bluuebunny/crossref_metadata_2025 huggingface dataset:
The Exact Density of the Eigenvalues of the Wishart and Matrix-Variate Gamma and Beta Random Variables
Authors: Mathai A. M. , Provost Serge B.
Published: August 2024

The determination of the distributions of the eigenvalues associated with matrix-variate gamma and beta random variables of either type proves to be a challenging problem. Several of the approaches utilized so far yield unwieldy representations that, for instance, are expressed in terms of multiple integrals, functions of skew symmetric matrices, ratios of determinants, solutions of differential equations, zonal polynomials, and products of incomplete gamma or beta functions. In the present paper, representations of the density functions of the smallest, largest and jth largest eigenvalues of matrix-variate gamma and each type of beta random variables are explicitly provided as finite sums when certain parameters are integers and, as explicit series, in the general situations. In each instance, both the real and complex cases are considered. The derivations initially involve an orthonormal or unitary transformation whereby the wedge products of the differential elements of the eigenvalues can be worked out from those of the original matrix-variate random variables. Some of these results also address the distribution of the eigenvalues of a central Wishart matrix as well as eigenvalue problems arising in connection with the analysis of variance procedure and certain tests of hypotheses in multivariate analysis. Additionally, three numerical examples are provided for illustration purposes.
A New Variant of the Conjugate Descent Method for Solving Unconstrained Optimization Problems and Applications
Authors: Awwal Aliyu Muhammed , Yahaya Mahmoud Muhammad , Pakkaranang Nuttapol , Pholasa Nattawut
Published: August 2024

Unconstrained optimization problems have a long history in computational mathematics and have been identified as being among the crucial problems in the fields of applied sciences, engineering, and management sciences. In this paper, a new variant of the conjugate descent method for solving unconstrained optimization problems is introduced. The proposed algorithm can be seen as a modification of the popular conjugate descent (CD) algorithm of Fletcher. The algorithm of the proposed method is well-defined, and the sequence of the directions of search is shown to be sufficiently descending. The convergence result of the proposed method is discussed under the common standard conditions. The proposed algorithm together with some existing ones in the literature is implemented to solve a collection of benchmark test problems. Numerical experiments conducted show the performance of the proposed method is very encouraging. Furthermore, an additional efficiency evaluation is carried out on problems arising from signal processing and it works well.
Continuous Dynamic Recrystallization and Deformation Behavior of an AA1050 Aluminum Alloy during High-Temperature Compression
Authors: Yang Qi , Wojcik Tomasz , Kozeschnik Ernst
Published: August 2024

Continuous dynamic recrystallization (CDRX) forms a new recrystallized microstructure through the progressive increase in low-angle boundary misorientations (LAGBs) during the hot forming of metallic materials with high stacking fault energy (SFE), such as aluminum alloys. The present work investigates the effect of deformation parameters on the evolution of the dynamic recrystallization microstructures of an AA1050 aluminum alloy during compression at elevated temperatures. The alloy microstructure is investigated at deformation temperatures and strain rates in the range of 300 °C to 500 °C and 0.001 to 0.8 s−1. A well-defined substructure and subsequent DRX grains provide indication that recrystallization can proceed with continued strain under high-temperature compression. At a strain rate of 0.1 s−1, the DRX fraction is observed to be 0.25 at a temperature of 300 °C. This fraction increases to 0.32 as the temperature rises to 400 °C. The recrystallization mechanism is identified by analyzing the flow stress, the evolution of the subgrain misorientation angle, and the distribution of recrystallized grains. The observations of discontinuous dynamic recrystallization (DDRX) and CDRX under various deformation parameters are discussed. Moreover, the main substructure evolution laws observed from the high-temperature compression of an AA1050 Al alloy are summarized.
Machine-Learning-Assisted Design of Novel TiZrNbVAl Refractory High-Entropy Alloys with Enhanced Ductility
Authors: Zhao Xinyi , Wei Zihang , Zhao Junfeng , Jia Yandong , Cao Shuo , Wang Dan , Lei Yucheng
Published: August 2024

Refractory high-entropy alloys (RHEAs) typically exhibit excellent high-temperature strength but limited ductility. In this study, a comprehensive machine learning strategy with integrated material knowledge is proposed to predict the elongation of TiZrNbVAl RHEAs. By referring to the ductility theories, a set of cost-effective material features is developed with various mathematical forms of thermodynamic parameters. These features are proven to effectively incorporate material knowledge into ML modeling. They also offer potential alternatives to those obtained from costly first-principles calculations. Based on Pearson correlation coefficients, the linear relationships between pairwise features were compared, and the seven key features with the greatest impact on the model were selected for ML modeling. Regression tasks were performed to predict the ductility of TiZrNbVAl, and the CatBoost gradient boosting algorithm exhibiting the best performance was eventually selected. The established optimized model achieves high predictive accuracies exceeding 0.8. These key features were further analyzed using interpretable ML methods to elucidate their influences on various ductility mechanisms. According to the ML results, different compositions of TiZrNbVAl with excellent tensile properties were prepared. The experimental results indicate that Ti44Zr24Nb17V5Al10 and Ti44Zr26Nb8V13Al9 both exhibited ultimate tensile strengths of approximately 1180 MPa and elongations higher than 21%. They verified that the ML strategy proposed in this study is an effective approach for predicting the properties of RHEAs. It is a potential method that can replace costly first-principles calculations. Thermodynamic parameters have been shown to effectively predict alloy ductility to a certain extent.
Recent Progress in Photothermal, Photodynamic and Sonodynamic Cancer Therapy: Through the cGAS-STING Pathway to Efficacy-Enhancing Strategies
Authors: Fang Kelan , Zhang Huiling , Kong Qinghong , Ma Yunli , Xiong Tianchan , Qin Tengyao , Li Sanhua , Zhu Xinting
Published: August 2024

Photothermal, photodynamic and sonodynamic cancer therapies offer opportunities for precise tumor ablation and reduce side effects. The cyclic guanylate adenylate synthase-stimulator of interferon genes (cGAS-STING) pathway has been considered a potential target to stimulate the immune system in patients and achieve a sustained immune response. Combining photothermal, photodynamic and sonodynamic therapies with cGAS-STING agonists represents a newly developed cancer treatment demonstrating noticeable innovation in its impact on the immune system. Recent reviews have concentrated on diverse materials and their function in cancer therapy. In this review, we focus on the molecular mechanism of photothermal, photodynamic and sonodynamic cancer therapies and the connected role of cGAS-STING agonists in treating cancer.
An Organic Microcavity Laser Amplifier Integrated on the End Facet of an Optical Fiber
Authors: Wang Meng , Xu Zhuangzhuang , Ren Yaqi , Bai Xiaolei , Zhang Xinping
Published: August 2024

We report a thin-film optical amplifier integrated on a fiber facet based on polymer-coated distributed feedback (DFB) microcavities, which are fabricated on a planar substrate and then transferred onto fiber tips by means of a flexible transfer technique. The amplified light directly couples into the fiber and is detected when coupled out at the other end after propagating along the fiber for about 20 cm. A prominently amplification factor of about 4.33 at 578.57 nm is achieved by sending supercontinuum pulses into the hundreds of micrometers’ DFB microcavities along the normal direction, which is also the axis direction of the fiber. The random distortions of grating lines generated during the transfer process result in a larger amplification spectral range and a less strict polarization dependence for injected light. Benefitting from the device size of hundreds of micrometers and the ease of integration, polymer amplifiers based on DFB microcavities demonstrate significant application potentials in optical communication systems and miniaturized optical devices.
Ni-Based Molecular Sieves Nanomaterials for Dry Methane Reforming: Role of Porous Structure and Active Sites Distribution on Hydrogen Production
Authors: Al-Fatesh Ahmed S. , Ibrahim Ahmed A. , Fakeeha Anis H. , Osman Ahmed I. , Alanazi Yousef M. , Almubaddel Fahad Saleh , Abasaeed Ahmed E.
Published: August 2024

Global warming, driven by greenhouse gases like CH4 and CO2, necessitates efficient catalytic conversion to syngas. Herein, Ni containing different molecular sieve nanomaterials are investigated for dry reforming of methane (DRM). The reduced catalysts are characterized by surface area porosity, X-ray diffraction, Raman infrared spectroscopy, CO2 temperature-programmed desorption techniques, and transmission electron microscopy. The active sites over each molecular sieve remain stable under oxidizing gas CO2 during DRM. The reduced 5Ni/CBV10A catalyst, characterized by the lowest silica–alumina ratio, smallest surface area and pore volume, and narrow 8-ring connecting channels, generated the maximum number of active sites on its outer surface. In contrast, the reduced-5Ni/CBV3024E catalyst, with the highest silica–alumina ratio, more than double the surface area and pore volume, 12-ring sinusoidal porous channels, and smallest Ni crystallite, produced the highest H2 output (44%) after 300 min of operation at 700 °C, with a CH4:CO2 = 1:1, P = 1 atom, gas hour space velocity (GHSV) = 42 L gcat−1 h−1. This performance was achieved despite having 25% fewer initial active sites, suggesting that a larger fraction of these sites is stabilized within the pore channels, leading to sustained catalytic activity. Using central composite design and response surface methodology, we successfully optimized the process conditions for the 5Ni/CBV3024E catalyst. The optimized conditions yielded a desirable H2 to CO ratio of 1.00, with a H2 yield of 91.92% and a CO yield of 89.16%, indicating high efficiency in gas production. The experimental results closely aligned with the predicted values, demonstrating the effectiveness of the optimization approach.
Breastfeeding in PKU and Other Amino Acid Metabolism Disorders—A Single Centre Experience
Authors: Kowalik Agnieszka , Gudej-Rosa Sylwia , Nogalska Marta , Myszkowska-Ryciak Joanna , Sykut-Cegielska Jolanta
Published: August 2024

In addition to the numerous immunological and nutritional benefits that breast milk offers to infants, its proportion in the diet must be limited or even excluded in the case of inborn errors of amino acid metabolism (IEM). The objective of the study was to expand knowledge about breastfeeding and the degree of contribution of breast milk to the feeding of infants with IEM before and after the introduction of expanded newborn screening. A retrospective single-centre study was conducted on 127 infants born between 1997 and 2020: 66 with phenylketonuria (PKU), 45 with other IEM (non-PKU), all diagnosed through newborn screening (NBS), and 16 non-PKU diagnosed through selective screening (SS). The time of initiation of dietary treatment and the proportion of breast milk in the diet, both expressed and breastfed, with or without intake control, were analysed at 1, 3, and 6 months after birth. For 47% of the newborns in Groups 1 and 2, the dietary treatment was started before the 10th day of life; in Group 3, the dietary treatment was started after the 10th day of life for all children. During the first month of life, the proportion of infants receiving breast milk was higher in the NBS-PKU (74%) and the NBS non-PKU (80%) groups, compared with 38% in the SS non-PKU infants. In the subsequent months of life, the proportion of infants receiving human milk (either from the breast or a bottle) declined in all groups. This decline occurred more in bottle-fed rather than directly breast-fed infants. Our observations indicate that the model of feeding from a bottle with expressed milk may have had an adverse effect on maintaining lactation and may have contributed to a faster transition to formula milk. Maintaining lactation and extending the period of feeding the infant with human milk in the first 6 months of life is possible by breastfeeding on demand, under regular biochemical monitoring: preferably weekly in PKU infants, and at least every 2–4 weeks in infants with other IEM.
Polyphenols: Secondary Metabolites with a Biological Impression
Authors: Bolat Ecem , Sarıtaş Sümeyye , Duman Hatice , Eker Furkan , Akdaşçi Emir , Karav Sercan , Witkowska Anna Maria
Published: August 2024

Polyphenols are natural compounds which are plant-based bioactive molecules, and have been the subject of growing interest in recent years. Characterized by multiple varieties, polyphenols are mostly found in fruits and vegetables. Currently, many diseases are waiting for a cure or a solution to reduce their symptoms. However, drug or other chemical strategies have limitations for using a treatment agent or still detection tool of many diseases, and thus researchers still need to investigate preventive or improving treatment. Therefore, it is of interest to elucidate polyphenols, their bioactivity effects, supplementation, and consumption. The disadvantage of polyphenols is that they have a limited bioavailability, although they have multiple beneficial outcomes with their bioactive roles. In this context, several different strategies have been developed to improve bioavailability, particularly liposomal and nanoparticles. As nutrition is one of the most important factors in improving health, the inclusion of plant-based molecules in the daily diet is significant and continues to be enthusiastically researched. Nutrition, which is important for individuals of all ages, is the key to the bioactivity of polyphenols.
Validity and Reliability of the Turkish Version of the Screening Questionnaire of Highly Processed Food Consumption (sQ-HPF)
Authors: Erdoğan Gövez Nazlıcan , Köksal Eda , Martinez-Perez Celia , Daimiel Lidia
Published: August 2024

The global consumption of highly (ultra) processed foods (HPFs) is increasing, and it is associated with non-communicable diseases. This study aimed to assess the validity and reliability of the Screening Questionnaire of Highly Processed Food Consumption (sQ-HPF). This study included 94 adults. Sociodemographic data were collected, and anthropometric and blood pressure measurements were performed. The sQ-HPF was translated into Turkish and culturally adapted. Dietary intake was assessed using three-day dietary records. Factor analysis and Cronbach’s alpha were used to evaluate the validity and consistency of the sQ-HPF. Test–retest reliability was assessed with the intraclass correlation coefficient (ICC). Three items from the original sQ-HPF were excluded due to low factor loadings. The Kaiser–Meyer Olkin (KMO) coefficient for the measure of sample adequacy was found to be 0.642 and Bartlett’s test of sphericity was found to be significant (p < 0.001). A significant correlation was found between the sQ-HPF score and HPF consumption derived from the 3-day dietary records (p < 0.05). Cronbach’s alpha was found to be 0.65. Individuals with higher sQ-HPF scores consumed a significantly greater percentage of energy from HPFs (kcal/day) (p < 0.001). The sQ-HPF demonstrated good test–retest reliability (ICC = 0.76). The Turkish version of the sQ-HPF is a valid and reliable tool for assessing HPF consumption patterns and can be used in epidemiological and clinical studies.
Chemical Profile and In Vitro Gut Microbiota Modulation of Wild Edible Mushroom Phallus atrovolvatus Fruiting Body at Different Maturity Stages
Authors: Kaewsaen Raweephorn , Wichienchot Santad , Thayanukul Parinda , Charoensiddhi Suvimol , Chanput Wasaporn Preteseille
Published: August 2024

Phallus atrovolvatus, a wild edible mushroom, has attracted increasing interest for consumption due to its unique taste and beneficial health benefits. This study determined the chemical components in the so-called fruiting body during the egg and mature stages and investigated its gut microbiota-modulating activities. The egg stage contained higher total carbohydrates, dietary fiber, glucans, ash, and fat, while the total protein content was lower than in the mature stage. Two consumption forms, including cooked mushrooms and a mushroom aqueous extract from both stages, were used in this study. An in vitro gut fermentation was performed for 24 h to assess gut microbiota regulation. All mushroom-supplemented fermentations increased short-chain fatty acid (SCFA) production compared to the blank control. Furthermore, all mushroom supplementations promoted the growth of Bifidobacterium and Streptococcus. Samples from the mature stage increased the relative abundance of Clostridium sensu stricto 1, while those from the egg stage increased the Bacteroides group. The inhibition of harmful bacteria, including Escherichia-Shigella, Klebsiella, and Veillonella, was only observed for the mature body. Our findings demonstrate that P. atrovolvatus exhibits potential benefits on gut health by promoting SCFA production and the growth of beneficial bacteria, with the mature stage demonstrating superior effects compared to the egg stage.
The Effects of Parental Food Education on Children’s Food Literacy: The Mediating Role of Parent–Child Relationship and Learning Motivation
Authors: Xu Xing , Cai Huizi , Zhang Jieying , Xia Tiansheng
Published: August 2024

Parental food education has been recognized among the important factors influencing children’s food literacy; however, the intrinsic mechanisms through which this influence occurs are unclear. In this study, a mediation model was constructed to explore this issue, using the parent–child relationship and learning motivation as mediating variables. In total, 204 children, aged 9–14 years old, responded to questionnaires on parental food education, children’s food literacy, the parent–child relationship, and learning motivation, which were used to measure the variables of interest. The results showed that parental food education was significantly and positively related to the parent–child relationship, learning motivation, and children’s food literacy; the parent–child relationship was significantly and positively related to learning motivation; and learning motivation was significantly and positively related to children’s food literacy. Parental food education influenced children’s food literacy in the following two main ways: the mediating role of learning motivation and the chain-mediating roles of the parent–child relationship and learning motivation. In addition, we attempt to explore the moderating role of the teaching stage between parental food education and the parent–child relationship, learning motivation, and children’s food literacy. In this paper, we discuss possible guidelines for family food education and children’s health based on the findings of the current study.
Ethnobotany around the Virovitica Area in NW Slavonia (Continental Croatia)—Record of Rare Edible Use of Fungus Sarccoscypha coccinea
Authors: Vitasović-Kosić Ivana , Berec Dominik , Łuczaj Łukasz , Motti Riccardo , Juračak Josip
Published: August 2024

Slavonia is the most developed agricultural region in Croatia. With rich and fertile soils that have enabled the cultivation of a wide variety of fruits, vegetables, and cereals, Slavonia has always met the food needs of its population. Today, the biocultural diversity of local varieties and semi-natural vegetation has irretrievably disappeared. Our aim was to document the remaining local knowledge of plant use in this area through in-depth semi-structured interviews, which were conducted in 2022–2023. All possible aspects of the use of plants and fungi were recorded as food, animal feed, medicine, construction, jewelry, rituals and ceremonies, dyes, etc. The names and uses of local plant varieties were also recorded. The results show 1702 entries—a total of 296 plant taxa from 76 families and 28 fungi from 16 families. The most frequently named plants were: Urtica dioica, Robinia pseudoacacia, Rosa canina, and Sambucus nigra. The plants with the greatest variety of uses were Morus alba, Rosmarinus officinalis, Triticum aestivum, and Zea mays. Interesting uses were identified. The leaves of the ornamental plant Hosta sieboldiana are still used today as food for wrapping meat with rice, the aquatic plant Trapa natans is eaten like chestnuts, and Pteridium aquilinum was once consumed as a vegetable. In addition, Ambrosia artemisiifolia and Sambucus ebulus were given to horses to prevent and avoid blood poisoning. Some forest species had a special significance and were revered or favored. The most frequently mentioned edible fungi were Boletus sp., Cantharellus cibarius, and Lactarius piperatus. Auricularia auricula-judae is the only species stated to have been used exclusively as a raw snack. Evidence of edible use of Sarccoscypha coccinea, which was reported as traditionally consumed in the past, was of particular interest. Despite the modernization and agricultural nature of the region, many interesting uses of plants and fungi were identified. Further efforts should be directed towards documenting this knowledge to facilitate its dissemination in the communities that possess it, or at least to preserve it for future generations.
Combined BSA-Seq and RNA-Seq to Identify Potential Genes Regulating Fruit Size in Bottle Gourd (Lagenaria siceraria L.)
Authors: Fang Huarong , Huang Shishi , Li Ruirui , Wang Peng , Jiang Qingwei , Zhong Chuan , Yang Yanjuan , Yu Wenjin
Published: August 2024

Fruit size is a crucial agronomic trait in bottle gourd, impacting both yield and utility. Despite its significance, the regulatory mechanism governing fruit size in bottle gourd remains largely unknown. In this study, we used bottle gourd (small-fruited H28 and large-fruited H17) parent plants to measure the width and length of fruits at various developmental stages, revealing a single ‘S’ growth curve for fruit expansion. Paraffin section observations indicated that both cell number and size significantly influence bottle gourd fruit size. Through bulked segregant analysis and combined genotype–phenotype analysis, the candidate interval regulating fruit size was pinpointed to 17,747,353 bp–18,185,825 bp on chromosome 9, encompassing 0.44 Mb and including 44 genes. Parental fruits in the rapid expansion stage were subjected to RNA-seq, highlighting that differentially expressed genes were mainly enriched in pathways related to cell wall biosynthesis, sugar metabolism, and hormone signaling. Transcriptome and resequencing analysis, combined with gene function annotation, identified six genes within the localized region as potential regulators of fruit size. This study not only maps the candidate interval of genes influencing fruit size in bottle gourd through forward genetics, but also offers new insights into the potential molecular mechanisms underlying this trait through transcriptome analysis.
Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review
Authors: Ramos Edgar , Lopes Arminda Guerra , Mendonça Fábio
Published: August 2024

The intersection of artificial intelligence and environmental sustainability has become a relevant exploration domain in the contemporary era of rapid technological advancements and complex global challenges. This work reviews the application of machine learning (ML) models to address the pressing issue of plastic waste (PW) management. By systematically examining the state of the art with snowballing, this research aims to determine the efficiency and effectiveness of ML-based methods for PW detection and classification. Considering the increasing environmental concerns and information processing potential, this article hypothesised that ML models could contribute to more sustainable PW management practices. For this purpose, two scientific article repositories were examined from 2000 to 2023, and 188 articles were identified. After the systematic screening procedure, 28 were selected. Additionally, 28 more articles were included by snowballing. It was observed that accuracy in either detection or classification problems often exceeded the 80% detection accuracy benchmark, further improving when the model combination was employed. As a result, strong support was reached for the applicable potential of ML in PW. It was also concluded that models based on convolutional neural networks were the most commonly used.
Effects of Irrigation with Slightly Algae-Contaminated Water on Soil Moisture, Nutrient Redistribution, and Microbial Community
Authors: Zheng Han , Wang Xiaofei , Huang Chen , Bao Zhe , Zhao Xiaohui , Tan Zhanming , Xie En
Published: August 2024

The presence of excessive algae in water is always considered as a negative factor in irrigation. However, the optimal balance between algal removal and retention in irrigation water when the algal biomass is controllable remains unknown. Therefore, this study explores the impact of low-level algal presence (Scytonema javanicum) on soil and microbial activity through controlled soil column experiments. Soil moisture was measured, and 16S rRNA gene amplicons sequencing was applied to characterize the microbial community. Slight community changes indicated no negative impact on the local microbial community of S. javanicum. Enzyme assays and quantitative polymerase chain reaction (qPCR) revealed that algae improved soil moisture retention, and enhanced the nutrient content of the topsoil. The decrease in moisture in the treatment group (from 27.53% to 26.42%) was significantly reduced (p < 0.05) compared to the control (from 27.55% to 25.17%), while the contents of ammonium (NH3-N) and total nitrogen (TN) in the treatment (0.70 mg/kg and 0.54 g/kg) were also higher (p < 0.05) than that of the control (0.43 mg/kg and 0.49 g/kg). The results of the abundance of functional gene suggested algae facilitated nitrogen fixation and nitrification. This research offers innovative insights for diversifying the sources of irrigation water.
Study on the Structural Characteristics of Bulb Tubular Pumps Based on Fluid–Structure Interaction
Authors: Wang Wenjie , Li Jingyu , Wu Chunhui , Pei Ji , Luo Can , Hu Bo
Published: August 2024

As a special type of through-flow device, bulb turbine pumps have been widely used in the Eastern Route of the South-to-North Water Diversion Project due to their compact structure, flexible installation process, easy maintenance, high efficiency, and strong adaptability. Therefore, structural improvements to enhance their safety and stability through fluid–structure interaction analysis have significant engineering value. This paper conducts static and transient fluid–structure interaction analyses of the bulb turbine pump structure. The results show that the rotor structure experiences the greatest deformation under low-flow conditions, with maximum deformation (2.13 mm) occurring at the leading edge of the impeller inlet and decreasing radially along a gradient distribution. The damping effect of water changes the mode shapes of the rotor structure, and although the vibration modes under wet conditions are similar to those in the air, the frequencies decrease to varying degrees. In transient analyses under different conditions, the total deformation of the rotor system is greater than in static analyses, showing significant regularity. Under low-flow conditions, the deformation of the pressure surface at the inlet and outlet of the blade tip is greater than that of the suction surface, with a maximum total deformation of 3.656 mm. The maximum total deformation under design flow is 3.337 mm; under high flow, it is 2.646 mm. The total deformation of the casing mainly occurs on both sides of the internal bulb body bottom support, with a maximum deformation of 2.0355 mm and an equivalent stress maximum of 44.848 MPa. The equivalent stress and total deformation distribution of the support structure are similar, located at the top support and trailing edge, with a maximum value of 22.94 MPa at the trailing edge. The research results provide technical references and theoretical foundations for the structural optimization of bulb turbine pumps.
Analysis of Inter-Layer Interference in Multi-Layer Reservoir Commingled Production Wells
Authors: Wang Kai , Zheng Wei , He Yufei , Tang Chenyang , Pan Yue , Li Jing , Jiang Juanzhe , Cai Shaobin , Li Junjian
Published: August 2024

During the operation of commingled production wells, inter-layer interference is a key factor affecting the recovery rate and flooding extraction efficiency. This study proposes a well deliverability equation that is characterized by a multi-parameter coupled inter-layer interference coefficient, which is applied to quantify the commingled production wells in a multi-layer reservoir. This study used principal component analysis (PCA) to study the combined effects of correlation parameters for inter-layer interference. The results show that the starting pressure gradient and the crude oil viscosity contrast have the most significant impact on inter-layer interference. Changes in these two parameters directly enhance the heterogeneity between different oil layers, thereby intensifying inter-layer interference. Additionally, the positive correlation between permeability and permeability contrast also highlights the contribution of physical property differences in the oil layers to the interference. The sensitivity analysis shows the main influencing factors of inter-layer interference in commingled production wells. providing references for subsequent improvements and optimization of the exploitation schemes and adjustments in reservoir management measures. The results not only enhance the understanding of the mechanisms of inter-layer interference in the exploitation of multi-layer oil reservoirs but also provide scientific evidence and technical support for oilfield development.
A Novel Sample Generation Method for Deep Learning Lithological Mapping with Airborne TASI Hyperspectral Data in Northern Liuyuan, Gansu, China
Authors: Liu Huize , Wu Ke , Zhou Dandan , Xu Ying
Published: August 2024

High-resolution and thermal infrared hyperspectral data acquired from the Thermal Infrared Airborne Spectrographic Imager (TASI) have been recognized as efficient tools in geology, demonstrating significant potential for rock discernment. Deep learning (DL), as an advanced technology, has driven substantial advancements in lithological mapping by automatically extracting high-level semantic features from images to enhance recognition accuracy. However, gathering sufficient high-quality lithological samples for model training is challenging in many scenarios, posing limitations for data-driven DL approaches. Moreover, existing sample collection approaches are plagued by limited verifiability, subjective bias, and variation in the spectra of the same class at different locations. To tackle these challenges, a novel sample generation method called multi-lithology spectra sample selection (MLS3) is first employed. This method involves multiple steps: multiple spectra extraction, spectra combination and optimization, lithological type identification, and sample selection. In this study, the TASI hyperspectral data collected from the Liuyuan area in Gansu Province, China, were used as experimental data. Samples generated based on MLS3 were fed into five typical DL models, including two-dimensional convolutional neural network (2D-CNN), hybrid spectral CNN (HybridSN), multiscale residual network (MSRN), spectral-spatial residual network (SSRN), and spectral partitioning residual network (SPRN) for lithological mapping. Among these models, the accuracy of the SPRN reaches 84.03%, outperforming the other algorithms. Furthermore, MLS3 demonstrates superior performance, achieving an overall accuracy of 2.25–6.96% higher than other sample collection methods when SPRN is used as the DL framework. In general, MLS3 enables both the quantity and quality of samples, providing inspiration for the application of DL to hyperspectral lithological mapping.
A Novel SAR Imaging Method for GEO Satellite–Ground Bistatic SAR System with Severe Azimuth Spectrum Aliasing and 2-D Spatial Variability
Authors: Ti Jingjing , Suo Zhiyong , Liang Yi , Zhao Bingji , Xi Jiabao
Published: August 2024

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method.
Assessing Habitat Suitability: The Case of Black Rhino in the Ngorongoro Conservation Area
Authors: Borges Joana , Symeonakis Elias , Higginbottom Thomas P. , Jones Martin , Cain Bradley , Kisingo Alex , Maige Deogratius , Oliver Owen , Lobora Alex L.
Published: August 2024

Efforts to identify suitable habitat for wildlife conservation are crucial for safeguarding biodiversity, facilitating management, and promoting sustainable coexistence between wildlife and communities. Our study focuses on identifying potential black rhino (Diceros bicornis) habitat within the Ngorongoro Conservation Area (NCA), Tanzania, across wet and dry seasons. To achieve this, we used remote sensing data with and without field data. We employed a comprehensive approach integrating Sentinel-2 and PlanetScope images, vegetation indices, and human activity data. We employed machine learning recursive feature elimination (RFE) and random forest (RF) algorithms to identify the most relevant features that contribute to habitat suitability prediction. Approximately 36% of the NCA is suitable for black rhinos throughout the year; however, there are seasonal shifts in habitat suitability. Anthropogenic factors increase land degradation and limit habitat suitability, but this depends on the season. This study found a higher influence of human-related factors during the wet season, with suitable habitat covering 53.6% of the NCA. In the dry season, browse availability decreases and rhinos are forced to become less selective of the areas where they move to fulfil their nutritional requirements, with anthropogenic pressures becoming less important. Furthermore, our study identified specific areas within the NCA that consistently offer suitable habitat across wet and dry seasons. These areas, situated between Olmoti and the Crater, exhibit minimal disturbance from human activities, presenting favourable conditions for rhinos. Although the Oldupai Gorge only has small suitable patches, it used to sustain a large population of rhinos in the 1960s. Land cover changes seem to have decreased the suitability of the Gorge. This study highlights the importance of combining field data with remotely sensed data. Remote sensing-based assessments rely on the importance of vegetation covers as a proxy for habitat and often overlook crucial field variables such as shelter or breeding locations. Overall, our study sheds light on the imperative of identifying suitable habitat for black rhinos within the NCA and underscores the urgency of intensified conservation efforts. Our findings underscore the need for adaptive conservation strategies to reverse land degradation and safeguard black rhino populations in this dynamic multiple land-use landscape as environmental and anthropogenic pressures evolve.
On Unsupervised Multiclass Change Detection Using Dual-Polarimetric SAR Data
Authors: Kim Minhwa , Lee Seung-Jae , Park Sang-Eun
Published: August 2024

Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data.
Application of Digital Twin Technology in Synthetic Aperture Radar Ground Moving Target Intelligent Detection System
Authors: Liu Hui , Yan He , Hao Jialin , Xu Wenshuo , Min Zhou , Zhu Daiyin
Published: August 2024

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously.
A Phase-Only Optimization Null Control Method for FDA-MIMO Based on ADMM
Authors: Xiao Mengxuan , Hu Taiyang , Shao Xiaolang , Wu Yifan , Xiao Zelong
Published: August 2024

This paper investigates null control within the transmit–receive beampattern of Frequency Diverse Array-Multiple-Input and Multiple-Output (FDA-MIMO) systems, presenting a novel phase-only optimization approach for achieving null control in FDA-MIMO. We employ an alternating multiplier framework, which transforms the intricate and inherent constant modulus constraint and numerous amplitude constraints in optimization into more manageable projection problems. By employing a phase-only optimization strategy, the intricate hardware and computational burdens associated with null control in FDA-MIMO are effectively alleviated. The simulation results indicate that the algorithm proposed in this paper exhibits excellent null control ability while precisely maintaining constant modulus constraints, and it possesses an extremely high computational efficiency.
Closed-Loop Control of Melt Pool Temperature during Laser Metal Deposition
Authors: Wang Qing , Zhang Jinchao , Zhu Qingqing , Cao Yue
Published: August 2024

Laser metal deposition (LMD) is a technology for the production of near-net-shape components. It is necessary to control the manufacturing process to obtain good geometrical accuracy and metallurgical properties. In the present study, a closed-loop control method of melt pool temperature for the deposition of small Ti6Al4V blocks in open environment was proposed. Based on the developed melt pool temperature sensor and deposition height sensor, a closed-loop control system and proportional-integral (PI) controller were developed and tested. The results show that with a PI temperature controller, the melt pool temperature tends to the desired value and remains stable. Compared to the deposition block without the controller, a flatter surface and no oxidation phenomenon are obtained with the controller.
Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds
Authors: Zhou Yunyi , Feng Ziyi , Chen Chunling , Yu Fenghua
Published: August 2024

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network’s ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.
Distributed Wearable Ultrasound Sensors Predict Isometric Ground Reaction Force
Authors: King Erica L. , Patwardhan Shriniwas , Bashatah Ahmed , Magee Meghan , Jones Margaret T. , Wei Qi , Sikdar Siddhartha , Chitnis Parag V.
Published: August 2024

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R2 of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.
Exploring the Potential of a Smart Ring to Predict Postoperative Pain Outcomes in Orthopedic Surgery Patients
Authors: Morimoto Michael , Nawari Ashraf , Savic Rada , Marmor Meir
Published: August 2024

Poor pain alleviation remains a problem following orthopedic surgery, leading to prolonged recovery time, increased morbidity, and prolonged opioid use after hospitalization. Wearable device data, collected during postsurgical recovery, may help ameliorate poor pain alleviation because a patient’s physiological state during the recovery process may be inferred from sensor data. In this study, we collected smart ring data from 37 inpatients following orthopedic surgery and developed machine learning models to predict if a patient had postsurgical poor pain alleviation. Machine learning models based on the smart ring data were able to predict if a patient had poor pain alleviation during their hospital stay with an accuracy of 70.0%, an F1-score of 0.769, and an area under the receiver operating characteristics curve of 0.762 on an independent test dataset. These values were similar to performance metrics from existing models that rely on static, preoperative patient factors. Our results provide preliminary evidence that wearable device data may help control pain after orthopedic surgery by incorporating real-time, objective estimates of a patient’s pain during recovery.