bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulated Impacts of Thundercloud Charge Distributions on Sprite Halos Using a 3D Quasi-Electrostatic Field Model
Authors: Zhang Jinbo , Niu Jiawei , Xie Zhibin , Wang Yajun , Li Xiaolong , Zhang Qilin
Published: November 2024

Sprite halos are transient luminous phenomena in the lower ionosphere triggered by tropospheric lightning. The effect of removed charge distributions on sprite halos has not been sufficiently discussed. A three-dimensional (3D) quasi-electrostatic (QES) field model was developed in this paper, including the ionospheric nonlinear effect and optical emissions. Simulation results show that, for a total charge of 150 C removed within 1 ms with different spatial distributions, higher altitudes of charge removal lead to stronger electric fields and increase sprite halos’ emission intensities. The non-axisymmetric horizontal distribution of charge affects mesospheric electric fields, and the corresponding scales and intensities of emissions vary with observation orientations. Considering the tilted dipole charge structure due to wind shear, the generated electric field and the corresponding position of sprite halos shift accordingly with the tropospheric removed charge, providing an explanation for the horizontal displacement between sprite halos and the parent lightning.
Predicting the Potential Distribution of Aralia chinensis L. (Wild Vegetable) in China Under Different Climate Change Scenarios
Authors: Liu Longjiang , Liang Shanshan , Xie Chengshi , Liu Jie , Zheng Yaqiang , Xue Juan
Published: November 2024

Global climate change has a main impact on the distribution of plants. Aralia chinensis L. is economically valuable, making it necessary to predict the impact of climate change on its distribution. It is important for researching the effects of climate change on A. chinensis distribution to achieve sustainable utilization. Based on 340 occurrence records of A. chinensis covering all known provinces and 58 environmental factor data, we used MaxEnt to simulate the potential distribution of A. chinensis under current and different future climate scenarios, analyzing the key environmental variables affecting its distribution. The results were as follows: (1) Suitable A. chinensis habitats under current and different future climate scenarios were mainly distributed in the southern region of China, east of the Hu Huanyong line. (2) Annual precipitation, minimum temperature during the coldest month, precipitation during the driest month, and slope were the key environmental variables affecting its potential distribution, and annual precipitation was more important. (3) Suitable habitat areas were projected to increase under different future climate scenarios and expand westward and northward while shrinking in the central regions, such as Hubei and Hunan. The results provide a theoretical reference for the conservation and cultivation of A. chinensis.
Bioluminescent Whole-Cell Bioreporter Bacterial Panel for Sustainable Screening and Discovery of Bioactive Compounds Derived from Mushrooms
Authors: Trif Calin , Vunduk Jovana , Parcharoen Yardnapar , Bualuang Aporn , Marks Robert S.
Published: November 2024

This study presents a rapid and comprehensive method for screening mushroom extracts for the putative discovery of bioactive molecules, including those exhibiting antimicrobial activity. This approach utilizes a panel of bioluminescent bacteria, whose light production is a sensitive indicator of various cellular effects triggered by the extracts, including disruption of bacterial communication (quorum sensing), protein and DNA damage, fatty acid metabolism alterations, and oxidative stress induction. The bioassay’s strength is its ability to efficiently analyze a large number of extracts simultaneously while also assessing several different mechanisms of toxicity, significantly reducing screening time. All samples analyzed exhibited more than one cellular effect, as indicated by the reporter bacteria. Four samples (C. cornucopioides, F. fomentarius, I. obliquus, and M. giganteus) displayed the highest number (six) of possible mechanisms of antibacterial activity. Additionally, combining extraction and purification protocols with a bioluminescent bacterial panel enables simultaneous improvement of the desired antimicrobial properties of the extracts. The presented approach offers a valuable tool for uncovering the diverse antimicrobial mechanisms of mushroom extracts.
Adolescent Depressive Symptoms and Peer Dynamics: Distorted Perceptions in Liking and Disliking Networks
Authors: Palacios Diego , Caldaroni Silvia , Berger Christian , Di Tata Daniele , Barrera Davide
Published: November 2024

Depression in adolescents has been linked to poor life outcomes, including suicidal ideation, peer victimization, and fewer friendships. Less is known about how depressed adolescents perceive their peer interactions. Based on the depression-distortion model, we expected that adolescents with depressive symptoms misperceive their social ties by being less likely to like some peers, and more likely to dislike other peers. An Italian dataset about adolescent relationships was used, including 275 first-year secondary school students (M age = 11.80, 46% female) in 12 classrooms across nine schools. Adolescents were asked to nominate classmates they liked and disliked. Longitudinal social network analyses (stochastic actor-oriented models) were conducted, including structural network effects (reciprocity, transitivity, indegree-popularity) and covariates such as gender, immigrant origin, and highest parents’ education level. The results indicated that adolescents with depressive symptoms were less likely to send liking nominations, and conversely, they were more likely to send disliking nominations than non-depressed classmates. Interestingly, adolescents with depressive symptoms were not more disliked or less liked by their peers. These findings seem to support the depression-distortion model by suggesting that, compared to non-depressed peers, adolescents with depressive symptoms misperceive their relationships by overstating negative relationships and underestimating positive ones.
A Method to Detect Concealed Damage in Concrete Tunnels Using a Radar Feature Vector and Bayesian Analysis of Ground-Penetrating Radar Data
Authors: Wang Junfang , Chen Heng , Lin Jianfu , Li Xiangxiong
Published: November 2024

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process.
Seismic Performance Evaluation of a Frame System Strengthened with External Self-Centering Components
Authors: Fan Yulin , Song Jiaye , Zhou Xuelu , Liu Hang
Published: November 2024

In the context of China’s promotion of green buildings and resilient urban development, new reinforcement technologies offer significant development prospects, while traditional methods have limited effectiveness in enhancing structural resilience. To address this latter issue, this study proposes a novel reinforcement method that involves enlarging the structural cross-section and adding external self-resetting components to improve seismic performance. While this method has been validated through quasi-static tests, limitations in terms of sample size and experimental conditions necessitate further research into the seismic performance and dynamic behavior of the reinforced framework. Consequently, this study uses finite element analysis to explore the influencing factors and dynamic characteristics of the reinforcement method. The results show that finite element modeling effectively simulates the stress characteristics of reinforced frameworks. Installing prefabricated beams significantly enhances the load-bearing capacity by 18% and reduces the residual deformation rates after earthquakes by 26%. Increased pre-tensioning of the steel strands further improves seismic resilience. This reinforcement method enables older structures lacking self-resetting capabilities to achieve some degree of self-resetting ability, and it performs well under various earthquake conditions.
Intelligent Forecast Model for Project Cost in Guangdong Province Based on GA-BP Neural Network
Authors: Li Changqing , Xiao Yang , Xu Xiaofu , Chen Zhuoyu , Zheng Haofeng , Zhang Huiling
Published: November 2024

Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects.
Experimental and Numerical Investigation of Shear Strengthening of Simply Supported Deep Beams Incorporating Stainless Steel Plates
Authors: Hamoda Ahmed , Yehia Saad A. , Ahmed Mizan , Sennah Khaled , Abadel Aref A. , Shahin Ramy I.
Published: November 2024

In this study, the effectiveness was investigated of shear strengthening techniques in reinforced concrete (RC) deep beams incorporating stainless steel plates (SSPs). Four RC deep beams were tested under incremental static loading until failure to examine the proposed strengthening techniques. The key parameters considered in this study included the arrangement of the externally bonded SSPs. The experimental findings demonstrated that strengthening using SSPs led to substantial improvements in their performance compared to the unstrengthened control beam. The use of SSPs increased the ultimate shear capacity by 129 to 175% over the control specimen. Finite element models (FEMs) were developed to simulate the responses of the tested beams strengthened using SSPs. Parametric studies were then conducted using the validated FEM to investigate to identify the effects of the area of SSPs on the shear capacity of the beams. The parametric studies concluded that increasing the plate thickness resulted in the enhanced shear capacity of the deep beam specimens up to a critical point upon which the increases in the thickness have insignificant effects on the shear strength. The accuracy of the design equations given by European and American codes in predicting the shear strength of the deep beams is examined.
Energy-Efficiency-Oriented Spatial Configuration of VRV Outdoor Units in an Equipment Layer Under Background Wind Conditions
Authors: Liu Lin , Huang Haoran , Tian Xiaoyu
Published: November 2024

This study provides a spatial configuration method to improve the cooling efficiency of multiple VRV outdoor units placed on equipment layers with high floors. Relevant factors include wind parameters, the placement of multiple outdoor units, and louver. A total of 96 cases were designed. CFD simulations were used to obtain the inlet air temperature distributions of multiple outdoor units and then calculate their cooling efficiency. The results found that these factors have effects on the average cooling efficiency of outdoor units in a single row to a certain extent. The influencing degrees of these factors, from large to small, were the louver angle, wind parameters, and the placement of multiple outdoor units. When the cooling efficiency of outdoor units was maximum and the louver angle was 15°, the louvers could be oriented perpendicular to the dominant wind direction (90°) when wind speed was ≥6 m/s, and the unit spacing was 600 mm. Based on this, when the number of outdoor units was expanded in the limited space, staggered arrangements with different directions of heat exchange surfaces were a recommended optimization layout. This study provides technical support for improving the working efficiency of VRV outdoor units in an equipment layer.
Shear Bond Performance of UHPC-to-NC Interfaces with Varying Sizes: Experimental and Numerical Evaluations
Authors: He Shaohua , Huang Xu , Huang Jiale , Zhang Youyou , Wan Zhiyong , Yu Zhitao
Published: November 2024

This paper explores the effect of bonding size on the shear performance of ultra-high-performance concrete (UHPC) and normal concrete (NC). The study includes two sets of direct shear tests on a total of 16 Z-shaped UHPC-NC bonded specimens. The first set consists of eight direct shear tests on the chiseled UHPC-NC interface with an average roughness of 4 mm (referred to as series C), from the authors’ previous study. The second set involves eight direct shear tests on the chiseled UHPC-NC interface with additional short shear steel rebars (referred to as series CS) that possess identical roughness to the first set of tests. The study discusses the failure modes, shear stress–slip behavior, and strain histories of the UHPC-NC interfaces with varying bonding sizes and shear mechanisms. A finite element model incorporating the cohesive zone model for the UHPC-NC interface was developed to gain insights into the shear bond evolutions. Our experimental results show that the two sets of direct shear specimens exhibit similar size effects in the shear stiffness, bonding strength, and interfacial slippage of the UHPC-NC interface. The use of shear steel rebars mitigated the impact of interfacial size on the bond shear behavior, thereby enhancing shear stiffness and reducing susceptibility to brittle damage. Numerical simulations indicate that the shear stress inhomogeneity coefficients for the CS specimens with bonding heights of 100 mm, 200 mm, 330 mm, and 440 mm were 1.2%, 1.8%, 11.9%, and 17.4%, respectively. The findings of this study provide valuable insights for optimizing UHPC applications in the repair and strengthening of concrete structures.
Investigation of the Effect of Compression Force on the Tensile Strength and Infiltration Rate of Pervious Concrete Blocks
Authors: Akkaya Ahmet , Çağatay İsmail Hakkı
Published: November 2024

Pervious concrete is widely used as a paving material. Pervious pavement is generally constructed by pouring fresh pervious concrete and compacting. However, it has some difficulties such as finishing and curing. In addition, the road has to be closed, until the pervious concrete gains enough strength. Pervious concrete block is a new material that can overcome these difficulties. In this study, the effect of compression force on the strength and infiltration rate of pervious concrete blocks has been investigated. The compaction process was different from traditional methods in this study, and was applied according to predesignated compression forces on fresh pervious concrete mixtures sensitively. Within the scope of the study, 36 different mixtures were produced by applying four different compression forces (25, 50, 75, and 100 kN) in three different sample thicknesses (60, 80, and 100 mm) with three different aggregate sizes (2–4, 4–8, and 8–16 mm). As a result, it was found that while the increase in the compression force increases the splitting tensile strength of pervious concrete blocks with 2–4 and 4–8 mm aggregate, it causes a decrease in the strength due to the aggregate crushing phenomenon in mixtures with 8–16 mm aggregate, 6 cm thick samples. In this study, it was seen that the expectation that the increase in compaction would always cause an increase in strength is not valid, contrary to the literature. The infiltration rate decreased as the compression force increased, as expected. It was determined that the new infiltration rate measurement method has been found effective. Considering the strength requirement in the TS 2824 EN 1338 standard, pervious concrete blocks produced with 4–8 mm aggregate, compressed with 75 kN force and having 80 mm thickness have been determined as the optimum block type.
Response to Bridging Therapy as a Prognostic Indicator of Post-Transplantation Hepatocellular Carcinoma Recurrence and Survival: A Systematic Review
Authors: Topolewski Paweł , Łaski Dariusz , Łukasiewicz Martyna , Domagała Piotr , de Wilde Roeland F. , Polak Wojciech G.
Published: November 2024

Liver transplantation (LT) is one of the most effective treatments for hepatocellular carcinoma (HCC) in cirrhotic livers. Neoadjuvant bridging treatment in patients qualifying and listed for LT is advised but is still debatable owing to the low level of evidence. The aim of this study was to perform a systematic review to assess the prognostic value of bridging therapy, in terms of radiological and histopathological examination outcomes, for survival after LT. The systematic review was performed according to the PRISMA 2020 guidelines. The MEDLINE and Web of Science databases were searched. In total, five studies were included. An evaluation with the ROBINS-I resulted in studies classified as the following: moderate risk of bias (n = 1) and serious risk of bias (n = 4). The results of the analysis indicated that favorable LT outcomes were most common with complete response or partial radiological response. Poor radiological response or progressive disease during bridging treatment was generally associated with worse overall LT survival. There were not enough data to support the use of this approach to achieve a complete pathologic response. Radiological, pathological, histological, cellular, and molecular tumor features should be included in future LT qualification models.
g-C3N4-Based Heterojunction for Enhanced Photocatalytic Performance: A Review of Fabrications, Applications, and Perspectives
Authors: Pei Junxiang , Li Haofeng , Yu Dechao , Zhang Dawei
Published: November 2024

In recent years, photocatalysts have attracted wide attention in alleviating energy problems and environmental governance, among which, g-C3N4, as an ideal photocatalyst, has shown excellent application potential in achieving the sustainable development of energy. However, its photocatalytic performance needs to be further improved in some applications. Rational construction of heterostructures with two or more semiconductor materials can combine the advantages of multi-components to simultaneously improve the photo-induced charge separation, which is very conducive to improving the absorption of visible light and obtaining more efficient redox capacity. With the rapid development in photocatalysis of g-C3N4-based heterostructures, a systematic summary and prospection of performance improvement are urgent and meaningful. This review focuses on various kinds of effective methods of heterogeneous combination; as well, strategies for improving the photocatalytic performance are fully discussed. In addition, the applications in efficient photocatalytic hydrogen production, photocatalytic carbon dioxide reduction, and organic pollutant degradation are systematically demonstrated. Finally, the remaining issues and prospects of further development are proposed as a kind of guidance for g-C3N4-based heterostructures with high efficiency at photocatalysis.
Highly Efficient Hydrogenation of Guaiacol over Ru/Al2O3-TiO2 Catalyst at Low Temperatures
Authors: Song Yumeng , Chen Ping , Lou Hui , Zheng Xiaoming , Song Xiangen
Published: November 2024

In this work, the highly efficient hydrogenation of guaiacol catalyzed by ruthenium supported on Al2O3-TiO2 (Ru/Al2Ti1) at very mild conditions was carried out. At temperatures as low as 25 °C and 2 MPa H2, about 60% of guaiacol could be converted to 2-methoxycyclohexanol (MCH) with a selectivity as high as 94% on the Ru/Al2Ti1 catalyst with an appropriate hydrogen pressure. At temperatures above 50 °C, almost all of the guaiacol could be converted with the catalyst of Ru/Al2Ti1, mainly into hydrogenated products such as MCH. The surprisingly efficient hydrogenation of guaiacol at low temperatures was most likely due to the ability of Ru particles loaded on the specific complex metal oxide carriers, particularly the reduction of the edge effect of Ru, to activate phenyl and hydrogen and reduce the competition of the dimethoxy process. These findings about the high activity of the Ru/Al2Ti1 catalyst at nearly room temperature may be helpful to upgrading the industrial process of the pyrolysis bio-oils.
Knocking Out TAAR5: A Pathway to Enhanced Neurogenesis and Dopamine Signaling in the Striatum
Authors: Vaganova Anastasia N. , Fesenko Zoia S. , Efimova Evgeniya V. , Chekrygin Sergei A. , Shafranskaya Daria D. , Prjibelski Andrey D. , Katolikova Nataliia V. , Gainetdinov Raul R.
Published: November 2024

The member of trace-amine associated receptor family, TAAR5 receptor was suggested to recognize tertiary amines, mostly in the olfactory system; however, knocking out the receptor TAAR5 in mice showed an enhancing effect on adult neurogenesis and dopamine neurotransmission in the striatum. To estimate the role of the TAAR5, we performed gene expression profiling of striatal samples from TAAR5 knockout (KO) mice and their wild-type littermates. The higher expression of several genes involved in dopaminergic signaling and the downregulation of genes associated with gliogenesis were revealed in TAAR5-KO mice. Meanwhile, the upregulating effect of TAAR5 knockout on genes was associated with neurogenesis and synaptogenesis. The estimation of cell-type relative abundance through the deconvolution of RNA sequencing data demonstrated that TAAR5-KO striatum samples contain more D2 dopamine receptor-expressing medium spiny neurons but fewer astrocytes than wild-type mice. Our findings indicate that previously identified improvement in cognitive functions and motor coordination in TAAR5-KO mice may activate genes involved in neurogenesis, synaptogenesis, and synapse organization in the striatum. These data suggest that the pharmaceutical targeting of TAAR5 may improve striatum-dependent cognitive or motor functions. At the same time, a more detailed investigation of future TAAR5 antagonists’ effect on glia development is necessary.
Decoding the Significance of Alpha Cell Function in the Pathophysiology of Type 1 Diabetes
Authors: Carroll Jordan , Chen Jessie , Mittal Rahul , Lemos Joana R. N. , Mittal Mannat , Juneja Shreya , Assayed Amro , Hirani Khemraj
Published: November 2024

Alpha cells in the pancreas, traditionally known for their role in secreting glucagon to regulate blood glucose levels, are gaining recognition for their involvement in the pathophysiology of type 1 diabetes (T1D). In T1D, autoimmune destruction of beta cells results in insulin deficiency, which in turn may dysregulate alpha cell function, leading to elevated glucagon levels and impaired glucose homeostasis. This dysfunction is characterized by inappropriate glucagon secretion, augmenting the risk of life-threatening hypoglycemia. Moreover, insulin deficiency and autoimmunity alter alpha cell physiological responses, further exacerbating T1D pathophysiology. Recent studies suggest that alpha cells undergo transdifferentiation and interact with beta cells through mechanisms involving gamma-aminobutyric acid (GABA) signaling. Despite these advances, the exact pathways and interactions remain poorly understood and are often debated. Understanding the precise role of alpha cells in T1D is crucial, as it opens up avenues for developing new therapeutic strategies for T1D. Potential strategies include targeting alpha cells to normalize glucagon secretion, utilizing glucagon receptor antagonists, enhancing GABA signaling, and employing glucagon-like peptide-1 (GLP-1) receptor agonists. These approaches aim to improve glycemic control and reduce the risk of hypoglycemic events in individuals with T1D. This review provides an overview of alpha cell function in T1D, highlighting the emerging focus on alpha cell dysfunction in the context of historically well-developed beta cell research.
Disruptions of rpiAB Genes Encoding Ribose-5-Phosphate Isomerases in E. coli Increases Sensitivity of Bacteria to Antibiotics
Authors: Seregina Tatyana A. , Shakulov Rustem S. , Sklyarova Svetlana A. , Mironov Alexander S.
Published: November 2024

In Escherichia coli cells, the main enzymes involved in pentose interconversion are ribose-5-phosphate isomerases RpiA and RpiB and ribulose-5-phosphate epimerase Rpe. The inactivation of rpiAB limits ribose-5-phosphate (R5P) synthesis via the oxidative branch of the pentose phosphate pathway (PPP) and unexpectedly results in antibiotic supersensitivity. This type of metabolism is accompanied by significant changes in the level of reducing equivalents of NADPH and glutathione, as well as a sharp drop in the ATP pool. However, this redox and energy imbalance does not lead to the activation of the soxRS oxidative stress defense system but the increased sensitivity to oxidants paraquat and H2O2. The deletion of rpiAB leads to a significant increase in the activity of transketalase (Tkt), a key enzyme of the nonoxidative branch of the PPP and increased sensitivity to ribose added in the growth medium. The phenotype of supersensitivity of rpiAB to antibiotics and ribose can be suppressed by activating the utilization of sedoheptulose-7-phosphate, which originates from R5P, to LPS synthesis or limitation of nucleoside catabolism by the inactivation of the DeoB enzyme, responsible for conversion of ribose-1-phospate to R5P. Our results indicate that the induction of unidirectional synthesis of R5P is the cause of supersensitivity to antibiotics in rpiAB mutant.
Effect of Powder Preparation Techniques on Microstructure, Mechanical Properties, and Wear Behaviors of Graphene-Reinforced Copper Matrix Composites
Authors: Phuong Doan Dinh , Van Trinh Pham , Minh Phan Ngoc , Shtertser Alexandr A. , Ulianitsky Vladimir Y.
Published: November 2024

In this study, the effect of powder preparation techniques on microstructure, mechanical properties, and wear behaviors of graphene-reinforced copper matrix (Gr/Cu) composites was investigated. The composite powders were prepared by two different techniques including high-energy ball (HEB) milling and nanoscale dispersion (ND). The obtained results showed that the ND technique allows the preparation of the composite powder with a smaller and more uniform grain size compared to the HEB technique. By adding Gr, the mechanical properties and wear resistance of the composite were much improved compared to pure Cu. In addition, the composite using the powder prepared by the ND technique exhibits the best performance with the improvement in hardness (40%), tensile strength (66%) and wear resistance (38%) compared to pure Cu. This results from the uniform grain size of the Cu matrix and the good bonding between Cu matrix and Gr. The strengthening mechanisms were also analyzed to clarify the contribution of the powder preparation techniques on the load transfer strengthening mechanisms of the prepared composite.
Comparison of Craniofacial Anthropometric Measurement Accuracy of Manual Technique vs. Cone-Beam CT Scanning
Authors: Misăiloaie Alexandru , Tărăboanță Ionuț , Stan Cristinel Ionel , Budacu Cristian Constantin , Misăiloaie Denisa-Mihaela , Sava Anca
Published: November 2024

Background: This study aimed to compare the accuracy of linear measurements obtained using the classical (manual) method versus cone-beam computed tomography (CBCT) in craniofacial anthropometry, specifically targeting the infraorbital foramen (IOF). Methods: This study involved two sample groups: one of 40 dry skulls measured manually using digital calipers, and the other of 40 CBCT 3D images digitally measured. Measurements included IOF height, distances between the IOF and nasion (N), IOF and frontomalar orbital (FMO), and between the two IOFs. Statistical analysis was performed using an ANOVA, t-test, and Fisher’s test with a significance level of 0.05. Results: The manual method recorded a mean IOF height of 2.1 mm, while CBCT showed a mean of 3.52 mm. Significant differences were recorded between the two methods when measuring IOF height, with a p < 0.001. However, CBCT measurements generally yielded higher accuracy and lower variance due to the absence of significant differences (p > 0.05). The distance between the two IOFs measured by both methods differed significantly (p = 0.03157), with CBCT measurements showing higher values. Conclusions: In conclusion, although no significant differences were found in the overall accuracy of the two methods, CBCT proved to be a more reliable tool for detailed craniofacial measurements due to its higher accuracy and reproducibility. CBCT demonstrated superior consistency in measurements, offering enhanced precision in assessing craniofacial structures. These findings support the growing preference for CBCT in maxillofacial surgery, where precision is critical for successful outcomes. Nonetheless, manual techniques remain valuable in settings where advanced imaging is not accessible.
The Role of Simulation in Exposing Hidden Gender Biases: A Study of Motivational Discourse in Mathematics Education
Authors: Zuckerman Dafna , Yablon Yaacov B. , Iluz Shira
Published: November 2024

This study investigated the value of simulation workshops designed to enhance motivational discourse between mathematics teachers and struggling students who have difficulty keeping up with the curriculum, especially in advanced mathematics. Grounded in the self-determination theory, we examined teachers’ motivational discourse by having them participate in simulated individual dialogues with students, with a focus on the differences in the motivational discourse with male and female students. Twenty-nine middle school mathematics teachers (89.6% female; mean experience = 9.4 years, SD = 8.7) participated in the online simulations, each of which presented a scenario where an actor portrayed a struggling student contemplating dropping out of math class. Based on the observational measures of motivational discourse, the findings reveal significant gender disparities in that teachers tended to provide more support and autonomy to male students. Moreover, they tend to direct more frequent and intense autonomy-suppressing behaviors toward female students. The results highlight the efficacy of simulation-based workshops in uncovering teachers’ hidden behavioral patterns. It also highlights the importance of simulation-based learning to tailor professional development issues and for addressing unconscious gender biases in mathematics education.
Ground Fault Detection of Photovoltaic and Energy Storage DC Converter Load on User Side
Authors: Zhang Kai , Yang Jian , Li Jian , Zhang Zhongying , Gu Ling , Dongye Zhonghao
Published: November 2024

With the rapid development of DC power supply technology, the operation, maintenance, and fault detection of DC power supply equipment and devices on the user side have become important tasks in power load management. DC/DC converters, as core components of photovoltaic and energy storage DC systems, have issues with detecting ground faults on the positive and negative input/output buses, leading to difficulties in troubleshooting device malfunctions and potentially endangering user safety. To address these issues, a method for detecting ground faults on the positive and negative buses of a synchronous buck photovoltaic and energy storage DC/DC converter is proposed, which involves the comprehensive measurement of multi-point common-mode voltages. This method collects the input positive bus voltage, output positive bus voltage, switch voltage, and the common-mode voltage at the midpoint of the bridge arm, then sums these after removing the switching harmonics. By analyzing the characteristic differences of the summed voltage under the ground fault modes of the positive and negative input/output buses, characteristic parameters are extracted to establish a ground fault identification method, thereby achieving effective detection of ground faults in the photovoltaic and energy storage DC/DC converter. Finally, the effectiveness of the method proposed in this paper was validated through simulations and experiments.
A Multi-Mode Pressure Stabilization Control Method for Pump–Valve Cooperation in Liquid Supply System
Authors: Xu Peng , Kou Ziming
Published: November 2024

In order to solve the problems of frequent pressure fluctuations caused by frequent action of the unloading valve of the pump station and serious hydraulic shock due to the variable amount of fluid used in the hydraulic support system of the coal mining face and the irregularity of the load suffered by the system, a pump–valve cooperative multi-mode stabilizing control method based on a digital unloading valve was proposed. Firstly, a prototype of a digital unloading valve under high-pressure and high water-based conditions was developed, and a digital control scheme was proposed to control the pilot valve by a servo motor to adjust the system pressure in real time. Then, an experimental platform for simulating the hydraulic bracket and a co-simulation model was constructed, and the validity of the co-simulation model was verified through experiments. Secondly, a collaborative multi-mode pressure stabilization control method for the pump valve based on a GRNN (General Regression Neural Network) was established to control the flow and pressure output of the emulsion pumping station according to the actual working conditions. Finally, numerical research and experimental verification were carried out for different working conditions to prove the effectiveness of this method. The results showed that the proposed pressure stabilization control method could adaptively adjust the working state of the digital unloading valve and the liquid supply flow of the emulsion pump station according to the working condition of the hydraulic support, effectively reducing the frequency and amplitude of the system pressure fluctuations and making the system pressure more stable.
DLT-GAN: Dual-Layer Transfer Generative Adversarial Network-Based Time Series Data Augmentation Method
Authors: Chen Zirui , Pang Yongheng , Jin Shuowei , Qin Jia , Li Suyuan , Yang Hongchen
Published: November 2024

In actual production processes, analysis and prediction tasks commonly rely on large amounts of time-series data. However, real-world scenarios often face issues such as insufficient or imbalanced data, severely impacting the accuracy of analysis and predictions. To address this challenge, this paper proposes a dual-layer transfer model based on Generative Adversarial Networks (GANs) aiming to enhance the training speed and generation quality of time-series data augmentation under small-sample conditions while reducing the reliance on large training datasets. This method introduces a module transfer strategy based on the traditional GAN framework which balances the training between the discriminator and the generator, thereby improving the model’s performance and convergence speed. By employing a dual-layer network structure to transfer the features of time-series signals, the model effectively reduces the generation of noise and other irrelevant features, improving the similarity of the generated signals’ characteristics. This paper uses speech signals as a case study, addressing scenarios where speech data are difficult to collect and the limited number of speech samples available for effective feature extraction and analysis. Simulated speech timbre generation is conducted, and the experimental results on the CMU-ARCTIC database show that, compared to traditional methods, this approach achieves significant improvements in enhancing the consistency of generated signal features and the model’s convergence speed.
The Study of Scheduling Optimization for Multi-Microgrid Systems Based on an Improved Differential Algorithm
Authors: Dong Ang , Lee Seon-Keun
Published: November 2024

As traditional power grids are unable to meet growing demand, extensive research on multi-microgrid scheduling has begun to address the issues present in conventional power grids. However, existing studies on the scheduling of grid-connected multi-microgrids still lack sufficient focus on system demand-side and interaction-side aspects. At the same time, the uncertainties of renewable energy and demand-side responses further complicate this research. To address this, this paper proposes an operational scheduling strategy based on an improved differential evolution algorithm, aiming to incorporate power interactions between microgrids, demand-side responses, and the uncertainties of renewable energy, thus enhancing the operational reliability and economic efficiency of multi-microgrid systems. The research in this paper is divided into the following steps: (1) constructing a multi-microgrid model primarily based on renewable energy; (2) formulating an optimization model with the objective of minimizing economic costs while ensuring stable system operation and solving it; (3) proposing an improved differential evolution algorithm for optimizing system scheduling; (4) testing and validating the improved differential algorithm; and (5) designing an operational strategy that accounts for the uncertainties of renewable energy and load demand. Through the application of real-world cases, the feasibility and effectiveness of the operational scheduling strategy based on the improved differential evolution algorithm are verified.
Properties and Analysis of the Guard Interval in Infinite Impulse Response–Orthogonal Frequency Division Multiplexing Systems
Authors: Jiang Mengwan , Luo Jiehao , Kong Dejin
Published: November 2024

Recently, an orthogonal frequency division multiplexing (OFDM) technique for the infinite impulse response (IIR) channel (IIR-OFDM) was proposed, which carries the dedicated guard interval to maintain the circular convolution of the received signal and channel coefficients. Therefore, the loop of an IIR channel can be converted to the frequency domain, and single-tap equalization can still be used to equalize loop interference, like classical OFDM. In this paper, we describe how to build the IIR system based on the channel with loops and derive the properties of the dedicated guard interval for a general multi-order IIR channel, which is different from the classical cyclic prefix (CP) obtained by replicating the samples at the tail end of the signal. In particular, we address two special models for first-order and delay IIR channels. It is demonstrated that the guard interval composition and power characteristics of the two special models are similar. Moreover, the complexity of the guard interval depends not only on the maximum delay of the loop, but also on the number of loops. Finally, we simulate the IIR-OFDM performance under different IIR channels.
A Study of Occluded Person Re-Identification for Shared Feature Fusion with Pose-Guided and Unsupervised Semantic Segmentation
Authors: Qu Junsuo , Zhang Zhenguo , Zhang Yanghai , He Chensong
Published: November 2024

The human body is often occluded by a variety of obstacles in the monitoring system, so occluded person re-identification is still a long-standing challenge. Recent methods based on pose guidance or external semantic clues have improved the representation and related performance of features; there are still problems, such as weak model representation and unreliable semantic clues. To solve the above problems, we proposed a feature extraction network, named shared feature fusion with pose-guided and unsupervised semantic segmentation (SFPUS). This network will extract more discriminative features and reduce the occlusion noise on pedestrian matching. Firstly, the multibranch joint feature extraction module (MFE) is used to extract feature sets containing pose information and high-order semantic information. This module not only provides robust extraction capabilities but can also precisely segment occlusion and the body. Secondly, in order to obtain multiscale discriminant features, the multiscale correlation feature matching fusion module (MCF) is used to match the two feature sets, and the Pose–Semantic Fusion Loss is designed to calculate the similarity of the feature sets between different modes and fuse them into a feature set. Thirdly, to solve the problem of image occlusion, we use unsupervised cascade clustering to better prevent occlusion interference. Finally, performances of the proposed method and various existing methods are compared on the Occluded-Duke, Occluded-ReID, Market-1501 and Duke-MTMC datasets. The accuracy of Rank-1 reached 65.7%, 80.8%, 94.8% and 89.6%, respectively, and the mAP accuracy reached 58.8%, 72.5%, 91.8% and 80.1%. The experiment results demonstrate that our proposed SFPUS holds promising prospects and performs admirably compared with state-of-the-art methods.
A Directional Enhanced Adaptive Detection Framework for Small Targets
Authors: Li Chao , Chang Yifan , Yang Shimeng , Li Kaiju , Yin Guangqiang
Published: November 2024

Due to the challenges posed by limited size and features, positional and noise issues, and dataset imbalance and simplicity, small object detection is one of the most challenging tasks in the field of object detection. Consequently, an increasing number of researchers are focusing on this area. In this paper, we propose a Directional Enhanced Adaptive (DEA) detection framework for small targets. This framework effectively combines the detection accuracy advantages of two-stage methods with the detection speed advantages of one-stage methods. Additionally, we introduce a Multi-Scale Object Adaptive Slicing (MASA) module and an improved IoU-based aggregation module that integrate with this framework to enhance detection performance. For better comparison, we use the F1 score as one of the evaluation metrics. The experimental results demonstrate that our DEA framework improves the performance of various backbone detection networks and achieves better comprehensive detection performance than other proposed methods, even though our network has not been trained on the test dataset while others have.
Advantages of Co-Pyrolysis of Sewage Sludge with Agricultural and Forestry Waste
Authors: Gusiatin Mariusz Z.
Published: November 2024

This paper explores the advantages of the co-pyrolysis of municipal sewage sludge with agricultural and forestry biomass, emphasizing its potential for environmental and economic benefits. Co-pyrolysis with lignocellulosic biomass significantly enhances biochar quality, reduces the heavy metal content, increases porosity, and improves nutrient retention, which are essential for soil applications. The biochar produced through co-pyrolysis demonstrates enhanced stability and a lower oxygen-to-carbon (O/C) ratio, making it more suitable for long-term carbon (C) sequestration and pollutant adsorption. Additionally, co-pyrolysis generates bio-oil and syngas with improved calorific value, contributing to renewable energy recovery from sewage sludge. This synergistic process also addresses waste management challenges by reducing harmful emissions and immobilizing heavy metals, thus mitigating the environmental risks associated with sewage sludge disposal. This paper covers key sections on the properties of waste materials, improvements in biochar quality and energy products, and the environmental benefits of co-pyrolysis, such as emissions reduction and heavy metal immobilization. The paper highlights trends and challenges in co-pyrolysis technology, aiming to optimize parameters for maximizing biochar yield and energy recovery while aligning with sustainability and circular economy goals. The paper concludes with recommendations for optimizing co-pyrolysis processes and scaling applications to support sustainable waste management. Overall, co-pyrolysis represents a sustainable approach to valorizing sewage sludge, transforming it into valuable resources while supporting environmental conservation.
Study of Gas Amplification Impact on Plateau Curve Characteristics in Boron-Coated Proportional Counters
Authors: Liu Yaolong , Chen Zhi , Huang Youjun , Luo Tingfang , Zhu Hongliang , Wu Wenchao
Published: November 2024

The characteristics of the plateau curve, specifically its length and slope, in boron-coated proportional counters are key performance indicators that impact the detector’s overall performance. Currently, the lack of research on the plateau curve of boron-coated proportional counters is holding back progress in engineering design and scientific research. This study harnesses the Diethorn formula, a calculation method for gas gain, to explore the relationship between gas amplification and the plateau curve in a boron-coated proportional counter. Based on the analysis of experimental data, this study proposes methods for improving the characteristics of the plateau curve in a proportional counter, including modifications to the counter structure and gas pressure, along with an evaluation method for assessing the effectiveness of these improvements. First, the causes of the plateau curve in a boron-coated proportional counter are analyzed through a physical process perspective, identifying factors that influence gas amplification and subsequently affect the plateau curve. Building upon this foundation, the Diethorn formula is utilized to explore the effects of structural parameters and gas pressure on gas multiplication. Finally, experimental validation is conducted, resulting in the proposal of three methods for improving the characteristics of the plateau curve and an evaluation method for assessing the effectiveness of improvements.