bluuebunny/crossref_metadata_2025 huggingface dataset:
The Perspective of Arctic–Alpine Species in Southernmost Localities: The Example of Kalmia procumbens in the Pyrenees and Carpathians
Authors: Walas Łukasz , Pietras Marcin , Mazur Małgorzata , Romo Ángel , Tasenkevich Lydia , Didukh Yakiv , Boratyński Adam
Published: September 2023

High-mountain and arctic plants are considered especially sensitive to climate change because of their close adaptation to the cold environment. Kalmia procumbens, a typical arctic–alpine species, reaches southernmost European localities in the Pyrenees and Carpathians. The aim of this study was the assessment and comparison of the current potential niche areas of K. procumbens in the Pyrenees and Carpathians and their possible reduction due to climate change, depending on the scenario. The realized niches of K. procumbens in the Pyrenees are compact, while those in the Carpathians are dispersed. In both mountain chains, the species occurs in the alpine and subalpine vegetation belts, going down to elevations of about 1500–1600 m, while the most elevated localities in the Pyrenees are at ca. 3000 m, about 500 m higher than those in the Carpathians. The localities of K. procumbens in the Carpathians have a more continental climate than those in the Pyrenees, with lower precipitation and temperatures but higher seasonality of temperature and precipitation. The species covered a larger area of geographic range during the Last Glacial Maximum, but its geographic range was reduced during the mid-Holocene. Due to climate warming, a reduction in the potential area of occurrence could be expected in 2100; this reduction is expected to be strong in the Carpathians and moderate in the Pyrenees.
Horticultural Crops under Stresses
Authors: de Melo Alberto Soares , Gheyi Hans Raj
Published: September 2023

Climate change causes alterations in the spatio-temporal temperature and rainfall distribution [...]
Positive Effect of Green Photo-Selective Filter on Graft Union Formation in Tomatoes
Authors: Carmach Constanza , Castro Mónica , Peñaloza Patricia , Guzmán Leda , Marchant María José , Valdebenito Samuel , Kopaitic Iván
Published: September 2023

This study investigated the effects of green and red photo-selective filters (shade nets) on the process of graft union formation (healing and acclimation) in grafted tomato plants. The research evaluated oxidative stress, physiological characteristics, and anatomical development of graft unions. Plants were subjected to green-netting, red-netting, and no-netting treatments for 28 days, starting 4 days after grafting. Markers of oxidative stress, including reactive oxygen species (ROS), superoxide dismutase (SOD), peroxidase (POD), and malondialdehyde (MDA), as well as protein concentration of SOD/POD enzyme-enriched extracts, were quantified. The anatomical development of the graft unions was examined using microscopy. The results demonstrated that the red and green photo-selective filters increased ROS production by 5% and 4% after 3 days of exposure, by 58% and 14% after 7 days, and by 30% and 13% after 14 days in comparison to the control treatment. The increase in ROS activates the defense mechanism, enhancing the activity of SOD and POD enzymes. In terms of anatomy, the green netting resulted in enhanced cell proliferation and early differentiation of vascular tissue cells. Notably, at the 28-day mark, when the plants were ready for transplanting, the green-net treatment showed a reduction in lipid peroxidation damage and increases of 20% and 54% in dry weight compared with the control and red-net treatments, respectively. Finally, our results suggest that the use of a green photo-selective filter has a positive effect on oxidative stress, anatomical development, and overall growth of grafted tomato plants during the process of graft union formation.
The Potential of Essential Oils from Active Packaging to Reduce Ethylene Biosynthesis in Plant Products. Part 1: Vegetables (Broccoli and Tomato)
Authors: López-Gómez Antonio , Navarro-Martínez Alejandra , Garre Alberto , Artés-Hernández Francisco , Villalba Pedro , Martínez-Hernández Ginés Benito
Published: September 2023

Essential oils (EOs) extracted from plants have a high potential to reduce ethylene biosynthesis, although their effects have not been deeply studied yet on the key components of the ethylene biosynthesis pathway: l-aminocyclopropane-1-carboxylic (ACC) oxidase activity, ACC synthase activity, and ACC content. Hence, the present study aimed to elucidate the effects of released EOs from active packaging (with different EO doses ranging from 100 to 1000 mg m−2) on the ethylene biosynthesis key components of broccoli and tomato under different storage temperature scenarios. The largest ethylene inhibitory effects on broccoli and tomatoes were demonstrated by grapefruit EO and thyme essential EO (up to 63%), respectively, which were more pronounced at higher temperatures. Regarding EO doses, active packaging with a thyme EO dose of 1000 mg m−2 resulted in the strongest reduction (33–38%) of ethylene production in tomatoes. For broccoli, identical results were shown with a lower grapefruit EO dose of 500 mg m−2. The studied EO-active packaging decreased ACC synthase and ACC oxidase activities by 40–50% at 22 °C. Therefore, this EO-active packaging is a natural and effective technology to reduce ethylene biosynthesis in broccoli and tomatoes when they are stored, even in unsuitable scenarios at high temperatures.
Babassu Coconut Fibers: Investigation of Chemical and Surface Properties (Attalea speciosa.)
Authors: Chaves Yago Soares , da Silveira Pedro Henrique Poubel Mendonça , Monteiro Sergio Neves , Nascimento Lucio Fabio Cassiano
Published: September 2023

To complement previous results, an analysis of the chemical and morphological properties of babassu fibers (Attalea speciosa Mart. ex Spreng.) was conducted in order to evaluate their potential as reinforcements in the production of composites with epoxy matrix. The diameter distribution was analyzed in a sample of one hundred fibers, allowing the verification of its variation. The determination of the chemical properties involved experimental analyses of the constituent index and X-ray diffraction. The diffractogram was used to calculate the crystallinity index and the microfibril angle, which are crucial parameters that indicate the consistency of the mechanical properties of babassu fibers and the feasibility of their use in composites. The results revealed that babassu fiber has a chemical composition, with contents of 28.53% lignin, 32.34% hemicellulose, and 37.97% cellulose. In addition, it showed a high crystallinity index of 81.06% and a microfibril angle of 7.67°. These characteristics, together with previous results, indicate that babassu fibers have favorable chemical and morphological properties to be used as reinforcements in composites, highlighting its potential as an important material for applications in technology areas.
Microcanonical Analysis of Helical Homopolymers: Exploring the Density of States and Structural Characteristics
Authors: Williams Matthew J.
Published: September 2023

This study investigates the density of states and structural characteristics of helical homopolymers. Comprising repeating identical units, the model enables the exploration of complex behaviors arising from a simple, yet generalized, set of potentials. Utilizing microcanonical analysis, transitions between helical structures are identified and categorized. Through a systematic comparison of results under varying conditions, we develop a nuanced understanding of the system’s general behavior. A two-dimensional plot illustrates the relative distribution of different structural types, effectively showcasing their prevalence. The findings of this study substantially advance our understanding of the density of states and structural transformations of helical homopolymers across a range of conditions. Additionally, the prevalence plot offers valuable insights into the occurrence of suppressed intermediate states, particularly in models featuring stiff helix segments. This research significantly enhances our understanding of the complex interactions governing helix bundling phenomena within the context of helical homopolymers.
The Behavior of Polymeric Pipes in Drinking Water Distribution System—Comparison with Other Pipe Materials
Authors: Stefan Daniela Simina , Bosomoiu Magdalena , Teodorescu Georgeta
Published: September 2023

The inner walls of the drinking water distribution system (DWDS) are expected to be clean to ensure a safe quality of drinking water. Complex physical, chemical, and biological processes take place when water comes into contact with the pipe surface. This paper describes the impact of leaching different compounds from the water supply pipes into drinking water and subsequent risks. Among these compounds, there are heavy metals. It is necessary to prevent these metals from getting into the DWDS. Those compounds are susceptible to impacting the quality of the water delivered to the population either by leaching dangerous chemicals into water or by enhancing the development of microorganism growth on the pipe surface. The corrosion process of different pipe materials, scale formation mechanisms, and the impact of bacteria formed in corrosion layers are discussed. Water treatment processes and the pipe materials also affect the water composition. Pipe materials act differently in the flowing and stagnation conditions. Moreover, they age differently (e.g., metal-based pipes are subjected to corrosion while polymer-based pipes have a decreased mechanical resistance) and are susceptible to enhanced bacterial film formation. Water distribution pipes are a dynamic environment, therefore, the models that are used must consider the changes that occur over time. Mathematical modeling of the leaching process is complex and includes the description of corrosion development over time, correlated with a model for the biofilm formation and the disinfectants–corrosion products and disinfectants–biofilm interactions. The models used for these processes range from simple longitudinal dispersion models to Monte Carlo simulations and 3D modeling. This review helps to clarify what are the possible sources of compounds responsible for drinking water quality degradation. Additionally, it gives guidance on the measures that are needed to maintain stable and safe drinking water quality.
Research on Optimal Scheduling of Multi-Energy Microgrid Based on Stackelberg Game
Authors: Li Bo , Li Yang , Li Ming-Tong , Guo Dan , Zhang Xin , Zhu Bo , Zhang Pei-Ru , Wang Li-Di
Published: September 2023

In recent years, rapid industrialization has driven higher energy demand, depleting fossil-fuel reserves and causing excessive emissions. China’s “dual carbon” strategy aims to balance development and sustainability. This study optimizes microgrid efficiency with a tiered carbon-priced economy. A Stackelberg game establishes microgrid-user equilibrium, solved iteratively with a multi-population algorithm (MPGA). Comparative analysis can be obtained without considering demand response scenarios, and the optimization cost of microgrid operation considering price-based demand response scenarios was reduced by 5%; that is 668.95 yuan. In addition, the cost of electricity purchase was decreased by 23.8%, or 778.6 yuan. The model promotes user-driven energy use, elevating economic and system benefits, and therefore, the scheduling expectation of “peak shaving and valley filling” is effectively realized.
Energy Efficiency State Identification for Cogeneration Units Based on Benchmark Value
Authors: Li Xin , Gu Yujiong , Wang Zijie
Published: September 2023

In China, cogeneration units predominantly employ a flexible operation mechanism. However, it is possible that this could lead to a decline in performance and an increase in energy consumption. This paper introduces a methodology that utilizes the data mining technique to ascertain the benchmark value section of the energy efficiency status index for cogeneration units. The equal interval division method is utilized for the purpose of categorizing the operating conditions. The Gaussian mixture model is utilized to ascertain the benchmark value section in relation to the fluctuating operating conditions by estimating the probability of historical data. The methodology is verified by utilizing historical data from a functioning cogeneration unit. The findings suggest that the unit’s total heat consumption can be decreased by 32.5–50 kJ·(kW·h)−1 when compared to the design-based approach.
Comparison of the Proteomes and Phosphoproteomes of S. cerevisiae Cells Harvested with Different Strategies
Authors: Rossio Valentina , Paulo Joao A.
Published: September 2023

The budding yeast Saccharomyces cerevisiae is a powerful model system that is widely used to investigate many cellular processes. The harvesting of yeast cells is the first step in almost every experimental procedure. Here, yeast cells are isolated from their growth medium, collected, and used for successive experiments or analysis. The two most common methods to harvest S. cerevisiae are centrifugation and filtration. Understanding if and how centrifugation and filtration affect yeast physiology is essential with respect to downstream data interpretation. Here, we profile and compare the proteomes and the phosphoproteomes, using isobaric label-based quantitative mass spectrometry, of three common methods used to harvest S. cerevisiae cells: low-speed centrifugation, high-speed centrifugation, and filtration. Our data suggest that, while the proteome was stable across the tested conditions, hundreds of phosphorylation events were different between centrifugation and filtration. Our analysis shows that, under our experimental conditions, filtration may cause both cell wall and osmotic stress at higher levels compared to centrifugation, implying harvesting-method-specific stresses. Thus, considering that the basal activation levels of specific stresses may differ under certain harvesting conditions is an important, but often overlooked, aspect of experimental design.
Disputes and Doctrines of the Threefold Middle Way in the Early Sanlun School
Authors: Cho Yoon Kyung
Published: September 2023

This paper examines the development of the Sanlun School’s Threefold Middle Way doctrine, focusing on the contributions of Sengquan and Falang. Often seen as pillars of Sanlun thought preceding Jizang, their teachings remain under-explored in scholarship. This study combines a close reading of their philosophical texts with historical context to generate an in-depth understanding of how the Threefold Middle Way evolved over generations. Sengquan introduced a complex double negation to amplify the transcendental quality of his theory, contrasting with the simpler “neither A nor B” negation of Chengshi masters. Falang, building on this, solidified his interpretation of the middle way and provisional names amidst disputes with contemporary masters. He formulated Three Types of Utterance; the First is directly inherited from Sengquan, while the Second and Third represent the two axes of his two truths theory: gradual abandonment and equality. Falang’s critical response to his fellow masters, particularly his removal of provisional names, heightens the transcendental quality of his theories. A thorough understanding of Sengquan and Falang’s doctrines provides an informed basis to assess the true scope of Jizang’s contributions to Sanlun thought.
Uniting Divided Religious Leaders to Democratize Zimbabwe
Authors: Tagwirei Kimion
Published: September 2023

Following the presidential announcement of Zimbabwe’s harmonized 2023 electoral date, most Christian, African Traditional, and other religious leaders commendably resumed advocating for peaceful, free, and fair elections. However, as history usually repeats itself, the tables eventually turned. Some of the leaders regressively became divided, submitted to political infiltration, aligned themselves with oppressive politicians, betrayed their fellow leaders as well as God and humanity, and inadvertently supported totalitarianism. By employing Richard Osmer’s practical theological methodology and engaging with the existing literature, this paper reviewed the (dis)unity and (dis)engagement between Christian and African Traditional Religious (ATR) leaders. It also endeavored to address the underlying (dis)connections in view of Jesus Christ’s all-encompassing servant leadership model, diaconal mission, and the African Ubuntu philosophy. Observing that Zimbabwean religious leaders are retrogressively divided by their varying spiritualities, leadership styles, and political orientations, it is recommended to embrace inclusivity and unity amidst diversity. This can be achieved by resisting selfishness and promoting selflessness, unconditional love, generosity, hospitality, tolerance, peace-making, justice, and social cohesion. These values collectively play a role in the democratization of Zimbabwe and are imperative for its advancement.
Comprehensive Recovery of Metals in Tailings Utilization with Mechanochemical Activation
Authors: Golik Vladimir I. , Mitsik Mikhail F. , Aleksakhina Yulia V. , Alenina Elena E. , Ruban-Lazareva Natalia V. , Kruzhkova Galina V. , Kondratyeva Olga A. , Trushina Ekaterina V. , Skryabin Oleg O. , Khayrutdinov Marat M.
Published: September 2023

The paper analyzes the results of metal extraction from tailings of ore processing based on traditional approaches. The history of methods of secondary processing of mineral raw materials is described. The technique and features of mechanochemical activation of the leaching process of metal ores and coals are described. The results of laboratory studies of a new mechanochemical technology for metal extraction are presented. A description of the compromise optimal criterion formulated in the model of extraction of metals from tailings with substandard mineral raw materials based on the mathematical planning of the experiment, regression analysis methods and consideration of sanitary standards for the disposal of processing tailings is proposed. Mechanochemical technology results in economic and environmental effects from the utilization of processing tailings and radical strengthening of the mineral resource base of the mining industry.
Investigating the Performance of IGS Real-Time Global Ionospheric Maps under Different Solar Conditions
Authors: Liu Hang , Ren Xiaodong , Xu Guozhen
Published: September 2023

In recent years, real-time global ionospheric map (RT-GIM) products have been actively developed by the international global navigation satellite system (GNSS) service (IGS) and its ionosphere associate analysis centers (IAACs) along with the increase of RT-GNSS multi-frequency and multi-constellation observations. In this study, the accuracy and consistency of three RT-GIM products from the Chinese Academy of Sciences (CAS), Wuhan University (WHU), and IGS are evaluated and analyzed utilizing three validation methods, namely, comparison with JASON-3 vertical total electron content (VTEC), the difference of slant total electron content (dSTEC), and IGS combined final GIM (IGSG) data. The test period was from 1 January 2019 to 31 December 2022, including the different solar activities. First, the comparison with JASON-3 data illustrates that the quality of the three RT-GIM products over oceans is in great consistency with that of the IGSG during different levels of solar activity and the daily mean bias (MEAN) values in low and high solar activities are approximately 5 and 10 TECU, respectively. The root mean square (RMS) values under low and high solar activities can be up to 7 and 12 TECU. Furthermore, the dSTEC validation results present that the MEAN values of RT-GIM products from different IAACs at high- and mid-latitude stations are about 0.5 TECU, which is smaller than those at low-latitude stations at about 1 TECU over continental regions. The standard deviation (STD) and RMS values for various RT-GIM products are within 3 and 4 TECU at low latitudes, respectively. In terms of the comparison with IGSG, the result shows that IGS combined RT-GIM (IRTG) presents better consistency than CAS RT-GIM (CRTG) and WHU RT-GIM (WRTG) in 2021 and 2022, with average annual STD and RMS values of 2.56 and 2.78 TECU, respectively. The daily biases of the RT-GIM products relative to IGSG can reach 4 TECU in high solar activities and the daily STD and RMS values are mainly within the 5 to 6 TECU range, respectively.
Coastal Waveform Retracking for Synthetic Aperture Altimeters Using a Multiple Optimization Parabolic Cylinder Algorithm
Authors: Zheng Jincheng , Xu Xi-Yu , Xu Ying , Guo Chang
Published: September 2023

The importance of monitoring sea level in coastal zones becomes more and more obvious in the era of global climate change, because, in coastal zones, although satellite altimetry is an ideal tool in measuring sea level over open ocean, but its accuracy often decreases significantly at coast due to land contamination. Although the accuracy of waveform processing algorithms for synthetic aperture altimeters has been improved in the last decade, the computational speed is still not fast enough to meet the requirements of real-time processing, and the accuracy cannot meet the needs of nearshore areas within 1 km from the coast. To improve the efficiency and accuracy in the coastal zone, this study proposed an innovative waveform retracking scheme for the coastal zone based on a multiple optimization parabolic cylinder algorithm (MOPCA) integrated with machine learning algorithms such as recurrent neural network and Bayesian estimation. The algorithm was validated using 153-pass repeat cycle data from Sentinel-6 over Qianliyan Island and Hong Kong–Wanshan Archipelago. The computational speed of the proposed algorithm was four to five times faster than the current operational synthetic aperture radar (SAR) retracking algorithm, and its accuracy within 0–20 km from the island was comparable to the most popular SAMOSA+ algorithm, better than the official data product provided by Sentinel-6. Especially, the proposed algorithm demonstrates remarkable stability in the sense of proceeding speed. It maintains consistent performance, even when dealing with intricate wave patterns within a proximity of 1 km from the coast. The results showed that the proposed scheme greatly improved the quality of coastal altimetry waveform retracking.
SCDA: A Style and Content Domain Adaptive Semantic Segmentation Method for Remote Sensing Images
Authors: Xiao Hongfeng , Yao Wei , Chen Haobin , Cheng Li , Li Bo , Ren Longfei
Published: September 2023

Due to the differences in imaging methods and acquisition areas, remote sensing datasets can exhibit significant variations in both image style and content. In addition, the ground objects can be quite different in scale even within the same remote sensing image. These differences should be considered in remote sensing image segmentation tasks. Inspired by the recently developed domain generalization model WildNet, we propose a domain adaption framework named “Style and Content Domain Adaptation” (SCDA) for semantic segmentation tasks involving multiple remote sensing datasets with different data distributions. SCDA uses residual style feature transfer (RSFT) in the shallow layer of the baseline network model to enable source domain images to obtain style features from the target domain and reduce the loss of source domain content information. Considering the scale difference of different ground objects in remote sensing images, SCDA uses the projection of the source domain images, the style-transferred source domain images, and the target domain images to construct a multiscale content adaptation learning (MCAL) loss. This enables the model to capture multiscale target domain content information. Experiments show that the proposed method has obvious domain adaptability in remote sensing image segmentation. When performing cross-domain segmentation tasks from VaihingenIRRG to PotsdamIRRG, mIOU is 48.64%, and the F1 is 63.11%, marking improvements of 1.21% and 0.45%, respectively, compared with state-of-the-art methods. When performing cross-domain segmentation tasks from VaihingenIRRG to PotsdamRGB, the mIOU is 44.38%, an improvement of 0.77% over the most advanced methods. In summary, SCDA improves the semantic segmentation of remote sensing images through domain adaptation for both style and content. It fully utilizes multiple innovative modules and strategies to enhance the performance and the stability of the model.
Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)
Authors: Peng Feifei , Lu Wei , Hu Yunfeng , Jiang Liangcun
Published: September 2023

Accurate geographic data of slums are important for handling urban poverty issues. Previous slum mapping studies using high-resolution or very-high-resolution (HR/VHR) remotely sensed (RS) images are commonly not suitable for city-wide scale tasks. This study aims to efficiently generate a slum map on a city-wide scale using freely accessed multispectral medium-resolution (MR) Sentinel-2 images. Composite slum spectral indices (CSSIs) were initially proposed based on the shapes of spectral profiles of slums and nonslums and directly represent slum characteristics. Specifically, CSSI-1 denotes the normalized difference between the shortwave infrared bands and the red edge band, while CSSI-2 denotes the normalized difference between the blue band and the green band. Furthermore, two methods were developed to test the effectiveness of CSSIs on slum mapping, i.e., the threshold-based method and the machine learning (ML)-based method. Experimental results show that the threshold-based method and the ML-based method achieve intersection over unions (IoU) of 43.89% and 54.45% in Mumbai, respectively. The accuracies of our methods are comparable to or even higher than the accuracies reported by existing methods using HR/VHR images and transfer learning. The threshold-based method exhibits a promising performance in mapping slums larger than 5 ha, while the ML-based method refines mapping accuracies for slum pockets smaller than 5 ha. The threshold-based method and the ML-based method produced the slum map in Mumbai in 2 and 28 min, respectively. Our methods are suitable for rapid large-area slum mapping owing to the high data availability of Sentinel-2 images and high computational efficiency.
Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes
Authors: Xue Wei , Ko Jonghan , Cao Ruyin , Yu Zhiguo
Published: September 2023

Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a simple, but effective “Light and Temperature-Driven Growth model and Double Logistic function fusion algorithm” (LTDG_DL). The empirical basis of the LTDG_DL algorithm was traced from the de Wit crop growth simulation model and the commonly observed nonlinear correlation between the EVI and the Leaf Area Index (LAI). It assimilates the ground daily solar radiation and air temperature to generate seasonal profiles of the empirical LAI and EVI and conducts the within-season calibration of the empirical EVI by adjusting crop growth using cloud-free Landsat EVI observations. The initial date of seedling emergence (DOYini) and the accumulated Growing Degree Days for completion of the vegetative and Flowering stage (FGDDs) were variables to which the algorithm’s accuracy was most sensitive. The variable-constrained optimization of the LTDG_DL algorithm was performed by loading the seedling emergence calendar of local prevailing crops and establishing an FGDD lookup table with an exhaustive sampling without replication method. Compared to temporal interpolation functions and Landsat–MODIS spatiotemporal fusion algorithms, the LTDG_DL algorithm had superior performance in the predictions of the EVI increment slope at the vegetative growth stage, the timing of the peak EVI, and the protection of key Landsat EVI observations over cloud-contaminated and complex landscape agricultural systems. Finally, the advantages and limitations of the LTDG_DL algorithm are discussed.
Discovery, Quantitative Recurrence, and Inhibition of Motion-Blur Hysteresis Phenomenon in Visual Tracking Displacement Detection
Authors: Shi Lixiang , Tan Jianping
Published: September 2023

Motion blur is common in video tracking and detection, and severe motion blur can lead to failure in tracking and detection. In this work, a motion-blur hysteresis phenomenon (MBHP) was discovered, which has an impact on tracking and detection accuracy as well as image annotation. In order to accurately quantify MBHP, this paper proposes a motion-blur dataset construction method based on a motion-blur operator (MBO) generation method and self-similar object images, and designs APSF, a MBO generation method. The optimized sub-pixel estimation method of the point spread function (SPEPSF) is used to demonstrate the accuracy and robustness of the APSF method, showing the maximum error (ME) of APSF to be smaller than others (reduced by 86%, when motion-blur length > 20, motion-blur angle = 0), and the mean square error (MSE) of APSF to be smaller than others (reduced by 65.67% when motion-blur angle = 0). A fast image matching method based on a fast correlation response coefficient (FAST-PCC) and improved KCF were used with the motion-blur dataset to quantify MBHP. The results show that MBHP exists significantly when the motion blur changes and the error caused by MBHP is close to half of the difference of the motion-blur length between two consecutive frames. A general flow chart of visual tracking displacement detection with error compensation for MBHP was designed, and three methods for calculating compensation values were proposed: compensation values based on inter-frame displacement estimation error, SPEPSF, and no-reference image quality assessment (NR-IQA) indicators. Additionally, the implementation experiments showed that this error can be reduced by more than 96%.
Shallow Marine High-Resolution Optical Mosaics Based on Underwater Scooter-Borne Camera
Authors: Liu Yiyuan , Wang Xinwei , Sun Liang , Chen Jianan , He Jun , Zhou Yan
Published: September 2023

Optical cameras equipped with an underwater scooter can perform efficient shallow marine mapping. In this paper, an underwater image stitching method is proposed for detailed large scene awareness based on a scooter-borne camera, including preprocessing, image registration and post-processing. An underwater image enhancement algorithm based on the inherent underwater optical attenuation characteristics and dark channel prior algorithm is presented to improve underwater feature matching. Furthermore, an optimal seam algorithm is utilized to generate a shape-preserving seam-line in the superpixel-restricted area. The experimental results show the effectiveness of the proposed method for different underwater environments and the ability to generate natural underwater mosaics with few artifacts or visible seams.
Simplifying Rogowski Coil Modeling: Simulation and Experimental Verification
Authors: Mingotti Alessandro , Betti Christian , Tinarelli Roberto , Peretto Lorenzo
Published: September 2023

The integration of renewable energy sources, electric vehicles, and other electrical assets has introduced complexities in monitoring and controlling power networks. Consequently, numerous grid nodes have been equipped with sensors and complex measurement systems to enhance network observability. Additionally, real-time power network simulators have become crucial tools for predicting and estimating the behavior of electrical quantities at different network components, such as nodes, branches, and assets. In this paper, a new user-friendly model for Rogowski coils is presented and validated. The model’s simplicity stems from utilizing information solely from the Rogowski coil datasheet. By establishing the input/output relationship, the output of the Rogowski coil is obtained. The effectiveness and accuracy of the proposed model are tested using both simulations and commercially available Rogowski coils. The results confirm that the model is simple, accurate, and easily implementable in various simulation environments for a wide range of applications and purposes.
TranStutter: A Convolution-Free Transformer-Based Deep Learning Method to Classify Stuttered Speech Using 2D Mel-Spectrogram Visualization and Attention-Based Feature Representation
Authors: Basak Krishna , Mishra Nilamadhab , Chang Hsien-Tsung
Published: September 2023

Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the accurate classification of stuttering types. The researchers introduce “TranStutter”, a pioneering Convolution-free Transformer-based DL model, designed to excel in speech disfluency classification. Unlike conventional methods, TranStutter leverages Multi-Head Self-Attention and Positional Encoding to capture intricate temporal patterns, yielding superior accuracy. In this study, the researchers employed two benchmark datasets: the Stuttering Events in Podcasts Dataset (SEP-28k) and the FluencyBank Interview Subset. SEP-28k comprises 28,177 audio clips from podcasts, meticulously annotated into distinct dysfluent and non-dysfluent labels, including Block (BL), Prolongation (PR), Sound Repetition (SR), Word Repetition (WR), and Interjection (IJ). The FluencyBank subset encompasses 4144 audio clips from 32 People Who Stutter (PWS), providing a diverse set of speech samples. TranStutter’s performance was assessed rigorously. On SEP-28k, the model achieved an impressive accuracy of 88.1%. Furthermore, on the FluencyBank dataset, TranStutter demonstrated its efficacy with an accuracy of 80.6%. These results highlight TranStutter’s significant potential in revolutionizing the diagnosis and treatment of stuttering, thereby contributing to the evolving landscape of speech pathology and neurodevelopmental research. The innovative integration of Multi-Head Self-Attention and Positional Encoding distinguishes TranStutter, enabling it to discern nuanced disfluencies with unparalleled precision. This novel approach represents a substantial leap forward in the field of speech pathology, promising more accurate diagnostics and targeted interventions for individuals with stuttering disorders.
An Access Control Scheme Based on Blockchain and Ciphertext Policy-Attribute Based Encryption
Authors: Hu Ronglei , Ma Ziwei , Li Li , Zuo Peiliang , Li Xiuying , Wei Jiaxin , Liu Sihui
Published: September 2023

Ciphertext policy–attribute-based encryption (CP-ABE), which provides fine-grained access control and ensures data confidentiality, is widely used in data sharing. However, traditional CP-ABE schemes often choose to outsource data to untrusted third-party cloud service providers for storage or to verify users’ access rights through third parties, which increases the risk of privacy leakage and also suffers from the problem of opaque permission verification. This paper proposes an access control scheme based on blockchain and CP-ABE, which is based on multiple authorization centers and supports policy updating. In addition, blockchain technology’s distributed, decentralized, and tamper-proof features are utilized to solve the trust crisis problem in the data-sharing process. Security analysis and performance evaluation show that the proposed scheme improves the computational efficiency by 18%, 26%, and 68% compared to previous references. The proposed scheme also satisfies the indistinguishability under chosen-plaintext attack (IND-CPA).
Simultaneous Assay of CA 72-4, CA 19-9, CEA and CA 125 in Biological Samples Using Needle Three-Dimensional Stochastic Microsensors
Authors: Bratei Alexandru-Adrian , Stefan-van Staden Raluca-Ioana , Ilie-Mihai Ruxandra-Maria , Gheorghe Damaris-Cristina
Published: September 2023

Two-needle 3D stochastic microsensors based on boron- and nitrogen-decorated gra-phenes, modified with N-(2-mercapto-1H-benzo[d]imidazole-5-yl), were designed and used for the molecular recognition and quantification of CA 72-4, CA 19-9, CEA and CA 125 biomarkers in biological samples such as whole blood, urine, saliva and tumoral tissue. The NBGr-2 sensor yielded lower limits of determination. For CEA, the LOD was 4.10 × 10−15 s−1 g−1 mL, while for CA72-4, the LOD was 4.00 × 10−11 s−1 U−1 mL. When the NBGr-1 sensor was employed, the best results were obtained for CA12-5 and CA19-9, with values of LODs of 8.37 × 10−14 s−1 U−1 mL and 2.09 × 10−13 s−1 U−1 mL, respectively. High sensitivities were obtained when both sensors were employed. Broad linear concentration ranges favored their determination from very low to higher concentrations in biological samples, ranging from 8.37 × 10−14 to 8.37 × 103 s−1 U−1 mL for CA12-5 when using the NBGr-1 sensor, and from 4.10 × 10−15 to 2.00 × 10−7 s−1 g−1 mL for CEA when using the NBGr-2 sensor. Student’s t-test showed that there was no significant difference between the results obtained utilizing the two microsensors for the screening tests, at a 99% confidence level, with the results obtained being lower than the tabulated values.
Smart Grid Outlier Detection Based on the Minorization–Maximization Algorithm
Authors: Qiao Lina , Gao Wengen , Li Yunfei , Guo Xinxin , Hu Pengfei , Hua Feng
Published: September 2023

Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data, affect the completeness and accuracy of the data, and thus affect the monitoring analysis and control of the power system. Therefore, timely identification and treatment of outliers are essential to ensure stable and reliable operation of the power system. In this paper, we consider the problem of detecting and localizing outliers in power systems. The paper proposes a Minorization–Maximization (MM) algorithm for outlier detection and localization and an estimation of unknown parameters of the Gaussian mixture model (GMM). To verify the performance of the method, we conduct simulation experiments by simulating different test scenarios in the IEEE 14-bus system. Numerical examples show that in the presence of outliers, the MM algorithm can detect outliers better than the traditional algorithm and can accurately locate outliers with a probability of more than 95%. Therefore, the algorithm provides an effective method for the handling of outliers in the power system, which helps to improve the monitoring analyzing and controlling ability of the power system and to ensure the stable and reliable operation of the power system.
Security Aspects of Social Robots in Public Spaces: A Systematic Mapping Study
Authors: Oruma Samson Ogheneovo , Ayele Yonas Zewdu , Sechi Fabien , Rødsethol Hanne
Published: September 2023

Background: As social robots increasingly integrate into public spaces, comprehending their security implications becomes paramount. This study is conducted amidst the growing use of social robots in public spaces (SRPS), emphasising the necessity for tailored security standards for these unique robotic systems. Methods: In this systematic mapping study (SMS), we meticulously review and analyse existing literature from the Web of Science database, following guidelines by Petersen et al. We employ a structured approach to categorise and synthesise literature on SRPS security aspects, including physical safety, data privacy, cybersecurity, and legal/ethical considerations. Results: Our analysis reveals a significant gap in existing safety standards, originally designed for industrial robots, that need to be revised for SRPS. We propose a thematic framework consolidating essential security guidelines for SRPS, substantiated by evidence from a considerable percentage of the primary studies analysed. Conclusions: The study underscores the urgent need for comprehensive, bespoke security standards and frameworks for SRPS. These standards ensure that SRPS operate securely and ethically, respecting individual rights and public safety, while fostering seamless integration into diverse human-centric environments. This work is poised to enhance public trust and acceptance of these robots, offering significant value to developers, policymakers, and the general public.
Various-Order Low-Pass Filter with the Electronic Change of Its Approximation
Authors: Langhammer Lukas , Sotner Roman , Theumer Radek
Published: September 2023

The design of a low-pass-frequency filter with the electronic change of the approximation characteristics of resulting responses is presented. The filter also offers the reconnection-less reconfiguration of the order (1st-, 2nd-, 3rd- and 4th-order functions are available). Furthermore, the filter offers the electronic control of the cut-off frequency of the output response. The feature of the electronic change in the approximation characteristics is investigated for the Butterworth, Bessel, Elliptic, Chebyshev and Inverse Chebyshev approximations. The design is verified by PSpice simulations and experimental measurements. The results are also supported by the transient domain response (response to the square waveform), comparison of the group delay, sensitivity analysis and implementation feasibility based on given approximation. The benefit of the proposed electronic change in the approximation characteristics feature (in general signal processing or for sensors in particular) is presented and discussed for an exemplary scenario.
Political Participation in China: Towards a New Definition and Typology
Authors: Ma Zhihao , Cao Yujue
Published: September 2023

Political participation has become an important topic in contemporary political thinking. Western scholars’ views that reflect experiences in Western societies have become predominant theoretical articulations on political participation. This study offers a critical review of the conceptual frameworks of political participation in democratic and non-democratic societies. Drawing on previous research, it has developed a new definition and typology of political participation in China. It eschews voluntarism in the Western definition of political activities and participation to creatively propose a well-developed definition of political participation in China for which it constructs a ladder model. This study summarises Chinese political participation according to four categories: non-participation, tokenism, active participation, and radical participation. This study proposes a typology that motivates us to think of many issues, including online interactions. This study makes a much-needed contribution to the literature on the theoretical development of political participation in China.
Gender Is the Name of the Frame: Understanding Gender through the Lens of Relational Frame Theory
Authors: Farrell Lynn , Mizael Táhcita M. , Gould Evelyn R.
Published: September 2023

While researchers continue to develop their understanding of gender as a complex and multifaceted concept, the detrimental impact of gender-related inequity and social injustice persists. This conceptual paper describes the potential benefits of incorporating Relational Frame Theory (RFT) as a contextual and pragmatic approach to gender. An RFT lens might enhance our understanding of gender as a language-based phenomenon, involving patterns of derived relational responding and rule-governed behavior. Such an understanding might then facilitate the development of assessments and context-sensitive interventions that support flexible and expansive experiences of gender that promote thriving. Despite the potential utility of an RFT approach, RFT has rarely been applied to gender-related concerns. This paper aims to provide a starting point for exploring gender from an RFT perspective, highlight relevant RFT studies, acknowledge limitations of current lines of research, and provide recommendations regarding future research. We hope that the paper will also act as a call to action for contextual behavioral scientists, as well as demonstrate how an RFT perspective might both align with, as well as add to, existing perspectives from other disciplines.
Powerful Knowledge and the 2017 Swedish National Test in Religious Education
Authors: Andersson Klas , Börjesson Mattias , Larsson Kristoffer
Published: September 2023

In the 21st century, there has been a recurring discussion about social realism in educational research: the idea that subject knowledge derived from academic disciplines should constitute the heart of the school curriculum. The argument is that this knowledge, because it is produced according to specific standards, is powerful knowledge, and has better claims to truth and is more valid than knowledge gained from students’ everyday experiences. Because of its validity and universality, this knowledge empowers the knower to transcend her or his everyday experiences and take part in society’s conversations, which are central goals of social studies education. The aim of this study is to identify in which ways aspects of powerful knowledge are manifested in the 2017 national test in RE. For the analysis, both items and students’ answers from the 2017 test are used. The focus with regard to the items is on whether the students are expected to demonstrate knowledge and abilities in relation to powerful knowledge. We also analyze the students’ answers to these items using the same framework.
Terroir in View of Bibliometrics
Authors: Stefanis Christos , Giorgi Elpida , Tselemponis Giorgios , Voidarou Chrysa , Skoufos Ioannis , Tzora Athina , Tsigalou Christina , Kourkoutas Yiannis , Constantinidis Theodoros C. , Bezirtzoglou Eugenia
Published: September 2023

This study aimed to perform a bibliometric analysis of terroir and explore its conceptual horizons. Advancements in terroir research until 2022 were investigated using the Scopus database, R, and VOSviewer. Out of the 907 results, the most prevalent document types were articles (771) and reviews (70). The annual growth rate of published manuscripts in this field was 7.8%. The research on terroir encompassed a wide range of disciplines, with significant contributions from Agricultural and Biological Sciences, Social Sciences, Environmental Science, Biochemistry, Genetics, and Molecular Biology. Through keyword analysis, the study identified the most frequently occurring terms in titles, abstracts, and keywords fields, including ‘terroir’, ‘wine’, ‘soil’, ‘wines’, ‘grape’, ‘analysis’, ‘vineyard’, ‘composition’, and ‘climate’. A trend topic analysis revealed that research in terroir primarily focused on the geo-ecology and physiology of grapes. Furthermore, considerable attention was given to methods and techniques related to the physicochemical, sensory, and microbial characterization of terroir and various aspects of the wine industry. Initially, the research in this domain was focused on terroir, authenticity, grapevine, soils, soil moisture, and wine quality. However, over time, the research agenda expanded to include topics such as food analysis, viticulture, wine, taste, sustainability, and climate change. New research areas emerged, including phenolic compounds, anthocyanin, phenols, sensory analysis, and precision agriculture—all of which became integral components of the scientific studies on terroir. Overall, this study provided valuable insights into the historical trends and current developments in terroir research, contributing to our understanding of the frontiers in this field.