bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning
Authors: Yang Huanyu , Wang Jun , Wang Jiacun
Published: November 2023

Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention and damage mitigation. Combining deep learning techniques with UAV imagery holds potential in advancing forest fire smoke recognition. However, issues arise when using UAV-derived images, especially in detecting miniature smoke patches, complicating effective feature discernment. Common deep learning approaches for forest fire detection also grapple with limitations due to sparse datasets. To counter these challenges, we introduce a refined UAV-centric forest fire smoke detection approach utilizing YOLOv5. We first enhance anchor box clustering through K-means++ to boost the classification precision and then augment the YOLOv5 architecture by integrating a novel partial convolution (PConv) to trim down model parameters and elevate processing speed. A unique detection head is also incorporated to the model to better detect diminutive smoke traces. A coordinate attention module is embedded within YOLOv5, enabling precise smoke target location and fine-grained feature extraction amidst complex settings. Given the scarcity of forest fire smoke datasets, we employ transfer learning for model training. The experimental results demonstrate that our proposed method achieves 96% AP50 and 57.3% AP50:95 on a customized dataset, outperforming other state-of-the-art one-stage object detectors while maintaining real-time performance.
Circularly Polarized Ultra-Wideband Antenna for Uni-Traveling-Carrier Photodiode Terahertz Source
Authors: Li Qi , Nie Chuang , Liu Zihao , Zhou Xin , Cheng Xiaohe , Liang Song , Yao Yuan
Published: November 2023

This paper proposes a circularly polarized ultra-wideband (UWB) antenna for a Uni-Traveling-Carrier Photodiode (UTC-PD) to meet the growing demand for bandwidth and polarization diversity in terahertz (THz) communication. In the design of the UTC-PD integrated antenna, the planar electrodes of the chip are directly integrated with the antenna to simplify the integration process. However, this integration introduces new problems, such as asymmetry inside the spiral antenna, which leads to a deterioration in the corresponding high-frequency performance. To address this issue, the antenna’s structure is optimized, and a lens is integrated to enhance directivity and eliminate surface waves. As a result, the proposed antenna achieves a 100–1500 GHz (175%) impedance bandwidth and a 150–720 GHz (131%) axial ratio bandwidth for the UTC-PD. The maximum gain of the antenna is 21.05 dBi at 1 THz.
Reduced CPU Workload for Human Pose Detection with the Aid of a Low-Resolution Infrared Array Sensor on Embedded Systems
Authors: Alves Marcos G. , Chen Gen-Lang , Kang Xi , Song Guang-Hui
Published: November 2023

Modern embedded systems have achieved relatively high processing power. They can be used for edge computing and computer vision, where data are collected and processed locally, without the need for network communication for decision-making and data analysis purposes. Face detection, face recognition, and pose detection algorithms can be executed with acceptable performance on embedded systems and are used for home security and monitoring. However, popular machine learning frameworks, such as MediaPipe, require relatively high usage of CPU while running, even when idle with no subject in the scene. Combined with the still present false detections, this wastes CPU time, elevates the power consumption and overall system temperature, and generates unnecessary data. In this study, a low-cost low-resolution infrared thermal sensor array was used to control the execution of MediaPipe’s pose detection algorithm using single-board computers, which only runs when the thermal camera detects a possible subject in its field of view. A lightweight algorithm with several filtering layers was developed, which allowed the effective detection and isolation of a person in the thermal image. The resulting hybrid computer vision proved effective in reducing the average CPU workload, especially in environments with low activity, almost eliminating MediaPipe’s false detections, and reaching up to 30% power saving in the best-case scenario.
VP-SOM: View-Planning Method for Indoor Active Sparse Object Mapping Based on Information Abundance and Observation Continuity
Authors: Zhang Jiadong , Wang Wei
Published: November 2023

Active mapping is an important technique for mobile robots to autonomously explore and recognize indoor environments. View planning, as the core of active mapping, determines the quality of the map and the efficiency of exploration. However, most current view-planning methods focus on low-level geometric information like point clouds and neglect the indoor objects that are important for human–robot interaction. We propose a novel View-Planning method for indoor active Sparse Object Mapping (VP-SOM). VP-SOM takes into account for the first time the properties of object clusters in the coexisting human–robot environment. We categorized the views into global views and local views based on the object cluster, to balance the efficiency of exploration and the mapping accuracy. We developed a new view-evaluation function based on objects’ information abundance and observation continuity, to select the Next-Best View (NBV). Especially for calculating the uncertainty of the sparse object model, we built the object surface occupancy probability map. Our experimental results demonstrated that our view-planning method can explore the indoor environments and build object maps more accurately, efficiently, and robustly.
Estimation of Shape Error with Monitoring Signals
Authors: Kim Hyein , Nam Soohyun , Nam Eunseok
Published: November 2023

Recently, extensive research has actively been conducted in relation to intelligent manufacturing systems. During the machining process, various factors, such as geometric errors, vibrations, and cutting force fluctuations, lead to shape errors. When a shape error exceeds the tolerance, it results in improper assembly or functionality issues in the assembled part. Predicting shape errors before or during the machining process helps increase production efficiency. In this paper, we propose a methodology that uses monitoring signals and on-machine measurement (OMM) results to predict machining quality in real time. We investigate the correlation between monitoring signals and OMM results and then construct a machine learning model for shape error estimation. The developed model implements a tool offset compensation strategy. The performance of the proposed method is evaluated under various sliding window sizes and the compensation weights. The experimental results confirmed that the proposed algorithm is effective for obtaining a uniform machining quality.
Principal Component Analysis Applied to Digital Pulse Shape Analysis for Isotope Discrimination
Authors: Guerrero-Morejón Katherine , Hinojo-Montero José María , Muñoz-Chavero Fernando , Flores-Garrido Juan Luis , Gómez-Galán Juan Antonio , González-Carvajal Ramón
Published: November 2023

Digital pulse shape analysis (DPSA) techniques are becoming increasingly important for the study of nuclear reactions since the development of fast digitizers. These techniques allow us to obtain the (A, Z) values of the reaction products impinging on the new generation solid-state detectors. In this paper, we present a computationally efficient method to discriminate isotopes with similar energy levels, with the aim of enabling the edge-computing paradigm in future field-programmable gate-array-based acquisition systems. The discrimination of isotope pairs with analogous energy levels has been a topic of interest in the literature, leading to various solutions based on statistical features or convolutional neural networks. Leveraging a valuable dataset obtained from experiments conducted by researchers in the FAZIA Collaboration at the CIME cyclotron in GANIL laboratories, we aim to establish a comparative analysis regarding selectivity and computational efficiency, as this dataset has been employed in several prior publications. Specifically, this work presents an approach to discriminate between pairs of isotopes with similar energies, namely, 12,13C, 36,40Ar, and 80,84Kr, using principal component analysis (PCA) for data preprocessing. Consequently, a linear and cubic machine learning (ML) support vector machine (SVM) classification model was trained and tested, achieving a high identification capability, especially in the cubic one. These results offer improved computational efficiency compared to the previously reported methodologies.
Biodegradable Carrageenan-Based Force Sensor: An Experimental Approach
Authors: Žaimis Uldis , Petronienė Jūratė Jolanta , Dzedzickis Andrius , Bučinskas Vytautas
Published: November 2023

The development of low-cost biodegradable pressure or force sensors based on a carrageenan and iron (III) oxide mix is a promising way to foster the spread of green technologies in sensing applications. The proposed materials are inexpensive and abundant and are available in large quantities in nature. This paper presents the development and experimental study of carrageenan and iron (III)-oxide-based piezoresistive sensor prototypes and provides their main characteristics. The results show that glycerol is required to ensure the elasticity of the material and preserve the material from environmental impact. The composition of the carrageenan-based material containing 1.8% Fe2O3 and 18% glycerol is suitable for measuring the load in the range from 0 N to 500 N with a sensitivity of 0.355 kΩ/N when the active surface area of the sensor is 100 mm2. Developed sensors in the form of flexible film have square resistance dependence to the force/pressure, and due to the soft original material, they face the hysteresis effect and some plastic deformation effect in the initial use stages. This paper contains extensive reference analysis and found a firm background for a new sensor request. The research covers the electric and mechanical properties of the developed sensor and possible future applications.
Velocity Estimation from LiDAR Sensors Motion Distortion Effect
Authors: Haas Lukas , Haider Arsalan , Kastner Ludwig , Zeh Thomas , Poguntke Tim , Kuba Matthias , Schardt Michael , Jakobi Martin , Koch Alexander W.
Published: November 2023

Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.
Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold
Authors: Zhang Handuo , Na Jun , Zhang Bin
Published: November 2023

With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy.
Enhanced Detection Precision of the Taiji Program by Frequency Setting Strategy Based on a Hierarchical Optimization Algorithm
Authors: Zhang Jiafeng , Yang Zhen , Ma Xiaoshan , Peng Xiaodong , Gao Chen , Zhao Mengyuan , Tang Wenlin
Published: November 2023

For space-based gravitational wave detection, a laser interferometric measurement system composed of a three-spacecraft formation offers the most rewarding bandwidth of astrophysical sources. There are no oscillators available that are stable enough so that each spacecraft could use its own reference frequency. The conversion between reference frequencies and their distribution between all spacecrafts for the synchronization of the different metrology systems is the job of the inter-spacecraft frequency setting strategy, which is important for continuously acquiring scientific data and suppressing measurement noise. We propose a hierarchical optimization algorithm to solve the frequency setting strategy. The optimization objectives are minimum total readout displacement noise and maximum beat-note frequency feasible range. Multiple feasible parameter combinations were obtained for the Taiji program. These optimized parameters include lower and upper bounds of the beat note, sampling frequency, pilot tone signal frequency, ultrastable clock frequencies, and modulation depth. Among the 20 Pareto optimal solutions, the minimum total readout displacement noise was 4.12 pm/Hz, and the maximum feasible beat-note frequency range was 23 MHz. By adjusting the upper bound of beat-note frequency and laser power transmitted by the telescope, we explored the effects of these parameters on the minimum total readout displacement noise and optimal local laser power in greater depth. Our results may serve as a reference for the optimal design of laser interferometry system instrument parameters and may ultimately improve the detection performance and continuous detection time of the Taiji program.
Game Theory-Based Signal Control Considering Both Pedestrians and Vehicles in Connected Environment
Authors: Wang Anyou , Zhang Ke , Li Meng , Shao Junqi , Li Shen
Published: November 2023

Signal control, as an integral component of traffic management, plays a pivotal role in enhancing the efficiency of traffic and reducing environmental pollution. However, the majority of signal control research based on game theory primarily focuses on vehicular perspectives, often neglecting pedestrians, who are significant participants at intersections. This paper introduces a game theory-based signal control approach designed to minimize and equalize the queued vehicles and pedestrians across the different phases. The Nash bargaining solution is employed to determine the optimal green duration for each phase within a fixed cycle length. Several simulation tests were carried out by SUMO software to assess the effectiveness of this proposed approach. We select the actuated signal control approach as the baseline to demonstrate the superiority and stability of the proposed control strategy. The simulation results reveal that the proposed approach is able to reduce pedestrian and vehicle delay, vehicle queue length, fuel consumption, and CO2 emissions under different demand levels and demand patterns. Furthermore, the proposed approach consistently achieves more equalized queue length for each lane compared to the actuated control strategy, indicating a higher degree of fairness.
Analysis of Differences in Single-Joint Movement of Dominant and Non-Dominant Hands for Human-like Robotic Control
Authors: Kim Samyoung , Min Kyuengbo , Kim Yeongdae , Igarashi Shigeyuki , Kim Daeyoung , Kim Hyeonseok , Lee Jongho
Published: November 2023

Although several previous studies on laterality of upper limb motor control have reported functional differences, this conclusion has not been agreed upon. It may be conjectured that the inconsistent results were caused because upper limb motor control was observed in multi-joint tasks that could generate different inter-joint motor coordination for each arm. Resolving this, we employed a single wrist joint tracking task to reduce the effect of multi-joint dynamics and examined the differences between the dominant and non-dominant hands in terms of motor control. Specifically, we defined two sections to induce feedback (FB) and feedforward (FF) controls: the first section involved a visible target for FB control, and the other section involved an invisible target for FF control. We examined the differences in the position errors of the tracer and the target. Fourteen healthy participants performed the task. As a result, we found that during FB control, the dominant hand performed better than the non-dominant hand, while we did not observe significant differences in FF control. In other words, in a single-joint movement that is not under the influence of the multi-joint coordination, only FB control showed laterality and not FF control. Furthermore, we confirmed that the dominant hand outperformed the non-dominant hand in terms of responding to situations that required a change in control strategy.
Meshless Search SR-STAP for Airborne Radar Based on Meta-Heuristic Algorithms
Authors: Hou Yunfei , Zhang Yingnan , Gui Wenzhu , Wang Di , Dong Wei
Published: November 2023

The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples. However, when the cluttering is nonlinear in a spatial-Doppler profile, it will cause an off-grid effect and reduce the sparse recovery performance. A meshless search using a meta-heuristic algorithm (MH) can completely eliminate the off-grid effect in theory. Therefore, genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods are applied to SR-STAP for selecting exact clutter atoms in this paper. The simulation results show that MH-STAP can estimate the clutter subspace more accurately than the traditional algorithm; PSO-STAP and GWO-STAP showed better clutter suppression performance in four MH-STAP methods. To search for more accurate clutter atoms, PSO and GWO are combined to improve the method’s capacity for global optimization. Meanwhile, the fitness function is improved by using prior knowledge of the clutter distribution. The simulation results show that the improved PSO-GWO-STAP algorithm provides excellent clutter suppression performance, which solves the off-grid problem better than does single MH-STAP.
Sexual Victimization and Hypersexuality in College Women: Examining Alcohol Use as a Potential Mediator
Authors: Marshall Ethan
Published: November 2023

The past two decades have yielded a large body of literature that uncovered an unfortunate reality: sexual victimization is more prevalent than previously thought. This body of literature has also indicated a number of the negative consequences of experiencing sexual victimization, including mental illness, substance abuse, and sexual dysfunction. Recent research has also indicated that sexual victimization may lead to hypersexuality. What has yet to be researched is how other negative consequences of sexual victimization, such as substance abuse, may contribute to elevated levels of hypersexuality. Since these behaviors are associated with experiencing future instances of sexual violence, it is important to understand the relationship between these factors. The purpose of the current study is to address this gap in the research by examining whether alcohol use mediates the effect between sexual victimization and hypersexuality. Results indicate that alcohol use does partially mediate the relationship between sexual victimization and hypersexuality, but that sexual victimization still accounts for a significant amount of variation with respect to hypersexuality. These findings indicate that sexual victimization experiences may lead some to engage in problematic coping behaviors, such as risky sexual behavior and increased alcohol consumption, which may place individuals at an increased risk of future victimization experiences.
Money in Electoral Campaigns: The Relationship between Money and Politics as a Cause of the Judicialization of Electoral Processes in Brazil
Authors: Mancuso Wagner Pralon , de Oliveira Vanessa Elias , Speck Bruno Wilhelm , Horochovski Rodrigo Rossi
Published: November 2023

The electoral court is a specialized branch of the Brazilian judiciary that not only organizes and regulates elections in the country but also resolves conflicts between political actors involved in the electoral process. The (mis)use of money in electoral campaigns is one of the causes of the judicialization of electoral processes in Brazil. In fact, among many other reasons, such judicialization can occur when actors in the electoral process (candidates, parties, and party coalitions) are accused, either by their opponents and/or by the Electoral Public Prosecutor’s Office, of irregularities such as abuse of economic power, illegal fundraising or expenditure of electoral resources, electoral corruption, or falsification of the campaign’s accounting records. Through the study of electoral lawsuits filed between 2008 and 2016 against mayoral candidates, this work has two objectives: (i) to dimension the importance of the relationship between money and politics as a cause of the judicialization of electoral processes in Brazilian local elections and (ii) to characterize the candidates that appear as defendants in electoral lawsuits filed by that cause.
Influence of Cutting Intervals and Transition Periods on Chemical Composition Variability of Selected Tropical Grasses under Flooded Savanna Conditions of Arauca, Colombian Orinoquia
Authors: Vélez-Terranova Mauricio , Salamanca-Carreño Arcesio , Vargas-Corzo Oscar Mauricio , Parés-Casanova Pere M. , Pérez-López Otoniel
Published: November 2023

The floodplain savannah is a tropical ecosystem that sustains grazing livestock, mainly by its grass’s diversity, of which scarce knowledge regarding the chemical composition and influencing factors. The aim was to evaluate the chemical composition variability of some native and introduced grasses grown in different physiographic positions of the floodplain savannah at transition periods and different cutting intervals. Five grasses from the “bank” (native species: Paspalum plicatulum, Axonopus compressus, Panicum versicolor, and Paspalum sp.; introduced species: Mulato I) and four from the “low” (native species: Leersia hexandra, Acroceras zizanioides, and Hymenachne amplexicaulis; introduced species: Urochloa humidicola) were sampled at 30, 40, and 50 cutting interval days during the “dry–rainy” and “rainy–dry” transition periods. The cuts were made with a 1 m2 frame to estimate forage biomass. The chemical compositions were analyzed by near-infrared spectroscopy. The influences of the cutting intervals and transition periods on chemical composition variables were evaluated through principal component analysis (PCA). Grass chemical variability was explained by eleven variables, including a digestible fraction, namely crude protein (CP), ash, ether extract (EE), total digestible nutrients (TDN), dry matter digestibility (DMD), metabolic energy (ME), phosphorus (P), and sulfur (S); and a partial digestible or undigestible fraction, namely neutral detergent fiber (NDF), lignin, and hemicellulose (HC). Grasses from the “low” position or with 30 cutting interval days in the rainy–dry transition period presented the highest proportion of the digestible fraction. Introduced grasses showed reduced nutritional value from 40 days onwards, whereas the L. hexandra, H. amplexicaulis, A. zizanioides, and P. versicolor native grasses were the least affected by the studied cutting intervals and transition periods. These native grasses constitute an important sustainable food resource for livestock in the flooded savanna ecosystem.
Organogels for Low-Polar Organic Solvents: Potential Applications on Cultural Heritage Materials
Authors: Biribicchi Chiara , Giuliani Laura , Macchia Andrea , Favero Gabriele
Published: November 2023

Traditional cleaning methods for removing undesired substances from artworks often involve the use of toxic volatile solvents, raising concerns about human health and environmental impact. Over time, various cleaning systems, such as thickeners, rigid, peelable, and nanostructured gels, have been introduced in the conservation sector to minimize solvent use and toxicity. However, these methods are primarily tailored for aqueous solutions or medium-to-high-polar solvents, leaving sustainable organogels for low-polar solvents largely unexplored. This paper explores the application of Low-Molecular-Weight Gelators (LMWGs) in the field of cultural heritage conservation, focusing on their potential to gel low-polar organic solvents. LMWGs, including cholesterol derivatives, fatty acid-derived compounds, anthryl, anthraquinone, amino acid, and saccharide-based organogelators, offer biocompatible and cost-effective options by forming supramolecular gels that immobilize solvents and reduce their release into the environment. This study highlights the need to transition from traditional, often toxic, solvents to greener and more sustainable cleaning systems by emphasizing LMWGs’ biodegradability, biocompatibility, and sustainability. While challenges such as optimizing gel properties and ensuring compatibility with artwork surfaces still need to be addressed, LMWGs hold promise as organogelators in conservation practice. Further research into LMWGs should focus on their optimization for conservation applications by adjusting their rheological properties and physico-chemical stability.
Evaluating the Whole-Process Management of Future Communities Based on Integrated Fuzzy Decision Methods
Authors: Dong Wenli , Lin Lihan
Published: November 2023

Focusing on the potential risks involved in the entire process of developing and creating Future Communities for old and renovated developments, a Future Community evaluation system for life-cycle management was constructed, featuring ten aspects. The index is preliminarily screened through the Fuzzy Delphi Method (FDM). The index weight is produced using the comprehensive weighting model of game theory based on the G1 method and Entropy Weight method. Six sample communities are ranked based on the TOPSIS method. This article proposes a feasible evaluation system for the comprehensive process of developing Future Communities. The results can provide relatively scientific evaluation results regarding the entire process of Future Community development, while promoting their sustainable operation and providing reference for other community regeneration projects in Zhejiang Province, China, and worldwide.
Geographical Indications and Sustainable Viticulture: Empirical and Theoretical Perspectives
Authors: Reinhardt Tilman , Ambrogio Yasmine
Published: November 2023

Wine production faces numerous sustainability challenges. The backbone of European wine regulations is the framework for Geographical Indications (GIs), which is currently being reformed as part of the EU’s Farm-to-Fork Agenda. An important aspect of the reform is to strengthen the sustainability performance of GIs by allowing the integration of “sustainability undertakings” in GI product specifications. Our paper evaluates this policy approach based on the assessments of stakeholders from the German wine region Franconia and the Italian region of South Tyrol. We use a mixed-methods approach, combining semi-structured interviews and a quantitative survey among wine producers. We interpret our findings through the analytical lens of Elinor Ostrom’s Design Principles for the sustainable governance of common-pool resources. Our results show that localized “bottom—up” regulation in the framework of GIs corresponds to many practical and theoretical demands regarding sustainability governance. However, GIs seem to address certain sustainability aspects better than others. Sustainability undertakings in GIs should also avoid adding administrative burden to the already quite rigid GI regime and must be sufficiently inclusive and gradual. Overall, sustainability undertakings in GIs can only be considered as part of a broader policy mix.
The Effects of Trust, Perceived Risk, Innovativeness, and Deal Proneness on Consumers’ Purchasing Behavior in the Livestreaming Social Commerce Context
Authors: Sun Xuemei , Pelet Jean-Éric , Dai Shiying , Ma Yi
Published: November 2023

Livestreaming shopping platforms have emerged as dynamic and innovative channels for e-commerce, mobile commerce, and social commerce, revolutionizing the way consumers engage with online retail. Drawing upon the Technology Acceptance Model 3 framework, this research seeks to provide a comprehensive understanding of the interplay between perceived risk, trust, innovativeness and deal proneness in shaping consumers’ purchasing behavior in the livestreaming social commerce context. A snowball sampling method was applied to collect data from 675 Chinese livestreaming customers in December 2022. A PLS-SEM analysis was used to measure the proposed model. The results confirm that the present model has weak explanatory power except for medium predictive accuracy in explaining consumers’ purchasing behavior in the livestreaming social commerce context (R2 = 0.35; Q2 = 0.31). This research contributes to the social commerce literature by extending the Technology Acceptance Model 3 (TAM 3) to the novel domain of the livestreaming social commerce context, offering insights into the unique drivers of consumers’ purchasing behavior. It also provides practical implications for platform developers and marketers aiming to enhance consumer experiences and increase sales performance, thereby increasing economic growth.
TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase
Authors: Dong Zijing , Fan Boyi , Li Fan , Xu Xuezhi , Sun Hong , Cao Weiwei
Published: November 2023

Trajectory prediction (TP) is a vital operation in air traffic control systems for flight monitoring and tracking. The approach phase of general aviation (GA) aircraft is more of a visual approach, which is related to the safety of the flight and whether to go around. Therefore, it is important to accurately predict the flight trajectory of the approach phase. Based on the historical flight trajectories of GA aircraft, a TP model is proposed with deep learning after feature extraction in this study, and the hybrid model combines a time convolution network and an improved transformer model. First, feature extraction of the spatiotemporal dimension is performed on the preprocessed flight data by using TCN; then, the extracted features are executed by adopting the Informer model for TP. The performance of the novel architecture is verified by experiments based on real flight trajectory data. The results show that the proposed TCN-Informer architecture performs better according to various evaluation metrics, which means that the prediction accuracies of the hybrid model are better than those of the typical prediction models widely used today. Moreover, it has been verified that the proposed method can provide valuable suggestions for decision-making regarding whether to go around during the approach.
Sequential Application of Different Types of Coagulants as an Innovative Method of Phosphorus Inactivation, on the Example of Lake Mielenko, Poland
Authors: Grochowska Jolanta Katarzyna , Łopata Michał , Augustyniak-Tunowska Renata , Tandyrak Renata
Published: November 2023

The process of accelerated eutrophication forces the search for innovative, effective methods to restore the quality of surface waters. This study was conducted on shallow, urban Lake Mielenko (Maximum depth 1.9 m; Mean depth 1.3 m) in the context of implementing a new, sustainable method of lake restoration, i.e., phosphorus inactivation by sequential application of two types of coagulants. Approximately 9.9 tons of polyaluminium chloride (trade name PAX 18) were introduced into the profundal zone of Lake Mielenko, and 9.0 tons of iron chloride (trade name PIX 111) in the coastal area. The applications were divided into two spring and two autumn stages. Before restoration, the mean Pmin. concentration in Lake Mielenko water was 0.031 mg P/L, and TP was in the range of 0.091 to 0.346 mg P/L. After restoration, the average content of Pmin. was 0.007 mg P/L (a decrease of 80%), and the average value of TP was 0.096 mg P/L (a decrease of 72%). The obtained results indicate that phosphorus inactivation does not change nitrogen compounds’ content. However, due to the application of coagulants, P content decreased, i.e., the main factor limiting photosynthesis, which resulted in a significant decrease in primary production in Lake Mielenko. Before restoration, the average content of N-NH4 was 0.100 mg N/L, N-NO3—0.145 mg N/L, Norg. 1.70 mg N/L, and TN—1.86 mg N/L. After restoration, the average content of N-NH4 was 0.096 mg N/L, N-NO3—0.123 mg N/L, Norg. 1.28 mg N/L, and TN—1.50 mg N/L. This also resulted in a reduction in the Norg. content, as well as a reduction in the amount of chlorophyll a (from 30.51 to 13.41 mg/m3), organic compounds (BOD5 from 8.9 to 4.6 mg O2/L), and an increase in water transparency to the bottom (up to 1.45 m on average). The results obtained in Lake Mielenko indicate that the innovative method of phosphorus inactivation, which involves the sequential application of two types of phosphorus-binding preparations, is an excellent solution that ensures higher ecological safety in the coastal areas of the reservoir and also allows for a significant reduction in restoration costs.
Hamacher Operations for Complex Cubic q-Rung Orthopair Fuzzy Sets and Their Application to Multiple-Attribute Group Decision Making
Authors: Yu Qian
Published: November 2023

In this paper, based on the advantages of q-rung orthopair fuzzy sets (q-ROFSs), complex fuzzy sets (CFSs) and cubic sets (CSs), the concept of complex cubic q-rung orthopair fuzzy sets (CCuq-ROFSs) is introduced and their operation rules and properties are discussed. The objective of this paper was to develop some novel Maclaurin symmetric mean (MSM) operators for any complex cubic q-rung orthopair fuzzy numbers (CCuq-ROFNs) using Hamacher t-norm and t-conorm inspired arithmetic operations. The advantage of employing Hamacher t-norm and t-conorm based arithmetic operations with the MSM operator lies in their ability to take into account not only the interrelationships among multiple attributes but also to provide flexibility in the aggregation process due to the involvement of additional parameters. Also, the prominent characteristic of the MSM is that it can capture the interrelationship among the multi-input arguments and can provide more flexible and robust information fusion. Thus, based on the CCuq-ROF environment, we develop some new Hamacher operations for CCuq-ROFSs, such as the complex cubic q-rung orthopair fuzzy Hamacher average (CCuq-ROFHA) operator, the weighted complex cubic q-rung orthopair fuzzy Hamacher average (WCCuq-ROFHA) operator, the complex cubic q-rung orthopair fuzzy Hamacher Maclaurin symmetric mean (CCuq-ROFHMSM) operator and the weighted complex cubic q-rung orthopair fuzzy Hamacher Maclaurin symmetric mean (WCCuq-ROFHMSM) operator. Further, we develop a novel multi-attribute group decision-making (MAGDM) approach based on the proposed operators in a complex cubic q-rung orthopair fuzzy environment. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of the proposed method through a detailed comparison with existing methods.
Removal of Pb from Contaminated Kaolin by Pulsed Electrochemical Treatment Coupled with a Permeable Reactive Barrier: Tuning Removal Efficiency and Energy Consumption
Authors: Zhang Yinyin , Zang Libin , Zhao Yuyan , Wei Qiaoqiao , Han Jiangtao
Published: November 2023

Lead contamination in soil has emerged as a significant environmental concern. Recently, pulse electrochemical treatment (PECT) has garnered substantial attention as an effective method for mitigating lead ions in low-permeability soils. However, the impact of varying pulse time gradients, ranging from seconds to hours, under the same pulse duty cycle on lead removal efficiency (LRE) and energy consumption in PECT has not been thoroughly investigated. In this study, a novel, modified PECT method is proposed, which couples PECT with a permeable reaction barrier (PRB) and adds acetic acid to the catholyte. A comprehensive analysis of LRE and energy consumption is conducted by transforming pulse time. The results show that the LREs achieved in these experiments were as follows: PCb-3 s (89.5%), PCb-1 m (91%), PCb-30 m (92.9%), and PCb-6 h (91.9%). Importantly, these experiments resulted in significant reductions in energy consumption, with decreases of 68.5%, 64.9%, 51.8%, and 47.4% compared to constant voltage treatments, respectively. It was observed that LRE improved with an increase in both pulse duration and voltage gradient, albeit with a corresponding rise in energy consumption. The results also revealed that corn straw biochar as a PRB could enhance LRE by 6.1% while adsorbing migrating lead ions. Taken together, the present data highlights the potential of modified PECT technology for remediation of lead-contaminated soil, which provides an optimal approach to achieve high LRE while minimizing energy consumption.
Current Status and Challenges in Anti-Hepatitis B Virus Agents Based on Inactivation/Inhibition or Elimination of Hepatitis B Virus Covalently Closed Circular DNA
Authors: Zhuang An-Qi , Chen Yan , Chen Shan-Mei , Liu Wen-Cheng , Li Yao , Zhang Wen-Jie , Wu Yi-Hang
Published: November 2023

There has been over half a century since the discovery of hepatitis B virus (HBV) to now, but approximately 300 million patients with chronic hepatitis B (CHB) still live in the world, resulting in about one million deaths every year. Although currently approved antivirals (e.g., nucleoside analogues) are effective at reducing HBV replication, they have almost no impact on the existing HBV covalently closed circular DNA (cccDNA) reservoir. HBV cccDNA is a critical obstacle to the complete elimination of the virus via antiviral therapy. The true cure of HBV infection requires the eradication of viral cccDNA from HBV-infected cells; thus, the development of new agents directly or indirectly targeting HBV cccDNA is urgently needed due to the limitations of current available drugs against HBV infection. In this regard, it is the major focus of current anti-HBV research worldwide via different mechanisms to either inactivate/inhibit (functional cure) or eliminate (complete cure) HBV cccDNA. Therefore, this review discussed and summarized recent advances and challenges in efforts to inactivate/silence or eliminate viral cccDNA using anti-HBV agents from different sources, such as small molecules (including epigenetic drugs) and polypeptides/proteins, and siRNA or gene-editing approaches targeting/attenuating HBV cccDNA via different mechanisms, as well as future directions that may be considered in efforts to truly cure chronic HBV infection. In conclusion, no breakthrough has been made yet in attenuating HBV cccDNA, although a number of candidates have advanced into the phase of clinical trials. Furthermore, the overwhelming majority of the candidates function to indirectly target HBV cccDNA. No outstanding candidate directly targets HBV cccDNA. Relatively speaking, CCC_R08 and nitazoxanide may be some of the most promising agents to clear HBV infection in small molecule compounds. Additionally, CRISPR-Cas9 systems can directly target HBV cccDNA for decay and demonstrate significant anti-HBV activity. Consequently, gene-editing approaches targeting HBV cccDNA may be one of the most promising means to achieve the core goal of anti-HBV therapeutic strategies. In short, more basic studies on HBV infection need to be carried out to overcome these challenges.
The Functional Implications of Broad Spectrum Bioactive Compounds Targeting RNA-Dependent RNA Polymerase (RdRp) in the Context of the COVID-19 Pandemic
Authors: Comunale Brittany A. , Larson Robin J. , Jackson-Ward Erin , Singh Aditi , Koback Frances L. , Engineer Lilly D.
Published: November 2023

Background: As long as COVID-19 endures, viral surface proteins will keep changing and new viral strains will emerge, rendering prior vaccines and treatments decreasingly effective. To provide durable targets for preventive and therapeutic agents, there is increasing interest in slowly mutating viral proteins, including non-surface proteins like RdRp. Methods: A scoping review of studies was conducted describing RdRp in the context of COVID-19 through MEDLINE/PubMed and EMBASE. An iterative approach was used with input from content experts and three independent reviewers, focused on studies related to either RdRp activity inhibition or RdRp mechanisms against SARS-CoV-2. Results: Of the 205 records screened, 43 studies were included in the review. Twenty-five evaluated RdRp activity inhibition, and eighteen described RdRp mechanisms of existing drugs or compounds against SARS-CoV-2. In silico experiments suggested that RdRp inhibitors developed for other RNA viruses may be effective in disrupting SARS-CoV-2 replication, indicating a possible reduction of disease progression from current and future variants. In vitro, in vivo, and human clinical trial studies were largely consistent with these findings. Conclusions: Future risk mitigation and treatment strategies against forthcoming SARS-CoV-2 variants should consider targeting RdRp proteins instead of surface proteins.
New Insights into the Applications of Viruses to Biotechnology
Authors: Materatski Patrick , Varanda Carla
Published: November 2023

Viruses are responsible for many devastating human and animal diseases, such as Ebola, rabies, HIV, smallpox, influenza, dengue, and SARS-CoV-2 [...]
Molecular Epidemiology and Evolution of Coxsackievirus A14
Authors: Yu Liheng , Guo Qin , Wei Haiyan , Liu Yingying , Tong Wenbin , Zhu Shuangli , Ji Tianjiao , Yang Qian , Wang Dongyan , Xiao Jinbo , Lu Huanhuan , Liu Ying , Li Jichen , Wang Wenhui , He Yun , Zhang Yong , Yan Dongmei
Published: November 2023

As the proportion of non-enterovirus 71 and non-coxsackievirus A16 which proportion of composition in the hand, foot, and mouth pathogenic spectrum gradually increases worldwide, the attention paid to other enteroviruses has increased. As a member of the species enterovirus A, coxsackievirus A14 (CVA14) has been epidemic around the world until now since it has been isolated. However, studies on CVA14 are poor and the effective population size, evolutionary dynamics, and recombination patterns of CVA14 are not well understood. In this study, 15 CVA14 strains were isolated from HFMD patients in mainland China from 2009 to 2019, and the complete sequences of CVA14 in GenBank as research objects were analyzed. CVA14 was divided into seven genotypes A-G based on an average nucleotide difference of the full-length VP1 coding region of more than 15%. Compared with the CVA14 prototype strain, the 15 CVA14 strains showed 84.0–84.7% nucleotide identity in the complete genome and 96.9–97.6% amino acid identity in the encoding region. Phylodynamic analysis based on 15 CVA14 strains and 22 full-length VP1 sequences in GenBank showed a mean substitution rate of 5.35 × 10−3 substitutions/site/year (95% HPD: 4.03–6.89 × 10−3) and the most recent common ancestor (tMRCA) of CVA14 dates back to 1942 (95% HPD: 1930–1950). The Bayesian skyline showed that the effective population size had experienced a decrease–increase–decrease fluctuation since 2004. The phylogeographic analysis indicated two and three possible migration paths in the world and mainland China, respectively. Four recombination patterns with others of species enterovirus A were observed in 15 CVA14 strains, among which coxsackievirus A2 (CVA2), coxsackievirus A4 (CVA4), coxsackievirus A6 (CVA6), coxsackievirus A8 (CVA8), and coxsackievirus A12 (CVA12) may act as recombinant donors in multiple regions. This study has filled the gap in the molecular epidemiological characteristics of CVA14, enriched the global CVA14 sequence database, and laid the epidemiological foundation for the future study of CVA14 worldwide.
Low Risk of SARS-CoV-2 Reinfection for Fully or Boosted mRNA Vaccinated Subjects in Sicily: A Population-Based Study Using Real-World Data
Authors: Maniscalco Laura , Genovese Dario , Ravazzolo Barbara , Vella Giuseppe , Sparacia Benedetta , Vitale Francesco , Matranga Domenica , Amodio Emanuele
Published: November 2023

Background: Reinfections occur as a response to natural infections wanes and novel strains of SARS-CoV-2 emerge. The present research explored the correlation between sex, age, COVID-19 vaccination, prior infection hospitalization, and SARS-CoV-2 reinfection in Sicily, Italy. Materials and Methods: A population-based retrospective cohort study was articulated using the vaccination flux from a regional registry and the Sicilian COVID-19 monitoring system of the Italian Institute of Health. Only adult Sicilians were included in the study, and hazard ratios were calculated using Cox regression. Results: Partial vaccination provided some protection (adj-HR: 0.92), when compared to unvaccinated individuals; furthermore, reinfection risk was reduced by full vaccination (adj-HR: 0.43), and the booster dose (adj-HR: 0.41). Males had a lower risk than females of reinfection with SARS-CoV-2 (adj-HR: 0.75). Reinfection with SARS-CoV-2 was diminished by hospitalization during the first infection (adj-HR: 0.78). Reinfection risk was higher among those aged 30–39 and 40–49 compared to those aged 18–29, whereas those aged 60–69, 70–79, and 80+ were statistically protected. Reinfection was significantly more frequent during the wild-type–Alpha, Delta, Delta–Omicron, and Omicron dominance/codominance waves compared to the wild type. Conclusions: This study establishes a solid base for comprehending the reinfection phenomenon in Sicily by pinpointing the most urgent policy hurdles and identifying some of the major factors. COVID-19 vaccination, one of the most effective public health tools, protects against reinfection, mostly caused by the Omicron strain. Elderly and hospitalized people’s lower risk suggests stricter PPE use.