bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling Structural Secrets: Active Learning for Assessing Ultimate Load Capacity in Parallel Wire Cable Systems under Time-Varying Force Identification with Frequency-Squeezing Processing and Vibration Frequency Method
Authors: Lu Hong-Xia , Li Gang , Fu Wenwei , Chen Yi , Li Bingchun , Ma Wenwen
Published: June 2024

One of the primary challenges in cable-stayed bridges is to assess the service performance of stay cables in response to applied loads and ensure that they meet safety requirements. This paper proposes a new strategy to analyze the time-varying reliability of the ultimate load-carrying capacity of stay cables under resistance and stress uncertainty conditions. Initially, we employ the frequency-squeezing processing (FSP) technique within the vibration frequency method (VFM) to enhance the accuracy and effectiveness of cable force identification through field measurement. Subsequently, we thoroughly discuss and establish the statistical characteristics and probabilistic models of stress, including both slow-varying trend and fast-varying trend components, as well as resistance considering the strengthening deterioration effect. The slow-varying trends of the cable forces are extracted using the moving average method (MAM), and both the extracted slow variation and the fast-varying trend components are analyzed in detail. Finally, we introduce a Gaussian process-based surrogate model to assess the time-varying structural reliability by analyzing the associated limit-state function for the ultimate load capacity of the stay cables. In this study, the proposed strategy is applied to quantify the ultimate load-carrying reliability of a stay cable under the uncertainty of the coupled action of corrosion and fatigue. Compared with conventional reliability analysis, the failure probability interval estimation shows the uncertainty boundaries and provides specific years of reliability failure, which can serve as an important reference for bridge maintenance and strengthening.
Experimental and Numerical Investigations of Mixing Performance of Mixing Agitators of Deep Cement Mixing Ships
Authors: Chen Pingshan , Teng Chao , Wang Haiyang , Wan Yuyang , Chen Shunhua , Cao Dingfeng , Zang Mengyan
Published: June 2024

Recent decades have witnessed the increasing usage of deep cement mixing (DCM) mixers in the field of marine infrastructure construction. The mixing performance, including the torque history, can be helpful for structural safety evaluation, design, and the optimization of agitators, which is of engineering significance. However, to the best of the authors’ knowledge, there are no related publications that have reported the mixing behaviors of deep cement mixing agitators. In light of this, the present work conducts experimental and numerical investigations of the mixing behaviors of a DCM ship mixing agitator. To achieve this end, a model test device is established, and mixing experiments using two- and three-blade mixers are respectively conducted. Silt and clay soils are considered in the experiments with a three-blade mixer, while clay soils are used for those with a two-blade mixer. In addition, this work designs a torque transducer placed inside the rotating rod to accurately measure the torque history of the agitator during model test experiments. The experimental results show that, when mixing clay using agitators with different blades, the average torque value required for a two-blade agitator is slightly larger than that for a three-blade one. This study also presents a computational framework based on the arbitrary Lagrangian–Eulerian (ALE) method for an efficient and accurate modeling of the soil-mixing behaviors of the agitator. The numerical results are found to be in good agreement with the experimental data from model tests in terms of torque history, which demonstrates the effectiveness and capacity of our presented computational framework. The numerical results show that the average torque value is smaller at a higher rotational speed during the mixing of clay using a two-bladed agitator, but the effect of rotational speed on the torque history is small. The experimental and numerical methods introduced in the present work can act as a useful tool for investigations of mixing behaviors of DCM agitators.
Simplified Modeling Method for Prefabricated Shear Walls Considering Sleeve Grouting Defects
Authors: Wang Yanan , Wang Hongchen , Wang Fujie
Published: June 2024

The sleeve grouting connection is the most common form of vertical connection for prefabricated shear walls. However, during construction, this type of connection is prone to defects such as insufficient anchorage length of reinforcement, deviation of reinforcement, and insufficient amount of sleeve grouting, which significantly impact the integrity and seismic performance of the prefabricated shear wall structure. The finite element analysis of the prefabricated shear wall with sleeve grouting connection is still based on solid element modeling. This method has the disadvantages of complex models and low computational efficiency. In this paper, a simplified modeling method for prefabricated shear walls considering sleeve grouting defects was proposed to address this issue. Firstly, the equivalent constitutive relationship of the sleeve grouting defect connector was constructed based on the uniaxial tensile test of the existing sleeve grouting defect connector, and the T3D2 element was used to simulate the sleeve grouting connector. Then, the mechanical behavior of the horizontal joint between the shear wall and the foundation beam was simulated by the cohesive force model, and the prefabricated shear wall models with sleeve grouting defects were established. The accuracy of the simplified modeling method was verified by comparing the experimental results and numerical simulation results of the seismic performance of the prefabricated shear wall with sleeve grouting defects. The results showed that the hysteresis curve, skeleton curve, and failure mode of the numerical simulation were in good agreement with the test results. However, the stiffness of the concrete degenerated rapidly due to the apparent development of plastic-damaged concrete, which made the falling section of the hysteresis curve of the numerical simulation different from that of the test. The proposed simplified modeling method can be further applied to the performance study of prefabricated shear walls with sleeve grouting defects and can provide a reference for structural performance evaluation, design optimization, and construction quality control to a certain extent.
Photocatalytic Degradation of Crystal Violet (CV) Dye over Metal Oxide (MOx) Catalysts
Authors: Sifat Mohammed , Shin Eugene , Schevon Anthony , Ramos Hugo , Pophali Amol , Jung Hye-Jung , Halada Gary , Meng Yizhi , Olynik Nicholas , Sprouster David J. , Kim Taejin
Published: June 2024

Crystal violet (CV) is an organic chloride salt and a triphenylmethane dye commonly used in the textile processing industry, also being used as a disinfectant and a biomedical stain. Although CV is widely used, it is carcinogenic to humans and is retained by industrial-produced effluent for an extended period. The different types of metal oxide (MOx) have impressive photocatalytic properties, allowing them to be utilized for pollutant degradation. The role of the photocatalyst is to facilitate oxidation and reduction processes by trapping light energy. In this study, we investigated different types of metal oxides, such as titanium dioxide (TiO2), zinc oxide (ZnO), zirconium dioxide (ZrO2), iron (III) oxide (Fe2O3), copper (II) oxide (CuO), copper (I) oxide (Cu2O), and niobium pentoxide (Nb2O5) for the CV decomposition reaction at ambient conditions. For characterization, BET and Raman spectroscopy were applied, providing findings showing that the surface area of the anatase TiO2 and ZnO were 5 m2/g and 12.1 m2/g, respectively. The activity tests over TiO2 and ZnO catalysts revealed that up to ~98% of the dye could be decomposed under UV irradiation in <2 h. The decomposition of CV is directly influenced by various factors, such as the types of MOx, the band gap–water splitting relationship, and the recombination rate of electron holes.
Review of TiO2 as Desulfurization Catalyst for Petroleum
Authors: Hamza Zahraa A. , Dawood Jamal J. , Jabbar Murtadha Abbas
Published: June 2024

In the relentless pursuit of sustainable energy solutions, the petroleum industry faces the imperative challenge of mitigating sulfur emissions. This comprehensive review scrutinizes Titanium Dioxide (TiO2) as an extraordinary catalyst, pushing the boundaries of desulfurization performance in petroleum refining. The abstract begins by underscoring the urgent need for advanced desulfurization technologies, driven by stringent environmental mandates and escalating global energy demands. The spotlight then shifts to the unparalleled physicochemical attributes of TiO2, showcasing its inherent advantages such as exceptional surface area, stability, and photocatalytic process. A profound exploration of TiO2’s catalytic mechanisms follows, unraveling its capacity to disintegrate stubborn sulfur–carbon bonds, thereby elevating desulfurization efficiency to unprecedented levels. This review meticulously dissects diverse forms of TiO2, ranging from nanoparticles to mesoporous structures, and provides a critical analysis of their respective strengths and limitations in catalyzing sulfur removal. Delving into operational nuances, this review examines the impact of temperature, pressure, and catalyst loading on TiO2 performance, offering crucial insights for optimizing desulfurization processes. The narrative then unfolds to explore cutting-edge developments in TiO2-based catalysts, encompassing ingenious modifications, composites, and hybrid materials designed to augment catalytic activity and selectivity. Anticipating the road ahead, this review contemplates the challenges and prospects of deploying TiO2 on an industrial scale, pointing toward avenues for future research and development. This abstract encapsulates a wealth of knowledge, serving as an indispensable resource for researchers, engineers, and policymakers navigating the dynamic landscape of sustainable petroleum refining. TiO2 emerges as a transformative force, propelling the industry toward cleaner, greener, and more efficient energy production.
Maxillary Hypoplasia and Non-Invasive Ventilation: Literature Review and Proposed New Treatment Protocol
Authors: Meazzini Maria Costanza , Moretti Mattia , Canzi Gabriele , Sozzi Davide , Novelli Giorgio , Mazzoleni Fabio
Published: June 2024

The impact of non-invasive ventilation (NIV) on pediatric maxillary growth is a subject of ongoing research considering its increased use in the pediatric population due to technological advancements and broader indications. This review examines the existing literature, encompassing original articles, case reports, and reviews, to evaluate the effects of NIV on maxillary development and explore potential treatment options. Although the majority of studies agree on the adverse effects of prolonged NIV on maxillary development, techniques for its correction remain understudied. Introducing a novel treatment protocol, we addressed the challenge of correcting severe midfacial hypoplasia in a child with congenital central hypoventilation syndrome (CCHS) undergoing NIV therapy, thus sidestepping the necessity for osteotomies. This proposed protocol holds promise in correcting the adverse impact of NIV on maxillary growth, emphasizing the need for further exploration into innovative treatment modalities.
Characterization of Primary IGF-1 Deficiency in a Cohort of Canadian Children with Short Stature Using a Novel Algorithm Tailored to Electronic Medical Records
Authors: Haridas Rinila , Baxter Carly , Dover Saunya , Goldbloom Ellen B. , Terekhov Ivan , Robinson Marie-Eve
Published: June 2024

(1) Background: Severe primary insulin-like growth factor-I deficiency (SPIGFD) is a rare disorder causing short stature in children due to low insulin-like growth factor 1 (IGF-1) levels. Given the sparsity of reported cases of SPIGFD worldwide, the condition may be underdiagnosed, potentially preventing affected children from receiving therapy with recombinant human IGF-1 (rhIGF-1). Our objective was to determine the prevalence of SPIGFD among children with short stature at a large pediatric tertiary care center through the use of a novel electronic medical record (EMR) algorithm. (2) Methods: We queried our EMR using an algorithm that detected all children seen at our center between 1 November 2013 and 31 August 2021 with short stature and low IGF-1. We then conducted chart reviews, applying established diagnostic criteria for those identified with potential SPIGFD. (3) Results: From a cohort of 4863 children with short stature, our algorithm identified 30 (0.6%) patients with potential SPIGFD. Using chart reviews, we determined that none of these patients had SPIGFD. (4) Conclusions: Our algorithm can be used in other EMRs to identify which patients are likely to have SPIGFD and thus benefit from treatment with rhIGF-1. This model can be replicated for other rare diseases.
Evaluation of Sleep Quality in Asthmatic Children with the Paediatric Sleep Questionnaire (PSQ)
Authors: Özkars Mehmet Yaşar , Çevik Seda , Ata Serap , Sarısaltık Alican , Altaş Uğur
Published: June 2024

Considering the high prevalence of sleep-related breathing disorders (SRBD) in asthmatic patients, we aimed to compare asthmatic children and healthy children in terms of SRBD according to Paediatric Sleep Questionnaire (PSQ) scores. A questionnaire covering sociodemographic characteristics of the patients and the PSQ, which evaluates sleep quality and consists of 22 questions, was administered. During the data collection process, 180 patients in the patient group and 170 patients in the control group were included. The patient group showed statistically significantly higher total scores and subscale scores for snoring, sleepiness, and inattention compared to the control group. Statistically significant correlations were found between the sleepiness subscale and body mass index z score in a negative direction and between age at presentation and duration of asthma in a positive direction. Our findings endorse employing the PSQ as a screening instrument in the outpatient environment to ensure timely referral of asthma patients to a sleep specialist for SRBD evaluation. Considering the widespread occurrence of snoring and asthma, this tool could aid in identifying patients with an elevated risk of SRBD and expedite the scheduling of nocturnal polysomnography for these children.
Experimental Analysis and Wear Prediction Model Based on Friction Heat for Dry Sliding Contact
Authors: Sun Qiming , Zheng Dayu
Published: June 2024

In this study, the influence of the frictional heat effect on the degree of wear is explored from the perspectives of initial contact positive pressure and frictional relative slip velocity. Experiments based on a multifunctional friction and wear machine show that the friction temperature increases with an increase in friction relative velocity and initial normal contact load, which exacerbates the frictional thermal expansion and normal load fluctuation, and with the generation of frictional heat, the normal force fluctuates periodically; the wear mass and temperature in the contact area iterate cyclically, which results in the wear mass increasing. 316L stainless steel, 5A06 aluminium alloy and pure titanium are used in the Archard wear model due to their applications in severe wear environments. Since 316L stainless steel, 5A06 aluminium alloy and pure titanium are mostly used in wear-intensive environments, the Archard wear model is optimised based on the frictional heat effect of these three materials, and the accuracy of the improved model in 316L stainless steel, 5A06 aluminium alloy and pure titanium is improved by 52.6%, 7.4% and 23.9%, respectively, when compared with the conventional model. This study lays a theoretical foundation for the wear prediction models of 316L stainless steel, 5A06 aluminium alloy and pure titanium.
Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making
Authors: Semerci Zeliha Merve , Yardımcı Selmi
Published: June 2024

Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.
An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography
Authors: Neumann Jonas , Nowak Tristan , Schmidt Bernhard , von Zanthier Joachim
Published: June 2024

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.
Impact of Different Glidepath Techniques on the Overall Performance of WaveOne Gold in an Artificial S-Shape Canal
Authors: Lup Vlad Mircea , Marcu Olivia Andreea , Gaeta Carlo , Ciavoi Gabriela
Published: June 2024

Objectives: The article’s aim is to test if rotary or reciprocating glide path influences the overall performance of WaveOne Gold in S-shaped canals. Methods: Sixty endo training blocks with an S-shape curvature were divided into three groups based on the glide path method used: no glide path; glide path preparation with ProGlider; glide path preparation with WaveOne Gold Glider. All blocks were then shaped with WaveOne Gold Primary. The time for shaping, the incidence in reaching working length and the number of pecking motions were recorded. ANOVA with Turkey’s test was used, and the p-value was set to 0.05. Results: WaveOne Gold Primary reached working length faster in the control group when comparing total working times. No significant differences in the ability of the WaveOne Gold Primary to reach working length in all groups (p > 0.05). The mean number of pecking motions was higher in the control group compared to other groups. Conclusions: No significant differences in the time needed to achieve a glide path between Proglider and WaveOne Gold Glider. WaveOne Gold Primary can shape a double curved canal faster if a glide path is present but takes less time to reach length if it is the only file used. No difference in the ability to reach working length.
New Quantum Private Comparison Using Four-Particle Cluster State
Authors: Hou Min , Wu Yue , Zhang Shibin
Published: June 2024

Quantum private comparison (QPC) enables two users to securely conduct private comparisons in a network characterized by mutual distrust while guaranteeing the confidentiality of their private inputs. Most previous QPC protocols were primarily used to determine the equality of private information between two users, which constrained their scalability. In this paper, we propose a QPC protocol that leverages the entanglement correlation between particles in a four-particle cluster state. This protocol can compare the information of two groups of users within one protocol execution, with each group consisting of two users. A semi-honest third party (TP), who will not deviate from the protocol execution or conspire with any participant, is involved in assisting users to achieve private comparisons. Users encode their inputs into specific angles of rotational operations performed on the received quantum sequence, which is then sent back to TP. Security analysis shows that both external attacks and insider threats are ineffective at stealing private data. Finally, we compare our protocol with some previously proposed QPC protocols.
IDEARR Model for STEM Education—A Framework Proposal
Authors: Aguilera David , Lupiáñez José Luis , Perales-Palacios Francisco Javier , Vílchez-González José Miguel
Published: June 2024

This article proposes a theoretical framework for STEM education. It begins by determining the epistemological (based on the Model of Educational Reconstruction and General Systems Theory) and pedagogical (grounded in Situated Learning Theory and co-teaching) alignments. Once these issues are established, a pedagogical model is proposed to facilitate the implementation of the STEM approach in the classroom. This is the IDEARR model, consisting of six phases (Initial, Deconstruction, Explanation, Application, Review, and Reporting) to address an ill-defined problem. This article concludes with a reflection on the educational implications that arise from adopting this theoretical framework for working on STEM education in classrooms, particularly those related to the organization and operation of educational institutions and the initial and ongoing training of teachers.
Parents’ Assumptions and Beliefs about the Impact of Cultural Diversity on Children: A Preliminary Study in Italy, Bulgaria, Germany, Greece, and Spain
Authors: Sierra-Huedo María Luisa , Romea Ana C. , Bruton Lindsey A.
Published: June 2024

This study investigates parents’ perceptions of the impact of cultural diversity on their children and their role in facilitating their children’s navigation through diverse cultural landscapes. A questionnaire, part of the Erasmus+ REACT project (the reciprocal maieutic approach), was distributed among 243 parents of secondary school children in Bulgaria, Italy, Greece, and Spain. It aimed to shed light on the effects of cultural diversity on young individuals and the influence of parents in fostering intercultural competences and critical thinking. The findings reveal a strong positive perception among parents regarding cultural diversity, with a significant majority acknowledging its beneficial impact on their children’s development. Parents identify themselves as crucial educators and role models, emphasizing the importance of open dialogue, positive exemplification, and the teaching of tolerance and respect. Despite recognizing the general adeptness of their children in interacting with cultural diversity, parents perceive challenges, particularly related to differences in beliefs, religions, and social classes. Parents favor experiential and participatory activities over traditional academic methods for fostering intercultural competence, suggesting a shift toward more inclusive educational practices that involve family and community. This study calls for educational initiatives that promote active participation, connection with the community, critical thinking, and empathy toward cultural differences.
Exploring Freehand Drawing Skills of Engineering Students as a Support of Visualization
Authors: Martín Erro Alfonso , Martínez Muneta María Luisa , Rodríguez Sevillano Ángel Antonio
Published: June 2024

The importance of having engineering students proficient in visual literacy has long been recognized. Within this objective, the role of freehand drawing is paramount. It is, therefore, important to reinforce its practice for engineering communication and visual thinking support. This paper reports the findings of a research project aimed at studying the visual literacy skills of engineering students and the use of freehand drawing for this purpose. This exploratory study focuses on visualization and its externalization through freehand drawing. An empirical study was conducted with 66 engineering students from the Universidad Politécnica de Madrid (Spain). Their skills for expressing visualized images through freehand drawing and their tendency to use drawings for problem-solving were evaluated. The findings of the study indicate that students generally lack skills in expressing ideas through freehand drawing. Additionally, they do not tend to apply drawing when finding solutions to given problems. Considering the importance of freehand drawing to reinforce visual literacy, the implementation of its practice is encouraged in order to have students competent in thinking and communicating through these means.
Defining Rural: Rural Teachers’ Perspectives and Experiences
Authors: Inouye Martha , Macias Meghan , Boz Tugba , Lee Min Jung , Hammack Rebekah , Iveland Ashley , Johansen Natalie
Published: June 2024

Public agencies and other funding organizations have often defined rural in reference to “urban” and using parameters such as population density, access to cities, and distance to market areas. Using such definitions of rurality within the context of K-12 education as a way to support these systems is challenging because of the diverse geographic and socio-cultural identities of these places despite a common “rural” designation. This study aims to analyze elementary teachers’ perceptions of their school context and role within that context to better understand the diversity of what it means to be rural. Semi-structured interviews with 3rd–5th-grade teachers (n = 35) were used. Data sources also included identity and community walk slides created by these teachers. Structured interview prompts were tailored to these activities. A priori and emergent coding analyses were used to examine teachers’ conception of their rural context and their role within that context. The results show that rural, as defined by teachers, is a diverse and connected place in which diverse community assets support teachers in their instruction in unique ways. By better understanding the diversity of what it means to be rural, we begin to understand the ways in which context shapes experience and best determine how to support rural educational experiences for both teachers and students.
Overview of AI-Models and Tools in Embedded IIoT Applications
Authors: Dini Pierpaolo , Diana Lorenzo , Elhanashi Abdussalam , Saponara Sergio
Published: June 2024

The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.
From Individual Device Usage to Household Energy Consumption Profiling
Authors: Tolas Ramona , Portase Raluca , Potolea Rodica
Published: June 2024

This paper presents a novel approach for electricity consumption profiling in households through the fusion of usage data for individual smart devices. The novelty of the approach consists of leveraging the data representing the usage of individual appliances rather than using direct measurements of energy consumption. Our methodology focuses on merging signals representing the interaction of the user with the device to compute patterns in the total energy consumption per household. Subsequently, we apply data mining techniques—specifically, unsupervised clustering—to analyze the resulting time-series data representing daily energy consumption. Through this approach, we aim to identify and characterize patterns in energy usage within households, enabling insights for energy optimization strategies and resource allocation. This information can be further used in practical tasks, such as flattening energy consumption. The proposed approach offers an alternative to the direct measurement of energy usage, considering the potential for sensor failure or malfunction. This underscores the importance of implementing a complementary method for verifying and validating energy consumption data.
Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
Authors: Zhang Yunfan , Han Chi , Chu Feihuang , Xiong Wei , Jia Luliang
Published: June 2024

Due to the openness of inter-satellite links (ISLs) in mega-constellations, the threat posed by jamming from non-cooperative constellations is becoming increasingly significant. Most of the existing approaches focus on the up/down link capacity between satellites and ground stations, which differs greatly from the situation whereby ISLs are subjected to non-cooperative jamming. Therefore, this work investigates the transmission rates of ISLs under jamming from non-cooperative mega-constellations. Based on this, a novel satellite network capacity calculation method is proposed to evaluate the mega-constellation network capacity when the transmission rates change dynamically. The simulation results show that the satellite number, jamming power and inclination of non-cooperative constellations have a significant influence on the network capacity. The optimal jamming efficiency occurs when the constellation inclinations are close.
Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System
Authors: Lv Dan , Wang Peng , Wang Qubeijian , Ding Yu , Han Zeyang , Zhang Yadong
Published: June 2024

With the exploration of next-generation network technology, visual internet of things (VIoT) systems impose significant computational and transmission demands on mobile edge computing systems that handle large amounts of offloaded video data. Visual users offload specific tasks to cloud or edge computing platforms to meet strict real-time requirements. However, the available scheduling and computational resources for offloading tasks constantly destroy the system’s reliability and efficiency. This paper proposes a mechanism for task offloading and resource optimization based on predictive perception. Firstly, we proposed two LSTM-based decision-making prediction methods. In resource-constrained scenarios, we improve resource utilization by encouraging edge devices to participate in task offloading, ensuring the completion of more latency-sensitive request tasks, and enabling predictive decision-making for task offloading. We propose a polynomial time optimal mechanism for pre-emptive decision task offloading in resource-abundant scenarios. We solve the 0–1 knapsack problem of offloading tasks to better meet the demands of low-latency tasks where the system’s available resources are not constrained. Finally, we provide numerical results to demonstrate the effectiveness of our scheme.
A Surrogate Model for the Rapid Evaluation of Electromagnetic-Thermal Effects under Humid Air Conditions
Authors: Zhu Hui , Wang Hui , Zhang Han , Wang Nan , Ren Qiang , Chen Yanning , Liu Fang , Gao Jie
Published: June 2024

Integrated circuits are being more and more extensively applied, and the reliability issues of devices are receiving increased attention from researchers. The study of electronic device performance is not limited to the device themselves; these studies also need to consider the operating environment, such as high temperature or high humidity, which requires fluid simulation. However, this approach inevitably increases the complexity of modeling and the difficulty of the equations to be solved. Aiming at the simulation of the thermal performance of a device under coupled humid air conditions, this paper proposes a surrogate model to quickly evaluate the multiphysical effects of humid air and a multiphysical solver based on it. In this research, the finite element method (FEM) is utilized to simulate the multiphysical problem, and the proposed method is verified as being efficient.
Fusion of Infrared and Visible Light Images Based on Improved Adaptive Dual-Channel Pulse Coupled Neural Network
Authors: Feng Bin , Ai Chengbo , Zhang Haofei
Published: June 2024

The pulse-coupled neural network (PCNN), due to its effectiveness in simulating the mammalian visual system to perceive and understand visual information, has been widely applied in the fields of image segmentation and image fusion. To address the issues of low contrast and the loss of detail information in infrared and visible light image fusion, this paper proposes a novel image fusion method based on an improved adaptive dual-channel PCNN model in the non-subsampled shearlet transform (NSST) domain. Firstly, NSST is used to decompose the infrared and visible light images into a series of high-pass sub-bands and a low-pass sub-band, respectively. Next, the PCNN models are stimulated using the weighted sum of the eight-neighborhood Laplacian of the high-pass sub-bands and the energy activity of the low-pass sub-band. The high-pass sub-bands are fused using local structural information as the basis for the linking strength for the PCNN, while the low-pass sub-band is fused using a linking strength based on multiscale morphological gradients. Finally, the fused high-pass and low-pass sub-bands are reconstructed to obtain the fused image. Comparative experiments demonstrate that, subjectively, this method effectively enhances the contrast of scenes and targets while preserving the detail information of the source images. Compared to the best mean values of the objective evaluation metrics of the compared methods, the proposed method shows improvements of 2.35%, 3.49%, and 11.60% in information entropy, mutual information, and standard deviation, respectively.
A Review of Coupled Geochemical–Geomechanical Impacts in Subsurface CO2, H2, and Air Storage Systems
Authors: Shi Zhuofan , Driba Dejene L. , Lopez Rivera Nora , Kariminasab Mohammad , Beckingham Lauren E.
Published: June 2024

Increased demand for decarbonization and renewable energy has led to increasing interest in engineered subsurface storage systems for large-scale carbon reduction and energy storage. In these applications, a working fluid (CO2, H2, air, etc.) is injected into a deep formation for permanent sequestration or seasonal energy storage. The heterogeneous nature of the porous formation and the fluid–rock interactions introduce complexity and uncertainty in the fate of the injected component and host formations in these applications. Interactions between the working gas, native brine, and formation mineralogy must be adequately assessed to evaluate the efficiency, risk, and viability of a particular storage site and operational regime. This study reviews the current state of knowledge about coupled geochemical–geomechanical impacts in geologic carbon sequestration (GCS), underground hydrogen storage (UHS), and compressed air energy storage (CAES) systems involving the injection of CO2, H2, and air. Specific review topics include (1) existing injection induced geochemical reactions in these systems; (2) the impact of these reactions on the porosity and permeability of host formation; (3) the impact of these reactions on the mechanical properties of host formation; and (4) the investigation of geochemical-geomechanical process in pilot scale GCS. This study helps to facilitate an understanding of the potential geochemical–geomechanical risks involved in different subsurface energy storage systems and highlights future research needs.
Research on Carbon Emissions Estimation in Key Industries Based on the Electricity–Energy–Carbon Model: A Case Study of Henan Province
Authors: Wang Yuanyuan , Ji Haoyang , Wang Shiqian , Wang Han , Shi Junyi
Published: June 2024

This study focuses on the carbon emissions of key industries in Henan Province, employing techniques of seasonal adjustment, frequency transformation, and statistical modeling to construct an industry-level “Electricity–Energy–Carbon” model to aid in the high-frequency monitoring of carbon emissions in the province’s industries. Based on relevant data, this research performs high-frequency calculations of carbon emissions from energy consumption in 34 typical industries and from the production processes of 53 typical sub-categories in the industrial sector of Henan. The findings reveal the following: Firstly, industrial energy consumption in Henan accounts for over half of the total provincial energy consumption, with most months seeing proportions around 60%. Industries such as energy, non-ferrous metals, building materials, steel, chemicals, petrochemicals, and paper making contribute to over 80% of the industrial energy consumption’s carbon emissions, often nearing 90% in most months. Secondly, among the major industries, such as non-ferrous metals, chemicals, building materials, and steel, there is a dual challenge of being restricted under the “high energy consumption and high emissions” project while also being required to build key industrial bases, leading to fluctuating trends in historical annual carbon emissions data. Thirdly, six sub-categories, namely plastic products, cement, flat glass, steel, ten types of non-ferrous metals, and alumina, have significant carbon emissions in their production processes, accounting for about 72.3% of the total production-related emissions.
GC–MS Combined with Fast GC E-Nose for the Analysis of Volatile Components of Chamomile (Matricaria chamomilla L.)
Authors: Lu Jiayu , Jiang Zheng , Dang Jingjie , Li Dishuai , Yu Daixin , Qu Cheng , Wu Qinan
Published: June 2024

Chamomile has become one of the world’s most popular herbal teas due to its unique properties. Chamomile is widely used in dietary supplements, cosmetics, and herbal products. This study aimed to investigate the volatile aromatic components in chamomile. Two analytical techniques, gas chromatography–mass spectrometry (GC-MS) and an ultra-fast gas chromatography electronic nose, were employed to examine samples from Xinjiang (XJ), Shandong (SD), and Hebei (HB) in China, and imported samples from Germany (GER). The results revealed that all chamomile samples contained specific sesquiterpene compounds, including α-bisabolol, bisabolol oxide, bisabolone oxide, and chamazulene. Additionally, forty potential aroma components were identified by the electronic nose. The primary odor components of chamomile were characterized by fruity and spicy notes. The primary differences in the components of chamomile oil were identified as (E)-β-farnesene, chamazulene, α-bisabolol oxide B, spathulenol and α-bisabolone oxide A. Significant differences in aroma compounds included geosmin, butanoic acid, 2-butene, norfuraneol, γ-terpinene. This study demonstrates that GC–MS and the ultra-fast gas chromatography electronic nose can preliminarily distinguish chamomile from different areas, providing a method and guidance for the selection of origin and sensory evaluation of chamomile. The current study is limited by the sample size and it provides preliminary conclusions. Future studies with a larger sample size are warranted to further improve these findings.
Boosting Synergistic Antioxidant and Anti-Inflammatory Properties Blending Cereal-Based Nutraceuticals Produced Using Sprouting and Hydrolysis Tools
Authors: Jiménez-Pulido Iván Jesús , Martín-Diana Ana Belén , Tomé-Sánchez Irene , de Luis Daniel , Martínez-Villaluenga Cristina , Rico Daniel
Published: June 2024

Nutraceuticals obtained from sprouted wheat and oat grains and processing by-products (bran and hull, respectively) naturally containing antioxidant and anti-inflammatory compounds were evaluated. The objective of this study was the development of a cereal-based nutraceutical formula combining extracts from sprouts and by-products and the exploration for potential synergetic effects in their bioactive properties. The antioxidant and anti-inflammatory capacities, glycemic index, phytic acid, and β-glucan of individual wheat bran hydrolysate (EH-WB), sprouted wheat (SW), oat hull hydrolysate (EH-OH), sprouted oat (SO), and combined ingredients (CI 1, CI 2, and CI3) were used to tailor an optimal nutraceutical formula. The three blend ingredients (CI 1, CI2, and CI3) were formulated at different ratios (EH-WB:SW:EH-OH:SO; 1:1:1:1, 2:1:2:1, and 1:2:1:2, w:w:w:w, respectively). The resulting mixtures showed total phenol (TPs) content ranging from 412.93 to 2556.66 µmol GAE 100 g−1 and antioxidant capacity values from 808.14 to 22,152.54 µmol TE 100 g−1 (ORAC) and 1914.05 to 7261.32 µmol TE 100 g−1 (ABTS•+), with Fe3+ reducing ability from 734. 02 to 8674.51 mmol reduced Fe 100 g−1 (FRAP) for the individual ingredients produced from EH-WB and EH-OH, where high antioxidant activity was observed. However, the anti-inflammatory results exhibited an interesting behavior, with a potentially synergistic effect of the individual ingredients. This effect was observed in CI2 and CI3, resulting in a higher ability to inhibit IL-6 and TNF-α than expected based on the anti-inflammatory values of their individual ingredients. Similar to the antioxidant properties, oat-based ingredients significantly contributed more to the anti-inflammatory properties of the overall mixture. This contribution is likely associated with the β-glucans and avenanthramides present in oats. To ensure the bioaccessibility of these ingredients, further studies including simulated digestion protocols would be necessary. The ingredient formulated with a 2:1 hydrolysate-to-sprout ratio was the most effective combination, reaching higher biological characteristics.
Exploring Microbial Influence on Flavor Development during Coffee Processing in Humid Subtropical Climate through Metagenetic–Metabolomics Analysis
Authors: Vale Alexander da Silva , Pereira Cecília Marques Tenório , De Dea Lindner Juliano , Rodrigues Luiz Roberto Saldanha , Kadri Nájua Kêmil El , Pagnoncelli Maria Giovana Binder , Kaur Brar Satinder , Soccol Carlos Ricardo , Pereira Gilberto Vinícius de Melo
Published: June 2024

Research into microbial interactions during coffee processing is essential for developing new methods that adapt to climate change and improve flavor, thus enhancing the resilience and quality of global coffee production. This study aimed to investigate how microbial communities interact and contribute to flavor development in coffee processing within humid subtropical climates. Employing Illumina sequencing for microbial dynamics analysis, and high-performance liquid chromatography (HPLC) integrated with gas chromatography–mass spectrometry (GC-MS) for metabolite assessment, the study revealed intricate microbial diversity and associated metabolic activities. Throughout the fermentation process, dominant microbial species included Enterobacter, Erwinia, Kluyvera, and Pantoea from the prokaryotic group, and Fusarium, Cladosporium, Kurtzmaniella, Leptosphaerulina, Neonectria, and Penicillium from the eukaryotic group. The key metabolites identified were ethanol, and lactic, acetic, and citric acids. Notably, the bacterial community plays a crucial role in flavor development by utilizing metabolic versatility to produce esters and alcohols, while plant-derived metabolites such as caffeine and linalool remain stable throughout the fermentation process. The undirected network analysis revealed 321 interactions among microbial species and key substances during the fermentation process, with Enterobacter, Kluyvera, and Serratia showing strong connections with sugar and various volatile compounds, such as hexanal, benzaldehyde, 3-methylbenzaldehyde, 2-butenal, and 4-heptenal. These interactions, including inhibitory effects by Fusarium and Cladosporium, suggest microbial adaptability to subtropical conditions, potentially influencing fermentation and coffee quality. The sensory analysis showed that the final beverage obtained a score of 80.83 ± 0.39, being classified as a specialty coffee by the Specialty Coffee Association (SCA) metrics. Nonetheless, further enhancements in acidity, body, and aftertaste could lead to a more balanced flavor profile. The findings of this research hold substantial implications for the coffee industry in humid subtropical regions, offering potential strategies to enhance flavor quality and consistency through controlled fermentation practices. Furthermore, this study contributes to the broader understanding of how microbial ecology interplays with environmental factors to influence food and beverage fermentation, a topic of growing interest in the context of climate change and sustainable agriculture.
Effect of Jellyfish Body Parts and Presentation Form on Consumers Liking, Sensory Perception, Emotions, and Food Pairings
Authors: Nervo Chiara , Ragazzini Claudia , Torri Luisa
Published: June 2024

Although jellyfish represent a food source in Asia, limited attention has been devoted to investigating Western consumers’ perception and acceptance. This study explored the role of jellyfish body parts and presentation form in determining consumer perception. A local consumer test with 106 untrained subjects (57.5% female, 18–45 years) was performed in Italy over two days on six samples of jellyfish (Rhopilema esculentum Kishinouye) differing in terms of body parts (umbrella and oral arms) and presentation form (minced, striped, and pieced). For each sample, participants expressed their overall liking and, through three check-all-that-apply tests, described their perceived sensory properties and emotions and potential preferred food pairings. The results showed a significant effect of presentation form on liking (with striped and minced samples liked more than pieced samples), 18 sensory properties, four emotions, and five food pairings. Moreover, different drivers of liking and emotions were observed for three clusters of subjects named “In favour of”, “Against”, and “Picky towards” eating jellyfish. In conclusion, this study found that at least one segment of consumers could accept jellyfish as novel food. Moreover, the provided results could be useful for developing innovative jellyfish-based products and dishes that meet consumers’ expectations.