bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
Authors: Iaroshev Ivan , Pillai Ramalingam , Vaglietti Leandro , Hanne Thomas
Published: October 2024

This study explores the application of retrieval-augmented generation (RAG) to improve the accuracy and reliability of large language models (LLMs) in the context of financial report analysis. The focus is on enabling private investors to make informed decisions by enhancing the question-and-answering capabilities regarding the half-yearly or quarterly financial reports of banks. The study adopts a Design Science Research (DSR) methodology to develop and evaluate an RAG system tailored for this use case. The study conducts a series of experiments to explore models in which different RAG components are used. The aim is to enhance context relevance, answer faithfulness, and answer relevance. The results indicate that model one (OpenAI ADA and OpenAI GPT-4) achieved the highest performance, showing robust accuracy and relevance in response. Model three (MiniLM Embedder and OpenAI GPT-4) scored significantly lower, indicating the importance of high-quality components. The evaluation also revealed that well-structured reports result in better RAG performance than less coherent reports. Qualitative questions received higher scores than the quantitative ones, demonstrating the RAG’s proficiency in handling descriptive data. In conclusion, a tailored RAG can aid investors in providing accurate and contextually relevant information from financial reports, thereby enhancing decision making.
Element Array Optimization for Skin-Attachable Ultrasound Probes to Improve the Robustness against Positional and Angular Errors
Authors: Noda Takumi , Azuma Takashi , Sakuma Ichiro , Tomii Naoki
Published: October 2024

Skin-attachable ultrasound probes face challenges in imaging the intended cross-section due to the difficulty in precisely adjusting the position and angle of attachment. While matrix element arrays are capable of imaging any cross-section within a three-dimensional field of view, their implementation presents a challenge due to the significant number of required ultrasound elements. We propose a method for optimizing the coordinates and shapes of elements based on the focusing quality onto the imaging points under the positional and angular errors in the element array. A 128-element array was optimized through the proposed method and its imaging performance was evaluated with simulated phantoms. The optimized array demonstrated the ability to clearly visualize the simulated wires, cysts, and blood vessels even with the positional error of 3 mm and the angular error of 20°. These results indicate the feasibility of developing a skin-attachable ultrasound probe that can be easily used in daily life without requiring precise positional and angular accuracy.
Using Petri Nets and 4M1E Identification Resolution for Manufacturing Process Control and Information Tracking: Case Study of Transformer Coil Production
Authors: Zhang Xuedong , Sun Wenlei , Song Shijie , Lu Chen
Published: October 2024

To solve the problems of chaotic information management and difficult traceability in the manufacturing process of transformer coils, a traceability and management method oriented towards the manufacturing process of transformer coils has been proposed. This method integrates industrial internet identification resolution and extension of Petri net modeling theory. A comprehensive identification and resolution framework for coil manufacturing processes has been constructed. In this manuscript, the authors proposed an industrial data-sharing space based on the producer-consumer model with unified coding identification. This enables information sharing for all resources, including personnel, machinery, materials, methods, environment, and measurements. A method for modeling extensible identification primitives of coil manufacturing process information was proposed, which formalizes the correlation and data structure of process information. A Petri net model for the comprehensive acquisition and integration of elemental information in coil manufacturing processes, as well as a mathematical model for quality traceability, were constructed, thereby forming a complete path for quality traceability information. Finally, based on the method proposed above, a software and hardware environment for identification and traceability for coil manufacturing was established. Taking a certain type of coil as an example, validation was carried out; the results indicate a significant enhancement in the production management and information traceability capabilities of the coil production workshop. This study provides reference and guidance for the process traceability management of power equipment manufacturing.
Dynamic Link Prediction in Jujube Sales Market: Innovative Application of Heterogeneous Graph Neural Networks
Authors: Wu Yichang , Heng Liang , Tan Fei , Yang Jingwen , Guo Li
Published: October 2024

Link prediction is crucial in forecasting potential distribution channels within the dynamic and heterogeneous Xinjiang jujube sales market. This study utilizes knowledge graphs to represent entities and constructs a complex network model for market analysis. Graph neural networks (GNNs) have shown excellent performance in handling graph-structured data, but they do not necessarily significantly outperform in link prediction tasks due to an overreliance on node features and a neglect of structural information. Additionally, the Xinjiang jujube dataset exhibits unique complexity, including multiple types, attributes, and relationships, distinguishing it from typical GNN datasets such as DBLP and protein-protein interaction datasets. To address these challenges, we introduce the Heterogeneous Multi-Head Attention Graph Neural Network model (HMAGNN). Our methodology involves mapping isomeric nodes to common feature space and labeling nodes using an enhanced Weisfeiler–Lehman (WL) algorithm. We then leverage HMAGNN to learn both structural and attribute features individually. Throughout our experimentation, we identify the critical influence of local subgraph structure and size on link prediction outcomes. In response, we introduce virtual nodes during the subgraph extraction process and conduct validation experiments to underscore the significance of these factors. Compared to alternative models, HMAGNN excels in capturing structural features through our labeling approach and dynamically adapts to identify the most pertinent link information using a multi-head attention mechanism. Extensive experiments on benchmark datasets consistently demonstrate that HMAGNN outperforms existing models, establishing it as a state-of-the-art solution for link prediction in the context of jujube sales market analysis.
Investigation of Temperature Variation Characteristics and a Prediction Model of Sandy Soil Thermal Conductivity in the Near-Phase-Transition Zone
Authors: Liu Jine , Liu Panting , He Huanquan , Tang Linlin , Liu Zhiyun , Zhai Yue , Zhang Yaxing
Published: October 2024

Soil thermal conductivity in the near-phase-transition zone is a key parameter affecting the thermal stability of permafrost engineering and its catastrophic thermal processes. Therefore, accurately determining the soil thermal conductivity in this specific temperature zone has important theoretical and engineering significance. In the present work, a method for testing the thermal conductivity of fine sandy soil in the near-phase-transition zone was proposed by measuring thermal conductivity with the transient plane heat source method and determining the volumetric specific heat capacity by weighing unfrozen water contents. The unfrozen water content of sand specimens in the near-phase-transition zone was tested, and a corresponding empirical fitting formula was established. Finally, based on the testing results, temperature variation trends and parameter influence laws of thermal conductivity in the near-phase-transition zone were analyzed, and thermal conductivity prediction models based on multiple regression (MR) and a radial basis function neural network (RBFNN) were also established. The results show the following: (1) The average error of the proposed test method in this work and the reference steady-state heat flow method is only 7.25%, which validates the reliability of the proposed test method. (2) The variation in unfrozen water contents in fine sandy soil in the range of 0~−3 °C accounts for over 80% of the variation in the entire negative temperature range. The unfrozen water content and thermal conductivity curves exhibit a similar trend, and the near-phase-transition zone can be divided into a drastic phase transition zone and a stable phase transition zone. (3) Increases in the thermal conductivity of fine sandy soil mainly occur the drastic phase transition zone, where these increases account for about 60% of the total increase in thermal conductivity in the entire negative temperature region. With the increase in density and total water content, the rate of increase in thermal conductivity in the drastic phase transition zone gradually decreases. (4) The R2, MAE, and RSME of the RBFNN model in the drastic phase transition zone are 0.991, 0.011, and 0.021, respectively, which are better than those of the MR prediction model.
A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
Authors: Magliano Alfonso , Perez Carrera Carlos , Pappalardo Carmine Maria , Guida Domenico , Berardi Valentino Paolo
Published: October 2024

In recent years, there has been a significant increase in research on hydrogen due to the urgent need to move away from carbon-intensive energy sources. This transition highlights the critical role of hydrogen storage technology, where hydrogen tanks are crucial for achieving cleaner energy solutions. This paper aims to provide a general overview of hydrogen treatment from a mechanical viewpoint, and to create a comprehensive review that integrates the concepts of hydrogen safety and storage. This study explores the potential of hydrogen applications as a clean energy alternative and their role in various sectors, including industry, automotive, aerospace, and marine fields. The review also discusses design technologies, safety measures, material improvements, social impacts, and the regulatory landscape of hydrogen storage tanks and safety technology. This work provides a historical literature review up to 2014 and a systematic literature review from 2014 to the present to fill the gap between hydrogen storage and safety. In particular, a fundamental feature of this work is leveraging systematic procedural techniques for performing an unbiased review study to offer a detailed analysis of contemporary advancements. This innovative approach differs significantly from conventional review methods, since it involves a replicable, scientific, and transparent process, which culminates in minimizing bias and allows for highlighting the fundamental issues about the topics of interest and the main conclusions of the experts in the field of reference. The systematic approach employed in the paper was used to analyze 55 scientific articles, resulting in the identification of six primary categories. The key findings of this review work underline the need for improved materials, enhanced safety protocols, and robust infrastructure to support hydrogen adoption. More importantly, one of the fundamental results of the present review analysis is pinpointing the central role that composite materials will play during the transition toward hydrogen applications based on thin-walled industrial vessels. Future research directions are also proposed in the paper, thereby emphasizing the importance of interdisciplinary collaboration to overcome existing challenges and facilitate the safe and efficient use of hydrogen.
The Behavior of Catalytic, Low-Temperature N2O Decomposition (LT-deN2O) in the Presence of Sulfur-Containing Compounds on Nitric Acid Plants
Authors: Moszowski Bartosz , Mulica-Musiał Martyna , Piszko Paweł J. , Dobrzyński Maciej
Published: October 2024

The production of nitric acid represents the primary source of nitrous oxide (N2O) emissions. During pilot-scale studies of N2O reduction on a low-temperature catalyst on nitric acid plants, it was observed that increasing the concentration of NH3 resulted in a decrease in the degree of N2O decomposition. This suggested that N2O was formed by the oxidation of NH3. Measurements at different temperatures, conducted after the N2O reduction trials, resulted in the N2O concentration at the inlet equal to the concentration at the outlet, indicating catalyst deactivation. To identify the causes of deactivation, the physicochemical properties of the catalyst were investigated. XRF analysis revealed the presence of sulfur. The results suggest the necessity of removing sulfur from the raw gas before the reduction of N2O on the low-temperature catalyst in practical applications.
A Data- and Model-Integrated Driven Method for Recommending the Maximum Safe Braking Deceleration Rates for Trucks on Horizontal Curves
Authors: Xin Tian , Xu Jinliang
Published: October 2024

Truck skidding crashes on horizontal curves pose a significant road safety concern, with improper braking being the primary cause. A data- and model-integrated driven method is proposed to investigate the mechanism and recommend the maximum safe braking deceleration rates without skidding (abbreviated as MSBDRs) for trucks on horizontal curves. Firstly, a comprehensive road–vehicle interaction model was developed, considering dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Secondly, leveraging the “HighD” data set and employing cluster analysis principles, parameter data were extracted using Python and Matlab. Finally, through parameterizing model inputs, the transient dynamic response of trucks was examined, the potential of truck skidding was predicted, and the MSBDRs were recommended. The results indicate the following. (1) There is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers. (2) The MSBDR is 4.5 m/s2 on a limit-minimum-radius horizontal curve; however, when combined with steep slopes, an overspeed exceeding 20%, and extremely wet road conditions, respectively, the MSBDRs decrease to 4 m/s2, 3 m/s2, and 2 m/s2. These results provide a theoretical foundation for braking strategies in autonomous vehicles.
Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers
Authors: Li Maofei , Jiang Zhihai , Liu Shucai , Chen Shangbin , Tong Xuerui
Published: October 2024

Coal-bearing strata belong to sedimentary strata, and there are multiple aquifers. The accurate detection of deep aquifers is helpful to the safe mining of the working face. In order to provide guidance for the interpretation of the surface-to-underground transient electromagnetic method (SUTEM) that can be used to detect deep aquifers, we used theoretical analysis and numerical simulation methods in this study. Taking uniform half-spaces, single aquifers, and double aquifers as examples, we systematically studied the data characteristics and degree of influence of SUTEM under the influence of shallow aquifers. The results indicate the following: Under the influence of the primary field distribution, the x or y component of the induced electromotive force received by the underground receiving point has a positive and negative inflection point, which increases the difficulty of data interpretation, and the z component is easier to use for data interpretation. The influence of the aquifer on the early data of the underground receiving point is much greater than that of the ground receiving point, and the late influence is closer to the ground receiving point. The change in resistivity of the shallow aquifer has the greatest influence on the ability of each measuring point to detect the data of the deep aquifer; this influence is followed by change in thickness, and change in depth has the least influence on the detection capability of each measuring point.
Face Stability Analysis for Tunnels under Steady Unsaturated Seepage and Inhomogeneity Conditions
Authors: Xie Yi , Liao Hong , Zhou De
Published: October 2024

In the field of tunnels, the stability of tunnel faces is generally considered in dry, saturated and homogeneous soils. However, the actual condition of some soils has been found to be inhomogeneous, with unsaturated seepage. In this paper, an analytical method is applied to estimate the safety factor when the supporting force at the tunnel face is zero under steady unsaturated seepage and inhomogeneous conditions. This method combines kinematic limit analysis techniques with strength reduction techniques; an efficient stress formulation utilizing suction stress is employed to determine the apparent cohesive force to obtain the solution of the steady unsaturated seepage problem, and indicators of soil inhomogeneity are attributed to the effect on cohesion. A 3D log-spiral collapse mechanism is used to find the zero supporting pressure and determine the safety factor through an iterative method. This paper analyzes the effect of variations in the unsaturated parameters, inhomogeneity parameters and tunnel dimensional parameters on the stability of the tunnel face.
Toxic Effects of Sublethal Pyriproxyfen on Baseline Hemolymph Biochemical Balance in Euschistus heros
Authors: Cremonez Paulo S. G. , Matsumoto Janaína F. , Andrello Avacir C. , Pinheiro Daniela O. , Neves Pedro M. O. J.
Published: October 2024

The sublethal toxic effects of pyriproxyfen, an insect juvenile hormone analog (JHA) insecticide, on the circulating metabolite balance in the Neotropical brown stink bug, Euschistus heros, one of the main agronomic pests in South America, were investigated. Our objectives were to evaluate changes in the baseline levels of lipids and carbohydrates, along with three selected micro-elements—nickel (Ni), copper (Cu), and zinc (Zn)—in the hemolymph of adult E. heros, following the application of a predetermined LC30 of pyriproxyfen (single topical application) in fourth-instar nymphs (N4). Hemolymph was sampled using glass capillaries, with the resulting concentrations of lipids and carbohydrates determined through vanillin- and anthrone-positive reactions, respectively, and micro-element analysis was performed through total reflection X-ray fluorescence (TXRF) spectrometry. Lipids are the main and more stable energy metabolites for E. heros, with a proportion of 2:1 compared to carbohydrate levels. A remarkable sensitivity of carbohydrate levels to sublethal pyriproxyfen exposure, irrespective of sex, was observed. Baseline micro-element levels based on untreated control insects indicate sex-based differences in Ni and Zn, but not in Cu, concentrations. After insecticide exposure, the levels of these micro-elements were variable, with Ni and Zn generally decreasing and Cu decreasing in females but nearly doubling in males. The observed disproportion in lipids, carbohydrates, and inorganic micro-elements suggests potential physiological shifts triggered by pyriproxyfen activity in E. heros during late juvenile stages.
Feature Selection with Small Data Sets: Identifying Feature Importance for Predictive Classification of Return-to-Work Date after Knee Arthroplasty
Authors: Rietdijk Harald H. , Strijbos Daniël O. , Conde-Cespedes Patricia , Dijkhuis Talko B. , Oldenhuis Hilbrand K. E. , Trocan Maria
Published: October 2024

In recent decades, the number of cases of knee arthroplasty among people of working age has increased. The integrated clinical pathway ‘back at work after surgery’ is an initiative to reduce the possible cost of sick leave. The evaluation of this pathway, like many clinical studies, faces the challenge of small data sets with a relatively high number of features. In this study, we investigate the possibility of identifying features that are important in determining the duration of rehabilitation, expressed in the return-to-work period, by using feature selection tools. Several models are used to classify the patient’s data into two classes, and the results are evaluated based on the accuracy and the quality of the ordering of the features, for which we introduce a ranking score. A selection of estimators are used in an optimization step, reorganizing the feature ranking. The results show that for some models, the proposed optimization results in a better ordering of the features. The ordering of the features is evaluated visually and identified by the ranking score. Furthermore, for all models, higher accuracy, with a maximum of 91%, is achieved by applying the optimization process. The features that are identified as relevant for the duration of the return-to-work period are discussed and provide input for further research.
Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
Authors: Zhou Cong , Zhou Sihang , Huang Jian , Wang Dong
Published: October 2024

Knowledge-aware recommendation systems have shown superior performance by connecting user item interaction graph (UIG) with knowledge graph (KG) and enriching semantic connections collected by the corresponding networks. Among the existing methods, self-supervised learning has attracted the most attention for its significant effects in extracting node self-discrimination auxiliary supervision, which can largely improve the recommending rationality. However, existing methods usually employ a single (either node or edge) perspective for representation learning, over-emphasizing the pair-wise topology structure in the graph, thus overlooking the important semantic information among neighborhood-wise connection, limiting the recommendation performance. To solve the problem, we propose Hierarchical self-supervised learning for Knowledge-aware Recommendation (HKRec). The hierarchical property of the method is shown in two perspectives. First, to better reveal the knowledge graph semantic relations, we design a Triple-Graph Masked Autoencoder (T-GMAE) to force the network to estimate the masked node features, node connections, and node degrees. Second, to better align the user-item recommendation knowledge with the common knowledge, we conduct contrastive learning in a hybrid way, i.e., both neighborhood-level and edge-level dropout are adopted in a parallel way to allow more comprehensive information distillation. We conduct an in-depth experimental evaluation on three real-world datasets, comparing our proposed HKRec with state-of-the-art baseline models to demonstrate its effectiveness and superiority. Respectively, Recall@20 and NDCG@20 improved by 2.2% to 24.95% and 3.38% to 22.32% in the Last-FM dataset, by 7.0% to 23.82% and 5.7% to 39.66% in the MIND dataset, and by 1.76% to 34.73% and 1.62% to 35.13% in the Alibaba-iFashion dataset.
Multi-Objective Optimal Control Method for the 6-DOF Robotic Crusher
Authors: Duan Guochen , Yao Lele , Zhan Zhanyu , Kang Tao , Guo Chaoyue
Published: October 2024

In order to achieve the best crushing effect of the 6-DOF robotic crusher, a multi-objective optimal control method for the 6-DOF robotic crusher has been proposed. Taking the mass fraction of crushed products below 12 mm, total energy consumption, effective energy consumption, output, and wear as the working indexes, and taking the suspension point, precession angle, and swing frequency of the mantle as the working conditions of the crusher, the working indexes under different working conditions are calculated. And, based on the above parameters, the optimization objective function of the 6-DOF robotic crusher is obtained. The weight determination method of fuzzy multiple attributes decision making (FMADM) is used to determine the equivalent wear and the optimization target weight. Compared with the original scheme, the output increases and the energy consumption decreases significantly. The results can be used as a reference for the control strategy of the 6-DOF robotic crusher. It can also be used as a reference for the design of a traditional cone crusher.
Quality Enhancement of Torrefied Biopellets Prepared by Unused Forest Biomass and Wood Chip Residues in Pulp Mills
Authors: Lee Tae-Gyeong , Kim Chul-Hwan , Park Hyeong-Hun , Park Ju-Hyun , Park Min-Sik , Lee Jae-Sang
Published: October 2024

The effects of torrefaction of the biopellets made from hardwood chip residue (HW), camellia oilseed cake (CO), and pruning remnants of the toothache tree (TA) and mulberry tree (MT) were evaluated. Torrefaction of the biopellets reduced the volatile matter content of biopellets by 18–58% and increased their heating value by 18–58% without negatively impacting durability or fines content. Torrefaction also reduced the initial ignition time of biopellets by 50–59% and prolonged their combustion duration by 15–24%. Regardless of the type of feedstock, all biopellets exhibited mass yields in the range of 60–80% and energy yields ranging from 80–95%. The novelty of this study lies in the application of torrefaction to already-formed biopellets, which enhances pellet quality without the need for binders, and the use of unused forest biomass and wood chip residue from pulp mills. The use of unused forest biomass and wood chip residue from pulp mills for biopellet production not only provides a sustainable and efficient method for waste utilization but also contributes to environmental conservation by reducing the reliance on fossil fuels. Overall, the torrefaction of biopellets represents a promising technology for producing high-quality solid biofuel from a variety of woody biomass feedstocks without compromising pelletizing efficiency.
Exploring the Effects of Gratitude Voice Waves on Cellular Behavior: A Pilot Study in Affective Mechanotransduction
Authors: del Rosario-Gilabert David , Carbajo Jesús , Valenzuela-Miralles Antonio , Vigué-Guix Irene , Ruiz Daniel , Esquiva Gema , Gómez-Vicente Violeta
Published: October 2024

Emotional communication is a multi-modal phenomenon involving posture, gestures, facial expressions, and the human voice. Affective states systematically modulate the acoustic signals produced during speech production through the laryngeal muscles via the central nervous system, transforming the acoustic signal into a means of affective transmission. Additionally, a substantial body of research in sonobiology has shown that audible acoustic waves (AAW) can affect cellular dynamics. This pilot study explores whether the physical–acoustic changes induced by gratitude states in human speech could influence cell proliferation and Ki67 expression in non-auditory cells (661W cell line). We conduct a series of assays, including affective electroencephalogram (EEG) measurements, an affective text quantification algorithm, and a passive vibro-acoustic treatment (PVT), to control the CO2 incubator environment acoustically, and a proliferation assay with immunolabeling to quantify cell dynamics. Although a larger sample size is needed, the hypothesis that emotions can act as biophysical agents remains a plausible possibility, and feasible physical and biological pathways are discussed. In summary, studying the impact of gratitude AAW on cell biology represents an unexplored research area with the potential to enhance our understanding of the interaction between human cognition and biology through physics principles.
Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
Authors: Łukaniszyn Marian , Majka Łukasz , Grochowicz Barbara , Mikołajewski Dariusz , Kawala-Sterniuk Aleksandra
Published: October 2024

Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets and omics to quickly interpret large datasets in order to generate health and disease indicators and to improve sampling and longitudinal analysis. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of apatient’s condition, treatment progress, and adjustments to the treatment plan), especially in the environments of smart cities and smart territories and through the wider use of 6G, blockchain (and soon maybe quantum cryptography), and the Internet of Things (IoT), as well as through medical technologies, such as multiomics. From a practical point of view, this requires not only efficient validation but also seamless integration with the existing healthcare infrastructure.
A Size Effect Model Combining Both Surface Effects and the Fracture Process Zone (FPZ) for Rocks under Uniaxial Compression
Authors: Liu Yang , Liu Xiaoyu , Zhang Huimei , Zhu Fengbo
Published: October 2024

Developing a size effect model that can encompass the surface effect and the fracture process zone (FPZ) is still challenging. Here, a combined size effect model (CSE model) is formulated by integrating the surface effect size model and the size effect model of fracture mechanics (SEFM model) associated with the FPZ for rocks under compression. Parametric studies indicate that an increased volume fraction of the interior zone as the sample size increases is responsible for the anomalous size effect (ASE). The normal size effect (NSE) is a result of the decrease in the surface layer’s volume fraction and the interior zone’s nominal strength as the sample size increases. Moreover, the mixed type ASE–NSE is caused by the competition among the reduced surface volume fraction, the weakened interior zone strength, and the increased interior zone volume fraction as the sample size increases. A validation study demonstrates that the CSE model accurately predicts the ASE, the NSE, and the mixed type ASE–NSE. It was observed that the determination coefficient R2 of the CSE model is greater than that of the SEFM model for the NSE, equivalent to that of the size effect model of surface effects for the ASE and close to that of the improved USEL (IUSEL) for the mixed type ASE–NSE.
VFLD: Voxelized Fractal Local Descriptor
Authors: Gomez-Donoso Francisco , Escalona Felix , Dargère Florian , Cazorla Miguel
Published: October 2024

A variety of methods for 3D object recognition and registration based on a deep learning pipeline have recently emerged. Nonetheless, these methods require large amounts of data that are not easy to obtain, sometimes rendering them virtually useless in real-life scenarios due to a lack of generalization capabilities. To counter this, we propose a novel local descriptor that takes advantage of the fractal dimension. For each 3D point, we create a descriptor by computing the fractal dimension of the neighbors at different radii. Our redmethod has many benefits, such as being agnostic to the sensor of choice and noise, up to a level, and having few parameters to tinker with. Furthermore, it requires no training and does not rely on semantic information. We test our descriptor using well-known datasets and it largely outperforms Fast Point Feature Histogram, which is the state-of-the-art descriptor for 3D data. We also apply our descriptor to a registration pipeline and achieve accurate three-dimensional representations of the scenes, which are captured with a commercial sensor.
High-Accuracy Entanglement Detection via a Convolutional Neural Network with Noise Resistance
Authors: Sun Qian , Song Yanyan , Liao Zhichuan , Jiang Nan
Published: October 2024

Quantum entanglement detection is one of the fundamental tasks in quantum information science. Conventional methods for quantum state tomography exhibit limitations in scalability as the number of qubits increases, leading to exponential growth in the number of unknown parameters and required measurements. Consequently, the accuracy enhancement achieved by these methods is constrained. In response to this challenge, we developed a tailored convolutional neural network (CNN) model capable of effectively detecting entanglement in two-qubit quantum states, achieving an accuracy exceeding 97.5%. Notably, even in the presence of noise, this model retains its robust performance, displaying resilience against a tolerable level of noise contamination. Furthermore, the inherent generalization power of CNNs allows our model, which was initially trained on a specific spectrum of quantum states, to extend its applicability to wider states, positioning it as an outstanding tool for the further application of machine learning in the field of quantum computing, opening up new pathways for solving entanglement detection problems in quantum information.
1-V Mixed-Mode Universal Filter Using Differential Difference Current Conveyor Transconductance Amplifiers
Authors: Kumngern Montree , Khateb Fabian , Kulej Tomasz
Published: October 2024

This paper presents a mixed-mode universal filter using differential difference current conveyor transconductance amplifiers (DDCCTA). Despite using a minimum number of MOS differential pairs, the proposed DDCCTA is a multiple-input, multiple-output device, that was achieved using the multiple-input bulk-driven MOS transistor (MIBD-MOST) technique, multiple-output current followers and transconductance gains. A subthreshold technique is used to achieve minimum power consumption of the DDCCTA. Thanks to the multiple-input and multiple-output of DDCCTA, the mixed-mode universal filter based on the proposed element can realize five standard filter responses, i.e., low-pass, high-pass, band-pass, band-stop, and all-pass responses, of four modes, i.e., voltage-mode, current-mode, transadmittance-mode, and transimpedance-mode, thus providing 194 filter responses from a single circuit. The natural frequency and quality factor of the filter response can be controlled electronically and orthogonally. The proposed DDCCTA and mixed-mode universal filter are simulated and designed using 0.18 μm CMOS technology to confirm the functionality of the new circuit. The mixed-mode universal filter uses ±0.5 V of supply voltage and consumes 0.374 mW of power when operating at a natural frequency of 10 kHz.
Impact of High-Pressure Processing on Quality and Safety of High-Oil-Content Pesto Sauce: A Comparative Study with Thermal Processing
Authors: Shad Ehsan , Raninen Kaisa , Podergina Svetlana , Chan Lok In , Tong Kam Pui , Hälikkä Heidi , Huovinen Marjo , Korhonen Jenni
Published: October 2024

High-pressure processing (HPP) is a promising technology for increasing the shelf life of food, with minimal effects on the nutritional or sensory quality. However, there has been a concern that high-oil-content foods may protect food pathogens in HPP, and that HPP can affect the quality of lipids. We inoculated Listeria monocytogenes and Salmonella Typhimurium into 34% and 54% oil-content pesto sauce, processed them either with HPP (600 MPa, 4 min) or thermal processing (82 °C, 5 min), and analyzed bacteria counts, pH, GC-MS (Terpene compounds), the time–kill kinetic study, and lipid oxidation value for 60 days in refrigerating storage (5 ± 2 °C). Our findings show that HPP significantly reduced the number of bacteria (more than 4-log) compared to thermal processing or non-processing. Additionally, we discovered terpene compounds (highest-level terpene: L-linalool, eugenol, and 1,8-cineol) in pesto oil that exhibit antimicrobial activity. Different oil content did not have any significant effect on bacteria levels. Regarding chemical results, all samples were of acceptable quality, and the processes did not show any negative effect on lipid oxidation (Peroxide and P-Anisidine value under 10 meq per kilogram of oil). In conclusion, our study indicates that HPP is a suitable method for high-oil-content pesto sauce. In addition, functional compounds naturally present in pesto may contribute to maintaining its microbial and chemical quality.
Stress and Deformation Failure Characteristics Surrounding Rock in Rectangular Roadways with Super-Large Sections
Authors: Zhao Bingchao , Chen Haonan , Wang Jingbin , Wang Ruifeng , Yang Zhonghao , Wen Jie , Tuo Yongsheng
Published: October 2024

This paper presents our study of the deformation and failure characteristics surrounding rock in roadways with super-large sections during the integrated coal pillar excavation, filling and retention process between coalfaces. Based on the theory of complex variable function, the mapping accuracy of conformal transformation is improved, and an analytical solution for surrounding rock stress in super-large sections of roadway is derived. The stress distribution law of the surrounding rock of rectangular roadways is analyzed, and numerical simulation software is used for supplementary analysis and verification. According to the research findings, the compressive stresses on two sides and the tensile stress on the roof of a rectangular roadway with super-large sections decreased with the increase in the side-pressure coefficient; however, when the side-pressure coefficient increased to a certain point, those two sides changed from a pressure-bearing status to a tensile force-bearing status, while the roof changed from a tensile force-bearing status to a pressure-bearing status. In these stress changes, all the stresses upon the surrounding rock of roadways were compressive stresses and the two critical side-pressure coefficient values were λup and λdown. As the aspect ratio of the roadway increased from 1 to 9, its λup increased from 1.823 to 5.865 and its λdown increased from 0.549 to 0.888. When those side-pressure coefficients in the environment where a roadway is located exceed their critical values, tensile stress will take place on the roadway boundary and result in tensile failure, thus leading to instability in the roadway super-large section. The impact of the side-pressure coefficient upon the plastic zone range of roadway surrounding rock is greater than the impact of the roadway width. In order to secure stability in the surrounding rock of roadways with super-large sections during the excavation process, the side-pressure coefficient should remain around 1; in this situation, the plastic zone covers the smallest range and the relevant support work is the easiest. These research findings provide theoretical references for the excavation and support of roadways with super-large sections.
Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
Authors: Wang Le , Chang Yuchen , Wang Kaiping
Published: October 2024

In the transition to fully automated driving, the interaction between drivers and vehicles is crucial as drivers’ emotions directly influence their behavior, thereby impacting traffic safety. Currently, relying solely on a backbone based on a convolutional neural network (CNN) to extract single RGB modal facial features makes it difficult to capture enough semantic information. To address this issue, this paper proposes a Dual-branch Multimodal Fusion Network (DMFNet). DMFNet extracts semantic features from visible–infrared (RGB-IR) image pairs effectively capturing complementary information between two modalities and achieving a more accurate understanding of the drivers’ emotional state at a global level. However, the accuracy of facial recognition is significantly affected by variations in the drivers’ head posture and light environment. Thus, we further propose a U-Shape Reconstruction Network (URNet) to focus on enhancing and reconstructing the detailed features of RGB modes. Additionally, we design a Detail Enhancement Block (DEB) embedded in a U-shaped reconstruction network for high-frequency filtering. Compared with the original driver emotion recognition model, our method improved the accuracy by 18.77% on the DEFE++ dataset, proving the superiority of the proposed method.
Contributions of DNA Sequencing Technologies to the Integrative Monitoring of Karstic Caves
Authors: Bontemps Zélia , Moënne-Loccoz Yvan , Hugoni Mylène
Published: October 2024

Cave microbiota knowledge has greatly expanded in the past decades, driven by the development of molecular techniques, which allow an in-depth characterization of diversity and its metabolic potential. This review focuses on the contribution of DNA sequencing technologies to depict the cave microbiome for the three domains of life (Bacteria, Archaea, and Microeukaryotes), assessing their advantages and limits. Cultural methods do not provide a representative view of cave microbial diversity but do offer, subsequently, the possibility to genomically characterize the strains isolated from caves. Next-generation DNA sequencing permits an exhaustive description of microbial biodiversity in caves, using metabarcoding (for taxonomic assessment) or metagenomics (for taxonomic and functional assessments). It proved useful to compare caves, different rooms, or substrata (water, soil, bedrock, etc.) within a cave, or the effect of cave disturbance in Lascaux and elsewhere. The integration of next-generation DNA sequencing with cultivation techniques, physico-chemical characterization, and other complementary approaches is important to understand the global functioning of caves and to provide key information to guide cave conservation strategies.
Investigation of the Conservatism in Multiple Cracks Coalescence Criteria Using Finite Element-Based Crack Growth Analysis
Authors: Hong Chan-Gi , Lee Gi-Bum , Huh Nam-Su
Published: October 2024

The interactions between multiple cracks significantly influence fracture mechanics parameters, necessitating their consideration in crack assessments. Codes such as ASME Section XI, API 579, BS 7910, and British Energy R6 provide guidelines for crack growth and coalescence, taking crack interactions into account. However, these guidelines often employ idealized crack models, which lead to overly conservative assessments. This study proposes a new criterion for multiple crack coalescence, based on the plastic zone size, to better model the growth and merging of natural cracks. This criterion was implemented using the Advanced Iterative Finite Element Method (AI-FEM), an automated crack-growth simulation program utilizing re-meshing. Fatigue crack growth (FCG) simulations using AI-FEM validated the proposed criterion by comparing it with experimental data. Additionally, the AI-FEM results were compared with those obtained through code-based procedures to evaluate the conservatism of current codes. The findings demonstrate that the proposed criterion closely matches experimental results, providing a more realistic simulation of crack growth and reducing the conservatism of existing codes.
Re-Imagining Trade Policy and Energy Efficiency: Groundbreaking Pathways to Strengthen Environmental Sustainability in South Korea
Authors: Wang Dongxue , He Yugang
Published: October 2024

This study explores the long-term interplay between trade policy, energy efficiency, and carbon dioxide (CO2) emissions in South Korea, using data spanning from 1985 to 2023. By applying the Fourier autoregressive distributed lag (FARDL) model, the analysis reveals that while trade liberalization initially leads to a 0.23% increase in CO2 emissions for each 1% rise in trade openness—driven by the energy demands of industrial expansion—integrating energy efficiency standards within trade agreements helps mitigate these effects over time; this results in a 0.26% reduction in emissions for every 1% improvement in energy efficiency. The study also highlights the dual role of foreign direct investment (FDI), which contributes to a short-term 0.08% rise in emissions but significantly reduces carbon intensity in the long term by facilitating the adoption of cleaner technologies. These findings underscore the importance of innovation and FDI in decoupling economic growth from environmental degradation. The study advocates for the incorporation of energy efficiency measures into trade agreements and the prioritization of green technologies, recommending strategies that could enable South Korea to reduce its CO2 emissions by up to 40% by 2030. This research positions South Korea as a key actor in achieving global climate goals while maintaining economic competitiveness, offering valuable insights into the balance between sustainable development and industrial growth.
CGADNet: A Lightweight, Real-Time, and Robust Crosswalk and Guide Arrow Detection Network for Complex Scenes
Authors: Wang Guangxing , Lin Tao , Dong Xiwei , Wang Longchun , Leng Qingming , Shin Seong-Yoon
Published: October 2024

In the context of edge environments with constrained resources, realizing real-time and robust crosswalk and guide arrow detection poses a significant challenge for autonomous driving systems. This paper proposes a crosswalk and guide arrow detection network (CGADNet), a lightweight visual neural network derived from YOLOv8. Specifically designed for the swift and accurate detection of crosswalks and guide arrows within the field of view of the vehicle, the CGADNet can seamlessly be implemented on the Jetson Orin Nano device to achieve real-time processing. In this study, we incorporated a novel C2f_Van module based on VanillaBlock, employed depth-separable convolution to reduce the parameters efficiently, utilized partial convolution (PConv) for lightweight FasterDetect, and utilized a bounding box regression loss with a dynamic focusing mechanism—WIoUv3—to enhance the detection performance. In complex scenarios, the proposed method in the stability of the mAP@0.5 was maintained, resulting in a 4.1% improvement in the mAP@0.5:0.95. The network parameters, floating point operations (FLOPs), and weights were reduced by 63.81%, 70.07%, and 63.11%, respectively. Ultimately, a detection speed of 50.35 FPS was achieved on the Jetson Orin Nano. This research provides practical methodologies for deploying crosswalk and guide arrow detection networks on edge computing devices.
A Study on the Design of a New Three-Dimensional Seismic Isolation Bearing Based on an Improved Genetic Algorithm for Bridge Engineering
Authors: Sheng Ying , Yang Zhenchao , Meng Yu , Jia Bin
Published: October 2024

(1) Background: In most cases, passive isolation control methods are commonly used for the seismic isolation design in bridge engineering. However, passive seismic isolation devices, due to their non-adjustable performance parameters, struggle to achieve effective seismic isolation across a wide frequency range of 0 Hz to 20 Hz in response to random and varying seismic loads. (2) Methods: The sensitivity of the design parameters of the seismic isolation bearing was analyzed using the optimization center gradient method, and an improved genetic algorithm was employed to quickly optimize and obtain the optimal design parameters. (3) Results: The effectiveness of the three-dimensional seismic isolation bearing was validated through experiments. (4) Conclusions: The multi-factor sensitivity analysis approach used in this study for designing novel isolation bearings is applicable not only to seismic design in bridges but also serves as a reference for parameter design in isolation bearings requiring medium to high precision in seismic performance.