bluuebunny/crossref_metadata_2025 huggingface dataset:
Nanoplatforms Potentiated Ablation-Immune Synergistic Therapy through Improving Local Control and Suppressing Recurrent Metastasis
Authors: Wei Zixuan , Yu Xiaoya , Huang Mao , Wen Liewei , Lu Cuixia
Published: May 2023

Minimally invasive ablation has been widely applied for treatment of various solid tumors, including hepatocellular carcinoma, renal cell carcinoma, breast carcinomas, etc. In addition to removing the primary tumor lesion, ablative techniques are also capable of improving the anti-tumor immune response by inducing immunogenic tumor cell death and modulating the tumor immune microenvironment, which may be of great benefit to inhibit the recurrent metastasis of residual tumor. However, the short-acting activated anti-tumor immunity of post-ablation will rapidly reverse into an immunosuppressive state, and the recurrent metastasis owing to incomplete ablation is closely associated with a dismal prognosis for the patients. In recent years, numerous nanoplatforms have been developed to improve the local ablative effect through enhancing the targeting delivery and combining it with chemotherapy. Particularly, amplifying the anti-tumor immune stimulus signal, modulating the immunosuppressive microenvironment, and improving the anti-tumor immune response with the versatile nanoplatforms have heralded great application prospects for improving the local control and preventing tumor recurrence and distant metastasis. This review discusses recent advances in nanoplatform-potentiated ablation-immune synergistic tumor therapy, focusing on common ablation techniques including radiofrequency, microwave, laser, and high-intensity focused ultrasound ablation, cryoablation, and magnetic hyperthermia ablation, etc. We discuss the advantages and challenges of the corresponding therapies and propose possible directions for future research, which is expected to provide references for improving the traditional ablation efficacy.
Intra-Cavity Cascaded Pumped 946/1030 nm Dual-Wavelength Vortex Laser Using a Spot-Defect Mirror
Authors: Yang Yashuai , Li Yongliang , Zhu Xinyi , Wang Chi , Yang Chao
Published: May 2023

Due to their unique properties, vortex lasers have high application value in frontier fields such as optical micromanipulation, super-resolution imaging, quantum entanglement, and optical communication. In this study, we demonstrated a 946/1030 nm Laguerre-Gaussian (LG01) mode dual-wavelength vortex laser by using an intracavity cascade pumped structure and a spot-defect output mirror. Using a coaxial linear cavity structure, the 808 nm laser diode (LD) was used to end-pump the Nd:YAG crystal to generate a 946 nm laser and then use it to directly pump the Yb:YAG crystal in the cavity to generate a 1030 nm laser, and finally a 946/1030 nm dual-wavelength laser came out. By making a spot defect in the center of the output mirror to suppress the oscillation of the fundamental Gaussian mode laser and carefully adjusting the position of the laser crystals, the LG01 mode dual-wavelength vortex laser was output in single handedness. When the pump power was 40 W, the total output was 664 mW (356 and 308 mW at 946 and 1030 nm LG01 mode vortex lasers), and the total optical-optical conversion efficiency was 1.7%; the output power fluctuations of 946 and 1030 nm LG01 mode vortex lasers within 1 h were 3.43% and 3.13%, respectively; the beam quality factors M2 of 946 and 1030 nm LG01 mode vortex lasers were 2.35 and 2.40, respectively. It was proved that the generated dual-wavelength vortex laser had the wavefront phase expiϕ by the self-interference method.
Growth of the Macroalgae Ulva lactuca Cultivated at Different Depths in a Biofloc Integrated System with Shrimp and Fish
Authors: Carvalho Andrezza , Costa Léa Carolina de Oliveira , Holanda Mariana , Gonçalves Mayra , Santos Jorge , Costa César S. B. , Turan Gamze , Poersch Luís H.
Published: May 2023

The constant production of solids in intensive shrimp and tilapia culture can affect the performance of macroalgae when cultivated in an integrated system, and little is known about culture structures that enhance the performance of macroalgae in biofloc systems. The objective of this work was to evaluate different depths of culture structure for the macroalgae Ulva lactuca in an integrated system with Litopenaeus vannamei and Oreochromis niloticus in a biofloc system. The experiment lasted 70 days, with six systems composed of: a 16 m3 shrimp tank, a 3 m3 tilapia tank, and a 3 m3 macroalgae tank, with water recirculation between tanks. Two treatments were carried out, shallow float, with a structural depth of 10 cm, and bottom float, where the depth was kept at 30 cm from the surface. The shallow float resulted in a growth rate of up to 0.95 ± 0.54% day−1, with biomass loss only at the end of the culture due to the high density of macroalgae, decreasing temperature, and increasing solids concentration. The bottom float had biomass loss throughout the culture cycle. The integrated culture of shrimp, fish, and macroalgae is feasible with the use of shallow floats within 10 cm from the surface.
The Involvement of Lipid Mediators in the Mechanisms of Exercise-Induced Muscle Damage
Authors: Gakis Athanasios G. , Nomikos Tzortzis , Philippou Anastassios , Antonopoulou Smaragdi
Published: May 2023

Lipid mediators are a class of signaling molecules that play important roles in various physiological processes, including inflammation, blood pressure regulation, and energy metabolism. Exercise has been shown to affect the production and metabolism of several types of lipid mediators, including prostaglandins, leukotrienes, sphingolipids, platelet-activating factors and endocannabinoids. Eicosanoids, which include prostaglandins and leukotrienes, are involved in the regulation of inflammation and immune function. Endocannabinoids, such as anandamide and 2-arachidonoylglycerol, are involved in the regulation of pain, mood, and appetite. Pro-resolving lipid mediators are involved in the resolution of inflammation. Sphingolipids have a role in the function of skeletal muscle during and after exercise. There are many studies that have examined the effects of exercise on the production and release of these and other lipid mediators. Some of these studies have focused on the effects of exercise on inflammation and immune function, while others have examined the effects on muscle function and metabolism. However, much less is known about their involvement in the phenomenon of exercise-induced muscle damage that follows after intense or unaccustomed exercise.
Inflated Ovary May Increase the Dispersal Ability of Three Species in the Cold Deserts of Central Asia
Authors: Mamut Jannathan , Chen Kewei , Baskin Carol C. , Tan Dunyan
Published: May 2023

Among the diaspores of angiosperms an inflated ovary (IO) is a novel morphological trait, but no studies have evaluated its effects on dispersal. The primary aim of this study was to determine the effect of the IO on diaspore dispersal in three cold desert species (Carex physodes, Calligonum junceum, and Sphaerophysa salsula). Various morphological features and the mass of fruits and seeds of each species were measured. The role of an IO in diaspore dispersal by wind and water was determined by comparing responses of intact (inflated) IOs and flattened fruits and seeds. Mature diaspores of three species were dispersed by wind, and the IO significantly increased dispersal distance in the field and at different wind speeds in the laboratory. The floating time on water was greater for inflated fruits than flattened fruits and seeds. Since the seed remains inside the IO until after dispersal is completed, the IO of the three species enhances diaspore dispersal. This is the first detailed study on how an IO increases diaspore/seed dispersal. Furthermore, after primary dispersal by wind, secondary dispersal can occur via wind or surface runoff of water, and each method is enhanced by the presence of an IO.
Apoplastic and Symplasmic Markers of Somatic Embryogenesis
Authors: Kurczynska Ewa , Godel-Jędrychowska Kamila
Published: May 2023

Somatic embryogenesis (SE) is a process that scientists have been trying to understand for many years because, on the one hand, it is a manifestation of the totipotency of plant cells, so it enables the study of the mechanisms regulating this process, and, on the other hand, it is an important method of plant propagation. Using SE in basic research and in practice is invaluable. This article describes the latest, but also historical, information on changes in the chemical composition of the cell wall during the transition of cells from the somatic to embryogenic state, and the importance of symplasmic communication during SE. Among wall chemical components, different pectic, AGP, extensin epitopes, and lipid transfer proteins have been discussed as potential apoplastic markers of explant cells during the acquisition of embryogenic competence. The role of symplasmic communication/isolation during SE has also been discussed, paying particular attention to the formation of symplasmic domains within and between cells that carry out different developmental processes. Information about the number and functionality of plasmodesmata (PD) and callose deposition as the main player in symplasmic isolation has also been presented.
Metabolic Responses in Leaves of 15 Italian Olive Cultivars in Correspondence to Variable Climatic Elements
Authors: Colzi Ilaria , Marone Elettra , Luti Simone , Pazzagli Luigia , Mancuso Stefano , Taiti Cosimo
Published: May 2023

This study aims to evaluate the metabolic changes that occurred in olive leaves as responses over time to variations in climatic elements. Rainfall, temperature, and solar radiation data were collected over 4 months (August–November) to assess the impact of different climatic trends on the metabolism of the leaves of 15 Italian olive cultivars, cultivated at the experimental farm of the University of Florence. The net photosynthetic rate (AN) and stomatal conductance (gs), measured as main indicators of primary metabolism, were mainly influenced by the “cultivar” effect compared to the “climate” effect. The lowest AN value was showed by “Bianchera”, while “Ascolana” recorded the highest (8.6 and 13.6 µmol CO2 m−2s−1, respectively). On the other hand, the secondary metabolism indicators, volatile organic compound (VOC) and oleuropein (OL) content, were much more influenced by climate trends, especially rainfall. A phase of high rainfall caused a significant increase in the VOCs emission from leaves, even with different behaviors among the genotypes. The highest differences were observed between “Maiatica di Ferrandina”, with the highest average values (~85,000 npcs), and “Frantoio”, which showed the lowest (~22,700 npcs). The OL content underwent considerable fluctuations in relation to the rainfall but also appeared to be controlled by the genotype. “Coratina” always showed the highest OL concentration (reaching the maximum ~98 mg g−1), indicating the great potential of this cultivar for the industrial recovery of OL.
Preparation of Eco-Friendly Chelating Resins and Their Applications for Water Treatment
Authors: Marin Nicoleta , Dolete Georgiana , Motelica Ludmila , Trusca Roxana , Oprea Ovidiu , Ficai Anton
Published: May 2023

In the present study, two chelating resins were prepared and used for simultaneous adsorption of toxic metal ions, i.e., Cr3+, Mn2+, Fe3+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+, and Pb2+ (MX+). In the first step, chelating resins were prepared starting with styrene-divinylbenzene resin, a strong basic anion exchanger Amberlite IRA 402(Cl−) with two chelating agents, i.e., tartrazine (TAR) and amido black 10B (AB 10B). Key parameters such as contact time, pH, initial concentration, and stability were evaluated for the obtained chelating resins (IRA 402/TAR and IRA 402/AB 10B). The obtained chelating resins show excellent stability in 2M HCl, 2M NaOH, and also in ethanol (EtOH) medium. The stability of the chelating resins decreased when the combined mixture (2M HCl:EtOH = 2:1) was added. The above-mentioned aspect was more evident for IRA 402/TAR compared to IRA 402/AB 10B. Taking into account the higher stability of the IRA 402/TAR and IRA 402/AB 10B resins, in a second step, adsorption studies were carried out on complex acid effluents polluted with MX+. The adsorption of MX+ from an acidic aqueous medium on the chelating resins was evaluated using the ICP-MS method. The following affinity series under competitive analysis for IRA 402/TAR was obtained: Fe3+(44 µg/g) > Ni2+(39.8 µg/g) > Cd2+(34 µg/g) > Cr3+(33.2 µg/g) > Pb2+(32.7 µg/g) > Cu2+ (32.5 µg/g) > Mn2+(31 µg/g) > Co2+(29 µg/g) > Zn2+ (27.5 µg/g). While for IRA 402/AB 10B, the following behavior was observed: Fe3+(58 µg/g) > Ni2+(43.5 µg/g) > Cd2+(43 µg/g) > Cu2+(38 µg/g) > Cr3+(35 µg/g) > Pb2+(34.5 µg/g) > Co2+(32.8 µg/g) > Mn2+(33 µg/g) > Zn2+(32 µg/g), consistent with the decreasing affinity of MX+ for chelate resin. The chelating resins were characterized using TG, FTIR, and SEM analysis. The obtained results showed that the chelating resins prepared have promising potential for wastewater treatment in the context of the circular economy approach.
Metal-Filled Polyvinylpyrrolidone Copolymers: Promising Platforms for Creating Sensors
Authors: Grytsenko Oleksandr , Dulebova Ludmila , Spišák Emil , Pukach Petro
Published: May 2023

This paper presents research results on the properties of composite materials based on cross-linked grafted copolymers of 2-hydroxyethylmethacrylate (HEMA) with polyvinylpyrrolidone (PVP) and their hydrogels filled with finely dispersed metal powders (Zn, Co, Cu). Metal-filled pHEMA-gr-PVP copolymers in the dry state were studied for surface hardness and swelling ability, which was characterized by swelling kinetics curves and water content. Copolymers swollen in water to an equilibrium state were studied for hardness, elasticity, and plasticity. The heat resistance of dry composites was evaluated by the Vicat softening temperature. As a result, materials with a wide range of predetermined properties were obtained, including physico-mechanical properties (surface hardness 240 ÷ 330 MPa, hardness number 0.06 ÷ 2.8 MPa, elasticity number 75 ÷ 90%), electrical properties (specific volume resistance 102 ÷ 108 Ω⋅m), thermophysical properties (Vicat heat resistance 87 ÷ 122 °C), and sorption (swelling degree 0.7 ÷ 1.6 g (H2O)/g (polymer)) at room temperature. Resistance to the destruction of the polymer matrix was confirmed by the results concerning its behavior in aggressive media such as solutions of alkalis and acids (HCl, H2SO4, NaOH), as well as some solvents (ethanol, acetone, benzene, toluene). The obtained composites are characterized by electrical conductivity, which can be adjusted within wide limits depending on the nature and content of the metal filler. The specific electrical resistance of metal-filled pHEMA-gr-PVP copolymers is sensitive to changes in moisture (with a moisture increase from 0 to 50%, ρV decreases from 108 to 102 Ω⋅m), temperature (with a temperature change from 20 °C to 175 °C, ρV of dry samples decreases by 4.5 times), pH medium (within pH from 2 to 9, the range of ρV change is from 2 to 170 kΩ⋅m), load (with a change in compressive stress from 0 kPa to 140 kPa, ρV of swollen composites decreases by 2–4 times), and the presence of low molecular weight substances, which is proven by the example involving ethanol and ammonium hydroxide. The established dependencies of the electrical conductivity of metal-filled pHEMA-gr-PVP copolymers and their hydrogels on various factors, in combination with high strength, elastic properties, sorption capacity, and resistance to aggressive media, suggest the potential for further research as a platform for the manufacture of sensors for various purposes.
Effect of Drug Encapsulation and Hydrothermal Exposure on the Structure and Molecular Dynamics of the Binary System Poly(3-hydroxybutyrate)-chitosan
Authors: Karpova S. G. , Olkhov A. A. , Varyan I. A. , Popov A. A. , Iordanskii A. L.
Published: May 2023

In this work, film materials based on binary compositions of poly-(3-hydroxybutyrate) (PHB) and chitosan with different ratios of polymer components in the range from 0/100 to 100/0 wt. % were studied. Using a combination of thermal (DSC) and relaxation (EPR) measurements, the influence of the encapsulation temperature of the drug substance (DS) of dipyridamole (DPD) and moderately hot water (at 70 °C) on the characteristics of the PHB crystal structure and the diffusion rotational mobility of the stable TEMPO radical in the amorphous regions of the PHB/chitosan compositions is shown. The low-temperature extended maximum on the DSC endotherms made it possible to obtain additional information about the state of the chitosan hydrogen bond network. This allowed us to determine the enthalpies of thermal destruction of these bonds. In addition, it is shown that when PHB and chitosan are mixed, significant changes are observed in the degree of crystallinity of PHB, degree of destruction of hydrogen bonds in chitosan, segmental mobility, sorption capacity of the radical, and the activation energy of rotational diffusion in the amorphous regions of the PHB/chitosan composition. The characteristic point of polymer compositions was found to correspond to the ratio of the components of the mixture 50/50%, for which the inversion transition of PHB from dispersed material to dispersion medium is assumed. Encapsulation of DPD in the composition leads to higher crystallinity and to a decrease in the enthalpy of hydrogen bond breaking, and it also slows down segmental mobility. Exposure to an aqueous medium at 70 °C is also accompanied by sharp changes in the concentration of hydrogen bonds in chitosan, the degree of PHB crystallinity, and molecular dynamics. The conducted research made it possible for the first time to conduct a comprehensive analysis of the mechanism of action of a number of aggressive external factors (such as temperature, water, and the introduced additive in the form of a drug) on the structural and dynamic characteristics of the PHB/chitosan film material at the molecular level. These film materials have the potential to serve as a therapeutic system for controlled drug delivery.
Unlocking the Potential Use of Reactive POSS as a Coagent for EPDM/PP-Based TPV
Authors: Çakır Nazlı Yazıcı , İnan Özgenur , Ergün Merve , Kodal Mehmet , Özkoç Güralp
Published: May 2023

Thermoplastic vulcanizates (TPVs) are multifunctional materials consisting of two or more phases with solid elastomeric properties at room temperatures and fluid-like properties above their melting point. They are produced through a reactive blending process known as dynamic vulcanization. The most widely produced TPV type is ethylene propylene diene monomer/polypropylene (EPDM/PP), which is the focus of this study. The peroxides are mainly selected to be used in crosslinking of EPDM/PP-based TPV. However, they still have some disadvantages, such as the side reactions resulting in the beta chain scission of the PP phase and undesired disproportionation reactions. To eliminate these disadvantages, coagents are used. In this study, for the first time, the use of vinyl functionalized polyhedral oligomeric silsesquioxane (OV-POSS) nanoparticles was investigated as a potential coagent in EPDM/PP-based TPV production via peroxide-initiated dynamic vulcanization. The properties of the TPVs having POSS were compared with the conventional TPVs containing conventional coagents, such as triallyl cyanurate (TAC). POSS content and EPDM/PP ratio were investigated as the material parameters. Mechanical properties of EPDM/PP TPVs exhibited higher values in the presence of OV-POSS, which resulted from the active participation of OV-POSS into the three-dimensional network structure of EPDM/PP during dynamic vulcanization.
A Method for Assessing Urban Ecological Resilience and Identifying Its Critical Distance Belt Based on the “Source-Sink” Theory: A Case Study of Beijing
Authors: Ning Xiaogang , Zhang Xiaoyuan , Zhang Xiaoyu , Wang Hao , Zhang Weiwei
Published: May 2023

A reasonable assessment of urban ecological resilience (UER), as well as quantitative identification of critical thresholds of UER, is an important theoretical basis for the formulation of scientific urban development planning. The existing UER assessment methods ignore the dynamic relationship between protection factors and disturbance factors in urban systems and do not address the question of where UER starts to become unstable. Therefore, based on the “source-sink” landscape theory, we constructed a UER assessment model and a method to quantitatively identify the UER’s critical distance belt (UER-CDB) using the transect gradient analysis. Additionally, we combined scenario simulation to analyze the change characteristics of UER and its critical distance belt in different urban development directions over past and future periods. The results show that: (1) Based on the “source-sink” theory and transect gradient method, the UER can be effectively assessed and the UER-CDB can be quantitatively identified. (2) The UER in Beijing shows a distribution pattern of high in the northwest and low in the southeast, and the High resilience area accounts for more than 40%. (3) The changes in UER-CDB in Beijing in different development directions have obvious variability, which is mainly influenced by topography and policy planning. (4) Compared with the natural development scenario (NDS), the ecological protection scenario (EPS) is more consistent with Beijing’s future urban development plan and more conducive to achieving sustainable development. The methodology of this paper provides a fresh perspective for the study of urban ecological resilience and the critical threshold of ecosystems.
Evaluation of Copernicus DEM and Comparison to the DEM Used for Landsat Collection-2 Processing
Authors: Franks Shannon , Rengarajan Rajagopalan
Published: May 2023

Having highly accurate and reliable Digital Elevation Models (DEMs) of the Earth’s surface is critical to orthorectify Landsat imagery. Without such accuracy, pixel locations reported in the data are difficult to assure as accurate, especially in more mountainous landscapes, where the orthorectification process is the most challenging. To this end, the Landsat Calibration and Validation Team (Cal/Val) compared the Copernicus DEM (CopDEM) to the DEM that is currently used in Collection-2 processing (called “Collection-2 DEM”). NGS ground-surveyed and lidar-based ICESat-2 points were used, and the CopDEM shows improvement to be less than 1 m globally, except in Asia where the accuracy and resolution of the DEM were greater for the CopDEM compared to the Collection-2 DEM. Along with slightly improved accuracy, the CopDEM showed more consistent results globally due to its virtually seamless source and consistent creation methods throughout the dataset. While CopDEM is virtually seamless, having greater than 99% of their data coming from a single source (Tandem-X), there are significantly more voids in the higher elevations which were mostly filled with SRTM derivatives. The accuracy of the CopDEM fill imagery was also compared to the Collection-2 DEM and the results were very similar, showing that the choice of fill imagery used by CopDEM was appropriate. A qualitative assessment using terrain-corrected products processed with different DEMs and viewing them as anaglyphs to evaluate the DEMs proved useful for assessing orbital path co-registration. While the superiority of the CopDEM was not shown to be definitive by the qualitative method for many of the regions assessed, the CopDEM showed a clear advantage in Northern Russia, where the Collection-2 DEM uses some of the oldest and least accurate datasets in the compilation of the Collection-2 DEM. This paper presents results from the comparison study, along with the justification for proceeding with using the Copernicus DEM in future Landsat processing. As of this writing, the Copernicus DEM is planned to be used in Collection-3 processing, which is anticipated to be released no earlier than 2025.
Improvement and Assessment of Gaofen-3 Spotlight Mode 3-D Localization Accuracy
Authors: Chen Nuo , Deng Mingjun , Wang Di , Zhang Zhengpeng , Yang Yin
Published: May 2023

The spotlight image acquired by the Gaofen-3 satellite has a resolution of 1 m, which has great potential for 3-D localization. However, there have been no public reports on the 3-D localization accuracy evaluation of Gaofen-3 spotlight synthetic aperture radar (SAR) images. Here, three study areas were selected from this perspective, and the SAR spotlight stereo images of the study area were acquired using Gaofen-3. In the case of no ground control points (GCPs), based on the Rational Polynomial Coefficient (RPC) model, these images were used for initial 3-D localization; the plane accuracy was better than 10 m in general, and the elevation accuracy was worse than 37 m in general. Subsequently, the RPC model was optimized using geometric calibration technology, and the 3-D localization accuracy was assessed again. The elevation accuracy was significantly improved, which was generally better than 5 m. The plan accuracy was also improved, and it was generally better than 6 m. It can be seen that Gaofen-3 spotlight stereo images are of good quality, and high plane accuracy can be obtained even without GCPs. Geometric calibration technology improves the 3-D localization accuracy, and the elevation accuracy optimization effect is remarkable. Moreover, the optimization effect of plane accuracy is affected by the properties of stereo-image pairs. The optimization effect of plane accuracy is obvious for asymmetric stereo-image pairs, and the optimization effect of plane accuracy is general for symmetric stereo-image pairs.
Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging
Authors: Seo Hyungjoon , Raut Aishwarya Deepak , Chen Cheng , Zhang Cheng
Published: May 2023

In the era of the first Industrial Revolution, many buildings were built with red bricks, and the heritage buildings built at that time are more than 100 years old. In these old heritage buildings, damage is bound to occur due to chemical and physical effects. Technologies such as automatic damage detection can effectively manage damage, but they can be affected by other categories present in heritage buildings. Therefore, this paper proposes a CNN algorithm that can automatically detect cracks and damage that occur in heritage buildings, as well as multi-label classification, such as doors, windows, arches, artwork, brick walls, stonewalls, and vents. A total of 2400 thermal infrared images are collected for 8 categories and automatic classification was performed using the CNN algorithm. The average precision and average sensitivity for the eight categories of heritage buildings are 97.72% and 97.43%, respectively. This paper defines the causes of misclassification as the following two causes: misclassification by multiple objects and misclassification by the perception of the CNN algorithm.
Hydrogen Sensing Mechanism of WS2 Gas Sensors Analyzed with DFT and NAP-XPS
Authors: Minezaki Tomoya , Krüger Peter , Annanouch Fatima Ezahra , Casanova-Cháfer Juan , Alagh Aanchal , Villar-Garcia Ignacio J. , Pérez-Dieste Virginia , Llobet Eduard , Bittencourt Carla
Published: May 2023

Nanostructured tungsten disulfide (WS2) is one of the most promising candidates for being used as active nanomaterial in chemiresistive gas sensors, as it responds to hydrogen gas at room temperature. This study analyzes the hydrogen sensing mechanism of a nanostructured WS2 layer using near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS) and density functional theory (DFT). The W 4f and S 2p NAP-XPS spectra suggest that hydrogen makes physisorption on the WS2 active surface at room temperature and chemisorption on tungsten atoms at temperatures above 150 °C. DFT calculations show that a hydrogen molecule physically adsorbs on the defect-free WS2 monolayer, while it splits and makes chemical bonds with the nearest tungsten atoms on the sulfur point defect. The hydrogen adsorption on the sulfur defect causes a large charge transfer from the WS2 monolayer to the adsorbed hydrogen. In addition, it decreases the intensity of the in-gap state, which is generated by the sulfur point defect. Furthermore, the calculations explain the increase in the resistance of the gas sensor when hydrogen interacts with the WS2 active layer.
Blockchain-Assisted Reputation Management Scheme for Internet of Vehicles
Authors: Liu Qian , Gong Junquan , Liu Qilie
Published: May 2023

With the rapid development of Internet of Vehicles (IoV), particularly the introduction of Mobile Edge Computing (MEC), vehicles can efficiently share data with one another. However, edge computing nodes are vulnerable to various network attacks, posing security risks to data storage and sharing. Moreover, the presence of abnormal vehicles during the sharing process poses significant security threats to the entire network. To address these issues, this paper proposes a novel reputation management scheme, which proposes an improved multi-source multi-weight subjective logic algorithm. This algorithm fuses the direct and indirect opinion feedback of nodes through the subjective logic trust model while considering factors such as event validity, familiarity, timeliness, and trajectory similarity. Vehicle reputation values are periodically updated, and abnormal vehicles are identified through reputation thresholds. Finally, blockchain technology is employed to ensure the security of data storage and sharing. By analyzing real vehicle trajectory datasets, the algorithm is proven to effectively improve the differentiation and detection rate of abnormal vehicles.
A Nonlinear-Model-Based High-Bandwidth Current Sensor Design for Switching Current Measurement of Wide Bandgap Devices
Authors: Du Xia , Du Liyang , Chen Yuxiang , Wei Yuqi , Stratta Andrea , Mantooth Homer Alan
Published: May 2023

With the growing adoption of wide bandgap devices in power electronic applications, current sensor design for switching current measurement has become more important. The demands for high accuracy, high bandwidth, low cost, compact size, and galvanic isolation pose significant design challenges. The conventional modeling approach for bandwidth analysis of current transformer sensors assumes that the magnetizing inductance remains constant, which does not always hold true in high-frequency operations. This can result in inaccurate bandwidth estimation and affect the overall performance of the current sensor. To address this limitation, this paper provides a comprehensive analysis of nonlinear modeling and bandwidth, considering the varying magnetizing inductance in a wide frequency range. A precise and straightforward arctangent-based fitting algorithm was proposed to accurately emulate the nonlinear feature, and the fitting results were compared with the magnetic core’s datasheet to confirm its accuracy. This approach contributes to more accurate bandwidth prediction in field applications. In addition, the droop phenomenon of the current transformer and saturation effects are analyzed in detail. For high-voltage applications, different insulation methods are compared and an optimized insulation process is proposed. Finally, the design process is experimentally validated. The bandwidth of the proposed current transformer is around 100 MHz and the cost is around $20, making it a low-cost and high-bandwidth solution for switching current measurements in power electronic applications.
Enhancing Safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit
Authors: Ishola Adenrele A. , Valles Damian
Published: May 2023

Firefighters face numerous challenges when entering burning structures to rescue trapped victims, assess the conditions of a residential structure, and extinguish the fire as quickly as possible. These challenges include extreme temperatures, smoke, toxic gases, explosions, and falling objects, which can hinder their efficiency and pose risks to their safety. Accurate information and data about the burning site can help firefighters make informed decisions about their duties and determine when it is safe to enter and evacuate, reducing the likelihood of casualties. This research presents unsupervised deep learning (DL) to classify the danger levels at a burning site and an autoregressive integrated moving average (ARIMA) prediction model to forecast temperature changes using the extrapolation of a random forest regressor. The DL classifier algorithms provide the chief firefighter with an awareness of the danger levels in the burning compartment. The prediction models forecast the rise in temperature from a height ranging from 0.6 m to 2.6 m and the changes in temperature over time at an altitude of 2.6 m. Predicting the temperature at this altitude is critical as the temperature increases faster with height, and elevated temperatures can weaken the building’s structural material. We also investigated a new classification method using an unsupervised DL autoencoder artificial neural network (AE-ANN). The prediction data analytical approach included using the autoregressive integrated moving average (ARIMA) with random forest regression implementation. The proposed AE-ANN model, with an accuracy score of 0.869, did not perform as well compared to previous work, with an accuracy of 0.989, at achieving high accuracy scores for the classification task using the same dataset. However, the random forest regressor and our ARIMA models are analyzed and evaluated in this work, while other research has not utilized this dataset, even though it is open-sourced. However, the ARIMA model demonstrated remarkable predictions of the trends of temperature changes in a burning site. The proposed research aims to classify fire sites into dangerous levels and predict temperature progression using deep learning and predictive modeling techniques. This research’s main contribution is using a random forest regressor and autoregressive integrated moving average models to predict temperature trends in burning sites. This research demonstrates the potential of using deep learning and predictive modeling to enhance firefighter safety and decision-making processes.
Highly Activated Neuronal Firings Monitored by Implantable Microelectrode Array in the Paraventricular Thalamus of Insomnia Rats
Authors: Shan Jin , Song Yilin , Wang Yiding , Fan Penghui , Lu Botao , Luo Jinping , Xu Wei , Jing Luyi , Mo Fan , Hu Ruilin , Luo Yan , Mao Gang , Wang Ying , Cai Xinxia
Published: May 2023

Insomnia is a common sleep disorder around the world, which is harmful to people’s health, daily life, and work. The paraventricular thalamus (PVT) plays an essential role in the sleep–wake transition. However, high temporal-spatial resolution microdevice technology is lacking for accurate detection and regulation of deep brain nuclei. The means for analyzing sleep–wake mechanisms and treating sleep disorders are limited. To detect the relationship between the PVT and insomnia, we designed and fabricated a special microelectrode array (MEA) to record electrophysiological signals of the PVT for insomnia and control rats. Platinum nanoparticles (PtNPs) were modified onto an MEA, which caused the impedance to decrease and improved the signal-to-noise ratio. We established the model of insomnia in rats and analyzed and compared the neural signals in detail before and after insomnia. In insomnia, the spike firing rate was increased from 5.48 ± 0.28 spike/s to 7.39 ± 0.65 spike/s, and the power of local field potential (LFP) decreased in the delta frequency band and increased in the beta frequency band. Furthermore, the synchronicity between PVT neurons declined, and burst-like firing was observed. Our study found neurons of the PVT were more activated in the insomnia state than in the control state. It also provided an effective MEA to detect the deep brain signals at the cellular level, which conformed with macroscopical LFP and insomnia symptoms. These results laid the foundation for studying PVT and the sleep–wake mechanism and were also helpful for treating sleep disorders.
Accuracy of a Smartwatch to Assess Heart Rate Monitoring and Atrial Fibrillation in Stroke Patients
Authors: Meza Claudia , Juega Jesus , Francisco Jaume , Santos Alba , Duran Laura , Rodriguez Maite , Alvarez-Sabin Jose , Sero Laia , Ustrell Xavier , Bashir Saima , Serena Joaquín , Silva Yolanda , Molina Carlos , Pagola Jorge
Published: May 2023

(1) Background: Consumer smartwatches may be a helpful tool to screen for atrial fibrillation (AF). However, validation studies on older stroke patients remain scarce. The aim of this pilot study from RCT NCT05565781 was to validate the resting heart rate (HR) measurement and the irregular rhythm notification (IRN) feature in stroke patients in sinus rhythm (SR) and AF. (2) Methods: Resting clinical HR measurements (every 5 min) were assessed using continuous bedside ECG monitoring (CEM) and the Fitbit Charge 5 (FC5). IRNs were gathered after at least 4 h of CEM. Lin’s concordance correlation coefficient (CCC), Bland-Altman analysis, and mean absolute percentage error (MAPE) were used for agreement and accuracy assessment. (3) Results: In all, 526 individual pairs of measurements were obtained from 70 stroke patients—age 79.4 years (SD ± 10.2), 63% females, BMI 26.3 (IQ 22.2–30.5), and NIHSS score 8 (IQR 1.5–20). The agreement between the FC5 and CEM was good (CCC 0.791) when evaluating paired HR measurements in SR. Meanwhile, the FC5 provided weak agreement (CCC 0.211) and low accuracy (MAPE 16.48%) when compared to CEM recordings in AF. Regarding the accuracy of the IRN feature, analysis found a low sensitivity (34%) and high specificity (100%) for detecting AF. (4) Conclusion: The FC5 was accurate at assessing the HR during SR, but the accuracy during AF was poor. In contrast, the IRN feature was acceptable for guiding decisions regarding AF screening in stroke patients.
Photoplethysmogram Biometric Authentication Using a 1D Siamese Network
Authors: Seok Chae Lin , Song Young Do , An Byeong Seon , Lee Eui Chul
Published: May 2023

In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived.
Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls
Authors: Asif Muhammad Sheharyar , Faisal Muhammad Shahzad , Dar Muhammad Najam , Hamdi Monia , Elmannai Hela , Rizwan Atif , Abbas Muhammad
Published: May 2023

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.
An Evidence Theoretic Approach for Traffic Signal Intrusion Detection
Authors: Chowdhury Abdullahi , Karmakar Gour , Kamruzzaman Joarder , Das Rajkumar , Newaz S. H. Shah
Published: May 2023

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon’s entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
Collective Cognition on Global Density in Dynamic Swarm
Authors: Gia Luan Phan , Thinh Nguyen Truong
Published: May 2023

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm size may decrease over time due to out-of-battery or faulty individuals during operation. This can result in the average swarm density over the whole workspace being unable to be measured or changed in real-time. The swarm performance may not be optimal due to unknown swarm density. If the swarm density is too low, inter-robot communication will rarely be established, and robot swarm cooperation will not be effective. Meanwhile, a densely-packed swarm compels robots to permanently solve collision avoidance issues rather than performing the main task. To address this issue, in this work, the distributed algorithm for collective cognition on the average global density is proposed. The main idea of the proposed algorithm is to help the swarm make a collective decision on whether the current global density is larger, smaller or approximately equal to the desired density. During the estimation process, the swarm size adjustment is acceptable for the proposed method in order to reach the desired swarm density.
Multidomain Convolution Neural Network Models for Improved Event-Related Potential Classification
Authors: Chen Xiaoqian , Gupta Resh S. , Gupta Lalit
Published: May 2023

Two convolution neural network (CNN) models are introduced to accurately classify event-related potentials (ERPs) by fusing frequency, time, and spatial domain information acquired from the continuous wavelet transform (CWT) of the ERPs recorded from multiple spatially distributed channels. The multidomain models fuse the multichannel Z-scalograms and the V-scalograms, which are generated from the standard CWT scalogram by zeroing-out and by discarding the inaccurate artifact coefficients that are outside the cone of influence (COI), respectively. In the first multidomain model, the input to the CNN is generated by fusing the Z-scalograms of the multichannel ERPs into a frequency-time-spatial cuboid. The input to the CNN in the second multidomain model is formed by fusing the frequency-time vectors of the V-scalograms of the multichannel ERPs into a frequency-time-spatial matrix. Experiments are designed to demonstrate (a) customized classification of ERPs, where the multidomain models are trained and tested with the ERPs of individual subjects for brain-computer interface (BCI)-type applications, and (b) group-based ERP classification, where the models are trained on the ERPs from a group of subjects and tested on single subjects not included in the training set for applications such as brain disorder classification. Results show that both multidomain models yield high classification accuracies for single trials and small-average ERPs with a small subset of top-ranked channels, and the multidomain fusion models consistently outperform the best unichannel classifiers.
Torque Measurement and Control for Electric-Assisted Bike Considering Different External Load Conditions
Authors: Ho Ping-Jui , Yi Chen-Pei , Lin Yi-Jen , Chung Wei-Der , Chou Po-Huan , Yang Shih-Chin
Published: May 2023

This paper proposes a novel torque measurement and control technique for cycling-assisted electric bikes (E-bikes) considering various external load conditions. For assisted E-bikes, the electromagnetic torque from the permanent magnet (PM) motor can be controlled to reduce the pedaling torque generated by the human rider. However, the overall cycling torque is affected by external loads, including the cyclist’s weight, wind resistance, rolling resistance, and the road slope. With knowledge of these external loads, the motor torque can be adaptively controlled for these riding conditions. In this paper, key E-bike riding parameters are analyzed to find a suitable assisted motor torque. Four different motor torque control methods are proposed to improve the E-bike’s dynamic response with minimal variation in acceleration. It is concluded that the wheel acceleration is important to determine the E-bike’s synergetic torque performance. A comprehensive E-bike simulation environment is developed with MATLAB/Simulink to evaluate these adaptive torque control methods. In this paper, an integrated E-bike sensor hardware system is built to verify the proposed adaptive torque control.