bluuebunny/crossref_metadata_2025 huggingface dataset:
Thymus Vulgaris Oil Nanoemulsion: Synthesis, Characterization, Antimicrobial and Anticancer Activities
Authors: Doghish Ahmed , Shehabeldine Amr , El-Mahdy Hesham , Hassanin Mahmoud , Al-Askar Abdulaziz , Marey Samy , AbdElgawad Hamada , Hashem Amr
Published: October 2023

Essential oil nanoemulsions have received much attention due to their biological activities. Thus, a thyme essential oil nanoemulsion (Th-nanoemulsion) was prepared using a safe and eco-friendly method. DLS and TEM were used to characterize the prepared Th-nanoemulsion. Our findings showed that the nanoemulsion was spherical and ranged in size from 20 to 55.2 nm. The micro-broth dilution experiment was used to evaluate the in vitro antibacterial activity of a Th-emulsion and the Th-nanoemulsion. The MIC50 values of the thymol nanoemulsion were 62.5 mg/mL against Escherichia coli and Klebsiella oxytoca, 250 mg/mL against Bacillus cereus, and 125 mg/mL against Staphylococcus aureus. Meanwhile, it emerged that the MIC50 values of thymol against four strains were not detected. Moreover, the Th-nanoemulsion exhibited promising antifungal activity toward A. brasiliensis and A. fumigatus, where inhibition zones and MIC50 were 20.5 ± 1.32 and 26.4 ± 1.34 mm, and 12.5 and 6.25 mg/mL, respectively. On the other hand, the Th-nanoemulsion displayed weak antifungal activity toward C. albicans where the inhibition zone was 12.0 ± 0.90 and MIC was 50 mg/mL. Also, the Th-emulsion exhibited antifungal activity, but lower than that of the Th-nanoemulsion, toward all the tested fungal strains, where MIC was in the range of 12.5–50 mg/mL. The in vitro anticancer effects of Taxol, Th-emulsion, and Th-nanoemulsion were evaluated using the standard MTT method against breast cancer (MCF-7) and hepatocellular carcinoma (HepG2). Additionally, the concentration of VEGFR-2 was measured, and the activities of caspase-8 (casp-8) and caspase-9 (casp-9) were evaluated. The cytotoxic effect was the most potent against the MCF-7 breast cancer cell line after the Th-nanoemulsion treatment (20.1 ± 0.85 µg/mL), and was 125.1 ± 5.29 µg/mL after the Th-emulsion treatment. The lowest half-maximal inhibitory concentration (IC50) value, 20.1 ± 0.85 µg/mL, was achieved when the MCF-7 cell line was treated with the Th-nanoemulsion. In addition, Th-nanoemulsion treatments on MCF-7 cells led to the highest elevations in casp-8 and casp-9 activities (0.66 ± 0.042 ng/mL and 17.8 ± 0.39 pg/mL, respectively) compared to those with Th-emulsion treatments. In comparison to that with the Th-emulsion (0.982 0.017 ng/mL), the VEGFR-2 concentration was lower with the Th-nanoemulsion treatment (0.672 ± 0.019ng/mL). In conclusion, the Th-nanoemulsion was successfully prepared and appeared in nanoform with a spherical shape according to DLS and TEM, and also exhibited antibacterial, antifungal, as well as anticancer activities.
The Complementary Effects of Dabigatran Etexilate and Exercise Training on the Development and Stability of the Atherosclerotic Lesions in Diabetic ApoE Knockout Mice
Authors: Kadoglou Nikolaos PE , Stasinopoulou Marianna , Gkougkoudi Evangelia , Christodoulou Eirini , Kostomitsopoulos Nikolaos , Valsami Georgia
Published: October 2023

Aim: To determine the complementary effects of dabigatran etexilate (DE), exercise training (ET), and combination (DE + ET) on the development and stability of the atherosclerotic lesions in diabetic apoE knockout (apoE−/−) mice. Methods: In 48 male apoE−/− diabetic mice, streptozotocin (STZ) was induced for 5 consecutive days. Mice received a high-fat diet (HFD) for 8 weeks and then were randomized into four groups (1. Control/CG, 2. DEG: HFD with DE, 3. ETG: ET on treadmill, 4. DE + ETG: combination DE and ET treatment). At the end of the eighth week, all mice were euthanatized and morphometry of the aortic lesions at the level of aortic valve was obtained. Collagen, elastin, MCP-1, TNF-a, matrix metalloproteinases (MMP-2,-3,-9), and TIMP-1 concentrations within plaques at the aortic valve were determined. Results: All active groups had significantly smaller aorta stenosis (DEG:7.9 ± 2.2%, ETG:17.3 ± 5.3%, DE + ETG:7.1 ± 2.7%) compared to CG (23.3 ± 5.5% p < 0.05), reduced the relative intra-plaque content of MCP-1, macrophages, MMP-3, and MMP-9, and considerably increased collagen, elastin, and TIMP-1 (p < 0.05). Group 4 showed the most pronounced results (p < 0.05). Both DEG and DE + ETG significantly reduced MMP-2 and TNF-a concentrations compared to ETG and CG (p < 0.010). Conclusion: DE and ET treatment of diabetic apoE−/− mice resulted in complementary amelioration of atherosclerotic lesions development and stability, mediated by the anti-inflammatory modulation of both DE and ET.
Dynamic and Thermal Buckling Behaviors of Multi-Span Honeycomb Sandwich Panel with Arbitrary Boundaries
Authors: Wang Min , Gao Junfu , Liu Lun , Liu Lixia , Sun Shupeng , Zeng Leilei
Published: October 2023

The dynamic characteristics and thermal buckling behaviors of a multi-span honeycomb sandwich panel with arbitrary boundaries are studied comprehensively in this paper. The concept of artificial springs is proposed and it was found that arbitrary boundaries can be achieved by adjusting the stiffness of artificial springs. The hinges which connect the base plates of this structure are simulated by massless torsion springs. The displacement field of the panel is expressed as a series of admissible functions which is a set of characteristic orthogonal polynomials generated directly by employing the Gram–Schmidt process. The stresses induced by the temperature change in the multi-span panel are considered, and then the eigenvalue equations of free vibration and thermal buckling are derived by using the Rayleigh–Ritz method. The theoretical formulations of the present research are validated by comparing the results of this paper with those obtained from the available literature and ABAQUS software. Subsequently, the influences of structural parameters on the critical buckling temperature and natural frequencies are investigated comprehensively, and some useful conclusions about dynamic optimization design for multi-span honeycomb sandwich panels are drawn from the present study.
Development and Research Application of Optical Waveguide Microstructure Component Manufacturing Process for Triangle Roller Imprinting
Authors: Weng Yung-Jin , Tsai Min-Ko , Chen Jian-Zhi
Published: October 2023

This research integrates the stable pressuring of the flat surface of roll-to-plate (R2P) imprinting, the fast production features of roll-to-roll (R2R) imprinting, and compound layer ring-type microstructure mold cavity manufacturing technology. Using the compound multilayer method with air molecule assistance, the stability of the roller imprinting process is enhanced. In addition, with precision modulation of the triangle roll-to-plate (TR2P) system, a stable microstructure roller imprinting manufacturing process is achieved. The experimental results indicate that the developed triangle roll-to-plate system can stabilize the imprinting process of the continuous microstructure array components. Also, by modulating the angles of the roller axis and the ring, the exterior features of the microstructure can also be adjusted. Gas-molecule-assisted continuous pressuring effectively elevated the roll imprinting angle and continuous pressuring time and reached a high replication rate of 99.14%. The optical waveguide microstructure component produced by this process and the average waveguide propagation losses of approximately 1.2~1.4 dB/cm show that it has optical stability and transparency after optical testing. The research proves that the manufacturing process can effectively provide an innovative process for the equipment and application of the microstructure component.
A Data-Driven Approach for the Ultra-Supercritical Boiler Combustion Optimization Considering Ambient Temperature Variation: A Case Study in China
Authors: Wang Zhi , Yao Guojia , Xue Wenyuan , Cao Shengxian , Xu Shiming , Peng Xianyong
Published: October 2023

To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the time-consuming problems of boiler online combustion optimization systems. Firstly, A whole year’s worth of the historical operating data preprocessing procedure of a coal-fired boiler in a power station including data resampling, data cleaning, steady-state selection, and cluster analysis is performed. In order to meet the applicable conditions of the linear model, the historical operating data are divided into different sub-datasets (combination mode of coal mills, main steam flow, ambient temperature, lower heating value of coal). Secondly, the multi-objective optimization strategy of economical, carbon, and NOx emissions indexes is employed to select operating optimum data packets, and a new dataset is established that is better than the average value of the optimization target in each sub-dataset. On this basis, a stepwise regression algorithm (SRA) is used to select the specific manipulated variables (MVs) that are significant to the multiple optimization targets from 47 candidate MVs in each sub-dataset (different partitions have different types of MVs), and an MLR prediction model is developed. In order to further realize combustion optimization control, the MVs are optimized by employing the MLR model. According to the deviation between the optimal value and the real-time value of the MVs, a boiler combustion closed-loop control system is developed, which is connected with the DCS using the sum of the deviation signal and the corresponding original one. Then, a boiler combustion application test was carried out under some working conditions to verify the feasibility and effectiveness of the approach. The update time of the system signals running on industrial computers is less than 1 s and suitable for online applications. Finally, a full-scale test of the combustion optimization online control system (OCS) is executed. The results show that the boiler thermal efficiency increased by 0.39% based on standard coal, the NOx emissions reduced by 2.85% and the decarbonization effect is significant.
A Knowledge Push Approach to Support the Green Concept Design of Products
Authors: Li Xinnian , Zhang Kai , Zhao Wu , Zeng Kehua
Published: October 2023

With the development of the manufacturing industry, environmental problems have become more and more serious. At present, green design is playing an increasingly important role in product design and manufacturing. How to combine traditional conceptual design methods with green designs to realize green conceptual design is of great practical significance. In order to realize the green concept design of a product and to allow the conceptual design scheme to meet the functional requirements and green performance requirements of the product at the same time, this study introduces the knowledge push process and the green filter process into product concept design. The knowledge of invention principles is retrieved by establishing the knowledge space of invention principles, by calculating the word segmentation based on the CRF (conditional random field), and calculating the word similarity using a thesaurus. This is performed in order to realize the transformation from product function requirements to invention principle knowledge. Then, by studying the relationship among the environmental efficiency factors, green design attributes, engineering technical parameters, and invention principles, the correlation mapping table for green design attributes and invention principles is established. Thus, the green filter is applied to the pushed knowledge according to the green performance requirements, so that the final push results after filtering can meet the functional and green performance requirements of the product. In the last section of this paper, a green concept design for cutting the fluid supply system in an enterprise is presented as an example to verify and analyse the proposed method.
Optimization of the Effect of Laser Power Bed Fusion 3D Printing during the Milling Process Using Hybrid Artificial Neural Networks with Particle Swarm Optimization and Genetic Algorithms
Authors: Kaid Husam , Dabwan Abdulmajeed , Alqahtani Khaled N. , Abualsauod Emad Hashiem , Anwar Saqib , Al-Samhan Ali M. , AlFaify Abdullah Yahia
Published: October 2023

Additive manufacturing (AM) is gaining popularity as it can produce near-net geometries and work with difficult-to-manufacture materials, such as stainless steel 316L. However, due to the low surface quality of AM parts, machining and other finishing methods are required. Laser powder bed fusion (LPBF) components can be difficult to finish as the surface roughness (Sa) can vary greatly depending on the part’s orientation, even when using the same machining parameters. This paper explored the effects of finishing (milling) SS 316L LPBF components in a variety of part orientations. The effect of layer thickness (LT) variation in LPBF-made components was also studied. LPBF parts of 30, 60, 80, and 100 μm layer thicknesses were created to analyze the effect of the LT on the final milling process. Additionally, the effect of cutting speed during the milling process on the surface roughness of the SS 316L LPBF component was investigated, along with the orientations and layer thicknesses of the LPBF components. The results revealed that the machined surface undergoes significant orientation and layer thickness changes. The investigations employed a factorial design, and analysis of variance (ANOVA) was used to analyze the results. In addition, an artificial neural network (ANN) model was combined with particle swarm optimization (denoted as ANN-PSO) and the genetic algorithm (denoted as ANN-GA) to determine the optimal process conditions for machining an SS 316L LPBF part. When milled along (Direction B) an orientation with a cutting speed of 80 m/min, the LPBF component produced, with a layer thickness of 60 μm, achieves the lowest surface roughness. For instance, the Sa of a milled LPBF part can be as low as 0.133 μm, compared to 7.54 μm for an as-fabricated LPBF part. The optimal surface roughness was 0.155 μm for ANN-GA and 0.137 μm for ANN-PSO, whereas the minimal surface roughness was experimentally determined to be 0.133 μm. Therefore, the surface quality of both hybrid algorithms has improved, making them more efficient.
Separation and Recovery of Rare Earths and Iron from NdFeB Magnet Scraps
Authors: Wang Houqing , Wang Jinliang , Lei Xiang , Wen Xiaochun , Li Dewei , Liu Fupeng , Zhou Wenyue , Xu Shengming
Published: October 2023

NdFeB magnet scraps contain large amounts of iron, which poses challenges in recycling and greatly hinders the recovery of rare earths through direct hydrometallurgical treatment. To address this issue, we conducted tests using a flash furnace to explore the low-temperature reduction behavior of NdFeB magnet scraps under an H2 atmosphere based on thermodynamic calculations comparing the reduction properties of rare earth oxides (REOs) and iron oxide (FeOx). The results demonstrated that the reduction rate of FeOx surpassed 95% under optimal conditions including a reduction temperature of 723 K, a particle size (D90) of 0.45 μm, and an H2 flow rate of 2 L/min. X-ray diffraction and electron probe microanalysis of the reduction product revealed that the flash reduction at 723 K facilitated the selective reduction of FeOx, owing to efficient mass and heat transfer. Consequently, a two-step magnetic separation process was employed to separate metallic Fe and REOs from the reduction product. Fe-rich phase, obtained with a remarkable Fe distribution ratio of 90.2%, can serve as an economical raw material for weathering steel. Additionally, the REOs are enriched in REO-rich phase, achieving a distribution ratio of 93.9% and significantly boosting the REO concentration from 30.2 to 82.8 wt%.
Production and Evaluation of Gluten-Free Pasta and Pan Bread from Spirulina Algae Powder and Quinoa Flour
Authors: Hussein Ahmed S. , Mostafa Sayed , Fouad Suzanne , Hegazy Nefisa A. , Zaky Ahmed A.
Published: October 2023

This study was carried out to evaluate semolina flour (SF), wheat flour (WF), quinoa (Chenopodium quinoa Willd.) flour (QF), spirulina algae powder (SAP) and their blends for production of gluten-free pasta and bread suitable for celiac patients. Pasta made of 100% semolina and pan bread made of 100% WF were prepared for comparison with pasta and pan bread from QF and blends with SAP at different levels (5, 10 and 15%). The chemical composition, rheological properties, color attributes, cooking quality, baking quality, sensory properties and texture analysis of the pasta and pan bread were investigated. SAP was added to QF at 5, 10 and 15% levels. The results show that SAP is marked by higher protein (63.65%), fat (6.18%), and ash (12.50%) contents. Thus, raising the mixing level of SAP with QF resulted in an increase in the nutritional value of pasta and pan bread. Moreover, these high-protein products improved basal metabolic rate, preserved body muscle mass, and decreased body fat percentage. Farinograph characteristics demonstrated that water absorption, arrival time, dough development time, and stability grew as the ratio of SAP in QF increased. The addition of SAP to QF in increasing proportions from 5% to 15% decreased the elasticity and proportional number, while the extensibility and energy of the dough increased. Also, addition of SAP to QF at different levels (5 to 15%) decreased all viscoamylograph parameters except for the temperature of transition, which increased. In regard to cooking quality, all the pasta samples prepared by mixing SAP with QF had higher weight, volume, and cooking loss than the control. Additionally, while all samples of pasta and pan bread passed the sensory test, those that contained SAP had greater sensory qualities and nutritional value. These products are suitable for athletes and for patients with celiac disease and obesity.
RETRACTED: Power Cable Status Evaluation Method Based on Electrical Tree Growth and Data Association Rules
Authors: Wang Leixiong , Wang Bo , Ma Hengrui , Zhang Jiaxing , He Yifan , Wang Hongxia , Zhang Hanqi
Published: October 2023

The partial discharge (PD) analysis method has remained one of the key technologies for cable condition assessment to diagnose cable operation state. In this paper, two power cable status evaluation methods are proposed based on electrical tree growth and data association rules, respectively. First, in the field of molecular thermodynamics, based on the law of energy conservation of electrical tree growth, this paper studies the relationship between PD signals in cable insulation and the length of the electrical tree. By using the length of electrical tree growth to calculate the probability of PD failure of cables, a cable state assessment method based on electrical tree growth is proposed. Second, for power cable status evaluation, the strong concealment characteristics of cables result in a long power supply recovery time and a serious threat to power supply reliability. Therefore, a group of comprehensive cable status parameters are set by analyzing the status parameters based on data association rules. The comprehensive state of power cables is divided into four elements, and the weight coefficients of cable states are calculated based on data association rule methods. Finally, the simulation results verify the effectiveness and accuracy for the two different power cable status evaluation methods.
The Preparation of Mn-Modified CeO2 Nanosphere Catalysts and Their Catalytic Performance for Soot Combustion
Authors: Gao Siyu , Yu Di , Zhou Shengran , Zhang Chunlei , Wang Lanyi , Fan Xiaoqiang , Yu Xuehua , Zhao Zhen
Published: October 2023

With the increasing stringency of environmental protection regulations, reducing the severe pollution caused by soot particles from diesel vehicle exhaust has become a widespread concern. Catalytic purification technology is currently an efficient method for eliminating soot particles, which needs to develop high-activity catalysts. This work uses a two-step hydrothermal method to synthesize MnOx/CeO2 nanosphere catalysts, which have synergistic effects between manganese and cerium, and have high reactive oxygen species. Among them, the MnCe-1:4 catalyst represents the optimal catalytic activity, and the values of T10, T50, and T90 are 289, 340, and 373 °C, respectively. On account of their simple synthetic procedure and low cost, the prepared MnOx/CeO2 nanosphere catalysts have good prospects for practical applications.
Multiple Passions and Psychosomatic Health
Authors: Mudło-Głagolska Karolina , Larionow Paweł
Published: October 2023

People are often passionate about different activities in their lives. This study examined the role of multiple passions in psychosomatic health (i.e., subjective vitality and somatic symptoms) using variable-centered and person-centered approaches. Our sample consisted of 267 Polish adults, who filled out the measures on harmonious passion (HP), obsessive passion (OP), subjective vitality as a trait, and somatic symptoms in four categories (exhaustion, gastrointestinal, musculoskeletal, and cardiovascular complaints). In general, HP showed protective properties against individual somatic complaints, whereas OP was associated with higher levels of somatic symptoms, chiefly cardiovascular complaints. We highlighted that, unlike the first passion, the second passion can explain the differences in well-being and ill-being. Our study indicated the moderate health-promoting effects of HP, and the moderate-to-strong adverse effects of OP on somatic health. Having multiple passions of obsessive nature may be harmful for somatic health. In order to be healthy, prevention of the development of multiple passions with high obsessive levels seems to be a priority. Potential psychosomatic pathways were discussed.
Kawasaki Disease Complicated with Macrophage Activation Syndrome: The Importance of Prompt Diagnosis and Treatment–Three Case Reports
Authors: Corinaldesi Elena , Fabi Marianna , Scalabrini Ilaria , Praticò Elena Rita , Andreozzi Laura , Torcetta Francesco , Lanari Marcello
Published: October 2023

Kawasaki disease (KD) is an acute vasculitis that mainly affects children under 5 years of age, leading to coronary artery alterations (CAAs) in 25% of untreated patients. Macrophage activation syndrome (MAS) is a secondary hemophagocytic lymphohistiocytosis (HLH) that can complicate the acute, subacute, and chronic phases of KD. We retrospectively reviewed three cases of children affected by KD complicated with MAS hospitalized in two pediatric units in Emilia Romagna, a northern region of Italy. Case 1: a previously healthy 23-month-old female with full clinical criteria of KD and a hemorrhagic rash due to MAS during the acute phase of the illness. This patient responded promptly to a high dose of intravenous immune globulin (IVIG) and three pulses of high doses of methylprednisolone (MPD) with improvement in clinical signs and laboratory tests without the development of CAA at any phase of illness. Case 2: a previously healthy 10-month-old female with incomplete KD with persistent fever and maculopapular rash. This patient did not respond to IVIG and developed MAS during the subacute phase, characterized by persistent fever, hypertransaminasemia, hyperferritinemia, and hypofibrinogenemia after two high doses of IVIG and boluses of MPD. The patient responded to the addition of IL-1 blocker and anakinra and did not present CAA alterations during any phase of the illness. Case 3: a previously healthy 26-month-old male with incomplete KD with fever, maculopapular rash, cheilitis, and hyperemic conjunctivitis. This patient developed gallbladder hydrops and CAA in the acute phase and did not respond to two high doses of IVIG and a high dose of MPD. In the subacute phase, this patient was complicated with MAS and responded to intravenous anakinra. During the subacute phase, the patient developed transient aneurysms that regressed during the chronic phase. These cases reiterate that prompt diagnosis and aggressive immunomodulatory treatment can limit the most severe complications of MAS complicating KD. High doses of IVIG and MPD may result in a favorable outcome or more aggressive adjunctive treatment may be needed. Anakinra, cyclosporine, monoclonal antibodies, and plasmapheresis can be used as adjunctive treatment in the case of unresponsive MAS in KD. Notably, MAS, present during the subacute phase in cases 2 and 3, promptly responded to anakinra, an IL-1 blocker, without the use of cyclosporine. Our experience confirms that the IL-1 blocker can be considered an optimal choice after non-response to IVIG and MPD in KD complicating with MAS, avoiding over-treatment with cytotoxic drugs.
Advancement in the Application of Geospatial Technology in Archaeology and Cultural Heritage in South Africa: A Scientometric Review
Authors: Matyukira Charles , Mhangara Paidamwoyo
Published: October 2023

Geospatial technologies have become an essential component of archaeological research, aiding in the identification, mapping, and analysis of archaeological sites. Several journals have published existing narratives on the development and impact of geospatial technologies in the study of archaeology and cultural heritage. However, this has not been supported by a systematic review of articles and papers, where meticulously collected evidence is methodically analysed. This article systematically reviews the trends in the use of geospatial technologies in archaeology and cultural heritage through the search for keywords or terms associated with geospatial technologies used in the two fields on the Scopus database from 1990 to 2022. Bibliometric analysis using the Scopus Analyze tool and analysis of bibliometric networks using VOSviewer visualisations reveals how modern archaeological studies are now a significant discipline of spatial sciences and how the discipline enjoys the tools of geomatic engineering for establishing temporal and spatial controls on the material being studied and observing patterns in the archaeological records. The key concepts or themes or distinct knowledge domains that shape research in the use of geospatial technologies in archaeology and cultural heritage, according to the Scopus database (1990–2022), are cultural heritage, archaeology, geographic information systems, remote sensing, virtual reality, and spatial analysis. Augmented reality, 3D scanning, 3D modelling, 3D reconstruction, lidar, digital elevation modelling, artificial intelligence, spatiotemporal analysis, ground penetrating radar, optical radar, aerial photography, and unmanned aerial vehicles (UAVs) are some of the geospatial technology tools and research themes that are less explored or less interconnected concepts that have potential gaps in research or underexplored topics that might be worth investigating in archaeology and cultural heritage.
Design of Robust Sparse Wideband Beamformers with Circular-Model Mismatches Based on Reweighted ℓ2,1 Optimization
Authors: Bao Yu , Zhang Haixiao , Liu Xiaoli , Jiang Yuhan , Tao Yu
Published: October 2023

Wideband beamformers have been widely studied in wireless communication, remote sensing and so on. Generally speaking, to improve the spatial filtering ability of beamformers, there usually needs more sensors, which implies increased computational complexity and hardware costs. Besides that, wideband beamformers are known to be exceedingly sensitive to sensor mismatches in practice. Nevertheless, there is still a gap in research on the design of robust sparse wideband beamformers. In this paper, a two-step design of this topic is proposed. Firstly, a robust design based on the worst-case performance optimization (WCPO) using circular-model (CM) sensor mismatches is reformulated to address shortcomings of constraint sensitivity. Secondly, inspired by the joint sparse technology in compressive sensing theory, we focus on the sparse design of wideband beamformer. The constraints for the response characteristics and robustness are set from first step, and an iterative algorithm based on reweighted ℓ2,1 optimization is adopted to achieve maximum sparsity of the sensor array. The mainly advantages of the work are that the proposed design exhibits accordant performance in terms of response and robustness, but few sensors compared with the counterpart with uniform array. Moreover, we surprisingly find that the optimized sparse array is also applicable to other design based on WCPO criterion. Simulation results are provided to verify the superior of the proposed methods compared to the existing counterparts.
Estimation of Winter Wheat Yield Using Multiple Temporal Vegetation Indices Derived from UAV-Based Multispectral and Hyperspectral Imagery
Authors: Liu Yu , Sun Liang , Liu Binhui , Wu Yongfeng , Ma Juncheng , Zhang Wenying , Wang Bianyin , Chen Zhaoyang
Published: October 2023

Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ‘b1−b2/b3−b4’ derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale.
The Prediction of Transmission Towers’ Foundation Ground Subsidence in the Salt Lake Area Based on Multi-Temporal Interferometric Synthetic Aperture Radar and Deep Learning
Authors: Jin Bijing , Zeng Taorui , Yang Taohui , Gui Lei , Yin Kunlong , Guo Baorui , Zhao Binbin , Li Qiuyang
Published: October 2023

Displacement prediction of transmission towers is essential for the early warning of transmission network deformation. However, there is still a lack of prediction on the ground subsidence of the tower foundation. In this study, we first used the multi-temporal interferometric synthetic aperture radar (MT-InSAR) approach to acquire time series deformation for the transmission lines in the Salt Lake area. Based on the K-shape clustering method and field investigation results, towers #95 and #151 with representative foundation deformation characteristics were selected for displacement prediction. Combined with field investigations and the characteristics of saline soil in the Salt Lake area, the trigger factors of transmission tower deformation were analyzed. Then, the displacement and trigger factors of the transmission tower were decomposed by variational mode decomposition (VMD), which could closely connect the characteristics of the foundation saline soil with the influence of the trigger factors. To analyze the contribution of each trigger factor, the maximum information coefficient (MIC) was quantified, and the best choice was made. Finally, the hyperparameters of the long short-term memory (LSTM) neural networks were optimized using a convolutional neural network (CNN) and the grey wolf optimizer (GWO). The findings reveal that the refined deep learning models outperform the initial model in generalization potential and prediction precision, with the CNN–LSTM model demonstrating the highest accuracy in predicting the total displacement of tower #151 (RMSE and R2 for the validation set are 0.485 and 0.972, respectively). Given the scant research on the multifactorial influence on the ground subsidence displacement of transmission towers, this study’s methodology offers a novel perspective for monitoring and early warning of ground subsidence disasters in transmission networks.
The Five Basic Human Senses Evoke Electrodermal Activity
Authors: Bari Dindar S. , Rammoo Mohammed Noor S. , Aldosky Haval Y. Y. , Jaqsi Mohammed K. , Martinsen Ørjan G.
Published: October 2023

Electrodermal activity (EDA) usually relates to variations in the electrical properties of palmar or plantar skin sites. EDA responses, namely skin conductance responses (SCRs), skin potential responses (SPRs) and skin susceptance responses (SSRs) are shown to be sensitive indexes of sympathetic nervous system activation and are studied in many research projects. However, the association between EDA responses and the five basic human senses has not been investigated yet. Our study aimed to explore the relationship between the three EDA responses (SCRs, SSRs and SPRs) and the five basic human senses. These three EDA responses were measured simultaneously at the same skin site on each of the 38 volunteers. The tested five senses were sight, hearing, touch, taste and smell. The results showed that the different tested senses led to different degrees of EDA responses due to activation of the sympathetic nervous system and corresponding secretion of sweat. Although a controlled study on the degree of EDA as a function of the strength of each stimulus was not performed, we noted that the largest EDA responses were typically associated with the smell sense test. We conclude that EDA responses could be utilized as measures for examining the sensitivity of the human senses. Hence, EDA devices may have important roles in sensory systems for future clinical applications.
Application of Data Particle Geometrical Divide Algorithms in the Process of Radar Signal Recognition
Authors: Dudczyk Janusz , Rybak Łukasz
Published: October 2023

The process of recognising and classifying radar signals and their radiation sources is currently a key element of operational activities in the electromagnetic environment. Systems of this type, called ELINT class systems, are passive solutions that detect, process, and analyse radio-electronic signals, providing distinctive information on the identified emission source in the final stage of data processing. The data processing in the mentioned types of systems is a very sophisticated issue and is based on advanced machine learning algorithms, artificial neural networks, fractal analysis, intra-pulse analysis, unintentional out-of-band emission analysis, and hybrids of these methods. Currently, there is no optimal method that would allow for the unambiguous identification of particular copies of the same type of radar emission source. This article constitutes an attempt to analyse radar signals generated by six radars of the same type under comparable measurement conditions for all six cases. The concept of the SEI module for the ELINT system was proposed in this paper. The main aim was to perform an advanced analysis, the purpose of which was to identify particular copies of those radars. Pioneering in this research is the application of the author’s algorithm for the data particle geometrical divide, which at the moment has no reference in international publication reports. The research revealed that applying the data particle geometrical divide algorithms to the SEI process concerning six copies of the same radar type allows for almost three times better accuracy than a random labelling strategy within approximately one second.
An Improved Wake-Up Receiver Based on the Optimization of Low-Frequency Pattern Matchers
Authors: Fromm Robert , Kanoun Olfa , Derbel Faouzi
Published: October 2023

Wake-up receivers are gaining importance in power-aware wireless sensor networks, as they significantly reduce power consumption during RF reception, enabling asynchronous communication with low latency. However, the performance of wake-up receivers still lags behind that of off-the-shelf RF transceivers. There is a growing demand for higher sensitivity, enhanced reliability, and lower latency while maintaining the lowest power consumption. In this article, our goal is to advance the performance of wake-up receivers based on off-the-shelf components and low-frequency pattern matchers. Through a systematic investigation, we proposed multiple improvements aimed at enhancing wake-up receiver performance and reliability. We introduced an improved passive envelope detector and realized a wake-up receiver for the 868 MHz band, which achieves a power consumption of 5.71 μW and latency of 9.02 ms. Our proposed wake-up receiver is capable of detecting signals down to an average power level of −61.6 dBm. These achievements represent significant advancements compared to the existing state of research on wake-up receivers based on low-frequency pattern matchers. Recent articles have not been able to attain such improved values in signal detection, power consumption, and latency.
Concurrent Validity and Reliability of the Sprint Force–Velocity Profile Assessed with K-AI Wearable Tech
Authors: Vantieghem-Nicolas Laurine , Morin Jean-Benoit , Cotte Thierry , Sangnier Sébastien , Rossi Jeremy
Published: October 2023

Establishing a sprint acceleration force–velocity profile is a way to assess an athlete’s sprint-specific strength and speed production capacities. It can be determined in field condition using GNSS-based (global navigation satellite system) devices. The aims of this study were to (1) assess the inter-unit and the inter-trial reliability of the force–velocity profile variables obtained with K-AI Wearable Tech devices (50 Hz), (2) assess the concurrent validity of the input variables (maximal sprint speed and acceleration time constant), and (3) assess the validity of the output variables (maximal force output, running velocity and power). Twelve subjects, including one girl, performed forty-one 30 m sprints in total, during which the running speed was measured using two GPS (global positioning system) devices placed on the upper back and a radar (Stalker® Pro II Sports Radar Gun). Concurrent validity, inter-device and inter-trial reliability analyses were carried out for the input and output variables. Very strong to poor correlation (0.99 to 0.38) was observed for the different variables between the GPS and radar devices, with typical errors ranging from small to large (all < 7.6%). Inter-unit reliability was excellent to moderate depending on the variable (ICC values between 0.65 and 0.99). Finally, for the inter-trial reliability, the coefficients of variation were low to very low (all < 5.6%) for the radar and the GPS. The K-AI Wearable Tech used in this study is a concurrently valid and reliable alternative to radar for assessing a sprint acceleration force–velocity profile.
Advancements in Passive Wireless Sensors, Materials, Devices, and Applications
Authors: He Denghui , Cui Yuanhui , Ming Fangchao , Wu Weiping
Published: October 2023

In recent years, passive wireless sensors have been studied for various infrastructure sectors, making them a research and development focus. While substantial evidence already supports their viability, further effort is needed to understand their dependability and applicability. As a result, issues related to the theory and implementation of wireless sensors still need to be resolved. This paper aims to review and summarize the progress of the different materials used in different passive sensors, the current status of the passive wireless sensor readout devices, and the latest peripheral devices. It will also cover other related aspects such as the system equipment of passive wireless sensors and the nanogenerators for the energy harvesting for self-powered sensors for applications in contemporary life scenarios. At the same time, the challenges for future developments and applications of passive wireless are discussed.
Blind Quality Assessment of Images Containing Objects of Interest
Authors: He Wentong , Luo Ze
Published: October 2023

To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of objects, the quality of images containing objects of interest should be better than that of images without objects of interest, even if the former exhibits more severe distortion than the latter. However, according to current methods, quality assessments produce the opposite results. In this study, we propose an end-to-end model, named DETR-IQA (detection transformer image quality assessment), which extends the capability to perform object detection and blind image quality assessment (IQA) simultaneously by adding IQA heads comprising simple multi-layer perceptrons at the top of the DETRs (detection transformers) decoder. Using IQA heads, DETR-IQA carried out blind IQAs based on the weighted fusion of the distortion degree of the region of objects of interest and the other regions of the image; the predicted quality score of images containing objects of interest was generally greater than that of images without objects of interest. Currently, the subjective quality score of all public datasets is in accordance with the distortion of images and does not consider objects of interest. We manually extracted the images in which the five predefined classes of objects were the main contents of the largest authentic distortion dataset, KonIQ-10k, which was used as the experimental dataset. The experimental results show that with slight degradation in object detection performance and simple IQA heads, the values of PLCC and SRCC were 0.785 and 0.727, respectively, and exceeded those of some deep learning-based IQA models that are specially designed for only performing IQA. With the negligible increase in the computation and complexity of object detection and without a decrease in inference speeds, DETR-IQA can perform object detection and IQA via multi-tasking and substantially reduce the workload.
Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions
Authors: Zhang Yongjun , Chen Longxi , Feng Huanhuan , Xiao Xinqing , Nikitina Marina A. , Zhang Xiaoshuan
Published: October 2023

(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection.
Climbing Technique Evaluation by Means of Skeleton Video Stream Analysis
Authors: Beltrán Beltrán Raul , Richter Julia , Köstermeyer Guido , Heinkel Ulrich
Published: October 2023

Due to the growing interest in climbing, increasing importance has been given to research in the field of non-invasive, camera-based motion analysis. While existing work uses invasive technologies such as wearables or modified walls and holds, or focuses on competitive sports, we for the first time present a system that uses video analysis to automatically recognize six movement errors that are typical for novices with limited climbing experience. Climbing a complete route consists of three repetitive climbing phases. Therefore, a characteristic joint arrangement may be detected as an error in a specific climbing phase, while this exact arrangement may not considered to be an error in another climbing phase. That is why we introduced a finite state machine to determine the current phase and to check for errors that commonly occur in the current phase. The transition between the phases depends on which joints are being used. To capture joint movements, we use a fourth-generation iPad Pro with LiDAR to record climbing sequences in which we convert the climber’s 2-D skeleton provided by the Vision framework from Apple into 3-D joints using the LiDAR depth information. Thereupon, we introduced a method that derives whether a joint moves or not, determining the current phase. Finally, the 3-D joints are analyzed with respect to defined characteristic joint arrangements to identify possible motion errors. To present the feedback to the climber, we imitate a virtual mentor by realizing an application on the iPad that creates an analysis immediately after the climber has finished the route by pointing out the detected errors and by giving suggestions for improvement. Quantitative tests with three experienced climbers that were able to climb reference routes without any errors and intentionally with errors resulted in precision–recall curves evaluating the error detection performance. The results demonstrate that while the number of false positives is still in an acceptable range, the number of detected errors is sufficient to provide climbing novices with adequate suggestions for improvement. Moreover, our study reveals limitations that mainly originate from incorrect joint localizations caused by the LiDAR sensor range. With human pose estimation becoming increasingly reliable and with the advance of sensor capabilities, these limitations will have a decreasing impact on our system performance.