bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Characterization of Multifunctional Chitosan–Silver Nanoparticles: An In-Vitro Approach for Biomedical Applications
Authors: Vanti Gulamnabi , Poondla Naresh , Manogaran Prasath , Teradal Nagappa , S Veeresh , Kaulgud Ram , Kurjogi Mahantesh
Published: September 2024

Antibiotics are successful in promoting health quality by preventing various infectious diseases and minimizing mortality and morbidity all over the world. However, the indiscriminate use of antibiotics has led to the emergence of multi-drug-resistant bacteria, which pose a serious threat to health care sector. Therefore, it is necessary to develop novel antimicrobial agents with versatile characteristics, such as antibacterial activity, low toxicity, wound healing potency, and antioxidant property. In this context, silver chitosan nanoparticles were synthesized in the present study, and their physical characterization revealed that the size of synthesized chitosan–silver nanoparticles was 14–25 nm, with positive surface charge. The functional groups and crystalline nature of the nanoparticles were confirmed by FT-IR and XRD analysis. Further, the silver chitosan nanoparticles showed antibacterial activity against two important clinical pathogens, S. aureus and E. coli. The MTT assay carried out in the present study showed that the synthesized nanoparticles are non-toxic to host cells. A scratch assay on fibroblast cells (L292) demonstrated that the silver chitosan nanoparticles showed promising wound healing activity. A fluorescent DCFH-DA staining assay revealed anantioxidant property of the synthesized nanoparticles. Overall, the study emphasizes the versatile nature of synthesized chitosan–silver nanoparticles, suggesting their great compatibility for biomedical applications.
Targeted Delivery of STING Agonist via Albumin Nanoreactor Boosts Immunotherapeutic Efficacy against Aggressive Cancers
Authors: Miao Zhijun , Song Xue , Xu Anan , Yao Chang , Li Peng , Li Yanan , Yang Tao , Shen Gang
Published: September 2024

Background: Activating the cytosolic innate immune sensor, the cGAS-STING pathway, holds great promise for enhancing antitumor immunity, particularly in combination with immune checkpoint inhibitors (ICIs). However, the clinical application of STING agonists is often hindered by poor tumor accumulation, limited cellular uptake, and rapid clearance. To address these challenges, we developed a human serum albumin (HSA)-based nanoreactor system for the efficient delivery of the STING agonist SR-717, aiming to improve its antitumor efficacy. Methods: Using a biomineralization technique, we encapsulated SR-717 within HSA nanocages to form SH-NPs. These nanoparticles were characterized in terms of size, stability, and cellular uptake, and their ability to activate the STING pathway was assessed in both in vitro and in vivo models, including freshly isolated human renal tumor tissues. In vivo antitumor efficacy was evaluated in a murine renal tumor model, and immune responses were measured. Results: SH-NPs exhibited enhanced stability, efficient cellular uptake, and superior tumor accumulation compared to free SR-717. They robustly activated the STING pathway, as evidenced by increased phosphorylation of TBK1 and IRF3, along with elevated IFN-β production. Additionally, SH-NPs reshaped the immunosuppressive tumor microenvironment, promoting T-cell-mediated immunity and improving the therapeutic efficacy of checkpoint blockade in murine models. The validation in human renal tumor tissues further highlighted their potential for clinical translation. Importantly, SH-NPs were well tolerated with minimal systemic toxicity. Conclusions: This study underscores the potential of HSA-based nanoparticles for the targeted delivery of STING agonists, effectively enhancing antitumor immunity and improving cancer immunotherapy outcomes. SH-NPs offer a promising solution to the limitations of current STING agonists in clinical settings.
Replication of the Venezuelan Equine Encephalitis Vaccine from a Synthetic PCR Fragment
Authors: Mathew Christine , Tucker Colin , Tretyakova Irina , Pushko Peter
Published: September 2024

Background/Objectives: There is no approved human vaccine for Venezuelan equine encephalitis (VEE), a life-threatening disease caused by the VEE virus (VEEV). In previous studies, plasmid DNA encoding the full-length RNA genome of the VEE V4020 vaccine was used for the preparation of experimental live virus VEE vaccines in the plasmid-transfected cell culture. Methods: Here, we used the high-fidelity polymerase chain reaction (PCR) to prepare synthetic, transcriptionally active PCR (TAP) fragments encoding the V4020 genome. Results: TAP fragment initiated the replication of the V4020 live virus vaccine in TAP fragment-transfected cells. A transfection of less than 1 ug of TAP fragment resulted in the replication of the V4020 vaccine virus in CHO cells. Conclusion: We conclude that not only plasmid DNA but also synthetic PCR-generated DNA fragments can be used for the manufacturing of live vaccines for VEEV and, potentially, other viruses.
Efficient Plantlet Regeneration from Branches in Mangifera indica L.
Authors: Zhou Huijing , Sun Jinglang , Zheng Keyuan , Zhang Xinyuan , Yao Yuan , Zhu Mulan
Published: September 2024

Mango (Mangifera indica L.) is one of the most significant tropical and subtropical fruit species, with high ecological and economic value. However, research on the in vitro culture of mangoes is relatively weak, so establishing an efficient and stable mango plant regeneration system is of great significance. In this study, a preliminary mango regeneration system was established with Mangifera indica L. cv. Keitt from young branches as the starting explants. The results showed that the optimal plant growth regulator (PGR) formula for direct adventitious shoot induction on the branches was 1 mg/L 6-benzylaminopurine (6-BA) + 0.1 mg/L a-naphthaleneacetic acid (NAA), with an adventitious shoot induction rate of 73.63% and an average of 6.76 adventitious shoots. The optimal basal medium for adventitious shoot induction was wood plant medium (WPM), with an adventitious shoot induction rate of 63.87% and an average of 5.21 adventitious shoots. The optimal culture medium for adventitious shoot elongation was WPM + 1 mg/L 6-BA + 0.5 mg/L NAA, with an adventitious shoot elongation rate of 89.33% and an average length of 5.17 cm. The optimal formula for the induction of mango rooting was Douglas fir cotyledon revised medium (DCR) + 3 mg/L indole-3-butyric acid (IBA), with a maximum rooting rate of 66.13% and an average rooting quantity of 6.43. The genetic fidelity of the in vitro-regenerated plants was evaluated using inter-simple sequence repeat (ISSR) molecular markers. There was no difference between the in vitro-regenerated plants and the parent plant. This study provides an efficient and stable propagation system for Mangifera indica L., laying the foundation for its rapid propagation and genetic improvement.
Internal Disorders of Mango Fruit and Their Management—Physiology, Biochemistry, and Role of Mineral Nutrients
Authors: Ullah Muhammad Asad , Khanal Amit , Joyce Priya , White Neil , Macnish Andrew , Joyce Daryl
Published: September 2024

Mango (Mangifera indica L.) is a popular fruit grown in tropical and subtropical regions. Mango has a distinctive aroma, flavour, and nutritional properties. Annual global mango production is >50 million tonnes. Major producers of mango include India, Bangladesh, China, Mexico, Pakistan, Indonesia, Brazil, Thailand, and the Philippines, and it is shipped worldwide. Harvested mango fruit are highly perishable, with a short shelf life. Physiological disorders are among the major factors limiting their postharvest quality and shelf life, including when fruit need phytosanitary treatments, such as hot water treatment, vapour heat treatment, and irradiation. This review focuses on problematic physiological disorders of mango flesh, including physiology and biochemistry. It considers factors contributing to the development and/or exacerbation of internal disorders. Improved production practices, including pruning, nutrient application, and irrigation, along with monitoring and managing environmental conditions (viz., temperature, humidity, and vapour pressure deficit), can potentially maintain fruit robustness to better tolerate otherwise stressful postharvest operations. As demand for mangoes on international markets is compromised by internal quality, robust fruit is crucial to maintaining existing and gaining new domestic and export consumer markets. Considering mango quality, a dynamic system, a more holistic approach encompassing pre-, at-, and post-harvest conditions as a continuum is needed to determine fruit predisposition and subsequent management of internal disorders.
Mechanical Properties and Thermal Decomposition Mechanism of Glycidyl Azide Polyol Energetic Thermoplastic Elastomer Binder with RDX Composite
Authors: Sun Qili , Yang Xiao-Mei , Yin Guang-Zhong
Published: September 2024

To improve the reinforcement effect between a binder and high solid filler in a propellant formula, grafting the bonding group into the binder to form a neutral polymeric is a practically novel approach to improving the interface properties of the propellant. In this work, a glycidyl azide polyol energetic thermoplastic elastomer binder with a –CN bonding group (GAP–ETPE) was synthesized, and the mechanical and thermal decomposition mechanism of GAP–ETPE with Hexogeon (RDX) model propellants were studied. The stress–strain results indicated that the tensile strength and strain of GAP–ETPE/RDX model propellants were 6.43 MPa and 32.1%, respectively. DMA data showed that the storage modulus (E’) of the GAP–ETPE/RDX model propellants could increase the glass transition temperature (Tg) values, those were shifted to higher temperature with the increase in filler RDX percentages. TG/DTG showed the four decomposition stages of the decomposition process of the GAP–ETPE/RDX model propellants, and the thermal decomposition equation was constructed. These efforts provide a novel method to improve GAP–ETPE/RDX propellants mechanical property, and the thermal decomposition behavior of GAP–ETPE/RDX propellants also provided technical support for the study of propellant combustion characteristics.
Effect of a Polypropylene Separator with a Thin Electrospun Ceramic/Polymer Coating on the Thermal and Electrochemical Properties of Lithium-Ion Batteries
Authors: Hwang Yeongsu , Kim Minjae
Published: September 2024

Lithium-ion batteries (LIBs) are well known for their energy efficiency and environmental benefits. However, increasing their energy density compromises their safety. This study introduces a novel ceramic-coated separator to enhance the performance and safety of LIBs. Electrospinning was used to apply a coating consisting of an alumina (Al2O3) ceramic and polyacrylic acid (PAA) binder to a polypropylene (PP) separator to significantly improve the mechanical properties of the PP separator and, ultimately, the electrochemical properties of the battery cell. Tests with 2032-coin cells showed that the efficiency of cells containing separators coated with 0.5 g PAA/Al2O3 was approximately 10.2% higher at high current rates (C-rates) compared to cells with the bare PP separator. Open circuit voltage (OCV) tests revealed superior thermal safety, with bare PP separators maintaining stability for 453 s, whereas the cells equipped with PP separators coated with 4 g PAA/Al2O3 remained stable for 937 s. The elongation increased from 88.3% (bare PP separator) to 129.1% (PP separator coated with 4 g PAA/Al2O3), and thermal shrinkage decreased from 58.2% to 34.9%. These findings suggest that ceramic/PAA-coated separators significantly contribute to enhancing the thermal safety and capacity retention of high-energy-density LIBs.
Fatigue Behaviour of High-Performance Green Epoxy Biocomposite Laminates Reinforced by Optimized Long Sisal Fibers
Authors: Zuccarello B. , Militello C. , Bongiorno F.
Published: September 2024

In recent decades, in order to replace traditional synthetic polymer composites, engineering research has focused on the development of new alternatives such as green biocomposites constituted by an eco-sustainable matrix reinforced by natural fibers. Such innovative biocomposites are divided into two different typologies: random short fiber biocomposites characterized by low mechanical strength, used for non-structural applications such as covering panels, etc., and high-performance biocomposites reinforced by long fibers that can be used for semi-structural and structural applications by replacing traditional materials such as metal (carbon steel and aluminum) or synthetic composites such as fiberglass. The present research work focuses on the high-performance biocomposites reinforced by optimized sisal fibers. In detail, in order to contribute to the extension of their application under fatigue loading, a systematic experimental fatigue test campaign has been accomplished by considering four different lay-up configurations (unidirectional, cross-ply, angle-ply and quasi-isotropic) with volume fraction Vf = 70%. The results analysis found that such laminates exhibit good fatigue performance, with fatigue ratios close to 0.5 for unidirectional and angle-ply (±7.5°) laminates. However, by passing from isotropic to unidirectional lay-up, the fatigue strength increases significantly by about four times; higher increases are revealed in terms of fatigue life. In terms of damage, it has been observed that, thanks to the high quality of the proposed laminates, in any case, the fatigue failure involves the fiber failure, although secondary debonding and delamination can occur, especially in orthotropic and cross-ply lay-up. The comparison with classical synthetic composites and other similar biocomposite has shown that in terms of fatigue ratio, the examined biocomposites exhibit performance comparable with the biocomposites reinforced by the more expensive flax and with common fiberglass. Finally, appropriate models, that can be advantageously used at the design stage, have also been proposed to predict the fatigue behavior of the laminates analyzed.
Innovative Fiber-Reinforced Polymer Rope-Based Closed-Form Retrofitting Methods Applied in Reinforced Concrete T-Shaped Beams under Torsion
Authors: Zapris Adamantis G. , Kytinou Violetta K. , Chalioris Constantin E.
Published: September 2024

The fiber-reinforced polymer (FRP) strengthening of reinforced concrete (RC) elements with torsional deficiencies has not yet been extensively studied. Existing studies have primarily focused on rectangular RC beams. The few studies on L or T-shaped beams have used open-form retrofitting methods. However, premature debonding of the retrofitting from concrete surfaces often leads to detachment before achieving enhanced torsional capacity. This study introduces an innovative application of closed-form FRP retrofitting for RC T-beams against torsion. Two novel closed-form torsional upgrading methods were proposed and investigated through a comprehensive experimental program involving eight large-scale T-beams. One method employs FRP ropes embedded in transverse grooves near the surface, while the other combines U-shaped EB-FRP strips with FRP ropes. Additionally, two configurations were examined replicating scenarios where the upper part of the slab is accessible or inaccessible. The results demonstrate that the closed-form methods improve torsional strength by 9% to 25% and twist at failure by 92% to 536% compared to unstrengthened beams, with beams retrofitting through the slab exhibiting superior performance. Step-by-step technical guidelines of the proposed methods are presented to minimize construction defects and ensure effective implementation in real RC structures.
Between North and South: Buddhist Cliff Sculpture in Northern Sichuan in the First Half of the Seventh Century CE
Authors: Yang Xiao
Published: September 2024

In the first half of the seventh century CE, clusters of Buddhist cliff sculptures were carved into cliffs and boulders in Guangyuan, Mianyang, Bazhong, and other locations nestled in the northern Sichuan Basin. They mark the start of large-scale Buddhist grotto construction in Sichuan, significantly impacting the establishment of regional grotto traditions in southwestern China. Through analysis of site forms, statue types, and devotional inscriptions, this article argues that these Buddhist cliff sculptures represent a reintegration of divergent Buddhist practices and artistic conventions that emerged during the Southern and Northern Dynasties in northern and southern China. While their niche-based site structure and collective sponsorship through the yiyi association can be traced back to northern China in the prior two centuries, the sculptural style primarily reflects a regional tradition exemplified by the free-standing statues unearthed in Chengdu, central Sichuan in the Southern Dynasties. The construction of these sites, catalyzed by the influx of northern officials and monks into Sichuan toward the end of the Sui and the early Tang, provides valuable material for exploring the social integration of northern and southern China, as well as the religious dynamics between Buddhism and Daoism in northern Sichuan.
Global Resources and Resource Justice—Reframing the Socioecological Science-to-Policy Landscape
Authors: Negrutiu Ioan
Published: September 2024

The lexical analysis of seminal policy-to-diplomacy documents from the socio-environmental discourse of the last fifty years of agendas has allowed examining the contextual affinities between resources, pollution, and health. The central role of resource stewardship, according to nature’s physical limits, is highlighted in the context of the boundary systems for the biosphere and societies, and the carrying capacity and inclusive systemic health (one health, planetary health, …). To reframe a rather fragmented conceptual and methodological landscape, this work proposes choices that consider core values, purpose, and best practice developments, allowing us to compare the dynamics of socioecological states across the planet and in specific social, economic, political, and cultural contexts. The prioritization of resource justice and responsibility becomes a societal project, embedding the economy in social and ecological frames through institutional reframing and tangible action on resource management, pollution control, and health outcomes. How? By recognizing the primacy of the law and economy of life—the adequacy between basic needs and accessible resources for all— over the rules and economy of the market through socioecosystemic checks and balances.
Complex Permittivity of Adobe Verses Frequency and Water Content
Authors: Price Steven R. , Donohoe J. Patrick , Price Stanton R. , Fairley Josh , Robert Stephanie
Published: September 2024

The complex permittivity of adobe is measured using a coaxial probe system verses frequency (500 MHz to 7 GHz) and water content (0% to 6%). Measurements are performed using adobe samples collected from abode bricks. The geotechnical properties of the compressed earth bricks are characterized by (1) percentage of gravel, sands, and fines; (2) Atterberg limits; and (3) grain-size distribution. The variation in adobe complex permittivity verses frequency is measured at discrete levels of water content using small adobe samples exposed to controlled levels of constant humidity in an environmental chamber. The typical water content profile verses depth for an adobe brick is also determined.
Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat
Authors: Okyere Frank Gyan , Cudjoe Daniel Kingsley , Virlet Nicolas , Castle March , Riche Andrew Bernard , Greche Latifa , Mohareb Fady , Simms Daniel , Mhada Manal , Hawkesford Malcolm John
Published: September 2024

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.
Spherical Magnetic Vector Forwarding of Isoparametric DGGS Cells with Natural Superconvergent Points
Authors: Chen Peng , Cao Shujin , Lu Guangyin , Zhang Dongxin , Chen Xinyue , Chen Zhiming
Published: September 2024

With the rapid advancement of satellite remote sensing technology, many scientists and organizations, including NASA, ESA, NAOC, and Roscosmos, observe and study significant changes in the geomagnetic field, which has greatly promoted research on the geomagnetic field and made it an important research direction in Earth system science. In traditional geomagnetic field research, tesseroid cells face degradation issues in high-latitude regions and accuracy limitations. To overcome these limitations, this paper introduces the Discrete Global Grid System (DGGS) to construct a geophysical model, achieving seamless global coverage through multi-level grid subdivision, significantly enhancing the processing capability of multi-source and multi-temporal spatial data. Addressing the challenges of the lack of analytical solutions and clear integration limits for DGGS cells, a method for constructing shape functions of arbitrary isoparametric elements is proposed based on the principle of isoparametric transformation, and the shape functions of isoparametric DGGS cells are successfully derived. In magnetic vector forwarding, considering the potential error amplification caused by Poisson’s formula, the DGGS grid is divided into six regular triangular sub-units. The triangular superconvergent point technique is adopted, and the positions of integration points and their weight coefficients are accurately determined according to symmetry rules, thereby significantly improving the calculation accuracy without increasing the computational complexity. Finally, through the forward modeling algorithm based on tiny tesseroid cells, this study comprehensively compares and analyzes the computational accuracy of the DGGS-based magnetic vector forwarding algorithm, verifying the effectiveness and superiority of the proposed method and providing new theoretical support and technical means for geophysical research.
Unifying Building Instance Extraction and Recognition in UAV Images
Authors: Hu Xiaofei , Zhou Yang , Lan Chaozhen , Gan Wenjian , Shi Qunshan , Zhou Hanqiang
Published: September 2024

Building instance extraction and recognition (BEAR) extracts and further recognizes building instances in unmanned aerial vehicle (UAV) images, holds with paramount importance in urban understanding applications. To address this challenge, we propose a unified network, BEAR-Former. Given the difficulty of building instance recognition due to the small area and multiple instances in UAV images, we developed a novel multi-view learning method, Cross-Mixer. This method constructs a cross-regional branch and an intra-regional branch to, respectively, extract the global context dependencies and local spatial structural details of buildings. In the cross-regional branch, we cleverly employed cross-attention and polar coordinate relative position encoding to learn more discriminative features. To solve the BEAR problem end to end, we designed a channel group and fusion module (CGFM) as a shared encoder. The CGFM includes a channel group encoder layer to independently extract features and a channel fusion module to dig out the complementary information for multiple tasks. Additionally, an RoI enhancement strategy was designed to improve model performance. Finally, we introduced a new metric, Recall@(K, iou), to evaluate the performance of the BEAR task. Experimental results demonstrate the effectiveness of our method.
A First Extension of the Robust Satellite Technique RST-FLOOD to Sentinel-2 Data for the Mapping of Flooded Areas: The Case of the Emilia Romagna (Italy) 2023 Event
Authors: Satriano Valeria , Ciancia Emanuele , Pergola Nicola , Tramutoli Valerio
Published: September 2024

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time.
AgeDETR: Attention-Guided Efficient DETR for Space Target Detection
Authors: Wang Xiaojuan , Xi Bobo , Xu Haitao , Zheng Tie , Xue Changbin
Published: September 2024

Recent advancements in space exploration technology have significantly increased the number of diverse satellites in orbit. This surge in space-related information has posed considerable challenges in developing space target surveillance and situational awareness systems. However, existing detection algorithms face obstacles such as complex space backgrounds, varying illumination conditions, and diverse target sizes. To address these challenges, we propose an innovative end-to-end Attention-Guided Encoder DETR (AgeDETR) model, since artificial intelligence technology has progressed swiftly in recent years. Specifically, AgeDETR integrates Efficient Multi-Scale Attention (EMA) Enhanced FasterNet block (EF-Block) within a ResNet18 (EF-ResNet18) backbone. This integration enhances feature extraction and computational efficiency, providing a robust foundation for accurately identifying space targets. Additionally, we introduce the Attention-Guided Feature Enhancement (AGFE) module, which leverages self-attention and channel attention mechanisms to effectively extract and reinforce salient target features. Furthermore, the Attention-Guided Feature Fusion (AGFF) module optimizes multi-scale feature integration and produces highly expressive feature representations, which significantly improves recognition accuracy. The proposed AgeDETR framework achieves outstanding performance metrics, i.e., 97.9% in mAP0.5 and 85.2% in mAP0.5:0.95, on the SPARK2022 dataset, outperforming existing detectors and demonstrating superior performance in space target detection.
Impact of Assimilating Geostationary Interferometric Infrared Sounder Observations from Long- and Middle-Wave Bands on Weather Forecasts with a Locally Cloud-Resolving Global Model
Authors: Xian Zhipeng , Zhu Jiang , Lin Shian-Jiann , Liang Zhi , Chen Xi , Chen Keyi
Published: September 2024

The Geostationary Interferometric InfraRed Sounder (GIIRS) provides a novel opportunity to acquire high-spatiotemporal-resolution atmospheric information. Previous studies have demonstrated the positive impacts of assimilating GIIRS radiances from either long-wave temperature or middle-wave water vapor bands on modeling high-impact weather processes. However, the impact of assimilating both bands on forecast skill has been less investigated, primarily due to the non-identical geolocations for both bands. In this study, a locally cloud-resolving global model is utilized to assess the impact of assimilating GIIRS observations from both long-wave and middle-wave bands. The findings indicate that the GIIRS observations exhibit distinct inter-channel error correlations. Proper inflation of these errors can compensate for inaccuracies arising from the treatment of the geolocation of the two bands, leading to a significant enhancement in the usage of GIIRS observations from both bands. The assimilation of GIIRS observations not only markedly reduces the normalized departure standard deviations for most channels of independent instruments, but also improves the atmospheric states, especially for temperature forecasting, with a maximum reduction of 42% in the root-mean-square error in the lower troposphere. These improvements contribute to better performance in predicting heavy rainfall.
Multi-Spectral Radiation Temperature Measurement: A High-Precision Method Based on Inversion Using an Enhanced Particle Swarm Optimization Algorithm with Multiple Strategies
Authors: Wang Xiaodong , Han Shuaifeng
Published: September 2024

Multi-spectral temperature measurement technology has been found to have extensive applications in engineering practice. Addressing the challenges posed by unknown emissivity in multi-spectral temperature measurement data processing, this paper adds emissivity constraints to the objective function. It proposes a multi-spectral radiation temperature measurement data processing model realized through a particle swarm optimization algorithm improved based on multiple strategies. This paper simulates six material models with distinct emissivity trends. The simulation results indicate that the algorithm calculates an average relative temperature error of less than 0.3%, with an average computation time of merely 0.24 s. When applied to the temperature testing of silicon carbide and tungsten, experimental data further confirmed its accuracy: the absolute temperature error for silicon carbide (tungsten) is less than 4 K (7 K), and the average relative error is below 0.4% (0.3%), while two materials maintain an average computation time of 0.33 s. In summary, the improved particle swarm optimization algorithm demonstrates strong performance and high accuracy in multi-spectral radiation thermometry, making it a feasible solution for addressing multi-spectral temperature measurement challenges in practical engineering applications. Additionally, it can be extended to other multi-spectral systems.
Accurate Low Complexity Quadrature Angular Diversity Aperture Receiver for Visible Light Positioning
Authors: Cincotta Stefanie , Neild Adrian , Helmerson Kristian , Zenere Michael , Armstrong Jean
Published: September 2024

Despite the many potential applications of an accurate indoor positioning system (IPS), no universal, readily available system exists. Much of the IPS research to date has been based on the use of radio transmitters as positioning beacons. Visible light positioning (VLP) instead uses LED lights as beacons. Either cameras or photodiodes (PDs) can be used as VLP receivers, and position estimates are usually based on either the angle of arrival (AOA) or the strength of the received signal. Research on the use of AOA with photodiode receivers has so far been limited by the lack of a suitable compact receiver. The quadrature angular diversity aperture receiver (QADA) can fill this gap. In this paper, we describe a new QADA design that uses only three readily available parts: a quadrant photodiode, a 3D-printed aperture, and a programmable system on a chip (PSoC). Extensive experimental results demonstrate that this design provides accurate AOA estimates within a room-sized test chamber. The flexibility and programmability of the PSoC mean that other sensors can be supported by the same PSoC. This has the potential to allow the AOA estimates from the QADA to be combined with information from other sensors to form future powerful sensor-fusion systems requiring only one beacon.
Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner
Authors: Mitka Bartosz , Klapa Przemysław , Gawronek Pelagia
Published: September 2024

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.
An Efficient SM9 Aggregate Signature Scheme for IoV Based on FPGA
Authors: Zhang Bolin , Li Bin , Zhang Jiaxin , Wei Yuanxin , Yan Yunfei , Han Heru , Zhou Qinglei
Published: September 2024

With the rapid development of the Internet of Vehicles (IoV), the demand for secure and efficient signature verification is becoming increasingly urgent. To meet this need, we propose an efficient SM9 aggregate signature scheme implemented on Field-Programmable Gate Array (FPGA). The scheme includes both fault-tolerant and non-fault-tolerant aggregate signature modes, which are designed to address challenges in various network environments. We provide security proofs for these two signature verification modes based on a K-ary Computational Additive Diffie–Hellman (K-CAA) difficult problem. To handle the numerous parallelizable elliptic curve point multiplication operations required during verification, we utilize FPGA’s parallel processing capabilities to design an efficient parallel point multiplication architecture. By the Montgomery point multiplication algorithm and the Barrett modular reduction algorithm, we optimize the single-point multiplication computation unit, achieving a point multiplication speed of 70776 times per second. Finally, the overall scheme was simulated and analyzed on an FPGA platform. The experimental results and analysis indicate that under error-free conditions, the proposed non-fault-tolerant aggregate mode reduces the verification time by up to 97.1% compared to other schemes. In fault-tolerant conditions, the proposed fault-tolerant aggregate mode reduces the verification time by up to 77.2% compared to other schemes. When compared to other fault-tolerant aggregate schemes, its verification time is only 28.9% of their consumption, and even in the non-fault-tolerant aggregate mode, the verification time is reduced by at least 39.1%. Therefore, the proposed scheme demonstrates significant advantages in both error-free and fault-tolerant scenarios.
Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm
Authors: Guo Yifeng , He Min , Tong Xubin , Zhang Min , Huang Limin
Published: September 2024

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR’s right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR.
SimpleTrackV2: Rethinking the Timing Characteristics for Multi-Object Tracking
Authors: Ding Yan , Ling Yuchen , Zhang Bozhi , Li Jiaxin , Guo Lingxi , Yang Zhe
Published: September 2024

Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios. This paper rethinks state prediction and fusion based on target temporal features to address these issues and proposes the SimpleTrackV2 algorithm, building on the previously designed SimpleTrack. Firstly, to address the poor prediction performance of linear motion models in complex scenes, we designed a target state prediction algorithm called LSTM-MP, based on long short-term memory (LSTM). This algorithm encodes the target’s historical motion information using LSTM and decodes it with a multilayer perceptron (MLP) to achieve target state prediction. Secondly, to mitigate the effect of occlusion on target state saliency, we designed a spatiotemporal attention-based target appearance feature fusion (TSA-FF) target state fusion algorithm based on the attention mechanism. TSA-FF calculates adaptive fusion coefficients to enhance target state fusion, thereby improving the accuracy of subsequent data association. To demonstrate the effectiveness of the proposed method, we compared SimpleTrackV2 with the baseline model SimpleTrack on the MOT17 dataset. We also conducted ablation experiments on TSA-FF and LSTM-MP for SimpleTrackV2, exploring the optimal number of fusion frames and the impact of different loss functions on model performance. The experimental results show that SimpleTrackV2 handles camera jitter and target occlusion better, achieving improvements of 1.6%, 3.2%, and 6.1% in MOTA, IDF1, and HOTA, respectively, compared to the SimpleTrack algorithm.
Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models
Authors: Chu Changyong , Yang Weikang , Chen Yajun
Published: September 2024

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an “anti-semantic” approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.
Target Recognition Based on Infrared and Visible Image Fusion and Improved YOLOv8 Algorithm
Authors: Guo Wei , Li Yongtao , Li Hanyan , Chen Ziyou , Xu Enyong , Wang Shanchao , Gu Chengdong
Published: September 2024

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives.
Signal Separation Operator Based on Wavelet Transform for Non-Stationary Signal Decomposition
Authors: Han Ningning , Pei Yongzhen , Song Zhanjie
Published: September 2024

This paper develops a new frame for non-stationary signal separation, which is a combination of wavelet transform, clustering strategy and local maximum approximation. We provide a rigorous mathematical theoretical analysis and prove that the proposed algorithm can estimate instantaneous frequencies and sub-signal modes from a blind source signal. The error bounds for instantaneous frequency estimation and sub-signal recovery are provided. Numerical experiments on synthetic and real data demonstrate the effectiveness and efficiency of the proposed algorithm. Our method based on wavelet transform can be extended to other time–frequency transforms, which provides a new perspective of time–frequency analysis tools in solving the non-stationary signal separation problem.
A Wide-Range TCSC Based ADN in Mountainous Areas Considering Hydropower-Photovoltaic-ESS Complementarity
Authors: Guo Yao , Wang Shaorong , Chen Dezhi
Published: September 2024

Due to the radial network structures, small cross-sectional lines, and light loads characteristic of existing AC distribution networks in mountainous areas, the development of active distribution networks (ADNs) in these regions has revealed significant issues with integrating distributed generation (DGs) and consuming renewable energy. Focusing on this issue, this paper proposes a wide-range thyristor-controlled series compensation (TCSC)-based ADN and presents a deep reinforcement learning (DRL)-based optimal operation strategy. This strategy takes into account the complementarity of hydropower, photovoltaic (PV) systems, and energy storage systems (ESSs) to enhance the capacity for consuming renewable energy. In the proposed ADN, a wide-range TCSC connects the sub-networks where PV and hydropower systems are located, with ESSs configured for each renewable energy generation. The designed wide-range TCSC allows for power reversal and improves power delivery efficiency, providing conditions for the optimization operation. The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. The simulation results demonstrate the effectiveness of the proposed method.