapor transmission, and mechanical properties, were examined. The D. bulbifera extract/honey-loaded patch exhibited a higher value of water absorption, tensile strength, and elongation than the honey-loaded patch and the unloaded patch, respectively. The biological activities of the patches were also investigated. All hydrogel patches protected wounds from external bacterial infection. The D. bulbifera extract/honey-loaded patch exhibited stronger antioxidant activity than the honey-loaded patch and the unloaded patch. Besides, all the hydrogel patches with concentrations of 0.5–2.5 mg/mL showed that they were nontoxic to fibroblast cells. The combination of D. bulbifera extract and honey in the patch affected fibroblast proliferation. In addition, all Gan/XG hydrogel patches significantly induced recovery of the scratch area. Therefore, the Gan/XG hydrogel patches could be candidates as wound dressings.
Metabolic Reprogramming in Cancer Cells: Emerging Molecular Mechanisms and Novel Therapeutic Approaches
Authors: Navarro Carla , Ortega Ángel , Santeliz Raquel , Garrido Bermary , Chacín Maricarmen , Galban Néstor , Vera Ivana , De Sanctis Juan Bautista , Bermúdez Valmore
Published: June 2022

The constant changes in cancer cell bioenergetics are widely known as metabolic reprogramming. Reprogramming is a process mediated by multiple factors, including oncogenes, growth factors, hypoxia-induced factors, and the loss of suppressor gene function, which support malignant transformation and tumor development in addition to cell heterogeneity. Consequently, this hallmark promotes resistance to conventional anti-tumor therapies by adapting to the drastic changes in the nutrient microenvironment that these therapies entail. Therefore, it represents a revolutionary landscape during cancer progression that could be useful for developing new and improved therapeutic strategies targeting alterations in cancer cell metabolism, such as the deregulated mTOR and PI3K pathways. Understanding the complex interactions of the underlying mechanisms of metabolic reprogramming during cancer initiation and progression is an active study field. Recently, novel approaches are being used to effectively battle and eliminate malignant cells. These include biguanides, mTOR inhibitors, glutaminase inhibition, and ion channels as drug targets. This review aims to provide a general overview of metabolic reprogramming, summarise recent progress in this field, and emphasize its use as an effective therapeutic target against cancer.
Metabolism and Regulation of Ascorbic Acid in Fruits
Authors: Zheng Xianzhe , Gong Min , Zhang Qiongdan , Tan Huaqiang , Li Liping , Tang Youwan , Li Zhengguo , Peng Mingchao , Deng Wei
Published: June 2022

Ascorbic acid, also known as vitamin C, is a vital antioxidant widely found in plants. Plant fruits are rich in ascorbic acid and are the primary source of human intake of ascorbic acid. Ascorbic acid affects fruit ripening and stress resistance and plays an essential regulatory role in fruit development and postharvest storage. The ascorbic acid metabolic pathway in plants has been extensively studied. Ascorbic acid accumulation in fruits can be effectively regulated by genetic engineering technology. The accumulation of ascorbic acid in fruits is regulated by transcription factors, protein interactions, phytohormones, and environmental factors, but the research on the regulatory mechanism is still relatively weak. This paper systematically reviews the regulation mechanism of ascorbic acid metabolism in fruits in recent decades. It provides a rich theoretical basis for an in-depth study of the critical role of ascorbic acid in fruits and the cultivation of fruits rich in ascorbic acid.
Failure Prediction of Notched Composites Using Multiscale Approach
Authors: Kwon Young W.
Published: June 2022

This paper presents multiscale-based failure criteria to predict the failure of polymer composites with any shape of defect. The multiscale technique consists of bi-directional processes called upscaling and downscaling processes to link two different length scales. One is the microscale at the fiber and matrix material level, and the other is the macroscale at the homogenized composite material level. Failure criteria are applied to the microscale level such as micro-stresses and/or micro-strains occurring at the fiber and matrix material level. Recently proposed unified failure criteria are applied to the micro-stresses and/or micro-strains to predict failure at a notch. The new failure criteria have two parts, and they can be applied to any shape of defect. One is the stress or strain condition, and the other is the stress or strain gradient condition. For failure to occur, both conditions must be satisfied simultaneously. The failure criteria provide not only failure locations but also directions of failure propagation.
Performance Optimization Approach of Polymer Modified Asphalt Mixtures with PET and PE Wastes: A Safety Study for Utilizing Eco-Friendly Circular Economy-Based SDGs Concepts
Authors: Mushtaq Faizan , Huang Zhian , Shah Syyed Adnan Raheel , Zhang Yinghua , Gao Yukun , Azab Marc , Hussain Sajid , Anwar Muhammad Kashif
Published: June 2022

Eco-friendly waste utilization helps in the development of sustainable infrastructures. Recently, researchers have focused on the production of road infrastructures using the circular economy concept of human safety. The objective of this study is to investigate and explore the utilization of optimum polymer waste content for the development of polymer-modified asphalt mixtures using response surface methodology (RSM). RSM based on Box–Behnken design (BBD) was employed to optimize experimental design and included three factors: X1, polymer type; X2, polymer contents; and X3, testing day. The optimized responses determined by the RSM were as follows: MS of 42.98 kN, MF of 5.08 mm, and MQ of 8.66 kN/mm, indicating a favorable and consistent precision in comparison with experimental values. Moreover, the Marshall characteristics of samples prepared with PE were quite improved compared to PET. In conclusion, the incorporation of such polymer wastes in road construction is a sustainable and cost-effective way of improving their engineering properties. This study will help in the development of sustainable road infrastructures supporting human safety and environmentally friendly practices.
Experimental Investigation of the Different Polyacrylamide Dosages on Soil Water Movement under Brackish Water Infiltration
Authors: Zhang Jihong , Wang Quanjiu , Mu Weiyi , Wei Kai , Guo Yi , Sun Yan
Published: June 2022

The use of soil conditioners in conjunction with brackish water irrigation is critical for the efficient development and use of brackish water as well as the enhancement of the structure of saline soil and stimulating crop growth. This study investigated the effects of different polyacrylamide (PAM) dosages (0, 0.02%, 0.04%, and 0.06%) on the water flow properties of sandy loam during brackish water infiltration using one-dimensional vertical and horizontal soil column infiltration experiments. The results showed that: (1) PAM could lower the soil infiltration rate and increase soil water retention performance under brackish water infiltration conditions. (2) PAM had a significant effect on the parameters of the Philip and Kostiakov infiltration models. The soil sorption rate S and the empirical coefficient λ were the smallest, and the empirical index β was the largest when the PAM dosage was 0.04%. (3) PAM dosage displayed a quadratic polynomial connection with the soil saturated water content and the saturated hydraulic conductivity. The soil saturated water content was highest when the PAM dosage was 0.04%, the intake suction hd of the Brooks-Corey model increased by 15.30%, and the soil water holding capacity was greatly improved. (4) Soil treated with PAM could absorb more water under the same soil water suction, whereas the soil unsaturated hydraulic conductivity and its growth rate decreased. The soil saturated diffusion rate Ds, as well as the soil water diffusion threshold, rose. Finally, the 0.04% PAM dosage could improve soil hydrodynamic characteristics under brackish water infiltration, which is beneficial for the efficient utilization of brackish water.
Eye-Safe Aerosol and Cloud Lidar Based on Free-Space Intracavity Upconversion Detection
Authors: Yue Wenjie , Chen Tao , Kong Wei , Chen Xin , Huang Genghua , Shu Rong
Published: June 2022

We report an eye-safe aerosol and cloud lidar with an Erbium-doped fiber laser (EDFL) and a free-space intracavity upconversion detector as the transmitter and receiver, respectively. The EDFL was home-made, which could produce linearly-polarized pulses at a repetition rate of 15 kHz with pulse energies of ~70 μJ and pulse durations of ~7 ns centered at 1550 nm. The echo photons were upconverted to ~631 nm via the sum frequency generation process in a bow-tie cavity, where a Nd:YVO4 and a PPLN crystal served as the pump and nonlinear frequency conversion devices, respectively. The upconverted visible photons were recorded by a photomultiplier tube and their timestamps were registered by a customized time-to-digital converter for distance-resolved measurement. Reflected signals peaked at ~6.8 km from a hard target were measured with a distance resolution of 0.6 m for an integral duration of 10 s. Atmospheric backscattered signals, with a range of ~6 km, were also detectable for longer integral durations. The evolution of aerosols and clouds were recorded by this lidar in a preliminary experiment with a continuous measuring time of over 18 h. Clear boundary and fine structures of clouds were identified with a spatial resolution of 9.6 m during the measurement, showing its great potential for practical aerosol and cloud monitoring.
Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar
Authors: Li Jiaming , Yang Qiang , Zhang Xin , Ji Xiaowei , Xiao Dezhu
Published: June 2022

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.
A Wearable Assistive Device for Blind Pedestrians Using Real-Time Object Detection and Tactile Presentation
Authors: Shen Junjie , Chen Yiwen , Sawada Hideyuki
Published: June 2022

Nowadays, improving the traffic safety of visually impaired people is a topic of widespread concern. To help avoid the risks and hazards of road traffic in their daily life, we propose a wearable device using object detection techniques and a novel tactile display made from shape-memory alloy (SMA) actuators. After detecting obstacles in real-time, the tactile display attached to a user’s hands presents different tactile sensations to show the position of the obstacles. To implement the computation-consuming object detection algorithm in a low-memory mobile device, we introduced a slimming compression method to reduce 90% of the redundant structures of the neural network. We also designed a particular driving circuit board that can efficiently drive the SMA-based tactile displays. In addition, we also conducted several experiments to verify our wearable assistive device’s performance. The results of the experiments showed that the subject was able to recognize the left or right position of a stationary obstacle with 96% accuracy and also successfully avoided collisions with moving obstacles by using the wearable assistive device.
Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data
Authors: Chao Ko-Chieh , Chou Chuan-Bi , Lee Ching-Hung
Published: June 2022

Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data, which is called cross-domain learning problem. In recent years, many domain adaptation methods have been proposed to solve this kind of problems and make much progress. However, existing domain adaptation approaches have a common assumption that the number of the data in source domain (labeled data) and target domain (unlabeled data) is matched. In this paper, the scenarios in real manufacturing site are considered, that the target domain data is much less than source domain data at the beginning, but the number of target domain data will increase as time goes by. A novel method is proposed for fault diagnosis of rolling bearing with online imbalanced cross-domain data. Finally, the proposed method which is tested on bearing dataset (CWRU) has achieved prediction accuracy of 95.89% with only 40 target samples. The results have been compared with other traditional methods. The comparisons show that the proposed online domain adaptation fault diagnosis method has achieved significant improvements. In addition, the deep transfer learning model by adaptive- network-based fuzzy inference system (ANFIS) is introduced to interpretation the results.
Efficient Illumination for a Light-Addressable Potentiometric Sensor
Authors: Yoshinobu Tatsuo , Miyamoto Ko-ichiro
Published: June 2022

A light-addressable potentiometric sensor (LAPS) is a chemical sensor that is based on the field effect in an electrolyte–insulator–semiconductor structure. It requires modulated illumination for generating an AC photocurrent signal that responds to the activity of target ions on the sensor surface. Although high-power illumination generates a large signal, which is advantageous in terms of the signal-to-noise ratio, excess light power can also be harmful to the sample and the measurement. In this study, we tested different waveforms of modulated illuminations to find an efficient illumination for a LAPS that can enlarge the signal as much as possible for the same input light power. The results showed that a square wave with a low duty ratio was more efficient than a sine wave by a factor of about two.
QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks
Authors: Ortega Andres , Tralli Velio
Published: June 2022

The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.
Real-Time Structural Health Monitoring and Damage Identification Using Frequency Response Functions along with Finite Element Model Updating Technique
Authors: Singh Tarunpreet , Sehgal Shankar , Prakash Chander , Dixit Saurav
Published: June 2022

Throughout service, damage can arise in the structure of buildings; hence, their dynamic testing becomes essential to verify that such buildings possess sufficient strength to withstand disturbances, particularly in the event of an earthquake. Dynamic testing, being uneconomical, requires proof of concept; for this, a model of a structure can be dynamically tested, and the results are used to update its finite element model. This can be used for damage detection in the prototype and aids in predicting its behavior during an earthquake. In this instance, a wireless MEMS accelerometer was used, which can measure the vibration signals emanating from the building and transfer these signals to a remote workstation. The base of the structure is excited using a shaking table to induce an earthquake-like situation. Four natural frequencies have been considered and six different types of damage conditions have been identified in this work. For each damage condition, the experimental responses are measured and the finite element model is updated using the Berman and Nagy method. It is seen that the updated models can predict the dynamic responses of the building accurately. Thus, depending on these responses, the damage condition can be identified by using the updated finite element models.
Research on Multi-Sensor Fusion Indoor Fire Perception Algorithm Based on Improved TCN
Authors: Li Yang , Su Yanmang , Zeng Xiangye , Wang Jingyi
Published: June 2022

Indoor fires cause huge casualties and economic losses worldwide. Thus, it is critical to quickly and accurately perceive the fire. In this work, an indoor fire perception algorithm based on multi-sensor fusion was proposed. Firstly, the sensor data features were fully extracted by improved temporal convolutional network (TCN). Then, the dimension of the extracted features was reduced by adaptive average pooling (AAP). Finally, the fire classification was realized by the support vector machine (SVM) classifier. Experimental results demonstrated that the proposed algorithm can improve accuracy of fire classification by more than 2.5% and detection speed by more than 15%, compared with TCN, back propagation (BP) neural network and long short-term memory (LSTM). In conclusion, the proposed algorithm can perceive the fire quickly and accurately, which is of great significance to improve the performance of the current fire prediction systems.
Dynamic Routings in Satellite Networks: An Overview
Authors: Cao Xiaoli , Li Yitao , Xiong Xingzhong , Wang Jun
Published: June 2022

The Satellite network is an important part of the global network. However, the complex architecture, changeable constellation topology, and frequent inter-satellite connection switching problems bring great challenges to the routing designs of satellite networks, making the study of the routing methods in satellite networks a research hotspot. Therefore, this paper investigates the latest existing routing works to tackle the dynamic routing problems in satellite networks. The architecture and development of satellite networks are presented and analyzed first. Afterward, dynamic routing problems in satellite networks are analyzed in detail based on the time-varying network topology. According to the latest works, the advanced satellite network routing schemes, including single-layer and multi-layer dynamic routing are introduced and analyzed. In addition, the merits, shortcomings, and applications of these schemes are analyzed and summarized. Finally, potential technologies and future directions are discussed.
Investigation on Rupture Initiation and Propagation of Traffic Tunnel under Seismic Excitation Based on Acoustic Emission Technology
Authors: Liu Xiling , Zeng Yuan , Fan Ling , Peng Shuquan , Liu Qinglin
Published: June 2022

Traffic tunnels are important engineering structures in transportation, and their stability is critical to traffic safety. In particular, when these tunnels are in an earthquake-prone area, the rupture process under seismic excitation needs to be studied in depth for safer tunnel design. In this paper, based on a construction project on the Nairobi-Malaba railway in East Africa, a laboratory shaking table test with 24 working cases of seismic excitation on a mountain tunnel is designed, and the acoustic emission (AE) technique is employed to investigate the tunnel rupture process. The results show that the high frequency components between 20 and 30 kHz of AE signals are the tunnel rupturing signals under the seismic excitation under such conditions. The tunnel vault and the arch foot are prone to rupture during the seismic excitation, and the initial rupture in the arch foot and vault of the tunnel occur under the horizontal and vertical Kobe wave seismic excitation, respectively, with a maximum acceleration of 0.4 g. After the rupture initiation, the tunnel arch foot continues to rupture in the subsequent working cases regardless of whether the excitation direction is horizontal or vertical, while the tunnel vault does not rupture continuously with the implementation of the subsequent excitations. Moreover, the Kobe seismic wave has a higher degree of damage potential to underground structures than the El seismic wave.
Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns
Authors: Chophuk Ponlawat , Chamnongthai Kosin , Chinnasarn Krisana
Published: June 2022

Most of the existing methods focus mainly on the extraction of shape-based, rotation-based, and motion-based features, usually neglecting the relationship between hands and body parts, which can provide significant information to address the problem of similar sign words based on the backhand approach. Therefore, this paper proposes four feature-based models. The spatial–temporal body parts and hand relationship patterns are the main feature. The second model consists of the spatial–temporal finger joint angle patterns. The third model consists of the spatial–temporal 3D hand motion trajectory patterns. The fourth model consists of the spatial–temporal double-hand relationship patterns. Then, a two-layer bidirectional long short-term memory method is used to deal with time-independent data as a classifier. The performance of the method was evaluated and compared with the existing works using 26 ASL letters, with an accuracy and F1-score of 97.34% and 97.36%, respectively. The method was further evaluated using 40 double-hand ASL words and achieved an accuracy and F1-score of 98.52% and 98.54%, respectively. The results demonstrated that the proposed method outperformed the existing works under consideration. However, in the analysis of 72 new ASL words, including single- and double-hand words from 10 participants, the accuracy and F1-score were approximately 96.99% and 97.00%, respectively.
A Low-Power Analog Processor-in-Memory-Based Convolutional Neural Network for Biosensor Applications
Authors: Byun Sung-June , Kim Dong-Gyun , Park Kyung-Do , Choi Yeun-Jin , Kumar Pervesh , Ali Imran , Kim Dong-Gyu , Yoo June-Mo , Huh Hyung-Ki , Jung Yeon-Jae , Kim Seok-Kee , Pu Young-Gun , Lee Kang-Yoon
Published: June 2022

This paper presents an on-chip implementation of an analog processor-in-memory (PIM)-based convolutional neural network (CNN) in a biosensor. The operator was designed with low power to implement CNN as an on-chip device on the biosensor, which consists of plates of 32 × 32 material. In this paper, 10T SRAM-based analog PIM, which performs multiple and average (MAV) operations with multiplication and accumulation (MAC), is used as a filter to implement CNN at low power. PIM proceeds with MAV operations, with feature extraction as a filter, using an analog method. To prepare the input feature, an input matrix is formed by scanning a 32 × 32 biosensor based on a digital controller operating at 32 MHz frequency. Memory reuse techniques were applied to the analog SRAM filter, which is the core of low power implementation, and in order to accurately grasp the MAC operational efficiency and classification, we modeled and trained numerous input features based on biosignal data, confirming the classification. When the learned weight data was input, 19 mW of power was consumed during analog-based MAC operation. The implementation showed an energy efficiency of 5.38 TOPS/W and was differentiated through the implementation of 8 bits of high resolution in the 180 nm CMOS process.
Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition
Authors: Al-Hammadi Muneer , Bencherif Mohamed A. , Alsulaiman Mansour , Muhammad Ghulam , Mekhtiche Mohamed Amine , Abdul Wadood , Alohali Yousef A. , Alrayes Tareq S. , Mathkour Hassan , Faisal Mohammed , Algabri Mohammed , Altaheri Hamdi , Alfakih Taha , Ghaleb Hamid
Published: June 2022

Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing people. Sign language recognition aims to facilitate this mastering difficulty and bridge the communication gap between hearing-impaired people and others. This study presents an efficient architecture for sign language recognition based on a convolutional graph neural network (GCN). The presented architecture consists of a few separable 3DGCN layers, which are enhanced by a spatial attention mechanism. The limited number of layers in the proposed architecture enables it to avoid the common over-smoothing problem in deep graph neural networks. Furthermore, the attention mechanism enhances the spatial context representation of the gestures. The proposed architecture is evaluated on different datasets and shows outstanding results.
A Simple Optical Sensor Based on Multimodal Interference Superimposed on Additive Manufacturing for Diameter Measurement
Authors: Cardoso Victor H. R. , Caldas Paulo , Giraldi Maria Thereza R. , Fernandes Cindy Stella , Frazão Orlando , Costa João C. W. Albuquerque , Santos José Luís
Published: June 2022

In many areas, the analysis of a cylindrical structure is necessary, and a form to analyze it is by evaluating the diameter changes. Some areas can be cited: pipelines for oil or gas distribution and radial growth of trees whose diameter changes are directly related to irrigation and the radial expansion since it depends on the water soil deficit. For some species, these radial variations can change in around 5 mm. This paper proposes and experimentally investigates a sensor based on a core diameter mismatch technique for diameter changes measurement. The sensor structure is a combination of a cylindrical piece developed using a 3D printer and a Mach–Zehnder interferometer. The pieces were developed to assist in monitoring the diameter variation. It is formed by splicing an uncoated short section of MMF (Multimode Fiber) between two standard SMFs (Singlemode Fibers) called SMF-MMF-SMF (SMS), where the MMF length is 15 mm. The work is divided into two main parts. Firstly, the sensor was fixed at two points on the first developed piece, and the diameter reduction caused dips or peaks shift of the transmittance spectrum due to curvature and strain influence. The fixation point (FP) distances used are: 5 mm, 10 mm, and 15 mm. Finally, the setup with the best sensitivity was chosen, from first results, to develop another test with an optimization. This optimization is performed in the printed piece where two supports are created so that only the strain influences the sensor. The results showed good sensitivity, reasonable dynamic range, and easy setup reproduction. Therefore, the sensor could be used for diameter variation measurement for proposed applications.
Consistent Extended Kalman Filter-Based Cooperative Localization of Multiple Autonomous Underwater Vehicles
Authors: Zhang Fubin , Wu Xingqi , Ma Peng
Published: June 2022

In order to solve the problem of inconsistent state estimation when multiple autonomous underwater vehicles (AUVs) are co-located, this paper proposes a method of multi-AUV co-location based on the consistent extended Kalman filter (EKF). Firstly, the dynamic model of cooperative positioning system follower AUV under two leaders alternately transmitting navigation information is established. Secondly, the observability of the standard linearization estimator based on the lead-follower multi-AUV cooperative positioning system is analyzed by comparing the subspace of the observable matrix of state estimation with that of an ideal observable matrix, it can be concluded that the estimation of state by standard EKF is inconsistent. Finally, aiming at the problem of inconsistent state estimation, a consistent EKF multi-AUV cooperative localization algorithm is designed. The algorithm corrects the linearized measurement values in the Jacobian matrix for cooperative positioning, ensuring that the linearized estimator can obtain accurate measurement values. The positioning results of the follower AUV under dead reckoning, standard EKF, and consistent EKF algorithms are simulated, analyzed, and compared with the real trajectory of the following AUV. The simulation results show that the follower AUV with a consistent EKF algorithm can keep synchronization with the leader AUV more stably.
Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems
Authors: Colpaert Alfred
Published: June 2022

Satellite and UAV (unmanned aerial vehicle) imagery has become an important source of data for Geographic Information Systems (GISs) [...]
Analysis of Extended Information Provided by Bluetooth Traffic Monitoring Systems to Enhance Short-Term Level of Service Prediction
Authors: Fernández Pozo Rubén , Rodríguez González Ana Belén , Wilby Mark Richard , Vinagre Díaz Juan José
Published: June 2022

Bluetooth monitoring systems (BTMS) have opened a new era in traffic sensing, providing a reliable, economical, and easy-to-deploy solution to uniquely identify vehicles. Raw data from BTMS have traditionally been used to calculate travel time and origin–destination matrices. However, we could extend this to include other information like the number of vehicles or their residence times. This information, together with their temporal components, can be applied to the complex task of forecasting traffic. Level of service (LOS) prediction has opened a novel research line that fulfills the need to anticipate future traffic states, based on a standard link-based variable, accepted for both researchers and practitioners. In this paper, we incorporate BTMS’s extended variables and temporal information to an LOS classifier based on a Random Undersampling Boost algorithm, which is proven to efficiently respond to the data unbalance intrinsic to this problem. By using this approach, we achieve an overall recall of 87.2% for up to 15-min prediction horizons, reaching 96.6% predicting congestion, and improving the results for the intermediate traffic states, especially complex given their intrinsic instability. Additionally, we provide detailed analyses on the impact of temporal information on the LOS predictor’s performance, observing improvements up to a separation of 50 min between last features and prediction horizons. Furthermore, we study the predictor importance resulting from the classifiers to highlight those features contributing the most to the final achievements.
A New Proportionate Filtered-x RLS Algorithm for Active Noise Control System
Authors: Liang Xiaobei , Yao Jinyong , Luo Lei , Zhu Wenzhao , Zhang Weifang , Wang Yanrong
Published: June 2022

The filtered-x recursive least square (FxRLS) algorithm is widely used in the active noise control system and has achieved great success in some complex de-noising environments, such as the cabin in vehicles and aircraft. However, its performance is sensitive to some user-defined parameters such as the forgetting factor and initial gain. Once these parameters are not selected properly, the de-noising effect of FxRLS will deteriorate. Moreover, the tracking performance of FxRLS for mutation is still restricted to a certain extent. To solve the above problems, this paper proposes a new proportional FxRLS (PFxRLS) algorithm. The forgetting factor and initial gain sensitivity are successfully reduced without introducing new turning parameters. The de-noising level and tracking performance have also been improved. Moreover, the momentum technique is introduced in PFxRLS to further improve its robustness and de-noising level. To ensure stability, its convergence condition is also discussed in this paper. The effectiveness of the proposed algorithms is illustrated by simulations and experiments with different user-defined parameters and time-varying noise environments.
Bioluminescence Sensing in 3D Spherical Microtissues for Multiple Bioactivity Analysis of Environmental Samples
Authors: Calabretta Maria Maddalena , Gregucci Denise , Guarnieri Tiziana , Bonini Marina , Neri Elisa , Zangheri Martina , Michelini Elisa
Published: June 2022

The development of predictive in vitro sensing tools able to provide rapid information on the different bioactivities of a sample is of pivotal importance, not only to monitor environmental toxicants, but also to understand their mechanisms of action on diverse molecular pathways. This mechanistic understanding is highly important for the characterization of toxicological hazards, and for the risk assessment of chemicals and environmental samples such as surface waters and effluents. Prompted by this need, we developed and optimized a straightforward bioluminescent multiplexed assay which enables the measurement of four bioactivities, selected for their relevance from a toxicological perspective, in bioluminescent microtissues. The assay was developed to monitor inflammatory, antioxidant, and toxic activity, and the presence of heavy metals, and was successfully applied to the analysis of river water samples, showing potential applicability for environmental analyses. The assay, which does not require advanced equipment, can be easily implemented in general laboratories equipped with basic cell culture facilities and a luminometer.