bluuebunny/crossref_metadata_2025 huggingface dataset:
Can We Predict Graft Intolerance Syndrome After Kidney Transplant Failure? External Validation of a Previously Developed Model
Authors: Bunthof Kim , Saboerali Khalid , Wetering Jacqueline Van De , Nurmohamed Azam , Bemelman Frederike , Zuilen Arjan Van , Brand Jan Van Den , Baas Marije , Hilbrands Luuk
Published: May 2023

Previously we established a prediction model for graft intolerance syndrome requiring graft nephrectomy in patients with late kidney graft failure. The aim of this study is to determine generalizability of this model in an independent cohort. The validation cohort included patients with late kidney graft failure between 2008 and 2018. Primary outcome is the prognostic performance of our model, expressed as the area under the receiver operating characteristic curve (ROC-AUC), in the validation cohort. In 63 of 580 patients (10.9%) a graft nephrectomy was performed because of graft intolerance. The original model, which included donor age, graft survival and number of acute rejections, performed poorly in the validation cohort (ROC-AUC 0.61). After retraining of the model using recipient age at graft failure instead of donor age, the model had an average ROC-AUC of 0.70 in the original cohort and of 0.69 in the validation cohort. Our original model did not accurately predict the graft intolerance syndrome in a validation cohort. However, a retrained model including recipient age at graft failure instead of donor age performed moderately well in both the development and validation cohort enabling identification of patients with the highest and lowest risk of graft intolerance syndrome.
Cooperative Attention-Based Learning between Diverse Data Sources
Authors: Srivastava Harshit , Sankar Ravi
Published: May 2023

Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread rate decision score. This work proposes a determination spread variable using a disease spread model and cooperative learning. It is a four-stage model that determines answers by identifying semantic cooperation using the spread model to identify events, infection factors, location spread, and change in spread rate. The proposed model analyses the spread of COVID-19 throughout the United States using a new approach by defining data cooperation using the dynamic variable of the spread rate and the optimal cooperative strategy. Game theory is used to define cooperative strategy and to analyze the dynamic variable determined with the help of a control algorithm. Our analysis successfully identifies the spread rate of disease from social data with an accuracy of 67% and can dynamically optimize the decision model using a control algorithm with a complexity of order O(n2).
Quantum Circuit-Width Reduction through Parameterisation and Specialisation
Authors: Moawad Youssef , Vanderbauwhede Wim , Steijl René
Published: May 2023

As quantum computing technology continues to develop, the need for research into novel quantum algorithms is growing. However, such algorithms cannot yet be reliably tested on actual quantum hardware, which is still limited in several ways, including qubit coherence times, connectivity, and available qubits. To facilitate the development of novel algorithms despite this, simulators on classical computing systems are used to verify the correctness of an algorithm, and study its behaviour under different error models. In general, this involves operating on a memory space that grows exponentially with the number of qubits. In this work, we introduce quantum circuit transformations that allow for the construction of parameterised circuits for quantum algorithms. The parameterised circuits are in an ideal form to be processed by quantum compilation tools, such that the circuit can be partially evaluated prior to simulation, and a smaller specialised circuit can be constructed by eliminating fixed input qubits. We show significant reduction in the number of qubits for various quantum arithmetic circuits. Divide-by-n-bits quantum integer dividers are used as an example demonstration. It is shown that the complexity reduces from 4n+2 to 3n+2 qubits in the specialised versions. For quantum algorithms involving divide-by-8 arithmetic operations, a reduction by 28=256 in required memory is achieved for classical simulation, reducing the memory required from 137 GB to 0.53 GB.
Fineness Ratio Effects on the Flow Past an Axisymmetric Body at High Incidence
Authors: Jiménez-Varona José , Liaño Gabriel
Published: May 2023

The flow past an axisymmetric body at a sufficiently high angle of attack becomes asymmetric and unsteady. Several authors identified three different flow regions for bodies of large fineness ratio at low subsonic flow and high incidence: a steady region in the forebody and two unsteady regions in the rear body. Unsteady Reynolds Averaged Navier–Stokes (URANS) codes with eddy viscosity turbulence models or Reynolds stress turbulence models fail to capture the unsteady flow region. These methods are overly dissipative and resolve only frequencies far lower than turbulent fluctuations. Scale-Adaptive-Simulation (SAS) provides an alternative method to afford the problem of these massively separated flows at high Reynolds numbers without addressing the problem to Large Eddy Simulation (LES). This paper applies SAS to study the effect of slenderness on the flow. The numerical solutions show that the flow becomes more unstable as the fineness ratio increases, and the three flow regions are clearly recognizable. For low fineness ratios, only one of the two unsteady regions is visible. The good agreement between the sectional forces and pressure coefficients with their corresponding experimental data for an ogive-cylinder configuration allows an analysis of the flow structure with a fair degree of confidence.
Morphological, Histological, and Glyphosate Residue Analysis of Helianthus annuus L. Plants Treated with Glyphosate
Authors: Kisvarga Szilvia , Hamar-Farkas Dóra , Horotán Katalin , Inotai Katalin , Mörtl Mária , Neményi András , Székács András , Orlóci László
Published: May 2023

Several recent studies have shown that glyphosate and its metabolite, aminomethylphosphonic acid (AMPA), resist rapid degradation and, therefore, can accumulate in plants. Continuing our previous research, we aimed to investigate the effect of indirectly spraying glyphosate on leaves and soil on non-target plants in the case of Helianthus annuus L. The plants were treated with glyphosate in their 5–6 leaf stages, the effects of which were assessed two weeks later from a morphological and histological point of view, as an evaluation of the residues of glyphosate and its metabolite, AMPA. They had an effect on both treated groups. In the case of the morphological parameters (plant height, number of leaves, and fresh and dried root and green mass), the data of the treated plants were statistically lower than in the case of the control group. The epidermis and the transport tissue system were damaged, and tissue death was observed in plants exposed to glyphosate. Both compounds were detected in all plant parts (roots, stems, lower leaves, and upper leaves), well above the limit of detection (0.025 mg/kg) and limit of quantitation 0.075 mg/kg showing a statistical difference with the control plants. This proved that glyphosate is incorporated into the plant organism even when applied indirectly.
Evaluation of Two Surface Renewal Methods for Calculating the Sensible Heat Flux over a Tea Field Ecosystem in Hilly Terrain
Authors: Hu Huijie , Lu Yongzong , Hu Yongguang , Ding Risheng
Published: May 2023

Seasonal drought happens frequently in the lower slope hilly areas of China, which leads to a huge economic loss to China’s famous tea production. An accurate determination of the evapotranspiration (ET) value of different seasons can provide a crucial decision parameter for irrigation management. The surface renewal (SR) method is an accurate and inexpensive method compared with the eddy covariance (EC) method, which is widely used to calculate the sensible heat flux (H). The latent heat flux (LE) evapotranspiration can be estimated indirectly when combined with the energy–balance equation. This research investigated the traditional and improved calculation methods of H (SRsnyder and SRchen), based on the surface renewal theory, over a tea field for one year. The calculation accuracy was obtained from the statistical analysis between the SR and EC methods. Different months’ applicability was evaluated to determine the best calculation method for the tea field. The traditional calculation method (SRsnyder) is based on the van slope model using the second, third, and fifth structure function. The improved SR model (SRchen) introduces a third order temperature function and friction velocity for calculation. The results indicate that SRchen shows a good calculation accuracy of H in the spring seasons (February to April), summer (May to July), and autumn (August to October). The determination coefficients of regression analysis (R2) ranges were [0.66, 0.88] with most values greater than 0.8. The root mean square error (RMSE) ranges were [34.15, 69.22] W/m2. However, during this period, SRsnyder had a poor calculation accuracy of H, and the range of R2 was [0.45, 0.74] with the RMSE ranges of [32.28, 63.25] W/m2. In the winter (November to January), the calculation accuracy of both models was relatively low with R2 almost 30% lower than that of other seasons. Therefore, this study suggests the use of the SRchen method to estimate the H of a tea field ecosystem in the low slope hilly area of the Yangtze River region in the spring, summer, and autumn. While in the winter, the SRsnyder method is recommended.
Ensiling Characteristics, In Vitro Rumen Fermentation Patterns, Feed Degradability, and Methane and Ammonia Production of Berseem (Trifolium alexandrinum L.) Co-Ensiled with Artichoke Bracts (Cynara cardunculus L.)
Authors: Ahmed Mariam G. , Al-Sagheer Adham A. , El-Waziry Ahmed M. , El-Zarkouny Samir Z. , Elwakeel Eman A.
Published: May 2023

This study investigated the effect of co-ensiling increasing levels of artichoke bracts (Cynara cardunculus L.) with berseem (Trifolium alexandrinum L.) (100:0, 75:25, 50:50, 25:75, and 0:100, respectively) on silage quality after 0, 30, 60, and 120 days. Moreover, the in vitro rumen fermentation characteristics and methane (CH4) and ammonia (NH3-N) production were evaluated using a buffalo inoculum source. The results showed that pH of the silage and the concentration of acetic, propionic, butyric acid, and NH3-N significantly decreased (L; p < 0.01) with the increasing amounts of artichoke bracts in the mixture. At 30 and 60 days of ensiling, the highest lactic acid concentration was observed at intermediate proportions of artichoke bracts (p < 0.01). Cumulative gas production was higher in artichoke bracts than in the berseem silage. After 24 h of incubation, the highest value (p < 0.05) of truly dry matter, organic matter, natural detergent fiber degradability, and NH3-N concentration was recorded with 500 g/kg of forage mixtures. As the artichoke bract concentration increased, the partitioning factor and ruminal pH declined linearly (p ≤ 0.05). No significant differences were observed for total volatile fatty acids and volatile fatty acids molar proportions. In summary, co-ensiling artichoke bracts with berseem at a ratio of 1:1 might be a promising and easy method for the production of high-quality silage from legume forage with positively manipulating rumen fermentation.
Animal and Human Dirofilariasis in India and Sri Lanka: A Systematic Review and Meta-Analysis
Authors: Thilakarathne Sandani S. , Yuen Nicholas K. Y. , Hassan Mohammad Mahmudul , Yahathugoda Thishan C. , Abdullah Swaid
Published: May 2023

Dirofilariasis is an emerging vector-borne tropical disease of public health importance that mainly affects humans and dogs. Dirofilaria immitis and D. repens are the two well-documented dirofilariasis-causing filarioid helminths of both medical and veterinary concerns in India and Sri Lanka. This systematic review and meta-analysis aimed to describe and summarize the current evidence of dirofilariasis prevalence and distribution in India and Sri Lanka. Interestingly, D. repens is reported to circulate in both dogs (prevalence of 35.8% (95% CI: 11.23–60.69)) and humans (97% of published case reports) in India and Sri Lanka, but D. immitis is reported to be present in the dog populations in India (prevalence of 9.7% (95% CI: 8.5–11.0%)), and so far, it has not been reported in Sri Lanka. This peculiar distribution of D. immitis and D. repens in the two neighbouring countries could be due to the interaction between the two parasite species, which could affect the pattern of infection of the two worm species in dogs and thus influence the geographical distribution of these two filarial worms. In medical and veterinary practice, histopathology was the most commonly used diagnostic technique (31.3%; 95% CI 2.5–60.2%). The low specificity of histopathology to speciate the various Dirofilaria spp. may lead to misdiagnosis. It was identified in this study that several regions of India and Sri Lanka have not yet been surveyed for dirofilariasis. This limits our understanding of the geographical distribution and interspecies interactions of the two parasites within these countries. Parasite distribution, disease prevalence, and interspecies interactions between the vectors and the host should be targeted for future research.
Teleost Piscidins—In Silico Perspective of Natural Peptide Antibiotics from Marine Sources
Authors: Asensio-Calavia Patricia , González-Acosta Sergio , Otazo-Pérez Andrea , López Manuel R. , Morales-delaNuez Antonio , Pérez de la Lastra José Manuel
Published: May 2023

Fish, like all other animals, are exposed to constant contact with microbes, both on their skin and on the surfaces of their respiratory and digestive systems. Fish have a system of non-specific immune responses that provides them with initial protection against infection and allows them to survive under normal conditions despite the presence of these potential invaders. However, fish are less protected against invading diseases than other marine vertebrates because their epidermal surface, composed primarily of living cells, lacks the keratinized skin that serves as an efficient natural barrier in other marine vertebrates. Antimicrobial peptides (AMPs) are one type of innate immune protection present in all life forms. AMPs have been shown to have a broader range of biological effects than conventional antibiotics, including antibacterial, antiviral, antiprotozoal, and antifungal effects. Although other AMPs, such as defensins and hepcidins, are found in all vertebrates and are relatively well conserved, piscidins are found exclusively in Teleost fish and are not found in any other animal. Therefore, there is less information on the expression and bioactivity of piscidins than on other AMPs. Piscidins are highly effective against Gram-positive and Gram-negative bacteria that cause disease in fish and humans and have the potential to be used as pharmacological anti-infectives in biomedicine and aquaculture. To better understand the potential benefits and limitations of using these peptides as therapeutic agents, we are conducting a comprehensive study of the Teleost piscidins included in the “reviewed” category of the UniProt database using bioinformatics tools. They all have amphipathic alpha-helical structures. The amphipathic architecture of piscidin peptides and positively charged residues influence their antibacterial activity. These alpha-helices are intriguing antimicrobial drugs due to their stability in high-salt and metal environments. New treatments for multidrug-resistant bacteria, cancer, and inflammation may be inspired by piscidin peptides.
GlyNAC (Glycine and N-Acetylcysteine) Supplementation in Old Mice Improves Brain Glutathione Deficiency, Oxidative Stress, Glucose Uptake, Mitochondrial Dysfunction, Genomic Damage, Inflammation and Neurotrophic Factors to Reverse Age-Associated Cognitive Decline: Implications for Improving Brain Health in Aging
Authors: Kumar Premranjan , Osahon Ob W. , Sekhar Rajagopal V.
Published: May 2023

Cognitive decline frequently occurs with increasing age, but mechanisms contributing to age-associated cognitive decline (ACD) are not well understood and solutions are lacking. Understanding and reversing mechanisms contributing to ACD are important because increased age is identified as the single most important risk factor for dementia. We reported earlier that ACD in older humans is associated with glutathione (GSH) deficiency, oxidative stress (OxS), mitochondrial dysfunction, glucose dysmetabolism and inflammation, and that supplementing GlyNAC (glycine and N-acetylcysteine) improved these defects. To test whether these defects occur in the brain in association with ACD, and could be improved/reversed with GlyNAC supplementation, we studied young (20-week) and old (90-week) C57BL/6J mice. Old mice received either regular or GlyNAC supplemented diets for 8 weeks, while young mice received the regular diet. Cognition and brain outcomes (GSH, OxS, mitochondrial energetics, autophagy/mitophagy, glucose transporters, inflammation, genomic damage and neurotrophic factors) were measured. Compared to young mice, the old-control mice had significant cognitive impairment and multiple brain defects. GlyNAC supplementation improved/corrected the brain defects and reversed ACD. This study finds that naturally-occurring ACD is associated with multiple abnormalities in the brain, and provides proof-of-concept that GlyNAC supplementation corrects these defects and improves cognitive function in aging.
Antioxidant and Anti-Inflammatory Capacities of Fractions and Constituents from Vicia tetrasperma
Authors: Le Duc Dat , Min Kyung Hyun , Lee Mina
Published: May 2023

The young leaves and shoots of V. tetrasperma are consumed daily as cooked vegetables and can provide various health benefits. The antioxidant and anti-inflammatory capacities of its total extract and fractions were accessed for the first time in this study. The bioactivities guided the separation of the active fraction (EtOAc), leading to the identification of nine flavonoid glycoside compositions from this plant for the first time. In addition, the fractions and all isolates were evaluated for their inhibition against NO and IL-8 production in LPS-stimulated RAW264.7 and HT-29 cell lines, respectively. The most active ingredient was further assayed for its inhibitory abilities to iNOS and COX-2 proteins. Indeed, its mechanisms of action modes were confirmed by Western blotting assays through the reduction in their expression levels. An in silico approach revealed the substantial binding energies of docked compounds into established complexes to verify their anti-inflammatory properties. In addition, the presence of active components in the plant was validated by an established method on the UPLC-DAD system. Our research has boosted the value of this vegetable’s daily use and provided a therapeutic approach for the development of functional food products for health improvement regarding the treatment of oxidation and inflammation.
Loss of Nuclear/DNA Integrity in Mouse Epididymal Spermatozoa after Short-Term Exposure to Low Doses of Dibutyl Phthalate or Bisphenol AF and Its Mitigation by Oral Antioxidant Supplementation
Authors: Hug Elisa , Villeneuve Pauline , Bravard Stephanie , Chorfa Areski , Damon-Soubeyrand Christelle , Somkuti Stephen G. , Moazamian Aron , Aitken R. John , Gharagozloo Parviz , Drevet Joël R. , Saez Fabrice
Published: May 2023

Routine exposure to chemicals omnipresent in the environment, particularly the so-called endocrine-disrupting chemicals (EDCs), has been associated with decreased sperm quality and increased anomalies in testis. The decline in semen quality and testicular abnormalities have been attributed to the disruption of endocrine signaling as well as oxidative stress. The present study set out to examine the effect of short-term exposure of two common EDCs widely used in the plastic industry: Dibutyl Phthalate (DBP) and Bisphenol AF (BPAF). Our research objective was to focus on the post-testicular compartment of the epididymis, where spermatozoa acquire their functional capacity and are stored. The data obtained indicated no significant effect for either chemicals on sperm viability, motility or acrosome integrity. Neither of the EDCs had a noticeable effect on the structures of the testis and epididymis. However, substantial impact on the integrity of the sperm nucleus and DNA structure was evidenced by a significant increase in nuclear decondensation and DNA base oxidation. The damage observed was postulated to arise from the pro-oxidant properties of the EDCs generating excess of reactive oxygen species (ROS) and triggering a state of oxidative stress. This hypothesis was confirmed when the observed damage was largely blocked by co-administering EDCs with an evidenced-based antioxidant formulation.
Early-Life Exposure to Commercial Formulation Containing Deltamethrin and Cypermethrin Insecticides Impacts Redox System and Induces Unexpected Regional Effects in Rat Offspring Brain
Authors: Mekircha Fatiha , Fedeli Donatella , Nasuti Cinzia , Kecies Hadjer , Gabbianelli Rosita , Bordoni Laura
Published: May 2023

Several studies have shown that the oxidative impact of pesticides is most prevalent in rural environments where they are intensively used. At different levels, pyrethroids are reported to promote neurodegeneration; they share the ability to promote oxidative stress, and to induce mitochondrial impairments, α-synuclein overexpression and neuronal cell loss. The present study evaluates the impact of early-life exposure to a commercial formulation containing deltamethrin (DM) and cypermethrin (CYP) at a dose of 1/100 LD50 (1.28 and 2.5 mg/kg, respectively). Rats aged 30 days old, treated from the 6th to the 21st day of life, were tested for brain antioxidant activity and α-synuclein levels. Four regions of the brain were analyzed: the striatum, cerebellum, cortex and hippocampus. Our data demonstrated a significant increase in catalase (CAT), superoxide dismutase (SOD) and glutathione (GSH) antioxidant levels in the brain regions compared to the controls. Pups exhibited no significant changes in protein carbonyl levels and lipid peroxidation. Striatal α-synuclein expression was significantly reduced in the rats exposed to DM + CYP, while the treatment resulted in a non-significant increase in the other brain areas. These findings indicate unexpected effects of postnatal treatment with the commercial formulation containing DM and CYP on brain redox state and α-synuclein expression, suggesting an adaptive response.
Radiotherapy and Immunotherapy—A Future Partnership towards a New Standard
Authors: Mireștean Camil Ciprian , Iancu Roxana Irina , Iancu Dragoș Teodor
Published: May 2023

The impressive results in terms of survival brought by immune checkpoint inhibitors (ICI) in metastatic malignant melanoma and the transformation of this disease with a poor prognosis into a chronic disease even with long-term survival cases have opened horizons for a new era in cancer treatments. Later, therapy with CTLA-4 and PD-1/PD-L1 inhibitors became standard in other solid tumors, especially in relapsed and metastatic settings. The PACIFIC clinical trial revolutionized the concept of consolidation immunotherapy after the favorable response to curative chemoradiotherapy in non-small cell lung carcinoma (NSCLC). Two new effects will govern the future of the immunotherapy–radiotherapy association: the local “in situ” vaccination effect and the systemic remote “abscopal” response. Even if stereotactic body irradiation (SBRT) or stereotactic radiosurgery (SRT) seems to be more effective in generating the synergistic effect, the PACIFIC trial demonstrates the role of conventional irradiation in combination with chemotherapy in modulating the host’s immune response. Thus, the radiotherapy–chemotherapy–immunotherapy triad may become the future standard in locally advanced disease. The different mechanisms of producing immune-mediated cell death and the indirect role of augmenting the immune effect induced by radiotherapy make the old theories related to the therapeutic sequence, fractionation, doses, and target volumes as well as the protection of healthy tissues to be re-evaluated. The new concept of immuno-radiotherapy in synergistic association has as its physiopathological substrate the dual immunosuppressive and enhancement of antitumor response to irradiation, including the activation of the immune effectors in the tumor microenvironment (TME). The choice of sequential treatment, a hypofractionated irradiation regime, and the possible omission of lymph node irradiation with the limitation of lymphopenia could tilt the balance in favor of the activation and potentiation of the antitumor immune response. The selection of therapeutic targets chosen for the combination of immunotherapy and associated radiotherapy can be conducted based on the classification of tumors in the three immune phenotypes that characterize “cold” and “hot” tumors from the point of view of the response to therapy.
Design and Development of a Flexible 3D-Printed Endoscopic Grasping Instrument
Authors: Sudarevic Boban , Troya Joel , Fuchs Karl-Hermann , Hann Alexander , Vereczkei Andras , Meining Alexander
Published: May 2023

(1) Background: Interventional endoscopic procedures are growing more popular, requiring innovative instruments and novel techniques. Three-dimensional printing has demonstrated great potential for the rapid development of prototypes that can be used for the early assessment of various concepts. In this work, we present the development of a flexible endoscopic instrument and explore its potential benefits. (2) Methods: The properties of the instrument, such as its maneuverability, flexibility, and bending force, were evaluated in a series of bench tests. Additionally, the effectiveness of the instrument was evaluated in an ex vivo porcine model by medical experts, who graded its properties and performance. Furthermore, the time necessary to complete various interventional endoscopic tasks was recorded. (3) Results: The instrument achieved bending angles of ±216° while achieving a bending force of 7.85 (±0.53) Newtons. The time needed to reach the operating region was 120 s median, while it took 70 s median to insert an object in a cavity. Furthermore, it took 220 s median to insert the instrument and remove an object from the cavity. (4) Conclusions: This study presents the development of a flexible endoscopic instrument using three-dimensional printing technology and its evaluation. The instrument demonstrated high bending angles and forces, and superior properties compared to the current state of the art. Furthermore, it was able to complete various interventional endoscopic tasks in minimal time, thus potentially leading to the improved safety and effectiveness of interventional endoscopic procedures in the future.
Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
Authors: Han Chaolin , Li Hongwei , Xu Jian , Dong Bing , Wang Yalin , Zhou Xiaowen , Zhao Shan
Published: May 2023

As a core task of computer vision perception, 3D scene understanding has received widespread attention. However, the current research mainly focuses on the semantic understanding task at the level of entity objects and often neglects the semantic relationships between objects in the scene. This paper proposes a 3D scene graph prediction model based on deep learning methods for scanned point cloud data of indoor scenes to predict the semantic graph about the class of entity objects and their relationships. The model uses a multi-scale pyramidal feature extraction network, MP-DGCNN, to fuse features with the learned category-related unbiased meta-embedding vectors, and the relationship inference of the scene graph uses an ENA-GNN network incorporating node and edge cross-attention; in addition, considering the long-tail distribution effect, a category grouping re-weighting scheme is used in the embedded prior knowledge and loss function. For the 3D scene graph prediction task, experiments on the indoor point cloud 3DSSG dataset show that the model proposed in this paper performs well compared with the latest baseline model, and the prediction effectiveness and accuracy are substantially improved.
An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
Authors: Guo Jiaxing , Tang Zhiyi , Zhang Changxing , Xu Wei , Wu Yonghong
Published: May 2023

Structural health monitoring systems continuously monitor the operational state of structures, generating a large amount of monitoring data during the process. The structural responses of extreme events, such as earthquakes, ship collisions, or typhoons, could be captured and further analyzed. However, it is challenging to identify these extreme events due to the interference of faulty data. Real-world monitoring systems suffer from frequent misidentification and false alarms. Unfortunately, it is difficult to improve the system’s built-in algorithms, especially the deep neural networks, partly because the current neural networks only output results and do not provide an interpretable decision-making basis. In this study, a deep learning-based method with visual interpretability is proposed to identify seismic data under sensor faults interference. The transfer learning technique is employed to learn the features of seismic data and faulty data with efficiency. A post hoc interpretation algorithm, termed Gradient-weighted Class Activation Mapping (Grad-CAM), is embedded into the neural networks to uncover the interest regions that support the output decision. The in situ seismic responses of a cable-stayed long-span bridge are used for method verification. The results show that the proposed method can effectively identify seismic data mixed with various types of faulty data while providing good interpretability.
A Quantitative Stability Evaluation Method of VSC for Optimizing Control Parameters and Extending Stability Boundary to Avoid Oscillations
Authors: Jiang Xiaofeng , Han Xiaoyan , Sun Huadong , Liu Lei , Pan Pengyu , Chen Gang , Si Qi , Jiang Pan
Published: May 2023

Oscillations caused by the interaction between voltage source converters (VSCs) and weak grids are vital threats to the stability of power systems. Determining the appropriate parameters for the control of VSCs is essential to prevent the occurrence of oscillations in advance. To achieve this goal, a quantitative evaluation method of system stability for VSCs is proposed in this article to specify the stability boundary of control parameters. Then, an active damping controller for current control and a parameter optimization method for the phase-locked loop (PLL) is proposed, and the related parameters are designed based on the guidance of the proposed evaluation method. With planting the parameters optimization in the control of VSC, the stability boundary of control parameters of VSC is extended, so that the stability of VSC can be significantly improved. Finally, simulations are presented to verify the effectiveness of the theoretical analysis.
Focusing on Driving Modes Rather than Drivers: Toward More Precise and Efficient Car-Following Behavior Modeling
Authors: Zhang Duo , Rao Hongyu , Wang Junhua , Sun Jian , Yue Lishengsa
Published: May 2023

Car-following (CF) behavior is one of the most important driving behaviors. Accurately understanding and modeling CF behavior is essential for traffic flow simulation and user-acceptable advanced driving assistance systems (ADASs). In previous decades, CF models were calibrated based on drivers or trajectories, with short-term changes ignored. Recent studies have indicated that these changes could be caused by occasional irritations or regular switches of driving modes, but there is still a lack of specific understanding of driving modes and how these modes affect simulation accuracy in the reproduction of CF behavior. This paper explored the existence of driving modes and the quantified modeling influence of driving modes. Specifically, we first extracted 4000 high-resolution CF events of 40 drivers from large-scale naturalistic driving data for the discovery of underlying driving modes. Then, we introduced a novel multivariate time series method, Toeplitz Inverse Covariance-based Clustering (TICC), to achieve the segmentation and classification extraction of different driving modes. Finally, calibrated by the CF dataset, the proper cluster number of the driving mode was determined, and a comparison of driving-mode-based modeling (DMBM) and driver-based modeling (DBM) was conducted. The results showed that the driving process could be viewed as five core driving modes, and the DMBM has the potential to bring upwards of a 13% accuracy improvement with fewer parameters.
Walnut Acreage Extraction and Growth Monitoring Based on the NDVI Time Series and Google Earth Engine
Authors: Shi Ziyan , Zhang Rui , Bai Tiecheng , Li Xu
Published: May 2023

Walnut (Juglans regia) planting is the main economic pillar industry in southern Xinjiang. Based on the Google Earth Engine (GEE) cloud platform, the NDVI maximum synthesis method was used to estimate changes in the walnut cultivation area in Ganquan Town, South Xinjiang, from 2017 to 2021. The simultaneous difference between NDVI and meteorological conditions was also used to monitor the growth and correlation analysis of walnuts from April to September 2021. To improve the classification accuracy of the extracted walnut plantation area, Sentinel-2 image data were selected, and features were trained using the random forest algorithm, and by combining topographic features, texture features, NDVI, and EVI. The results show that, compared with Statistical Yearbook data, the average error of the extracted walnut planted area is less than 10%, the overall classification accuracy is 92.828%, the average kappa coefficient is 90.344%, and the average walnut classification accuracy is 94.4%. The accuracy of the data was significantly improved by adding vegetation indices EVI and NDVI compared with the single vegetation index. An analysis of the results from monitoring comparative growth shows that the growth of walnuts in Ganquan was better during the hardcore and oil transformation stages compared with 2020, and in the fruit development stage, the growth was the same as in 2020, and overall, the growth of walnuts in 2021 was better than in previous years.
Design and Verification of a Large-Scaled Flapping-Wing Aircraft Named “Cloud Owl”
Authors: Meng Rui , Song Bifeng , Xuan Jianlin , Yang Xiaojun
Published: May 2023

The bionic flapping-wing aircraft has the advantages of high flexibility and strong concealment; however, in the existing flapping-wing aircraft, the platform performance is influenced by the payload capacity, endurance, and durability; additionally, the mission capability is constrained, making it challenging to put into use in real-world scenarios. In response to this issue, this article offers a thorough design approach for a large-span flapping-wing aircraft, focusing on effective flapping wings, effective flapping mechanism design, and enhancement of flapping mechanism reliability, and ultimately realizing the design and verification of a new bionic flapping-wing aircraft with a large wingspan, called “Cloud Owl”. It has a wingspan of 1.82 m and weighs 980 g. The aircraft is capable of autonomous flight and remote control, and it can carry a range of mission-specific equipment. More than 200 flights have been made by “Cloud Owl” so far in Xi’an, Beijing, Tianjin, Tibet, Ganzi, and other places. It has evolved into a flapping-wing aircraft platform with exceptional stability, payload capacity, and long endurance.
Side-Channel Power Analysis Based on SA-SVM
Authors: Zhang Ying , He Pengfei , Gan Han , Zhang Hongxin , Fan Pengfei
Published: May 2023

Support vector machines (SVMs) have been widely used in side-channel power analysis. The selection of the penalty factor and kernel parameter heavily influences how well support vector machines work. Setting reasonable SVM hyperparameters is a key issue in side-channel power analysis. The novel side-channel power analysis method SA-SVM, which combines simulated annealing (SA) and support vector machines (SVMs) to analyze the power traces and crack the key, is proposed in this paper as a solution to this issue. This method differs from other approaches in that it integrates SA and SVMs, enabling us to more effectively explore the search space and produce superior results. In this paper, we conducted experiments on SA-SVM and SVM models from three different aspects: the selection of kernel functions, the number of parameters, and the number of eigenvalues. To compare our results with previous research, we performed experimental evaluations on open datasets. The results indicate that, compared with the SVM model, the SA-SVM model improved the accuracy by 0.25% to 3.25% and reduced the required time by 39.96% to 98.02% when the point of interest was 53, recovering the key using only three power traces. The SA-SVM model outperforms existing methods in terms of accuracy and computation time.
Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
Authors: Gong Yongzhen , Zhou Shengzhi , Chen Guo
Published: May 2023

One of the significant factors contributing to high energy consumption is the unutilized waste heat from flue gas in industrial boilers. Thermoelectric generator (TEG) technology can directly convert thermal energy into electrical energy, and has been gradually applied in the field of waste heat recovery due to its simple and reliable structure, environmental protection, and other advantages. In this paper, a thermoelectric generator simulation system of boiler flue gas waste heat is proposed. The experimental platform is designed by simulating the flue gas waste heat temperature condition of boiler, and the structure of cold end module and hot end module is optimized. During the experiment, the fixed temperature difference was set at 120 °C (hot end:150 °C~cold end: 30 °C). An analysis is conducted on the volt-ampere characteristics and output power of the TEG module. The output characteristics of the TEG system are analyzed under the conditions of variable load, constant load, different pump speed, different heat dissipation modes, and series and parallel connection method. The results show that the experimental platform can instantaneously and accurately test the output parameters of the TEG system, and ensure the intended design requirements. When the ratio of the load resistance to the internal resistance of the TEG module is approximately 1–1.15, the output power of the system reaches its maximum. In order to optimize the output power of the TEG system, a power prediction-based adaptive variable step size maximum power point tracking (MPPT) algorithm is introduced. Additionally, a corresponding mathematical model is formulated. Simulations demonstrate that the time of the improved algorithm to reach the stable maximum power point is 1.54 s faster than that of the traditional algorithm. The improved MPPT algorithm satisfies the criteria for speed and accuracy, diminishes superfluous energy waste, and enhances the overall system efficiency. The research results have certain guiding significance for the design and application of subsequent TEG system.
Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models
Authors: Yeleussinov Arman , Amirgaliyev Yedilkhan , Cherikbayeva Lyailya
Published: May 2023

This paper aims to increase the accuracy of Kazakh handwriting text recognition (KHTR) using the generative adversarial network (GAN), where a handwriting word image generator and an image quality discriminator are constructed. In order to obtain a high-quality image of handwritten text, the multiple losses are intended to encourage the generator to learn the structural properties of the texts. In this case, the quality discriminator is trained on the basis of the relativistic loss function. Based on the proposed structure, the resulting document images not only preserve texture details but also generate different writer styles, which provides better OCR performance in public databases. With a self-created dataset, images of different types of handwriting styles were obtained, which will be used when training the network. The proposed approach allows for a character error rate (CER) of 11.15% and a word error rate (WER) of 25.65%.
Activity Prediction Based on Deep Learning Techniques
Authors: Park Jinsoo , Song Chiyou , Kim Mingi , Kim Sungroul
Published: May 2023

Studies on real-time PM2.5 concentrations per activity in microenvironments are gaining a lot of attention due to their considerable impact on health. These studies usually assume that information about human activity patterns in certain environments is known beforehand. However, if a person’s activity pattern can be inferred reversely using environmental information, it can be easier to access the levels of PM2.5 concentration that affect human health. This study collected the actual data necessary for this purpose and designed a deep learning algorithm that can infer human activity patterns reversely using the collected dataset. The dataset was collected based on a realistic scenario, which includes activity patterns in both indoor and outdoor environments. The deep learning models used include the well-known multilayer perception (MLP) model and a long short-term memory (LSTM) model. The performance of the designed deep learning algorithm was evaluated using training and test data. Simulation results showed that the LSTM model has a higher average test accuracy of more than 15% compared to the MLP model, and overall, we were able to achieve high accuracy of over 90% on average.
3D Photogrammetric Survey, Raking Light Photography and Mapping of Degradation Phenomena of the Early Renaissance Wall Paintings by Saturnino Gatti—Case Study of the St. Panfilo Church in Tornimparte (L’Aquila, Italy)
Authors: Lanteri Luca , Calandra Sara , Briani Francesca , Germinario Chiara , Izzo Francesco , Pagano Sabrina , Pelosi Claudia , Santo Alba Patrizia
Published: May 2023

This paper provides the results of a 3D photogrammetric survey of the apsidal conch of St. Panfilo Church in Tornimparte (L’Aquila, Italy). The images were acquired and then processed in order to obtain a three-dimensional model available on Sketchfab platform. The five panels and the vault of the apsidal conch were exported from the 3D model as orthomosaics and then imported into CAD software for the mapping of the main degradation phenomena. The examined surface was almost entirely covered by mural painting and restoring mortars, the latter mainly located in the lower part of the apsidal conch. The whole surface of the apsidal conch was also examined by means of raking light that enabled highlighting of conservation problems and the presence of incision marks and giornata/pontate boundaries indicating the use of the fresco painting technique. Several degradation phenomena, attributable both to the executive technique of wall painting and the microclimate conditions, could be observed. According to the overlapping of weathering forms and the material involved, most of the examined surfaces exhibited moderate to very severe degradation.
Yolo-Light: Remote Straw-Burning Smoke Detection Based on Depthwise Separable Convolution and Channel Attention Mechanisms
Authors: Hong Rui , Wang Xiujuan , Fang Yong , Wang Hao , Wang Chengpeng , Wang Huanqin
Published: May 2023

Straw burning is a long-term environmental problem in China’s agricultural production. At present, China relies mainly on satellite remote sensing positioning and manual patrol to detect straw burning, which are inefficient. Due to the development of machine learning, target detection technology can be used for the detection of straw burning, but the current research does not take into account the various scenarios of straw burning and the deployment of object detection models. Therefore, a lightweight network based on depthwise separable convolution and channel attention mechanisms is proposed to detect straw-burning smoke at a remote distance. Various regional and crop-burning smoke datasets were collected to make the algorithm more robust. The lightweight network was applied to automatically identify and detect straw-burning smoke in surveillance videos. The experiment showed that the amount of light network parameter was only 4.76 M, and the calculation performance was only 11.2 Gflops. For the intelligent detection of straw-burning smoke, performance verification accuracy was improved by 2.4% compared with Yolov5s. Meanwhile, the detection speed on the embedded Jetson Xavier NX device can reach 28.65 FPS, which is 24.67% better than the Yolov5s. This study proposes a lightweight target detection network, providing a possible method for developing low-cost, rapid straw-burning smoke detection equipment.
Applying a Multiple-Input Single-Output Interleaved High Step-Up Converter with a Current-Sharing Device Having Different Input Currents to Harvest Energy from Multiple Heat Sources
Authors: Hwu Kuo-Ing , Shieh Jenn-Jong , Chen Pin-Jung
Published: May 2023

In this paper, a thermoelectric conversion system for multiple heat sources is proposed. For design convenience, the overall system employs only a single-stage converter. Such a converter uses coupling inductors and switched capacitors to increase the voltage gain. In order to reduce the high-frequency voltage oscillation of the turn-off of the main switches created from leakage inductors, two active clamp circuits with zero voltage switching (ZVS) turn-on are employed. By doing so, although the current-sharing device with interleave control is embedded in the proposed converter, the input currents can be unequal. Therefore, the inputs of the converter can operate under individual maximum power points and transfer the energy from different thermoelectric generators (TGs) to a single load. Furthermore, the main switches have low voltage stress during the turn-off period. As for the maximum power point tracking (MPPT) method, it utilizes a three-point-weighting method to improve the tracking stability. In addition, the number of inputs of this converter can be extended. The MPPT simulation is presented to verify the feasibility as well as several experimental waveforms to demonstrate the effectiveness. The field programmable gate array (FPGA) is used as a digital control kernel to control the thermoelectric conversion system.