bluuebunny/crossref_metadata_2025 huggingface dataset:
The intermediate role of farmland transfer in the impact of digital financial inclusion on agricultural total factor productivity in China
Authors: Yang Jiali , Meseretchanie Abate
Published: October 2024

Introduction

Enhancing agricultural productivity and promoting sustainable development in China are critical priorities, given their significant contribution to food security and rural development. However, achieving consistent agricultural productivity growth across regions and sectors has been challenging, leading to inefficiencies and environmental pressures. To address this, the potential of digital inclusive finance (DIF) as a catalyst for progress has been explored.

Methods

This study investigates the impact of DIF on agricultural total factor productivity (ATFP) through farmland transfer, with implications for rural revitalization and high-quality agricultural development. Using the HMB index method, we calculate and decompose China’s inter-provincial ATFP from 2011 to 2020. Employing the dynamic panel fixed effect model and SYS-GMM method, we empirically test the influence of DIF on agricultural productivity in relation to farmland transfer.

Results and discussion

Our findings reveal a slow upward trend in China’s ATFP, driven primarily by scale efficiency. Notably, a 1% increase in the general index of DIF correlates with a 0.136% increase in ATFP. We analyze the sub-classification indexes of digital inclusive finance, namely coverage breadth, depth of use, and digitalization degree. Their contributions to agricultural productivity growth are significant, with coverage breadth having the greatest impact, followed by depth of use and digitalization degree. Farmland transfer acts as a crucial intermediary, enhancing agricultural allocation efficiency and driving the relationship between digital finance and total factor productivity growth. Interestingly, the western region exhibits the highest growth rates, followed by the eastern region, while the central region shows no significant effects.

Conclusion

To comprehensively enhance ATFP, aligning supply and demand for digital financial services, leveraging service depth, and guiding farmland transfer according to local conditions are essential. These findings offer valuable insights into effective strategies for maximizing the potential of DIF, promoting rural development, and achieving sustainable agricultural growth.
Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning Models
Authors: Mahmood Nasir , Bhatti Sohail Masood , Dawood Hussain , Pradhan Manas Ranjan , Ahmad Haseeb
Published: October 2024

Students’ behavioral and emotional engagement in the classroom environment may reflect the students’ learning experience and subsequent educational outcomes. The existing research has overlooked the measurement of behavioral and emotional engagement in an offline classroom environment with more students, and it has not measured the student engagement level in an objective sense. This work aims to address the limitations of the existing research and presents an effective approach to measure students’ behavioral and emotional engagement and the student engagement level in an offline classroom environment during a lecture. More precisely, video data of 100 students during lectures in different offline classes were recorded and pre-processed to extract frames with individual students. For classification, convolutional-neural-network- and transfer-learning-based models including ResNet50, VGG16, and Inception V3 were trained, validated, and tested. First, behavioral engagement was computed using salient features, for which the self-trained CNN classifier outperformed with a 97%, 91%, and 83% training, validation, and testing accuracy, respectively. Subsequently, the emotional engagement of the behaviorally engaged students was computed, for which the ResNet50 model surpassed the others with a 95%, 90%, and 82% training, validation, and testing accuracy, respectively. Finally, a novel student engagement level metric is proposed that incorporates behavioral and emotional engagement. The proposed approach may provide support for improving students’ learning in an offline classroom environment and devising effective pedagogical policies.
Tornado Occurrence in the United States as Modulated by Multidecadal Oceanic Oscillations Using Empirical Model Decomposition
Authors: Pan Zaitao
Published: October 2024

Studies have analyzed U.S. tornado variability and correlated F1–F5 tornado occurrence with various natural climate oscillations and anthropogenic factors. Using a relatively new empirical mode decommission (EMD) method that extracts time-frequency modes adaptively without priori assumptions like traditional time-series analysis methods, this study decomposes U.S. tornado variability during 1954–2022 into intrinsic modes on specific temporal scales. Correlating the intrinsic mode functions (IMFs) of EMD with climate indices found that 1. the U.S. overall tornado count is negatively (positively) correlated with the Atlantic Multidecadal Oscillation (AMO) index (the Southern Oscillation Index (SOI)); 2. the negative (positive) correlation tends to be more prevalent in the western (eastern) U.S.; 3. the increase in weak (F1–F2) and decrease in strong (F3–F5) tornadoes after around 2000, when both the AMO and the Pacific Decadal Oscillation (PDO) shifted phases, are likely related to their secular trends and low-frequency IMFs; and 4. the emerging Dixie Tornado Alley coincides with an amplifying intrinsic mode of the SOI that correlates positively with the eastern U.S. and Dixie Alley tornadoes. The long-term persistence of these climate indices can offer potential guidance for future planning for tornado hazards.
Role of Tail Dynamics on the Climbing Performance of Gecko-Inspired Robots: A Simulation and Experimental Study
Authors: Fang Shengchang , Chen Guisong , Liu Tong , Zhou Weimian , Wang Yucheng , Wang Xiaojie
Published: October 2024

Geckos are renowned for their exceptional climbing abilities, enabled by their specialized feet with hairy toes that attach to surfaces using van der Waals forces. Inspired by these capabilities, various gecko-like robots have been developed for high-risk applications, such as search and rescue. While most research has focused on adhesion mechanisms, the gecko’s tail also plays a critical role in maintaining balance and stability. In this study, we systematically explore the impact of tail dynamics on the climbing performance of gecko-inspired robots through both simulation and experimental analysis. We developed a dynamic climbing simulation system that models the robot’s specialized attachment devices and predicts contact failures. Additionally, an adjustable-angle force measurement platform was constructed to validate the simulation results. Our findings reveal the significant influence of the tail on the robot’s balance, stability, and maneuverability, providing insights for further optimizing climbing robot performance.
Evaluation of Automatic Signal Detection of In Situ Hybridization for Detecting HPV DNA in Cervical Tissue Derived from Patients with Cervical Intraepithelial Neoplasia
Authors: Przybylski Marcin , Millert-Kalińska Sonja , de Mezer Mateusz , Krzyżaniak Monika , Kurzawa Paweł , Żurawski Jakub , Jach Robert , Pruski Dominik
Published: October 2024

Background: Cervical cancer is fourth the most common cancer in women worldwide. Due to the prevalence of human papillomavirus (HPV) in the population (80–90%), scientists are likely to discover even more associations of this pathogen with other diseases in the future. In recent years, In Situ Hybridization (ISH) assays that use automated signal-detecting methods in formalin-fixed, paraffin-embedded (FFPE) cervical tissue, such as the enzyme-categorized signal-detecting system, have shown a higher sensitivity. Objectives and Methods: To evaluate automatic signal detection of ISH assay for detecting HPV DNA, we compared the ability of an ISH probe, Inform HPV II and III (Ventana Medical Systems, Tucson, AZ), to that of PCR assays to detect HPV DNA in cervical tissue specimens with cervical intraepithelial neoplasia (CIN; CIN 1, 28 cases; CIN 2, 22 cases; and CIN 3, 20 cases) and normal cervix (2 cases). Results: Our findings showed a significant relation was confirmed between ISH III level and HPV outcome (positive/negative). Patients with positive HPV outcomes had significantly lower ISH III levels, MD = −7961.82 CI95 [−17,230.00; −199.21], p = 0.005. Conclusions: Automatic signal detection of ISH assay is not particularly applicable to cervical tissue material. A more useful method of confirming the presence of HPV in the cervix is the HPV test with genotyping, as it allows for collecting a larger amount of material from the cervical disc and canal. The interpretation of a positive or negative ISH test must be guided in the context of clinical history and morphology.
Whole Exome Sequencing of Intracranial Epidermoid Cysts Reveals Immune-Associated Mechanistic and Potential Targets
Authors: Kondaboina Shruthi , Parrish Oscar , Parada Carolina Angelica , Ferreira Manuel
Published: October 2024

Background/Objectives: Intracranial Epidermoid Cysts (IECs) are rare intracranial tumors primarily treated through surgery. Cyst adherence complicates complete removal, leading to high rates of tumor progression after subtotal resection. The molecular drivers of IEC remain unknown. Consequently, advances in treatment have fallen short. Tumor genetic profiling has revealed potential targets for drug development, including FDA-approved options and reshaping treatment. The genetic landscape of IECs has not been explored. We applied Whole Exome Sequencing (WES) to IECs to gain insights into the mechanisms of oncogenesis and identify potential therapeutic targets. Methods: We performed WES on tumor tissue and matched blood samples, when available. Following GATK best practices, we conducted read processing, quality control, somatic variant calling, and copy-number inference. Data analyses and visualization were conducted in R. Results: Top altered genes are associated with the immune system and tumor microenvironment, suggesting a mechanism of immune evasion. Gene and pathway enrichment revealed a high mutation burden in genes associated with Extracellular Matrix (ECM) and PI3K-AKT-mTOR cascades. Recurrent and deleterious alterations in NOTCH2 and USP8 were identified in 50% and 30% of the cohort, respectively. Frequent amplifications in deubiquitinases and beta-defensins strengthened the involvement of immune mechanisms for oncogenic transformation. Conclusions: Top altered genes and recurrent mutations may play a role in shaping the microenvironment and modulating immune evasion in IECs. USP8 and NOTCH2 may serve as clinically relevant target for IECs. Finally, we present evidence that the crosstalk between the PI3K-Akt-mTOR and ECM signaling pathways may play a role in modulating the immune escape mechanism in IECs.
Implications of GPIIB-IIIA Integrin and Liver X Receptor in Platelet-Induced Compression of Ovarian Cancer Multi-Cellular Spheroids
Authors: Isingizwe Zitha Redempta , Sjoelund Virginie , Benbrook Doris Mangiaracina
Published: October 2024

Background: Platelets have been shown to promote ovarian cancer; however, the mechanism is poorly understood. Previously, we demonstrated that platelets reduce the size and increase the density of multi-cellular ovarian cancer spheroids in cell cultures. The objectives of this study were to determine if platelet inhibitors could counteract these effects, and to explore the mechanisms involved. Methods: FDA-approved platelet inhibitors were screened for their abilities to alter platelet effects on ovarian cancer spheroids. Mass spectrometry was used to identify proteins significantly altered in cancer cells upon exposure to platelets. The effects of platelets and/or liver x receptor agonists or antagonists on LXR activity were measured using ES-2 ovarian cancer cells transduced with an LXR-reporter vector. Results: Eptifibatide, a GPIIB-IIIA integrin inhibitor, and dipyridamole, an adenosine reuptake inhibitor, reduced and enhanced platelet effects on ovarian cancer spheroids, respectively. Proteomic studies identified the LXR/RXR and integrin pathways as mediators of platelet effects on ovarian cancer, and downstream effectors of eptifibatide. Conclusions: Integrin pathways and their downstream LXR/RXR effectors are implicated in how platelets alter ovarian cancer spheroid morphology. These results support studying eptifibatide and LXR/RXR agonists as candidate drugs for repurposing as therapeutic strategies to counteract platelet promotion of ovarian cancer.
Psychosocial Burden of Women Who Are to Undergo Additional Diagnostic Procedures Due to Positive Screening for Cervical Cancer
Authors: Ilic Irena , Babic Goran , Dimitrijevic Aleksandra , Sipetic Grujicic Sandra , Jakovljevic Vladimir , Ilic Milena
Published: October 2024

Background/Objectives: This study aimed to evaluate psychosocial burden and its associated factors in women who were referred for additional diagnostic procedures following receipt of a positive cervical-cancer-screening smear result. Methods: A cross-sectional study was performed in a consecutive cohort of only women who received an abnormal Papanicolaou screening result and therefore presented to a gynecologist for additional diagnostic examinations (colposcopy/biopsy/endocervical curettage) at the Clinic for Gynecology and Obstetrics of the Clinical Center. Multivariate linear regression was used for data analysis, with Bonferroni correction applied for multiple comparisons. Results: Significant independent predictors for the occurrence of psychosocial burden–worry in women with a positive Papanicolaou screening test before diagnostic procedures were the use of oral contraceptives (β = −0.174, p < 0.001), alcohol consumption (β = 0.188, p < 0.001), anxiety (β = −0.189, p = 0.001), high burden of depressive symptoms (β = 0.191, p = 0.001) and insufficient knowledge of the meaning of the term dysplasia/precancerous (β = −0.187, p < 0.001), according to the multivariate linear regression. The significant independent predictor for the occurrence of psychosocial burden–satisfaction with information/support in women with a positive Papanicolaou screening test before diagnostic procedures was psychological distress (β = −0.210, p = 0.001). Conclusions: Providing information in order to improve understanding of the term dysplasia/precancerous, as well as identifying which women are at risk of psychosocial burden, may help protect against this potential harm among women who receive a positive cervical-cancer-screening result and may facilitate their intention to undergo further diagnostic procedures.
An Early Increase in IL-10 and TNF-α Levels Following Atezolizumab Plus Bevacizumab Treatment Predicts Survival in Advanced Hepatocellular Carcinoma Patients: A Prospective Cohort Study
Authors: Lee Soon Kyu , Nam Soon Woo , Han Ji Won , Kwon Jung Hyun
Published: October 2024

Background/Objectives: Reliable biomarkers for predicting outcomes in hepatocellular carcinoma (HCC) treated with atezolizumab plus bevacizumab (Ate/Bev) are still lacking. Cytokines, which play a crucial role in immune regulation and HCC progression, have potential as predictive markers, but data supporting their use are limited. This study aimed to evaluate the impact of early changes in cytokine levels on the clinical outcomes of advanced HCC patients. Methods: We prospectively enrolled 32 advanced HCC patients, collecting blood samples before the first and second Ate/Bev treatments. These samples were analyzed for IL-2, IL-6, IL-10, IL-12, IL-17, IFN-γ, and TNF-α levels to assess changes post-treatment. The primary outcome was overall survival, with a secondary focus on progression-free survival (PFS) at 6 months. Results: The mean age of the participants was 64.2 years, with the majority being male (93.8%). Patients showing increased IL-10, IL-17, and TNF-α levels had significantly better survival (p < 0.05) and marginally improved PFS compared to those with decreased cytokine levels. Interestingly, a positive correlation was noted between changes in IL-10 and TNF-α levels (p = 0.009). Furthermore, a multivariable analysis revealed that increased levels of IL-10 and TNF-α were significant predictors of enhanced survival (hazard ratio, 0.07; 95% confidence interval, 0.01–0.46; p = 0.005). Conclusions: An early increases in IL-10 and TNF-α after Ate/Bev treatment may serve as effective biomarkers for clinical outcomes in advanced HCC patients.
The Neoangiogenic Transcriptomic Signature Impacts Hepatocellular Carcinoma Prognosis and Can Be Triggered by Transarterial Chemoembolization Treatment
Authors: Critelli Rosina Maria , Casari Federico , Borghi Alberto , Serino Grazia , Caporali Cristian , Magistri Paolo , Pecchi Annarita , Shahini Endrit , Milosa Fabiola , Di Marco Lorenza , Pivetti Alessandra , Lasagni Simone , Schepis Filippo , De Maria Nicola , Dituri Francesco , Martínez-Chantar María Luz , Di Benedetto Fabrizio , Giannelli Gianluigi , Villa Erica
Published: October 2024

Background/Objectives: We evaluated the relationship between the neoangiogenic transcriptomic signature (nTS) and clinical symptoms, treatment outcomes, and survival in hepatocellular carcinoma (HCC) patients. Methods: This study prospectively followed 328 patients in the derivation and 256 in the validation cohort (with a median follow-up of 31 and 22 months, respectively). The nTS was associated with disease presentation, treatments administered, and overall survival rates. Additionally, this study investigated how multiple treatments influenced changes in nTS status and alterations in microRNA expression. Results: The nTS was identified in 27.4% of patients, linked to aggressive features like multifocality and elevated alpha-fetoprotein (AFP), a pattern consistent with that of the validation cohort. Most patients in both cohorts received treatment for HCC. nTS+ patients had limited access to, and benefited less from, liver transplantation or radiofrequency ablation (RFA) compared to nTS− patients. By the end, 78.9% had died, with nTS− patients showing better median survival and response to treatments than their nTS+ counterparts, who had lower survival across all treatment types. Among those who received transarterial chemoembolization (TACE), 31.2% (21/80 patients after the initial treatment and another four following a second TACE) transitioned from an nTS− to an nTS+ status. This shift was associated with lower survival and alterations in microRNA expressions related to oncogenic pathways. Conclusions: The nTS markedly influences treatment eligibility and survival in patients with HCC. Notably, the nTS can develop after repeated TACE procedures, significantly impacting patient survival and altering oncogenic microRNA expression patterns. These findings highlight the critical role of the nTS in guiding treatment decisions and prognostication in HCC management.
Stereotactic Radiosurgery for Intracranial Breast Metastases: A Systematic Review and Meta-Analysis
Authors: Almeida Neil D. , Kuo Cathleen , Schrand Tyler V. , Rupp Julia , Madhugiri Venkatesh S. , Goulenko Victor , Shekher Rohil , Shah Chirag , Prasad Dheerendra
Published: October 2024

Background/Objectives: To determine the impact of stereotactic radiosurgery on outcomes of metastatic breast cancer with intracranial metastases. Methods: We systematically searched the PubMed and EMBASE databases for studies published between 1 January 1990 and 1 August 2024. Primary research articles evaluating the outcomes of stereotactic radiosurgery on intracranial metastases from breast cancer were included. Adverse events were defined as leptomeningeal disease, radiation necrosis, seizure, and headache. The pooled estimate was calculated using the DerSimonian and Laird approach. Results: Sixteen studies encompassing 1228 patients met the inclusion criteria. Our analysis revealed a median survival duration of 13.1 ± 3.8 months and a pooled 1-year overall survival rate of 53.1% after SRS treatment. There was a 29% local recurrence rate at 1 year and a 35% overall distant recurrence rate. In addition, our analysis found a relatively low rate of acute adverse events at 15.5%. Conclusions: SRS demonstrates promising efficacy and safety in managing intracranial metastases from breast cancer, with a favorable toxicity profile.
Introduction of an Educational Video to Enhance the Informed Consent Process in Postoperative Radiation Therapy of Breast Cancer Patients
Authors: Vorbach Samuel M. , Pointner Martin , Lehmann Jens , Mangesius Julian , Hart Tilmann , Gstir Claudia , Rändler Theresa , Seppi Thomas , Ganswindt Ute , Kollotzek Siegfried
Published: October 2024

Background/Objectives: Informed consent is crucial in medical practice, especially for complex treatments such as postoperative radiotherapy for patients with breast cancer. Conventional consent procedures are often based on verbal declarations with a highly condensed but nevertheless large amount of information, which can exceed the recording capacity of patients and lead to misunderstandings. The aim of this study was to develop and test an educational video on breast cancer patients to enhance the informed consent process by improving patients’ understanding and reducing the duration of the subsequent consultation. Methods: The educational video was created after the underlying content was determined by a modified Delphi method in which a panellist of radiation oncologists, nurses, radiation therapists, and former patients participated in successive rounds of topic scoring. After achieving content consent, the video included 19 items to cover key aspects of postoperative radiotherapy in a patient-friendly manner. Fifty breast cancer patients scheduled for postoperative radiotherapy were randomised to watch the video prior to their verbal consultation (n = 25) or to the control group participating in the traditional informed consent process only (n = 25). The duration of the informed consent processes in both arms was recorded. To assess the educational effect of the video, a multiple-choice test was created. In addition, patients’ satisfaction was recorded using a separate questionnaire. Both questionnaires were completed by the patients one to two hours after signing the consent form. Results: The patients in the video group evidenced significantly higher knowledge scores than those who received standard verbal explanations (median number of correct answers 9 vs. 8 out of 10, p = 0.0039). The whole informed consent process was also completed faster in the video group (mean duration 34.7 vs. 46.2 min, p < 0.001). Median satisfaction scores were high in both groups (34 vs. 33 out of 35 points), with no significant differences observed (p > 0.05). Conclusion: The educational video effectively improved patient understanding and streamlined the informed consent process without compromising patients’ satisfaction. This approach also helps to standardise the delivery of complex medical information, and it can also be adapted to improve the informed consent process for other cancer treatments.
Double-Q Checkerboard Bubble Crystal in Centrosymmetric Tetragonal Magnets
Authors: Hayami Satoru
Published: October 2024

We report our numerical studies on the emergence of a double-Q checkerboard bubble crystal in centrosymmetric tetragonal magnets. The double-Q checkerboard bubble crystal is characterized by a fourfold-symmetric collinear spin configuration consisting of a superposition of two sinusoidal waves with the out-of-plane spin modulations along the [110] and [1¯10] directions. The numerical calculations based on the simulated annealing for an effective spin model with the momentum-resolved easy-axis exchange interactions reveal that the double-Q checkerboard bubble crystal is energetically degenerate with the single-Q collinear state when the ordering wave vector lies on the quarter of the reciprocal lattice vector along the ⟨110⟩ direction. We show that such a degeneracy is lifted by considering the biquadratic interaction. We also find that the double-Q checkerboard bubble crystal turns into another double-Q state characterized by the in-plane spin modulations by increasing an external magnetic field.
Early Molecular Detection of Invasive Alien Plants in Urban and Peri-Urban Areas
Authors: Frigerio Jessica , Ouled Larbi Malika , Guidi Nissim Werther , Grassi Fabrizio , Cortis Pierluigi , Labra Massimo
Published: October 2024

Invasive alien plants represent one of the five major threats to biodiversity and the disruption of ecosystems. They are introduced through various routes, starting with commercial trade. Preventing their introduction is essential to avoid the spread of new invasive plants. In this paper, we propose a new early warning DNA barcoding tool for invasive plant detection. Eight invasive alien species of European Union concern (i.e., Ludwigia grandiflora, Elodea nuttallii, Myriophyllum aquaticum, Pontederia crassipes, Ailanthus altissima, Heracleum mantegazzianum, Impatiens glandulifera, Pueraria montana) were selected and analysed. A unique DNA marker for each species was identified and amplified using species-specific primers capable of identifying the presence of alien species. To verify whether the approach could detect the presence of alien plants in urban areas from lawn clippings, mixes with typical urban spontaneous plants and invasive species were tested. In all mixes, only the invasive species was identified. This rapid detection capability will enable environmental operators to intervene promptly to contain the spread of invasive plants before they can cause significant damage to the local ecosystem. This tool could have a significant impact on the protection of local biodiversity and the integrity of urban habitats.
Contribution of Artificial Intelligence (AI) to Code-Based 3D Modeling Tasks
Authors: Zichar Marianna , Papp Ildikó
Published: October 2024

The rapid advancement of technology and innovation is also impacting education across different levels. The rise of Artificial Intelligence (AI) is beginning to transform education in various areas, from course materials to assessment systems. This requires educators to reconsider how they evaluate students’ knowledge. It is crucial to understand if and to what extent assignments can be completed using AI tools. This study explores two hypotheses about the risks of using code-based 3D modeling software in education and the potential for students to delegate their work to AI when completing assignments. We selected two tasks that students were able to successfully complete independently and provided the same amount of information (both textual and image) to AI in order to generate the necessary code. We tested the widely used ChatGPT and Gemini AI bots to assess their current performance in generating code based on text prompts or image-based information for the two models. Our findings indicate that students are not yet able to entirely delegate their work to these AI tools.
Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks
Authors: Dong Wei , Zhang Weidong
Published: October 2024

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance.
Airspace Constrained Free-Flight Analysis: Implications for Uncrewed Air Traffic Management
Authors: Bruggemann Troy , McFadyen Aaron , Williams Brendan
Published: October 2024

This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic behaviour and quantitatively assess spatial–temporal airspace conflict as the airspace constraints vary. Findings indicate that airspace constraints have a much stronger influence on aircraft behaviour than the free-flight operational construct, with any benefits of free flight rapidly diminishing as the airspace becomes more constrained. We conclude that structured traffic route (or network) designs and associated risk modelling approaches should be considered for safe and efficient traffic management of highly constrained and congested (or dense) airspace. This work therefore provides evidence to inform new airspace design and management initiatives, including low-altitude uncrewed traffic.
Image Captioning Based on Semantic Scenes
Authors: Zhao Fengzhi , Yu Zhezhou , Wang Tao , Lv Yi
Published: October 2024

With the development of artificial intelligence and deep learning technologies, image captioning has become an important research direction at the intersection of computer vision and natural language processing. The purpose of image captioning is to generate corresponding natural language descriptions by understanding the content of images. This technology has broad application prospects in fields such as image retrieval, autonomous driving, and visual question answering. Currently, many researchers have proposed region-based image captioning methods. These methods generate captions by extracting features from different regions of an image. However, they often rely on local features of the image and overlook the understanding of the overall scene, leading to captions that lack coherence and accuracy when dealing with complex scenes. Additionally, image captioning methods are unable to extract complete semantic information from visual data, which may lead to captions with biases and deficiencies. Due to these reasons, existing methods struggle to generate comprehensive and accurate captions. To fill this gap, we propose the Semantic Scenes Encoder (SSE) for image captioning. It first extracts a scene graph from the image and integrates it into the encoding of the image information. Then, it extracts a semantic graph from the captions and preserves semantic information through a learnable attention mechanism, which we refer to as the dictionary. During the generation of captions, it combines the encoded information of the image and the learned semantic information to generate complete and accurate captions. To verify the effectiveness of the SSE, we tested the model on the MSCOCO dataset. The experimental results show that the SSE improves the overall quality of the captions. The improvement in scores across multiple evaluation metrics further demonstrates that the SSE possesses significant advantages when processing identical images.
Infidelity Analysis of Digital Counter-Diabatic Driving in Simple Two-Qubit System
Authors: Lei Ouyang
Published: October 2024

Digitized counter-diabatic (CD) optimization algorithms have been proposed and extensively studied to enhance performance in quantum computing by accelerating adiabatic processes while minimizing energy transitions. While adding approximate counter-diabatic terms can initially introduce adiabatic errors that decrease over time, Trotter errors from decomposition approximation persist. On the other hand, increasing the high-order nested commutators for CD terms may improve adiabatic errors but could also introduce additional Trotter errors. In this article, we examine the two-qubit model to explore the interplay between approximate CD, adiabatic errors, Trotter errors, coefficients, and commutators. Through these analyses, we aim to gain insights into optimizing these factors for better fidelity, a shallower circuit depth, and a reduced gate number in near-term gate-based quantum computing.
Fast and Accurate Numerical Integration of the Langevin Equation with Multiplicative Gaussian White Noise
Authors: Evstigneev Mykhaylo , Kacmazer Deniz
Published: October 2024

A univariate stochastic system driven by multiplicative Gaussian white noise is considered. The standard method for simulating its Langevin equation of motion involves incrementing the system’s state variable by a biased Gaussian random number at each time step. It is shown that the efficiency of such simulations can be significantly enhanced by incorporating the skewness of the distribution of the updated state variable. A new algorithm based on this principle is introduced, and its superior performance is demonstrated using a model of free diffusion of a Brownian particle with a friction coefficient that decreases exponentially with the kinetic energy. The proposed simulation technique proves to be accurate over time steps that are an order of magnitude longer than those required by standard algorithms. The model used to test the new numerical technique is known to exhibit a transition from normal diffusion to superdiffusion as the environmental temperature rises above a certain critical value. A simple empirical formula for the time-dependent diffusion coefficient, which covers both diffusion regimes, is introduced, and its accuracy is confirmed through comparison with the simulation results.
Rate Optimization of Intelligent Reflecting Surface-Assisted Coal Mine Wireless Communication Systems
Authors: Liu Yang , Yang Zhao , Wang Bin , Xu Yanhong
Published: October 2024

This paper proposes a three-step joint rate optimization method for intelligent reflecting surface (IRS)-assisted coal mine wireless communication systems. Different from terrestrial IRS-assisted communication scenarios, in coal mines, IRSs can be installed flexibly on the tops of rectangular tunnels to address the issues of signals being blocked and interfered with by mining equipment. Therefore, it is necessary to optimize the IRS deployment position, the transmit power and IRS phase shifts to achieve the maximum effective achievable rate at user stations equipped with the proposed system. However, due to the complex channel models of coal mines, the optimization problem of IRS deployment position is non-convex. To solve this problem, two auxiliary variables along with logarithmic operations and Taylor approximation are introduced. On this basis, a three-step joint rate optimization involving the transmit power, IRS phase shifts and IRS deployment position is proposed to maximize the effective achievable rates at the user station. The simulation results show that compared with other rate optimization schemes, the effective achievable rates at the user station using the proposed joint rate optimization scheme can be improved by approximately 12.32% to 54.17% for different parameter configurations. It is also pointed out that the deployment position of the IRS can converge to the same optimal position independent of the initial deployment position. Moreover, we investigate the effects of the roughness of the tunnel walls in a coal mine on the effective achievable rates at the user station, and the simulation results indicate that the proposed three-step joint rate optimization scheme performs better in the coal mine scenario regardless of the roughness.
Adaptive Privacy-Preserving Coded Computing with Hierarchical Task Partitioning
Authors: Zeng Qicheng , Nan Zhaojun , Zhou Sheng
Published: October 2024

Coded computing is recognized as a promising solution to address the privacy leakage problem and the straggling effect in distributed computing. This technique leverages coding theory to recover computation tasks using results from a subset of workers. In this paper, we propose the adaptive privacy-preserving coded computing (APCC) strategy, designed to be applicable to various types of computation tasks, including polynomial and non-polynomial functions, and to adaptively provide accurate or approximated results. We prove the optimality of APCC in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding, based on the optimal recovery threshold of Lagrange Coded Computing. We demonstrate that APCC guarantees information-theoretical data privacy preservation. Mitigation of the straggling effect in APCC is achieved through hierarchical task partitioning and task cancellation, which further reduces computation delays by enabling straggling workers to return partial results of assigned tasks, compared to conventional coded computing strategies. The hierarchical task partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. The simulation results show that APCC can reduce the task completion delay by a range of 20.3% to 47.5% when compared to other state-of-the-art benchmarks.
A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems
Authors: Varley Thomas F.
Published: October 2024

What does it mean for a complex system to “compute” or perform “computations”? Intuitively, we can understand complex “computation” as occurring when a system’s state is a function of multiple inputs (potentially including its own past state). Here, we discuss how computational processes in complex systems can be generally studied using the concept of statistical synergy, which is information about an output that can only be learned when the joint state of all inputs is known. Building on prior work, we show that this approach naturally leads to a link between multivariate information theory and topics in causal inference, specifically, the phenomenon of causal colliders. We begin by showing how Berkson’s paradox implies a higher-order, synergistic interaction between multidimensional inputs and outputs. We then discuss how causal structure learning can refine and orient analyses of synergies in empirical data, and when empirical synergies meaningfully reflect computation versus when they may be spurious. We end by proposing that this conceptual link between synergy, causal colliders, and computation can serve as a foundation on which to build a mathematically rich general theory of computation in complex systems.
The Relationship Between Astronomical and Developmental Times Emerging in Modeling the Evolution of Agents
Authors: Gusev Alexander O. , Martyushev Leonid M.
Published: October 2024

The simplest evolutionary model for catching prey by an agent (predator) is considered. The simulation is performed on the basis of a software-emulated Intel i8080 processor. Maximizing the number of catches is chosen as the objective function. This function is associated with energy dissipation and developmental time. It is shown that during Darwinian evolution, agents with an initially a random set of processor commands subsequently acquire a successful catching skill. It is found that in the process of evolution, a logarithmic relationship between astronomical and developmental times arises in agents. This result is important for the ideas available in the literature about the close connection of such concepts as time, Darwinian selection, and the maximization of entropy production.
Inverse Open Circuit Voltage Curve Model for LiCoO2 Battery at Different Temperatures
Authors: Barcellona Simone , Codecasa Lorenzo , Colnago Silvia
Published: October 2024

Lithium-ion batteries are widely used in a variety of applications. For effective battery management, accurate estimation of the state of charge (SOC) is essential. One of the most commonly employed methods for SOC estimation relies on the open circuit voltage (OCV) curve with respect to SOC. However, inverting the OCV-SOC function is not always straightforward. This paper proposes a novel analytical function that directly models the inverse OCV-SOC function, providing a more efficient and reliable method for SOC estimation. Moreover, the dependency of the proposed function on battery temperature is also being investigated, allowing for a wider application of the method under different OCV measuring conditions.
Centralised Control and Energy Management of Multiple Interconnected Standalone AC Microgrids
Authors: Udoha Ezenwa , Das Saptarshi , Abusara Mohammad
Published: October 2024

When microgrids operate autonomously, they must curtail the surplus of renewable energy sources (RES) while minimising reliance on gas. However, when interconnected, microgrids can collaboratively minimise RES curtailment and gas consumption due to the ability of exchanging power. This paper presents a centralised controller and energy management of multiple standalone AC microgrids interconnected to a common AC bus using back-to-back converters. Each microgrid consists of RES, a battery, a gas-powered auxiliary unit, and a load. The battery’s state of charge (SOC) is controlled and is used in the AC bus frequency to indicate whether the microgrid has a surplus or shortage of power. High-level global droop control exchanges power between the microgrids. The optimisation problem for this interconnected system is modelled cooperatively to determine the optimal dispatch solution that minimises the energy cost from the auxiliary unit. The optimal dispatch is solved in three cases using the Nelder–Mead simplex algorithm under different settings: one-variable optimisation, three-variable optimisation with the standard droop equation, and three-variable optimisation with a modified droop equation. The optimised performance results are compared with those of the non-optimised benchmark to determine the percentage of optimal performance. The simulation results show that the total energy cost from the auxiliary unit is minimised by 8.98%.
Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model
Authors: Fu Chen , Lan Li , Chen Su , Guo Mingxing , Jiang Xiaojing , Yin Xiaoran , Xu Chuanbo
Published: October 2024

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics.
Modeling and Simulation of Hybrid Electric Vehicles for Sustainable Transportation: Insights into Fuel Savings and Emissions Reduction
Authors: Keegan Govender , Nelendran Pillay , Oluwafemi Oni
Published: October 2024

Most motor vehicles have historically utilized Internal Combustion Engines powered by a fossil fuel. Despite the technological advancements in fuel-efficient engines, further improvements are required to reduce the effect of fuel costs and global warming. This study models and simulates Hybrid Electric Vehicles (HEVs) to evaluate their potential for fuel cost savings and emissions reduction compared to traditional vehicles. Using MATLAB® version R2023a Update 5 (9.14.0.2337262) Simulink version 10.7 (R2023a) and the Advanced Vehicle Simulator (ADVISOR) version 2003-00-r0116, the research examines Series and Parallel HEV configurations. The simulation explores various battery configurations, engine capacities, and power unit models to analyze their impact on energy utilization. The data, collected from the simulations, show significant fuel savings and emissions reduction with HEVs. The Parallel HEV configuration consistently saves fuel with fewer battery modules, while the Series HEV configuration performs better but requires more modules to maintain the system’s State of Charge.
New Thermochemical Salt Hydrate System for Energy Storage in Buildings
Authors: Galazutdinova Yana , Clark Ruby-Jean , Al-Hallaj Said , Kaur Sumanjeet , Farid Mohammed
Published: October 2024

This paper introduces an innovative design for an “inorganic salt-expanded graphite” composite thermochemical system. The storage unit is made of a perforated, compressed, expanded graphite block impregnated with molten CaCl2∙6H2O; the humid air passes through the holes that allow the moisture to diffuse and react with the salt. The prepared block underwent 90 hydration-dehydration cycles. Although most of the performed cycles were carried out with salt overhydration and deliquescence, the treated samples have remained mechanically and thermally stable with no drop in energy density. The volumetric energy density of the composite ranged from 135.5 to 277.6 kWh/m3, depending on airflow rate and absolute humidity. To ensure composite material cycling stability, the energy density of the block was measured during hydration at similar conditions of absolute humidity, inlet temperature, and airflow rate (0.01 kgwater/kgair, 20 °C, 400 l/min). The average energy density at these conditions was sustained at 219 kWh/m3. The block integrity was monitored by visual inspection after removing it from the reactor chamber every few cycles. Both the composite material and its manufacturing process are simple and easy to scale up for future commercialization.