bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced Methods for Monitoring and Fault Diagnosis of Control Loops in Common Rail Systems
Authors: Bacci di Capaci Riccardo , Pannocchia Gabriele
Published: October 2024

Common rail systems are a key component of modern diesel engines and highly increase their performance. During their working lifetime, there could be critical damages or failures related to aging, like backlash or friction, or out-of-spec operating conditions, like low-quality fuel with, e.g., the presence of water or particles or a high percentage of biodiesel. In this work, suitable data-driven methods are adopted to develop an automatic procedure to monitor, diagnose, and estimate some types of faults in common rail systems. In particular, the pressure control loop operating within the engine control unit is investigated; the system is described using a Hammerstein model composed of a nonlinear model for the control valve behavior and an extended linear model for the process dynamics, which also accounts for the presence of external disturbances. Three different sources of oscillations can be successfully detected and quantified: valve stiction, aggressive controller tuning, and external disturbance. Selected case studies are used to demonstrate the effectiveness of the developed methodology.
B-TBM: A Novel Deep Learning Model with Enhanced Loss Function for HAZOP Risk Classification Using Natural Language Statistical Laws
Authors: Xu Binxin , Lu Duhui , Gao Dong , Zhang Beike
Published: October 2024

HAZOP is a paradigm of industrial safety, and the introduction of deep learning-based HAZOP text categorization marks the arrival of an intelligent era of safety analysis. However, existing risk analysis methods have limitations in processing complex texts and extracting deep risk features. To solve this problem, this paper proposes a novel HAZOP risk event classification model based on BERT, BiLSTM, and TextCNN. The complexity of HAZOP text is revealed by introducing statistical laws of natural language, such as Zipf’s law and Heaps’ law, and the outputs of different levels of BERT are further combined linearly to collaborate with BiLSTM and TextCNN to capture long-term dependency and local contextual information for a more accurate classification task. Meanwhile, an improved loss function is proposed to effectively solve the deficiencies of the traditional cross-entropy loss function in the mislabeling process and improve the generalization ability of the model. It is experimentally demonstrated that the accuracy of the model is improved by 3% to 4% compared to the traditional BERT model in the task of severity and possibility classification of HAZOP reports. This study not only improves the accuracy and efficiency of HAZOP risk analysis, but also provides new ideas and methods for the application of natural language processing in industrial safety.
Comparative Techno-Economic Analysis of Parabolic Trough and Linear Fresnel Collectors with Evacuated and Non-Evacuated Receiver Tubes in Different Geographical Regions
Authors: Shokrnia Mehdi , Cagnoli Mattia , Grena Roberto , D’Angelo Antonio , Lanchi Michela , Zanino Roberto
Published: October 2024

In the context of Concentrated Solar Power (CSP) technology, this paper presents a comparison between the Parabolic Trough Collector (PTC) and the Linear Fresnel Collector (LFC), considering both evacuated and non-evacuated receiver tubes. The comparison was carried out in terms of the Levelized Cost of Electricity (LCOE) considering a reference year and four locations in the world, characterized by different levels of direct normal irradiation (DNI) from 2183 kWh/m2/year to 3409 kWh/m2/year. The LCOE depends on economic parameters and on the net energy generated by a plant on an annual basis. The latter was determined by a steady-state 1D model that solved the energy balance along the receiver axis. This model required computing the incident solar power and heat losses. While the solar power was calculated by an optical ray-tracing model, heat losses were computed by a lumped-parameter model developed along the radial direction of the tube. Since the LFC adopted a secondary concentrator, no conventional correlation was applicable for the convective heat transfer from the glass cover to the environment. Therefore, a 2D steady-state CFD model was also developed to investigate this phenomenon. The results showed that the PTC could generate a higher net annual energy compared to the LFC due to a better optical performance ensured by the parabolic solar collector. Nevertheless, the difference between the PTC and the LFC was lower in the non-evacuated tubes because of lower heat losses from the LFC receiver tube. The economic analysis revealed that the PTC with the evacuated tube also achieved the lowest LCOE, since the higher cost with respect to both the LFC system and the non-evacuated PTC was compensated by the higher net energy yield. However, the non-evacuated LFC demonstrated a slightly lower LCOE compared to the non-evacuated PTC since the lower capital cost of the non-evacuated LFC outweighed its lower net annual energy yield. Finally, a sensitivity analysis was conducted to assess the impact on the LCOE of the annual optical efficiency and of the economic parameters. This study introduces key technical parameters in LFC technology requiring improvement to achieve the level of productivity of the PTC from a techno-economic viewpoint, and consequently, to fill the gap between the two technologies.
Application of NACA 6412 Airfoil for Noise and Vibration Reduction in Evaporator Fan Blades
Authors: Moralar Aytaç , Ekim Serhat
Published: October 2024

In today’s industry, due to the importance placed on occupational health and safety, regulations are being implemented to reduce noise in factory conditions. This study aims to reduce fan noise levels while simultaneously enhancing fan efficiency. We focused specifically on the evaporator fans used in the ammonia refrigeration tunnels at Unilever’s Algida ice cream production facility. Both physical and computational models of the fan blades were developed using Fluent and Phoenics computational fluid dynamics (CFD) software. Vibration analyses of the fan blades were performed using the modal analysis module. Through the analysis of pressure distributions on the existing fan blades, it was found that aerodynamic irregularities were the primary contributors to the noise. To address this, the NACA 6412 airfoil model was selected for the redesign, resulting in balanced pressure distribution across the blade surfaces and a reduction in noise levels from 96 dB to 78 dB.
Multi-Objective Optimization and Design for Industrial Vinyl Chloride Reactor by Hybrid Model
Authors: Song Erwei , Wang Mingmei , Zhou Xiaoyu , Wang Erqiang , Guo Cun-Yue
Published: October 2024

The acetylene conversion rate and vinyl chloride production capacity are the main economic indexes for vinyl chloride synthesis, and the reaction temperature is an important operating parameter to prevent the Hg active component from being loss. These three factors have not been taken into consideration simultaneously in the traditional optimization process, making it difficult to achieve optimization targets perfectly for industrial application. To overcome this problem, an efficient strategy framework was proposed based on a hybrid model. Compared with conventional paradigms, the proposed framework could not only reduce computational expense but optimize these two economic indexes with a constrained reaction temperature simultaneously. In addition, a machine learning method was used to conduct a feature analysis, which can reveal the potential interaction between different variables so key variables of this reactor could be identified. To demonstrate and verify this framework, multi-objective optimization involving multiple variables with constrained conditions for the industrial reactor was conducted from design and operation perspectives, respectively. The proposed strategy could provide optimal operational direction for the industrial reactor from these design and operation aspects, which contribute to the sustainable and highly efficient process development in this field. For the first section of an industrial vinyl chloride reactor, this strategy could realize significant improvement in the acetylene conversion rate from 81.34% to over 95.00% and in the vinyl chloride production capacity from 2.60 to above 3.40 mol/h in the operation scenarios, which can meet production requirements. So, the second section of the traditional reactor system is not needed at all.
Parameter Identification and Energy Dissipation Analysis of Premium Connections Based on the Iwan Model
Authors: Yu Yang , Chen Yimeng , Dou Yihua , Yuan Qingying , Tang Jiarui , Cao Yinping
Published: October 2024

The premium connection is an important section of the tubing column. Under intricate downhole conditions, axial vibration generates alternating loads that cause energy dissipation between the sealing surfaces of the premium connection, reducing sealing performance. To investigate this issue, the mutual conversion process of sticking, slipping, and macroscopic slipping stages between the sealing surfaces of the premium connection under axial loads must be assessed. In this study, a finite element analysis model of a taper–taper Φ88.9 mm × 6.45 mm P110 premium connection is developed based on the discrete Iwan model’s ontological relationship, and the sealing surface’s force–displacement hysteresis curve is obtained. The equivalent Iwan model for this particular premium connection is constructed by discretizing the hysteresis curve and identifying the model’s four sets of parameters. The correctness of the parameter identification method of the equivalent Iwan model is verified by comparing and analyzing the similarity of the two models. The energy dissipation in the sealing surfaces of the premium connection for different working conditions under dynamic loading is analyzed. This study reveals that the area similarity of the hysteresis curves of the two models is more than 92%, while the positional error is less than 2%. The sealing surface displacement amplitude of the premium connection is between 0.04 mm and 0.07 mm, while the sealing surface energy dissipation increases linearly, which may lead to a decline in sealing performance.
The Cult of the Apollonian and the Dionysian in Ancient Greek Religion as Reflected in Edith Wharton’s Novels
Authors: Zeng Hong
Published: October 2024

The two basic conflicting forces throughout Wharton’s tragic novels have a great affinity with the cult of the Apollonian and Dionysian in ancient Greek religion and in Nietzsche’s The Birth of Tragedy. On the one hand, it is the Apollonian ideal of civilized society and individual restraint, which is a beautiful illusion maintained in a bright appearance. When it encounters Dionysus, it is its defensive social rules, the social restrictions, and oppression it imposes on individuals; on the other hand, it is the awakening of Dionysian primitive passion, which tears off the social and individual protective veil of Apollo and reaches the Dionysian tragic cognition at the root of existence, requiring breaking social barriers and indulging the vitality of primitive nature. In this paper, the cult of the Apollonian and Dionysian in ancient Greek religion and as defined by Nietzsche in his The Birth of Tragedy is used to analyze the inner conflicts in the protagonists of Wharton’s tragic novels and the patterns of tragedy in each of her studied novels. Through such archetypal criticism of religious cults in Wharton’s major works, this paper attempts to refute most of the negative criticism of her novels.
Arduino-Based Mobile Robotics for Fostering Computational Thinking Development: An Empirical Study with Elementary School Students Using Problem-Based Learning Across Europe
Authors: Barradas Rolando , Lencastre José Alberto , Soares Salviano Pinto , Valente António
Published: October 2024

The present article explores the impact of educational robotics on fostering computational thinking and problem-solving skills in elementary school students through a problem-based learning approach. This study involved the creation of a framework which includes a robot and two eBooks designed for students and teachers. The eBooks serve as a guide to the construction and programming of a small Arduino-based robot. Through integration with gamification elements, the model features a narrative with three characters to boost a student’s engagement and motivation. Through iteration of heuristic evaluations and practical tests, we refined the initial theoretical framework. An empirical study was conducted in two phases involving 350 students. The first empirical test involved a small group of 21 students, similar to end users, from five European schools. With a 100% completion rate for the tasks, 73.47% of these tasks were solved optimally. Later, we conducted a larger validation study which involved 329 students in a Portuguese school. This second phase of the study was conducted during the 2022–2023 and 2023–2024 school years with three study groups. The results led to a 91.13% success rate in problem-solving activities, and 56.99% of those students achieved optimal solutions. Advanced statistical techniques, including ANOVA, were applied to account for group differences and ensure the robustness of the findings. This study demonstrates that the proposed model which integrates educational robotics with problem-based learning effectively promotes computational thinking and problem-solving skills, which are essential for the 21st century. These findings support the inclusion of robotics into primary school curricula and provide a validated framework for educators.
Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images
Authors: Jeon Juil , Ji Myungin , Lee Jungho , Han Kyeong-Soo , Cho Youngsu
Published: October 2024

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical.
How to Learn More? Exploring Kolmogorov–Arnold Networks for Hyperspectral Image Classification
Authors: Jamali Ali , Roy Swalpa Kumar , Hong Danfeng , Lu Bing , Ghamisi Pedram
Published: October 2024

Convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification. However, these models require a significant number of training data and are computational resources. On the other hand, modern Multi-Layer Perceptrons (MLPs) have demonstrated a great classification capability. These modern MLP-based models require significantly less training data compared with CNNs and ViTs, achieving state-of-the-art classification accuracy. Recently, Kolmogorov–Arnold networks (KANs) were proposed as viable alternatives for MLPs. Because of their internal similarity to splines and their external similarity to MLPs, KANs are able to optimize learned features with remarkable accuracy, in addition to being able to learn new features. Thus, in this study, we assessed the effectiveness of KANs for complex HSI data classification. Moreover, to enhance the HSI classification accuracy obtained by the KANs, we developed and proposed a hybrid architecture utilizing 1D, 2D, and 3D KANs. To demonstrate the effectiveness of the proposed KAN architecture, we conducted extensive experiments on three newly created HSI benchmark datasets: QUH-Pingan, QUH-Tangdaowan, and QUH-Qingyun. The results underscored the competitive or better capability of the developed hybrid KAN-based model across these benchmark datasets over several other CNN- and ViT-based algorithms, including 1D-CNN, 2DCNN, 3D CNN, VGG-16, ResNet-50, EfficientNet, RNN, and ViT.
ILN-SSR: Improved Logarithmic Norm and Sparse Structure Refinement for Infrared Small Target Detection
Authors: Liu Liqi , Zhang Rongguo , Mei Jian , Ni Xinyue , Li Liyuan , Su Xiaofeng , Chen Fansheng
Published: October 2024

The effective discrimination of targets from backgrounds in environments characterized by a low signal-to-clutter ratio (SCR) is paramount for the advancement of infrared small target detection (IRSTD). In this work, we propose a novel detection framework predicated on low-rank sparse decomposition (LRSD), incorporating an improved logarithmic norm and a mechanism for sparse structure refinement, herein referred to as the improved logarithmic norm and sparse structure refinement (ILN-SSR). The ILN-SSR framework more precisely characterizes the sparse properties of both the background and the target, enabling a more effective distinction between the target and its background. Initially, our approach entails the utilization of an improved logarithmic norm to precisely estimate the low-rank attributes of the infrared image background. This is followed by the employment of a linear sparse regularization term alongside a target-traits-based sparse regularization term aimed at meticulously identifying targets within sparse regions and refining the sparse structure. Subsequently, we combine these components into the ILN-SSR framework, which formulates IRSTD as an optimization problem. The resolution of this framework is achieved through the implementation of the alternating direction method of multipliers (ADMM). The efficacy of the proposed framework is corroborated through the analysis of six image sequences. Comprehensive experimental assessments affirmed the framework’s substantial robustness in navigating various complex backgrounds.
COCCON Measurements of XCO2, XCH4 and XCO over Coal Mine Aggregation Areas in Shanxi, China, and Comparison to TROPOMI and CAMS Datasets
Authors: Tu Qiansi , Hase Frank , Qin Kai , Alberti Carlos , Lu Fan , Bian Ze , Cao Lixue , Fang Jiaxin , Gu Jiacheng , Guan Luoyao , Jiang Yanwu , Kang Hanshu , Liu Wang , Liu Yanqiu , Lu Lingxiao , Shan Yanan , Si Yuze , Xu Qing , Ye Chang
Published: October 2024

This study presents the first column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) in the coal mine aggregation area in Shanxi, China, using two portable Fourier transform infrared spectrometers (EM27/SUNs), in the framework of the Collaborative Carbon Column Observing Network (COCCON). The measurements, collected over two months, were analyzed. Significant daily variations were observed, particularly in XCH4, which highlight the impact of coal mining emissions as a major CH4 source in the region. This study also compares COCCON XCO with measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, revealing good agreement, with a mean bias of 7.15 ± 9.49 ppb. Additionally, comparisons were made between COCCON XCO2 and XCH4 data and analytical data from the Copernicus Atmosphere Monitoring Service (CAMS). The mean biases between COCCON and CAMS were −6.43 ± 1.75 ppm for XCO2 and 15.40 ± 31.60 ppb for XCH4. The findings affirm the stability and accuracy of the COCCON instruments for validating satellite observations and detecting local greenhouse gas sources. Operating COCCON spectrometers in coal mining areas offers valuable insights into emissions from these high-impact sources.
Research on Deep Learning Detection Model for Pedestrian Objects in Complex Scenes Based on Improved YOLOv7
Authors: Hu Jun , Zhou Yongqi , Wang Hao , Qiao Peng , Wan Wenwei
Published: October 2024

Objective: Pedestrian detection is very important for the environment perception and safety action of intelligent robots and autonomous driving, and is the key to ensuring the safe action of intelligent robots and auto assisted driving. Methods: In response to the characteristics of pedestrian objects occupying a small image area, diverse poses, complex scenes and severe occlusion, this paper proposes an improved pedestrian object detection method based on the YOLOv7 model, which adopts the Convolutional Block Attention Module (CBAM) attention mechanism and Deformable ConvNets v2 (DCNv2) in the two Efficient Layer Aggregation Network (ELAN) modules of the backbone feature extraction network. In addition, the detection head is replaced with a Dynamic Head (DyHead) detector head with an attention mechanism; unnecessary background information around the pedestrian object is also effectively excluded, making the model learn more concentrated feature representations. Results: Compared with the original model, the log-average miss rate of the improved YOLOv7 model is significantly reduced in both the Citypersons dataset and the INRIA dataset. Conclusions: The improved YOLOv7 model proposed in this paper achieved good performance improvement in different pedestrian detection problems. The research in this paper has important reference significance for pedestrian detection in complex scenes such as small, occluded and overlapping objects.
A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs
Authors: Li Gang , Zhong Lin , Sun Wenjun , Zhang Shaohua , Liu Qianjie , Huang Qingsheng , Hu Guoliang
Published: October 2024

To improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspension with air springs. Mathematical models are established for the magnetorheological dampers and air springs. Based on the improved hyperbolic tangent model, a forward model is established for the magnetorheological damper. The adaptive fuzzy neural network method is used to establish the inverse model of the magnetorheological damper. The relationship between different road excitation frequencies and the control effect of magnetorheological semi-active suspension with air springs is simulated, and the optimal prediction horizons under different conditions are obtained. The VHMPC method is designed to automatically switch the predictive horizon according to the road surface excitation frequency. The results demonstrate that under mixed conditions, compared with the traditional MPC, the VHMPC can improve the smoothness of the suspension by 2.614% and reduce the positive and negative peaks of the vertical vibration acceleration by 11.849% and 6.938%, respectively. Under variable speed road conditions, VHMPC improved the sprung mass acceleration, dynamic tire deformation, and suspension deflection by 7.191%, 7.936%, and 22.222%, respectively, compared to MPC.
Multi-Activity Step Counting Algorithm Using Deep Learning Foot Flat Detection with an IMU Inside the Sole of a Shoe
Authors: Lucot Quentin , Beurienne Erwan , Behr Michel
Published: October 2024

Step counting devices were previously shown to be efficient in a variety of applications such as athletic training or patient’s care programs. Various sensor placements and algorithms were previously experimented, with a best mean absolute percentage error (MAPE) close to 1% in simple mono-activity walking conditions. In this study, an existing running shoe was first instrumented with an inertial measurement unit (IMU) and used in the context of multi-activity trials, at various speeds, and including several transition phases. A total of 21 participants with diverse profiles (gender, age, BMI, activity style) completed the trial. The data recorded was used to develop a step counting algorithm based on a deep learning approach, and further validated against a k-fold cross validation process. The results revealed that the step counts were highly correlated to gyroscopes and accelerometers norms, and secondarily to vertical acceleration. Reducing input data to only those three vectors showed a very small decrease in the prediction performance. After the fine-tuning of the algorithm, a MAPE of 0.75% was obtained. Our results show that such very high performances can be expected even in multi-activity conditions and with low computational resource needs making this approach suitable for embedded devices.
The Influence of Temperature Disturbance on Space Inertial Sensors
Authors: Hao Jia , Wei Fulong , Yan Xingyu , Ma Jinlong , Zhou Zebing , Luo Xiaobing
Published: October 2024

The TianQin program is an independently proposed space-borne detection initiative from China. The inertial sensor, as a crucial component, is susceptible to disturbances from temperature fluctuations, the impact of which on acceleration measurements remains elusive. Therefore, a comprehensive analysis on the influence of temperature disturbance is necessary to avoid errors in acceleration measurement. In this study, we proposed a complete scheme for calculating the level of acceleration noise. Based on the traditional temperature control scheme of using a heat pipe, we developed a systematic heat transfer simulation model to obtain the temperature signals. These signals can then be converted into the frequency domain and used to calculate the acceleration noise level within the target bandwidth. Our results indicate that, with a temperature control of 0.13 K/Hz1/2@1 mHz, the largest contributions to acceleration noise come from the outgassing effect, radiometer effect, and radiative pressure effect, in descending order. The total noise peak is 3.6×10−12 m/(s2⋅Hz1/2) at 1 mHz, which is more than three orders of magnitude higher than the TianQin target of 10−15 m/(s2⋅Hz1/2). This study provides new avenues for evaluating measurement errors and solutions for the TianQin program.
Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction
Authors: El-Sayegh Batoul , Dumoulin Chantale , Leduc-Primeau François , Sawan Mohamad
Published: October 2024

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a ±1 scale point with 97% accuracy. These results demonstrate the system’s potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously.
Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links
Authors: Scognamiglio Giovanni , Rucci Andrea , Vaccaro Attilio , Adirosi Elisa , Sapienza Fabiola , Giannetti Filippo , Bacci Giacomo , Angeloni Sabina , Baldini Luca , Roversi Giacomo , Ortolani Alberto , Antonini Andrea , Melani Samantha
Published: October 2024

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations—including SNR measurements paired with disdrometer and rain-gauge data—we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth–satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.
LIG-Based High-Sensitivity Multiplexed Sensing System for Simultaneous Monitoring of Metabolites and Electrolytes
Authors: Park Sang Hyun , Pak James Jungho
Published: October 2024

With improvements in medical environments and the widespread use of smartphones, interest in wearable biosensors for continuous body monitoring is growing. We developed a wearable multiplexed bio-sensing system that non-invasively monitors body fluids and integrates with a smartphone application. The system includes sensors, readout circuits, and a microcontroller unit (MCU) for signal processing and wireless communication. Potentiometric and amperometric measurement methods were used, with calibration capabilities added to ensure accurate readings of analyte concentrations and temperature. Laser-induced graphene (LIG)-based sensors for glucose, lactate, Na+, K+, and temperature were developed for fast, cost-effective production. The LIG electrode’s 3D porous structure provided an active surface area 16 times larger than its apparent area, resulting in enhanced sensor performance. The glucose and lactate sensors exhibited high sensitivity (168.15 and 872.08 μAmM−1cm−2, respectively) and low detection limits (0.191 and 0.167 μM, respectively). The Na+ and K+ sensors demonstrated sensitivities of 65.26 and 62.19 mVdec−1, respectively, in a concentration range of 0.01–100 mM. Temperature sensors showed an average rate of resistance change per °C of 0.25%/°C, within a temperature range of 20–40 °C, providing accurate body temperature monitoring.
Class and Ethno-Gender Differences in Education and Labour Market Position—An Intersectional Analysis of Ethnic Integration in the UK
Authors: Li Yaojun
Published: October 2024

This paper analyses the socio-economic disadvantages of women from different ethnic minority heritages in the UK. Using data from the Labour Force Survey (2014–2023), which contains detailed information on parental class and respondents’ socio-economic conditions, we examine four domains of life chances which are crucial for ethnic integration: educational attainment at the degree level, risks of unemployment, access to professional-managerial (salariat) position and earning power. We proceeded with the gross differences and then examined the differences by ethno-gender status and parental class combinations, controlling for many confounding factors. We also examined the net ethno-gender differences over the life course and the trends of social fluidity over the period covered and across the ethno-gender groups. We found that women from all ethnic origins were doing well in education but faced multiple disadvantages in the labour market, especially in access to the salariat and in earning power. Women of Pakistani/Bangladeshi heritages faced pronounced unemployment risks, especially at the earlier life stages. There is a significant increase in fluidity over the period covered, but this is marked by considerable ethnic and class differences, with Black Caribbean, Black African, Pakistani and Bangladeshi women from more advantaged class origins being unable to secure advantaged class positions and those from working-class families unable to make long-range upward mobility as effectively as White men. Overall, Bangladeshi, Pakistani, Black African and Black Caribbean women are found to be considerably disadvantaged, but there are also signs of social progress.
Valorization of Sugarcane Bagasse Ash as an Alternative SCM: Effect of Particle Size, Temperature-Crossover Effect Mitigation & Cost Analysis
Authors: Francioso Vito , Lemos-Micolta Erika D. , Elgaali Husam H. , Moro Carlos , Rojas-Manzano Manuel Alejandro , Velay-Lizancos Mirian
Published: October 2024

The construction industry faces increasing pressure to reduce its environmental impact while meeting the growing demand for infrastructure. One approach to achieving this goal is the use of industrial waste as a replacement for traditional supplementary cementitious materials (SCMs). This study investigates sugarcane bagasse ash (SCBA), addressing the future scarcity and increased cost of other commonly used SCMs. Despite existing literature, the use of SCBA is hindered by several unknowns. This research evaluates SCBA’s performance in mortars, focusing on the effects of curing temperature and particle size variation. Mortar samples were prepared with SCBA replacements from 0% to 30% by mass of cement and cured at 21 °C and 45 °C for 7, 28, and 90 days. The results suggest potential for SCBA replacement up to 30%, emphasizing its sustainability and economic benefits. A cost analysis was also conducted, demonstrating the economic viability of SCBA as an alternative to traditional cement for practical applications.
Design-Driven Sustainable Poverty Alleviation: A Case Study of Precise Identification and Resource Optimization in Rural Areas
Authors: Li Hong , Shi Li , Wen Kuo-Hsun
Published: October 2024

China’s transition from precise poverty alleviation to rural revitalization represents a critical development stage. This study aims to explore how a design-driven approach can achieve sustainable poverty alleviation and development by addressing challenges such as relative poverty, imprecise targeting, insufficient endogenous motivation, and the risk of returning to poverty. Understanding the inherent relationship between poverty alleviation and sustainable development is crucial for deepening precise poverty alleviation strategies and promoting rural revitalization. This study employs the TOPSIS method to accurately identify target groups for poverty eradication and select optimal poverty alleviation strategies based on multidimensional poverty governance criteria. A case study of Village M in Guangdong Province is conducted, where questionnaire surveys and sampling methods are used to establish the TOPSIS model for precise identification and the design of targeted interventions to support poverty eradication. Both qualitative and quantitative approaches are applied to ensure comprehensive analysis and results. The findings indicate that targeted design interventions, particularly those leveraging local bamboo resources, led to a significant increase in the annual per capita income of identified families by approximately 176.32%, from RMB 3146 (approximately USD 431) to RMB 8700 (approximately USD 1192). This design-driven approach effectively stimulated the innovation consciousness of impoverished farmers, demonstrating the critical role of sustainable design strategies in optimally matching resources to the needs of the poor. This study contributes to the development of a novel framework integrating design methodologies with poverty alleviation strategies, providing empirical evidence on the effectiveness of these interventions. The findings highlight the importance of user-centered design in promoting sustainable development and underscore the need to incorporate design-driven methodologies in poverty alleviation efforts.
New Approach to Identifying Barriers and Potentials in Peripheral and Less-Known Areas Using a Multi-Criteria Analytical Matrix
Authors: Gorgoń Justyna , Piasecka Joanna , Głogowska Magdalena , Długosz Jacek , Sitko Katarzyna , Krzyżak Jacek , Janota Paulina , Martín-Lerones Pedro , Barrientos Francisco
Published: October 2024

The purpose of the article is to present the author’s methodology for identifying and assessing potentials and barriers occurring in various locations related to cultural tourism. The research aimed to develop a methodology to identify barriers and potentials for tourism development in peripheral and less-known locations. The potentials and barriers identified in a given location are important determinants of the development of cultural tourism and therefore economic development in the mentioned area. The identification of development potentials and barriers refers to the existing features of a given area or place and factors that activate or limit the possibilities of its using cultural resources. The study was based on the analysis of eight case studies of destinations with interesting cultural values. The entire series of analyses addressed various aspects of these destinations. Areas of different sizes and scales were considered, including areas in transboundary regions, rural areas, historic city centres, and archaeological or industrial sites. The work presents the author’s methodology for integrating the various elements that define the features and factors of cultural tourism and establishes a framework for a strategy for its sustainable development. To achieve this, a tool in the form of a comprehensive matrix was developed to assess the situation of each pilot site. Based on this tool, the pilot sites identified key features or factors as a barrier or potential and furthermore defined their territorial scope and their impact on their chosen dominant types of tourism. Barriers and potentials were identified based on a set of 47 previously developed features and factors. During the research, 38 most important potentials and 15 barriers were identified. The tool presented in the paper is comprehensive and universal, so it can be applied to any destination.
Exploring Alternative Revenue Models for Construction Consulting Firms in the Digital Era
Authors: Tafti Abbas , Sandberg Marcus , Andersson Matts
Published: October 2024

The AEC industry, including construction consulting, is experiencing digitalization through technologies like Building Information Modeling (BIM), impacting firms’ business models. The traditional hourly-based model does not align with shortened project timelines driven by digitalization, design automation, and product-based approaches. This study uses a case study approach and explores and evaluates four selected alternative revenue models (freemium, subscription, out-come-based, and performance-based) for value creation using a digital tool developed by the consulting firm. The freemium model enhances market reach but requires a high conversion rate. The subscription model offers steady recurring revenue and supports client relationships. Out-come-based and performance-based models link payments to client outcomes and performance metrics, fostering efficiency and competitive advantages. The study identified multiple target markets for this tool and emphasized the importance of a portfolio revenue model to meet specific client needs in the digital era. Implementing alternative revenue models aids return on investments and avoids the digitalization paradox. Construction consulting firms must adeptly manage these models’ complexities to address diverse client needs and leverage digital transformation opportunities.
Constructing the Early-Stage Framework of Cultural Identity Enlightenment in Kindergarten Heritage Education
Authors: Zheng Ran , Guo Liangjing
Published: October 2024

Education and heritage are fundamental to achieving sustainable development goals. Integrating heritage into education is crucial for cultivating children’s connection to heritage and promoting sustainable development. However, the lack of a scientific framework to guide educational goals across age groups results in unclear integration. This study aims to develop a framework for cultural identity enlightenment within heritage education by conducting literature research and validating it through field observations in kindergartens. It identifies cognitive, emotional and behavioral dimensions, outlining the developmental trajectory of these goals. This framework illustrates how children’s understanding progresses from direct experiences to deeper appreciation and creative involvement. It provides teachers with a tool to set goals that empower children as agents, fostering the emergence of cultural awareness and identity. This approach not only enhances cultural identity but also supports sustainable development by advancing the transmission and innovation of cultural heritage.
Female CEOs and Green Innovation: Evidence from Asian Firms
Authors: Mansour Marwan , Shubita Mohammad Fawzi , Lutfi Abdalwali , Saleh Mohammed W. A. , Saad Mohamed
Published: October 2024

This study aims to examine how female CEOs influence green innovation and whether firm size moderates this connection. Our paper focuses on CEOs, who are considered the strategic leaders of corporations, because of their crucial role in making important decisions. This research paper examines how female CEOs influence green innovation (GI) in the Asian industrial sector. The primary goal is to address these research questions: Do Asian industrial firms with female and male CEOs differ in their GI efforts? Is there a positive moderating influence of Asian industrial enterprises’ size on the nexus between women in CEO positions and eco-innovation? Based on our research questions, firm size is likely a determining factor in the GI of female CEOs. This research employs rigorous econometric modeling to analyze a substantial dataset of listed Asian industrial companies from 2013 to 2022. We have found a significant positive correlation between female CEOs and GI in Asian industrial firms. It has been proven that female CEOs in the industrial sector are more inclined to promote environmentally friendly practices. Furthermore, the size of an industrial firm amplifies the beneficial influence of a female CEO on the firm’s chances of engaging in GI initiatives. Regarding the moderating effect of size, the size of companies significantly magnifies the impact of female CEOs on GI. The effectiveness of female CEOs on environmentally friendly practices is more prominent in large corporations than in smaller ones. Our outcomes remain robust with respect to endogeneity issues using two-step GMM estimators. This study proposes that stakeholders, particularly in Asian countries, should promote the increased representation of females in CEO roles, particularly within large corporations. This is because women-led companies demonstrate superior performance in GI endeavors. Hence, regulators must establish policies that facilitate the participation of women in CEO positions within large-scale enterprises. These policies may strengthen the private sector’s capacity to foster sustainable innovation.
A Survey of Deep Learning-Based Information Cascade Prediction
Authors: Wang Zhengang , Wang Xin , Xiong Fei , Chen Hongshu
Published: October 2024

Online social media have significantly boosted the creation and transmission of information, accelerating the dissemination and interaction of vast amounts of data, thereby making the prediction of information cascades increasingly important. In recent years, deep learning has been extensively applied in the domain of information cascade prediction. This paper primarily classifies, organizes, and summarizes the current research status and classic algorithms of information cascade prediction methods based on deep learning. According to the different focuses on characterizing information cascade features, studies on deep learning-based information cascade prediction are classified from two perspectives, i.e., prediction targets and prediction methods. Each category is explained in detail, along with its principles, advantages, and disadvantages, and the commonly used datasets and evaluation metrics in this field are introduced. Additionally, this paper explores the role of symmetry in the structural patterns of information diffusion networks, analyzing how symmetry impacts the pathways and efficiency of information dissemination. Finally, this paper summarizes the potential future research directions and development trends in this domain.
Laboratory Experiments on Passive Thermal Control of Space Habitats Using Phase-Change Materials
Authors: Ongil Claudia , Martínez Úrsula , Salgado Sánchez Pablo , Borshchak Kachalov Andriy , Ezquerro Jose Miguel , Olfe Karl
Published: October 2024

Here, we investigate the performance of phase-change materials (PCMs) in the passive thermal control of space habitats. PCMs are able to absorb and release large amounts energy in the form of latent heat during their (typically, solid-to-liquid) phase transition, which makes them an ideal choice for passive temperature control. In this study, a conceptual design of an igloo-shaped habitat is proposed. A scaled model for laboratory experiments is manufactured via 3D printing, using tap water as the PCM. The setup is used to conduct experiments and analyze PCM performance, based on temperature measurements inside and outside the habitat. Results demonstrate the effectiveness of PCMs in increasing thermal inertia and stabilizing the habitat interior temperature around the melting temperature, confirming that PCMs can be a suitable alternative for passive thermal control. The present study holds significant interest for the future of space exploration, with the emerging need to design habitats that are capable of accommodating astronauts.