bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Preparation of Dry Mixed Mortar Using Waste Incineration Fly Ash
Authors: Zhang Shengjun , Cheng Yue , Wu Huiyong , Cong Junjun , Zhou Zhengyuan , Wei Dechong
Published: October 2024

This study investigated the effect of water-washed municipal solid waste incineration fly ash (MSWI FA) as an admixture on the performance of dry mixed mortar and used X-ray diffraction (XRD) and scanning electron microscope (SEM) detection methods to conduct microscopic analysis. The experiment investigated the effects of the amount and water content of washed municipal solid waste incineration fly ash, cement, additives, sand and gravel, and curing time on the compressive flexural strength of dry mixed mortar at 28 days. The results show that when the content of water-washed MSWI FA is 9.80%, the content of sand and gravel is 73.50%, the content of ordinary Portland cement (PO42.5) is 16.66%, the content of water-reducing agent is 1.47‰, the content of cellulose is 0.03‰, the content of the expansion agent is 0.49‰, the addition of water is 130–160 mL/kg, the consistency of the sample can reach 91.8 mm, and the water retention rate can reach 93.6%. The flexural strength of the sample at 28 days can reach 7.5 MPa, and the compressive strength at 28 days can reach 28.30 MPa. Metal ions, such as Pb2+ and Gd2+ in MSWI FA, under the combined action of silicate cement in dry mixed mortar and fibers in cellulose, crisscross and form a solidified material, which will not be leached out. This quality meets the requirements of dry mixed mortar for ordinary plastering and masonry mortar (GB-T 25181-2019), and the leaching toxicity of the sample meets the “Identification Standard for Hazardous Waste” (GB5085.3-2007). This work provides a meaningful exploration of the resource utilization of water-washed MSWI FA.
Impact of Heat Input on the Cladding of Super Austenitic Stainless Steel Through the Gas Tungsten Arc Welding Process on ASTM A516 Grade 70 Steel
Authors: da Cruz Junior Eli Jorge , Varasquim Francisco M. F. A. , De Mendonça Vagner Romito , Ventrella Vicente A. , Gonçalves Aparecido Carlos , Fagundes Junior José Gedael , Zambon Andrea , Calliari Irene
Published: October 2024

The cladding process reduces manufacturing costs by depositing super austenitic stainless steel onto low-carbon steel. Arc welding techniques, especially gas tungsten arc welding (GTAW), are commonly used for this purpose. This study evaluates the influence of heat input on cladding performance. Macroscopic analysis showed good fusion of the weld beads to the base metal with no defects. Higher heat input resulted in a lower dilution rate due to increased reinforcement. A microstructural analysis of the heat-affected zones revealed similar characteristics, with martensite formation attributed to cooling conditions. Increased microhardness was observed at the interface between the cladding and base metal, corroborating the microstructural findings. Additionally, a significant enhancement in corrosion resistance was noted in the deposited layers. This research contributes to optimizing cladding processes, ensuring better material performance in industrial applications.
Terahertz Non-Destructive Testing of Porosity in Multi-Layer Thermal Barrier Coatings Based on Small-Sample Data
Authors: Ye Dongdong , Xu Zhou , Liu Houli , Zhang Zhijun , Wang Peiyong , Wu Yiwen , Yin Changdong
Published: October 2024

Accurately characterizing the internal porosity rate of thermal barrier coatings (TBCs) was essential for prolonging their service life. This work concentrated on atmospheric plasma spray (APS)-prepared TBCs and proposed the utilization of terahertz non-destructive detection technology to evaluate their internal porosity rate. The internal porosity rates were ascertained through a metallographic analysis and scanning electron microscopy (SEM), followed by the reconstruction of the TBC model using a four-parameter method. Terahertz time-domain simulation data corresponding to various porosity rates were generated employing the time-domain finite difference method. In simulating actual test signals, white noise with a signal-to-noise ratio of 10 dB was introduced, and various wavelet transforms were utilized for denoising purposes. The effectiveness of different signal processing techniques in mitigating noise was compared to extract key features associated with porosity. To address dimensionality challenges and further enhance model performance, kernel principal component analysis (kPCA) was employed for data processing. To tackle issues related to limited sample sizes, this work proposed to use the Siamese neural network (SNN) and generative adversarial network (GAN) algorithms to solve this challenge in order to improve the generalization ability and detection accuracy of the model. The efficacy of the constructed model was assessed using multiple evaluation metrics; the results indicate that the novel hybrid WT-kPCA-GAN model achieves a prediction accuracy exceeding 0.9 while demonstrating lower error rates and superior predictive performance overall. Ultimately, this work presented an innovative, convenient, non-destructive online approach that was safe and highly precise for measuring the porosity rate of TBCs, particularly in scenarios involving small sample sizes facilitating assessments regarding their service life.
Phototuning of Multi-Color Emission in PMMA Composite Films for Information Encryption Applications
Authors: Chen Guang , Han Dongxue , Cui Songya , Peng Liang
Published: October 2024

A strategy centered on dynamically tunable excited-state proton transfer (ESPT) processes is proposed for the design and synthesis of luminescent compounds. An emitter based on guanidine-substituted 1,8-naphthalimide (R-1) with ESPT characteristics has been meticulously engineered. Upon incorporation into poly (methyl methacrylate) (PMMA) matrices, the tunable ESPT process, transitioning between blue and yellow-green emission within the composite film, can be precisely controlled through irradiation in different pH environments. Moreover, the luminescence of the R-1/PMMA composite film exhibits variations in response to environmental changes, and demonstrates excellent fatigue resistance. Exploiting this characteristic, information such as “2020” can be encoded, and this encoded information automatically manifests in response to fluctuations in external pH. Specifically, employing a designated method is essential for accurately deciphering the information. The pH-dependent nature of this feature imparts a higher level of security to the material and offers new insights into information encryption.
Effects of Anisotropy, Convection, and Relaxation on Nonlinear Reaction-Diffusion Systems
Authors: Ramos Juan I.
Published: October 2024

The effects of relaxation, convection, and anisotropy on a two-dimensional, two-equation system of nonlinearly coupled, second-order hyperbolic, advection–reaction–diffusion equations are studied numerically by means of a three-time-level linearized finite difference method. The formulation utilizes a frame-indifferent constitutive equation for the heat and mass diffusion fluxes, taking into account the tensorial character of the thermal diffusivity of heat and mass diffusion. This approach results in a large system of linear algebraic equations at each time level. It is shown that the effects of relaxation are small although they may be noticeable initially if the relaxation times are smaller than the characteristic residence, diffusion, and reaction times. It is also shown that the anisotropy associated with one of the dependent variables does not have an important role in the reaction wave dynamics, whereas the anisotropy of the other dependent variable results in transitions from spiral waves to either large or small curvature reaction fronts. Convection is found to play an important role in the reaction front dynamics depending on the vortex circulation and radius and the anisotropy of the two dependent variables. For clockwise-rotating vortices of large diameter, patterns similar to those observed in planar mixing layers have been found for anisotropic diffusion tensors.
Effect of DC Micro-Pulsing on Microstructure and Mechanical Properties of TIG Welded Ti-6Al-4V
Authors: Vypana Jose , Babu Nagumothu Kishore , Talari Mahesh Kumar , Krishna Karni Vamsi , Krishna Chakravarthula Gopi , Rehman Ateekh Ur
Published: October 2024

This paper deals with the influence of micro-pulsed direct current on microstructure and mechanical properties of gas tungsten arc welding (GTAW) weldments of Ti-6Al-4V (Ti-64). Bead-on-plate GTA welds were made on the samples in the un-pulsed and micro-pulsed (125 Hz and 250 Hz) conditions. Post-weld heat treatment (PWHT) was performed on a few coupons at 700 °C for 3 h in an inert atmosphere, followed by furnace cooling. In the microstructure, the fusion zone (FZ), base metal (BM), and heat-affected zone (HAZ) can be easily distinguished. The top surface of the FZ has large columnar grains because of lower heat loss to the surrounding atmosphere, and the bottom region of the FZ has comparatively smaller equiaxed grains. The micro-pulsed samples’ FZ grain size was lower than that of those made without pulsing. This shows that high-frequency current has substantially refined prior β grains. The microstructure of the FZ is characterized by an acicular morphology composed of α, martensitic α′, and retained β phases. The FZ’s hardness was higher than the BM due to the presence of martensitic α′. Additionally, the hardness in the HAZ was elevated due to the formation of finer martensitic α′. Micro-pulsed DC welding led to improved mechanical properties, including higher hardness, ultimate tensile strength (UTS), and ductility compared to un-pulsed welding. This enhancement is attributed to the grain refinement achieved with micro-pulsed DC. After PWHT, the prior β grain size remained relatively unchanged compared to the as-welded condition. However, the hardness in the FZ decreased due to the decomposition of α′ into α and β phases. The ductility of all samples improved as a result of the widening of the diffusional α phase.
Prognostic Value of Sarcopenia in Elderly Patients with Metastatic Non-Small-Cell Lung Cancer Undergoing Radiotherapy
Authors: Nardone Valerio , Reginelli Alfonso , Patanè Vittorio , Sangiovanni Angelo , Grassi Roberta , Russo Anna , Correale Pierpaolo , Giordano Diego Sandro , Zaccaria Carmine , Belfiore Maria Paola , Cappabianca Salvatore
Published: October 2024

Background: Sarcopenia, a syndrome characterized by age-related loss of muscle mass and function, lacks universally accepted diagnostic criteria, particularly for its role as a prognostic factor in elderly patients with non-small-cell lung cancer (NSCLC). This study aimed to evaluate the prognostic significance of sarcopenia, assessed by psoas muscle size on baseline CT scans, in patients over 70 years of age with metastatic NSCLC. Methods: We retrospectively analyzed 85 elderly patients undergoing palliative radiation therapy between August 2022 and July 2024. Using morphometric analysis of psoas size, we investigated its correlation with overall survival (OS) and progression-free survival (PFS). Results: Our results showed that decreased psoas size was significantly associated with shorter OS and PFS, with median OS of 10 months and PFS of 4 months in sarcopenic patients compared to longer survival times in non-sarcopenic patients. Median survival of non-sarcopenic vs. sarcopenic patients was 21 ± 7 months (muscle area > median) versus 5 ± 2.3 months (muscle area < median). Multivariate analysis confirmed that psoas size, along with ECOG performance status and treatment of primary NSCLC, was a significant predictor of survival. Discussion: These findings suggest that psoas muscle size is a valuable prognostic marker for elderly NSCLC patients, potentially guiding treatment decisions and patient management. Further research is needed to validate these results and refine prognostic models for this population.
Towards a Taxonomy Machine: A Training Set of 5.6 Million Arthropod Images
Authors: Steinke Dirk , Ratnasingham Sujeevan , Agda Jireh , Ait Boutou Hamzah , Box Isaiah C. H. , Boyle Mary , Chan Dean , Feng Corey , Lowe Scott C. , McKeown Jaclyn T. A. , McLeod Joschka , Sanchez Alan , Smith Ian , Walker Spencer , Wei Catherine Y.-Y. , Hebert Paul D. N.
Published: October 2024

The taxonomic identification of organisms from images is an active research area within the machine learning community. Current algorithms are very effective for object recognition and discrimination, but they require extensive training datasets to generate reliable assignments. This study releases 5.6 million images with representatives from 10 arthropod classes and 26 insect orders. All images were taken using a Keyence VHX-7000 Digital Microscope system with an automatic stage to permit high-resolution (4K) microphotography. Providing phenotypic data for 324,000 species derived from 48 countries, this release represents, by far, the largest dataset of standardized arthropod images. As such, this dataset is well suited for testing the efficacy of machine learning algorithms for identifying specimens into higher taxonomic categories.
Root Coverage Techniques: Coronally Advancement Flap vs. Tunnel Technique: A Systematic Review and Meta-Analysis
Authors: Chauca-Bajaña Luis , Pérez-Jardón Alba , Silva Fábio França Vieira E , Conde-Amboage Mercedes , Velásquez-Ron Byron , Padín-Iruegas Elena , Pérez-Sayáns Mario
Published: October 2024

Introduction: Gingival recession, characterized by the apical displacement of the gingival margin, presents challenges to oral health. This study compares the effectiveness of the coronally advanced flap (CAF) and the tunnel technique (TT) for treating gingival recessions. Methods: Bibliographical searches included PubMed, Embase, Web of Science, Cochrane, Scopus, and the grey literature, with keywords “root coverage” “coronary advanced flap”, and “tunnel”. A systematic coreview was performed that included 26 studies evaluating root coverage, and 14 articles were included for the meta-analysis. Three groups were analyzed: Group 1 compared TT with connective tissue graft (CTG) versus CAF with CTG; Group 2 examined TT with CTG and/or other biomaterials versus TT with CTG alone; Group 3 compared TT with CAF, regardless of complementary biomaterials. Meta-analysis assessed mean root coverage (MRC), complete root coverage (CRC), and keratinized tissue gain (KTG). Results: In Group 1, TT with CTG demonstrated superior MRC compared with CAF with CTG (−8.68 CI95% −17.19 to −0.17; p = 0.0457). In Group 2, TT with CTG and/or other biomaterials showed similar MRC (4.17 CI95% −17.91 to 26.26; p = 0.7110) and CRC (0.37 CI95% −1.14 to 1.89; p = 0.6269) to TT with CTG alone, with variations in keratinized tissue gain. Group 3 indicated higher potential MRC for TT compared with CAF (5.73 CI95% −8.90 to 13.55; p = 0.685) but without statistically significant differences. Conclusions: This study suggests that TT with CTG might offer better root coverage than CAF with CTG; however, biomaterial selection requires consideration.
Railway Tracks Extraction from High Resolution Unmanned Aerial Vehicle Images Using Improved NL-LinkNet Network
Authors: Wang Jing , Fan Xiwei , Zhang Yunlong , Zhang Xuefei , Zhang Zhijie , Nie Wenyu , Qi Yuanmeng , Zhang Nan
Published: October 2024

The accurate detection of railway tracks from unmanned aerial vehicle (UAV) images is essential for intelligent railway inspection and the development of electronic railway maps. Traditional computer vision algorithms struggle with the complexities of high-precision track extraction due to challenges such as diverse track shapes, varying angles, and complex background information in UAV images. While deep learning neural networks have shown promise in this domain, they still face limitations in precisely extracting track line edges. To address these challenges, this paper introduces an improved NL-LinkNet network, named NL-LinkNet-SSR, designed specifically for railway track detection. The proposed NL-LinkNet-SSR integrates a Sobel edge detection module and a SimAM attention module to enhance the model’s accuracy and robustness. The Sobel edge detection module effectively captures the edge information of track lines, improving the segmentation and extraction of target edges. Meanwhile, the parameter-free SimAM attention module adaptively emphasizes significant features while suppressing irrelevant information, broadening the model’s perceptual field and improving its responsiveness to target areas. Experimental results show that the NL-LinkNet-SSR significantly outperforms the original NL-LinkNet model across multiple key metrics, including a more than 0.022 increase in accuracy, over a 4% improvement in F1-score, and a more than 3.5% rise in mean Intersection over Union (mIoU). These enhancements suggest that the improved NL-LinkNet-SSR offers a more reliable solution for railway track detection, advancing the field of intelligent railway inspection.
Quantum Machine Learning—Quo Vadis?
Authors: Wichert Andreas
Published: October 2024

The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due to the exponential speed-up and the possibility of compressed data representation. However, can we really apply quantum machine learning for real-world applications? What are the advantages of quantum machine learning algorithms in addition to some proposed artificial problems? Is the promised exponential or quadratic speed-up realistic, assuming that real quantum computers exist? Quantum machine learning is based on statistical machine learning. We cannot port the classical algorithms directly into quantum algorithms due to quantum physical constraints, like the input–output problem or the normalized representation of vectors. Theoretical speed-ups of quantum machine learning are usually analyzed in the literature by ignoring the input destruction problem, which is the main bottleneck for data encoding. The dilemma results from the following question: should we ignore or marginalize those constraints or not?
Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models
Authors: Scrucca Luca
Published: October 2024

Volatility in financial markets refers to the variation in asset prices over time. High volatility indicates increased risk, making its evaluation essential for effective risk management. Various methods are used to assess volatility, with the standard deviation of log-returns being a common approach. However, this implicitly assumes that log-returns follow a Gaussian distribution, which is not always valid. In this paper, we explore the use of (differential) entropy to evaluate the volatility of financial log-returns. Estimation of entropy is obtained using a Gaussian mixture model to approximate the underlying density of log-returns. Following this modeling approach, popular risk measures such as Value at Risk and Expected Shortfall can also be computed. By integrating Gaussian mixture modeling and entropy into the analysis of log-returns, we aim to provide a more accurate and robust framework for assessing financial volatility and risk measures.
Diversification and the Resource Curse: An Econometric Analysis of GCC Countries
Authors: Abdelkawy Nagwa Amin
Published: October 2024

This research explores the effects of significant global economic shocks, such as the 2008 Global Financial Crisis and the 2020 COVID-19 pandemic, on GDP growth in the Gulf Cooperation Council (GCC) nations. Employing a dynamic generalized method of moments (GMM) model, the analysis highlights the strong momentum effect of lagged GDP growth, where past performance plays a critical role in shaping current economic outcomes. The findings also reveal that natural resources continue to positively influence short-term growth, but with diminishing returns over time, supporting the resource curse hypothesis and underscoring the need for broader structural reforms to ensure long-term sustainability. In addition, the results show that external investments flowing into the country, trade balance, and inflation emerge as key drivers of economic growth. While moderate inflation is positively associated with economic expansion, unemployment exerts a significant negative effect on GDP growth, particularly in models that account for country-specific characteristics. This emphasizes the need for labor market reforms to improve employment rates and support sustainable development. The role of gross capital formation, particularly in both the dynamic GMM and random effects models, further underscores the importance of strategic domestic investment, especially during periods of global disruption. These findings emphasize the critical need for economic diversification in the GCC. Policymakers should focus on attracting foreign investment, managing inflation, enhancing human capital, and boosting domestic investment to mitigate the adverse effects of the resource curse and secure sustainability. While market capitalization and oil rents may stimulate short-term growth, their long-term sustainability remains uncertain without greater diversification. Both external and domestic investments emerge as critical drivers of long-term growth, while persistent challenges such as inflation and unemployment continue to pose risks to economic stability. The study highlights the need to reduce reliance on oil and leverage human capital to build more resilient economies capable of adapting to future challenges. By offering dynamic, empirical insights into the balance between resource reliance and sustainable growth, this research adds valuable insights to the policy discussion on economic diversification in the GCC. Policymakers are urged to prioritize FDI, inflation management, domestic capital formation, and human capital development to mitigate vulnerabilities and ensure sustainable economic growth in the face of ongoing global uncertainties.
FPGA-Based Design of a Ready-to-Use and Configurable Soft IP Core for Frame Blocking Time-Sampled Digital Speech Signals
Authors: Srinivas Nettimi Satya Sai , Sugan Nagarajan , Kumar Lakshmi Sutha , Nath Malaya Kumar , Kanhe Aniruddha
Published: October 2024

‘Frame blocking’ or ‘Framing’ is a technique that divides a time-sampled speech or audio signal into consecutive and equi-sized short-time frames, either overlapped or non-overlapped, for analysis. The framing hardware architectures (FHA) in the literature support framing speech or audio samples of specific word size with specific frame size and frame overlap size. However, speech and audio applications often require framing signal samples of varied word sizes with varied frame sizes and frame overlap sizes. Therefore, the existing FHAs must be redesigned appropriately to keep up with the variability in word size, frame size and frame overlap size, as demanded across multiple applications. Redesigning the existing FHAs for each specific application is laborious, prompting the need for a configurable intellectual property (IP) core. The existing FHAs are inappropriate for creating configurable IP cores as they lack adaptability to accommodate variability in frame size and frame overlap size. Therefore, to address these issues, a novel FHA, adaptable to accommodate the desired variability, is proposed. Furthermore, the proposed FHA is transformed into a field-programmable gate array-based soft, ready-to-use and configurable frame blocking IP core using the Xilinx® Vivado™ tool. The resulting IP core is versatile, offering configurability for framing in numerous applications incorporating real-time digital speech and audio systems. This research article discusses the proposed FHA and frame blocking IP core in detail.
Recognition of Street Landscape Patterns in Kunming City Based on Intelligent Decision Algorithm and Regional Cultural Expression
Authors: Zhu Xingxiao , Xing Zhizhong , Chen Xia , Wang Jing , Yang Xinyue , Yang Lei , Wang Lin , Li Ruimin , Wang Yayu
Published: October 2024

The integration of intelligent decision-making algorithms with urban cultural expression is becoming a hot topic in both academic and practical fields for exploring urban street landscapes. Exploring the application strategies of intelligent decision-making algorithms and regional cultural expression in street landscape pattern recognition and innovative design is a key step. The single layout of urban street construction, cultural deficiency, ecological imbalance, and low resident participation seriously constrain the overall quality improvement of the city. To address this dilemma, this study delved into Kunming City and selected the ten “most beautiful streets”, such as Dianchi Road, for research. By using the Analytic Hierarchy Process, a comprehensive evaluation system covering multiple dimensions, such as the street layout, plant landscape, and historical culture, was constructed to analyze the street landscape of Kunming. The research results indicate that the top four roads in terms of weight evaluation scores are Cuihu Ring Road, Jiaochang Middle Road, Qingnian Road, and Beijing Road, with values of 0.2076, 0.1531, 0.1274, and 0.1173. The weight reveals that each street has its unique landscape factors, such as the profound cultural heritage of Cuihu Ring Road and the beautiful plant landscape of Jiaochang Middle Road. Further analysis also reveals the close relationship between various factors in the evaluation model, emphasizing the importance of supplementing material and cultural elements in street landscape design. The significance of this study goes beyond a single analysis of the street landscape in Kunming City. Drawing a regional street landscape pattern map sets an example for other cities to build distinctive, eco-friendly, culturally rich, and highly humanized street spaces, providing reference and inspiration. More importantly, this study promotes the application and development of intelligent decision-making algorithms in the field of urban landscapes. Future research will further optimize algorithms to improve their adaptability and accuracy in complex environments.
Physics-Driven Image Dehazing from the Perspective of Unmanned Aerial Vehicles
Authors: Cui Tong , Dai Qingyue , Zhang Meng , Li Kairu , Ji Xiaofei , Hao Jiawei , Yang Jie
Published: October 2024

Drone vision is widely used in change detection, disaster response, and military reconnaissance due to its wide field of view and flexibility. However, under haze and thin cloud conditions, image quality is usually degraded due to atmospheric scattering. This results in issues like color distortion, reduced contrast, and lower clarity, which negatively impact the performance of subsequent advanced visual tasks. To improve the quality of unmanned aerial vehicle (UAV) images, we propose a dehazing method based on calibration of the atmospheric scattering model. We designed two specialized neural network structures to estimate the two unknown parameters in the atmospheric scattering model: the atmospheric light intensity A and medium transmission t. However, calculation errors always occur in both processes for estimating the two unknown parameters. The error accumulation for atmospheric light and medium transmission will cause the deviation in color fidelity and brightness. Therefore, we designed an encoder-decoder structure for irradiance guidance, which not only eliminates error accumulation but also enhances the detail in the restored image, achieving higher-quality dehazing results. Quantitative and qualitative evaluations indicate that our dehazing method outperforms existing techniques, effectively eliminating haze from drone images and significantly enhancing image clarity and quality in hazy conditions. Specifically, the compared experiment on the R100 dataset demonstrates that the proposed method improved the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics by 6.9 dB and 0.08 over the second-best method, respectively. On the N100 dataset, the method improved the PSNR and SSIM metrics by 8.7 dB and 0.05 over the second-best method, respectively.
23.5–27.5 GHz Band Doherty Power Amplifier Integrated Circuit Using 28 nm Bulk CMOS Process Based on Dynamic Power Dividing Network
Authors: Choi Young Chan , Bin Soohyun , Hwang Keum Cheol , Lee Kang-Yoon , Yang Youngoo
Published: October 2024

This paper presents a Doherty power amplifier (DPA) integrated circuit (IC) designed to have enhanced gain, efficiency, and AM-AM characteristics through a dynamic power dividing technique, which can control the power dividing ratio according to the input power. Since this multi-purpose dynamic power dividing network also provides the phase offset and impedance matching at the interstage network needed for appropriate DPA operation, the active IC area could be reduced. To verify the proposed technique and its analysis, the DPA was implemented with a 28 nm bulk CMOS process for the fifth-generation (5G) new radio (NR) millimeter-wave frequency band of 23.5–27.5 GHz. The measured results showed a gain of 20.3–21.9 dB, saturated output power of 14.0–15.2 dBm, power added efficiency (PAE) of 22.8–26.7% at the peak power, and PAE of 14.6–17.6% at the 6 dB output power back-off (OBO).
Research on the Technology of Improving the Compatibility of DC Measurement Devices Based on the Operation Experience of Siemens Hardware Solutions
Authors: Li Hao , Zhang Zicong , Ding Binghou , Bai Yaoxing , Zheng Yong , Liu Chao , Tian Xincui
Published: October 2024

The aim of this study was to manufacture alternative Siemens DC measurement system equipment to solve the problems of increased failure rates of foreign DC measurement equipment and the stopped production of key parts. In this paper, the overall structure and common faults of the Siemens DC measurement system are introduced, the difference between TDM signal frame structure and FT3 frame structure is detailed, and the related functional structure, chip type, and function of the Siemens distal module and merging unit (including laser function) are studied. The remote module, merging unit (including laser function), and related hardware design and software design to replace the Siemens DC measurement system were developed, and the test environment and control protection, fault recording wave for communication, and other functional tests were built. The test results show that the domestic remote module, merging unit (laser function), and other technologies communicate normally with the existing Siemens and the domestic mainstream manufacturers, and the accuracy meets the requirements of the 0.2 level.
CPEQA: A Large Language Model Based Knowledge Base Retrieval System for Chinese Confidentiality Knowledge Question Answering
Authors: Cao Jian , Cao Jiuxin
Published: October 2024

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to efficiently provide accurate and concise answers. This paper focuses on the national confidentiality publicity and education field, aiming to address the dilemma of inaccurate knowledge retrieval in this field. Therefore, we design an intelligent confidentiality question-answering system CPEQA by comprehensively utilizing the LLMs platform and information retrieval technique. CPEQA is capable of providing professional answers to questions about Chinese confidentiality publicity and education raised by users. Additionally, we also integrate the conventional database retrieval technique and LLMs into the database query construction, enabling CPEQA to perform real-time queries and data analysis for both single-table and multi-table querying tasks. Through extensive experiments with generated query sentences, we show both methodological comparisons and empirical evaluations of CPEQA’s performance. Experimental results indicate that CPEQA has achieved competitive results on answering precision, recall rate and other metrics. Finally, we explore the challenges of the CPEQA system associated with these techniques and outline potential avenues for future research in this emerging field.
Simulation and Experiments on Optimization of Vortex-Induced Vibration Power Generation System Based on Side-by-Side Double Blunt Bodies
Authors: Fan Liguo , Liu Guoqiang , Song Xianjin , Xiang Ce , Wei Jiacheng , Xia Hui
Published: October 2024

In order to improve the utilization efficiency of converting low-flow current energy into electric energy for Reynolds number 10,000 ≤ Re ≤ 40,000, this paper proposes a vortex-induced vibration power generation system based on a side-by-side double blunt body. In this system, the side-by-side double blunt body structure is used in the current energy capture part to enhance the collection of low-flow current energy; the permanent magnet linear motor is used in the electric energy conversion part to improve the efficiency of electric energy conversion; and a laboratory device is constructed for testing. The effects of the blunt body structure parameters and the center spacing ratio on the energy harvesting performance of the system are qualitatively explained by constructing a simulation model. Compared with the single blunt body energy capture structure, the side-by-side double blunt body structure increases the vibration amplitude by 1.04 times and the lift by 1.14 times at the center spacing S/D = 2.4. Meanwhile, energy harvesting can be realized at a lower flow velocity, increasing the vortex-induced vibration’s energy capture range. Finally, the power generation system was experimentally verified in the laboratory, and the results showed that the vibration amplitude of the double blunt body structure was increased by 1.12 times compared to the single blunt body. The maximum output power of the generator is 10.55 W when the water velocity is 0.7 m/s. The energy conversion efficiency of the power generation system can reach a maximum of 52.93%, which is 12.33% higher than that of a single blunt body structure, which proves that the system has a higher power conversion efficiency than that of a conventional single conversion system.
Effect of the Cathodic Gas Diffusion Layer on the Performance of a Proton Exchange Membrane Electrolyzer
Authors: Canelas Montaño Daniela Lorena , Ruiz García Pablo , Jerez Navarro Andrés , Aguirre Gomez Modesto , López Cascales José Javier
Published: October 2024

Hydrogen production through electrolysis using renewable resources is highly promising for reducing greenhouse gas emissions. While significant efforts have focused on developing more efficient and cost-effective catalysts to lower hydrogen production costs, catalysts are not the primary expense in electrolyzer fabrication. In the case of Proton Exchange Membrane Water Electrolyzers (PEMWEs), other components—such as the proton membrane, gas diffusion layer (GDL), and bipolar plates—contribute more to overall costs. To explore this, a study was conducted on the performance of PEMWEs with various carbon paper GDLs, developed in the lab, on the cathodic side. This study examined how properties like electrical conductivity, porosity, and gas permeability affect performance. These findings emphasize the need to optimize components beyond catalysts to improve the cost-effectiveness of hydrogen production through electrolysis.
Visualization Study on Oil Return Characteristics of Vapor Compression Heat Pump System
Authors: Tang Chuhao , Huang Zhanfeng , Li Tingxun
Published: October 2024

Vapor compression heat pump technology is a widely utilized method for energy conversion. Lubricating oil plays a crucial role in the heat pump system cycle by effectively reducing wear on the compressor’s moving parts and preventing refrigerant leakage. However, it can also create an oil film in the heat exchange equipment, which increases thermal resistance and diminishes heat transfer efficiency. This study utilizes a vapor compression heat pump system test bench to investigate factors influencing the system’s oil circulation rate, the two-phase flow patterns of refrigerant and lubricating oil, and the impact of oil circulation on system performance. The findings reveal that as the compressor speed increases, the oil circulation rate initially decreases before increasing again. Additionally, a decrease in the evaporator’s heat load leads to a reduction in oil circulation at high temperatures, while it increases at low temperatures. Furthermore, increasing the opening of the electronic expansion valve results in a gradual decrease in the oil circulation rate, whereas an increase in the refrigerant charge correlates with a rise in the oil circulation rate. The oil return flow pattern can primarily be categorized into three states: slow oil return, oil film flow, and high-speed oil return. These patterns are closely related to the degree of superheat, with lower superheat levels intensifying oil return.
Study on Quantitative Assessment of CO2 Leakage Risk Along the Wellbore Under the Geological Storage of the Salt Water Layer
Authors: Gao Shaobo , Jia Shanpo , Zhu Yanwei , Zhao Long , Cao Yuxuan , Qi Xianyin , Guan Fatian
Published: October 2024

In the process of CO2 geological storage in the salt water layer, CO2 leakage along the wellbore will seriously affect the effective storage of CO2 in the target geological area. To solve this problem, based on the investigation of a large number of failure cases of CO2 storage along the wellbore and failure cases of gas storage wells in the injection stage of the wellbore, the influencing factors of CO2 leakage risk along the wellbore were investigated in detail. Based on the analytic hierarchy process (AHP) and extension theory, 17 basic evaluation indexes were selected from 6 perspectives to establish the evaluation index system of CO2 leakage risk along the wellbore. The established evaluation system was used to evaluate the leakage risk of a CO2 storage well in the X gas field of BZ Block. The results showed that the influencing factors of tubing had the smallest weight, followed by cement sheath, and the influencing factors of casing–cement sheath interface and cement sheath–formation interface had the largest weight, accounting for 23.73% and 34.32%, respectively. The CO2 storage well leakage risk evaluation grade was Ι, with minimal leakage risk. The CO2 storage effect was excellent. The evaluation system comprehensively considers the tubing string, cement sheath, and micro-annulus interface, which can provide a scientific basis for the risk assessment of CO2 leakage along the wellbore under the CO2 geological storage of the salt water layer.
Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines
Authors: Karaköse Mustafa , Yücel Özgün
Published: October 2024

In this study, we aim to develop advanced machine learning regression models for the prediction of hydrate temperature based on the chemical composition of sweet gas mixtures. Data were collected in accordance with the BOTAS Gas Network Code specifications, approved by the Turkish Energy Market Regulatory Authority (EMRA), and generated using DNV GasVLe v3.10 software, which predicts the phase behavior and properties of hydrocarbon-based mixtures under various pressure and temperature conditions. We employed linear regression, decision tree regression, random forest regression, generalized additive models, and artificial neural networks to create prediction models for hydrate formation temperature (HFT). The performance of these models was evaluated using the hold-out cross-validation technique to ensure unbiased results. This study demonstrates the efficacy of ensemble learning methods, particularly random forest with an R2 and Adj. R2 of 0.998, for predicting hydrate formation conditions, thereby enhancing the safety and efficiency of gas transport and processing. This research illustrates the potential of machine learning techniques in advancing the predictive accuracy for hydrate formations in natural gas pipelines and suggests avenues for future optimizations through hybrid modeling approaches.
Modeling of Liquefied Natural Gas Cold Power Generation for Access to the Distribution Grid
Authors: Qi Yu , Zuo Pengliang , Lu Rongzhao , Wang Dongxu , Guo Yingjun
Published: October 2024

Cold energy generation is an important part of liquefied natural gas (LNG) cold energy cascade utilization, and existing studies lack a specific descriptive model for LNG cold energy transmission to the AC subgrid. Therefore, this paper proposes a descriptive model for the grid-connected process of cold energy generation at LNG stations. First, the expansion kinetic energy transfer of the intermediate work mass is derived and analyzed in the LNG unipolar Rankine cycle structure, the mathematical relationship between the turbine output mechanical power and the variation in the work mass flow rate and pressure is established, and the variations in the LNG heat exchanger temperature difference, seawater flow rate, and the turbine temperature difference in the cycle system are investigated. Secondly, based on the fifth-order equation of state of the synchronous generator, the expressions of its electromagnetic power, output AC frequency, and voltage were analyzed. Finally, the average equivalent models of the machine-side and grid-side converters are established using a direct-fed grid-connected structure, thus forming a descriptive model of the overall drive process. The ORC model is built in Aspen HYSIS to obtain the time series expression of the torque output of the turbine; based on the ORC output torque, the permanent magnet synchronous generator (PMGSG) as well as the direct-fed grid-connected structure are built in MATLAB/Simulink, and the active power and current outputs of the grid-following-type voltage vector control method and the grid-forming-type power-angle synchronous control method are also verified.
Transcription Factors Are Involved in Wizened Bud Occurrence During the Growing Season in the Pyrus pyrifolia Cultivar ‘Sucui 1’
Authors: Li Hui , Kan Jialiang , Liu Chunxiao , Yang Qingsong , Lin Jing , Li Xiaogang
Published: October 2024

Background: Flowers are important plant organs, and their development is correlated with yield in woody fruit trees. For Pyrus pyrifolia cultivar ‘Sucui 1’, the research on how DNA methylation accurately regulates the expression of TFs and affects the specific regulatory mechanism of flower bud wizening will help reduce wizened buds. Methods: Here, the DNA methylomes and transcriptomes of two types of flower buds from the Pyrus pyrifolia cultivar ‘Sucui 1’ were compared. Results: 320 differentially expressed transcription factors (TFs), in 43 families, were obtained from the wizened bud transcriptome versus the normal bud transcriptome. Most were members of the AP2/ERF, bHLH, C2H2, CO-like, MADS, MYB, and WRKY families, which are involved in flower development. As a whole, the methylation level of TFs in the ‘Sucui 1’ genome increased once flower bud wizening occurred. A cytosine methylation analysis revealed that the methylation levels of the same gene regions in TFs from two kinds of buds were similar. However, differentially methylated regions were found in gene promoter sequences. The combined whole-genome bisulfite sequencing and RNA-Seq analyses revealed 162 TF genes (including 164 differentially methylated regions) with both differential expression and methylation differences between the two flower bud types. Among them, 126 were classified as mCHH-type methylation genes. Furthermore, the transcriptional down regulation of PpbHLH40, PpERF4, PpERF061, PpLHW, PpMADS6, PpZF-HD11, and PpZFP90 was accompanied by increased DNA methylation. However, PpbHLH130, PpERF011, and PpMYB308 displayed the opposite trend. The expression changes for these TFs were negatively correlated with their methylation states. Conclusions: Overall, our results offer initial experimental evidence of a correlation between DNA methylation and TF transcription in P. pyrifolia in response to bud wizening. This enriched our understanding of epigenetic modulations in woody trees during flower development.
Transcriptomic Profiling Analyses Revealed Candidate Genes Under Freezing Stress in Siberian Apricot (Prunus sibirica)
Authors: Liu Quangang , Li Wenying , Zhao Xin , Zhang Hongrui , Chen Jianhua , Liu Qingbai , Li Tianlai , Dong Shengjun
Published: October 2024

Siberian apricot (Prunus sibirica) is an important ecological and commercial woody plant that is negatively affected by spring frosts. However, the mechanisms that control gene expression in adaptation to freezing remain largely unknown. In this work, we investigated the physiological, molecular, and phenotypic characteristics of pistils of two P. sibirica clones that differ in their ability to withstand freezing stress. A total of 14,717 unigenes categorized into 38 functional groups were identified. Additionally, the two P. sibirica clones included 3931 up-regulated and 2070 down-regulated differentially expressed genes (DEGs). Many DEGs are related to Ca2+ and MAPK signaling, carbohydrate biosynthesis and metabolism, plant hormone signal transduction, biosynthesis of amino acids, and photosynthesis. The metabolism of carbohydrates, amino acids, lipids, secondary metabolites, plant hormone signal transduction, and terpenoid metabolism were the transcriptome modifications most significantly altered by freezing stress. Real-time quantitative PCR (RT-qPCR) was used to verify the precision of the RNA-seq data. PsbHLH18, PsMYB4, PsMYB44, PsPOD1, and PsCDPK5 may play important roles in the freezing tolerance of the P. sibirica floral organ. This study provides a foundation for further studies on the complex mechanisms of freezing stress response in P. sibirica.
Estimation of Tree Diameter at Breast Height from Aerial Photographs Using a Mask R-CNN and Bayesian Regression
Authors: Kwon Kyeongnam , Im Seong-kyun , Kim Sung Yong , Lee Ye-eun , Kwon Chun Geun
Published: October 2024

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions.