bluuebunny/crossref_metadata_2025 huggingface dataset:
A Time-Limited Adaptive Reclosing Method in Active Distribution Networks Considering Anti-Islanding Protection
Authors: Yang Fan , Chen Hechong , Fan Kaijun , Xu Bingyin , Chen Yu , Cai Yong , Yang Zhichun
Published: December 2024

In active distribution networks (DNs), distributed energy resources (DERs) must be disconnected from the grid prior to automatic reclosing actions. Many scholars have proposed non-voltage checking reclosing methods, but a significant challenge arises; many substations lack line-side voltage transformers (LSVTs), making these schemes impractical. To address this, we introduce a time-limited adaptive automatic reclosing (TLAR) method that integrates DERs’ anti-islanding protection (AIP) with automatic reclosing. This method estimates the AIP action time using bus-side voltage measurements before the system-side protection (SSP) is tripped and adjusts the reclosing time accordingly to enhance power supply reliability. Simulations using PSCAD validate the method’s effectiveness. The TLAR method is well-suited for distribution lines without conditions for non-voltage checking, is cost-effective, easy to implement, and contributes to power system stability.
Stochastic Plantwide Optimizing Control for an Acrylic Acid Plant
Authors: Duque Andrés , Tusso-Pinzón Ricardo , Ochoa Silvia
Published: December 2024

This work addresses the design of an optimized control system for an acrylic acid plant through the lens of the Stochastic Plant-Wide Optimizing Control (S-PWOC) framework. The S-PWOC employs stochastic optimization methods and advanced computer modeling to optimize plant performance by dynamically adjusting operational parameters under varying uncertainties. A comparison between the proposed S-PWOC model and two conventional approaches, the two-level identification method and the typical plant-wide decentralized control structure, highlights the advantages of S-PWOC despite its higher computational demands. Experimental results demonstrate significant improvements, including a 15% increase in process efficiency, a 10% reduction in energy consumption, enhanced product quality consistency, and greater economic viability. Additionally, S-PWOC proves effective in reducing safety risks and improving control efficiency, making it a robust solution for handling uncertainties in real-world plant operations.
Multiscale Qualitative–Quantitative Characterization of the Pore Structure in Coal-Bearing Reservoirs of the Yan’an Formation in the Longdong Area, Ordos Basin
Authors: Wang Rong , Shi Baohong , Wang Tao , Lin Jiahao , Li Bo , Fan Sitong , Liu Jiahui
Published: December 2024

Accurate characterization of coal reservoir micro- and nanopores is crucial in evaluating coalbed methane storage and gas production capacity. In this work, 12 coal-bearing rock samples from the Jurassic Yan’an Formation, Longdong area, Ordos Basin were taken as research objects, and micro- and nanopore structures were characterized via scanning electron microscopy, high-pressure mercury pressure, low-temperature N2 adsorption and low-pressure CO2 adsorption experiments. The main factors controlling coal pore structure development and the influence of pore development on the gas content were studied by combining the reflectivity of specular samples from the research area, the pore microscopic composition and the pore gas content determined through industrial analyses and isothermal absorption experiments. The results show that the coal strata of the Yan’an coal mine are a very important gas source, and that the coal strata of the Yan’an Formation in the study area exhibit remarkable organic and clay mineral pore development accompanied by clear microfractures and clay mineral interlayer joints, which together optimize the coal gas storage conditions and form efficient microseepage pathways for gas. Coalstone, carbonaceous mudstone and mudstone show differential distributions in pore volume and specific surface area. The general trend is that coal rock is the best, carbonaceous mudstone is the second best, and mudstone is the weakest. The coal samples’ microporous properties are positively correlated with the coal sample composition for the specular group, whereas there is no clear correlation for the inert group. An increase in the moisture content of the air-dried matrix promotes adsorption pore development, leading to increases in the microporous volume and specific surface area. CH4 adsorption in coal rock increases with increasing pressure, and the average maximum adsorption is approximately 8.13 m3/t. The limit of the amount of methane adsorbed by the coal samples, VL, is positively correlated with the pore volume and specific surface area, indicating that the larger the pore volume is, the greater the amount of gas that can be adsorbed by the coal samples, and the larger the specific surface area is, the greater the amount of methane that can be adsorbed by the coal samples. The PL value, pore volume and specific surface area are not correlated, indicating that there is no direct mathematical relationship between them.
Estimation Model of Rockfall Trajectory Lateral Dispersion on Slopes with Loose Granular Cushion Layer Based on Three-Dimensional Discrete Element Method Simulations
Authors: Qian Tingbin , Luo Wei , Zheng Baojing , Feng Yixin , Yin Desheng
Published: December 2024

Rockfall is a typical successive hazard with a high incidence rate following primary geological disasters such as landslides, rock avalanches, and debris flows. The lateral dispersion of rockfall is significantly affected by the loose granular cushion layer deposited on slopes. This paper aims to develop a quick estimation model for this effect based on the 3D-DEM (discrete element method) numerical simulations. The DEM model employs particles with different bonding properties to create a modeling double-layer granular slope. The present model is also verified by comparing the data from the antecedent large-scale outdoor rockfall experiment with the numerical simulations. Accordingly, the influences of four factors: the initial horizontal release velocity, the size of the rock mass, the granular cushion thickness, and the slope angle on the lateral dispersion of the rockfall trajectory are analyzed, and the underlying physical mechanism is discussed thoroughly. Ultimately, we identify a nondimensional parameter that demonstrates a strong correlation with the evolution of the lateral dispersion ratio of the rockfall trajectory. Based on this insight, we propose an estimation model for predicting the lateral dispersion of the rockfall trajectory. This model can assist engineering and construction personnel in rapidly determining the lateral dispersion range of the rockfall.
Comparative Analysis of Volatile Components and Sensory Profiles of Four Basil Varieties Based on HS-SPME and SD Coupled with GC-MS
Authors: Jiang Rongyue , Liu Jinzhen , Liu Qingchuan , Jin Zhigang , Zhu Huixia , Han Huipei , Ma Xiaojing
Published: December 2024

This study utilized gas chromatography-mass spectrometry (GC-MS) to analyze the volatile components and sensory profiles of four basil varieties, both in their fresh state and as essential oils (EOs) extracted via steam distillation (SD). By employing headspace solid-phase microextraction (HS-SPME) and SD/GC-MS, a comprehensive comparison was conducted to elucidate the changes in volatile profiles before and after drying and extraction processes. In total, 47 volatile components were identified in fresh basil samples. Methyl chavicol was predominant in Thai basil (66.53%), lemon basil (90.18%), and sweet basil (89.19%), whereas linalool (58.56%) was the major component in purple basil. For EOs, 66 volatile components were detected, with methyl chavicol remaining significant in Thai basil (65.27%) and lemon basil (81.03%), though its proportion decreased in sweet basil (29.34%). Purple basil EOs showed a higher proportion of alcohols (54.54%) and terpenoids (31.31%), with the notable presence of linalool (20.08%) and τ-juniper alcohol (18.18%). Multivariate analyses, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), revealed significant variations in volatile profiles among basil varieties. Sensory evaluation, supported by radar fingerprinting, demonstrated that the drying and extraction processes notably impacted the aroma profiles. Distinctive aroma profiles revealed that Thai basil was noted for its aniseed aroma, lemon basil for its lemon scent, sweet basil for its pungency, and purple basil for its floral notes. These findings highlight the diverse applications of basil varieties and their EOs, providing valuable insights into flavoring, fragrance, and therapeutic products based on their volatile compositions and sensory attributes.
Experimental and Numerical Investigations of the Sediment Abrasion Mechanism at the Leading Edge of an Airfoil
Authors: Liu Zhen , Zhu Lei , Lu Li , Li Tieyou , Wang Wanpeng , Meng Long
Published: December 2024

Multiple engineering projects have confirmed that hydraulic machinery operating in sediment-laden rivers undergoes sediment abrasion. Guide vanes are among the most severely worn flow-passing components and have long been a key research focus in hydraulic machinery. In this research, a wear test of the NACA0012 cascade under a 10° incoming flow angle was carried out in the Venturi test system, and the evolution process of the wear was analyzed. The three-dimensional flow channel of the cascade was constructed, and the Finnie wear model was adopted for computational fluid dynamics (CFD) simulations to analyze the wear mechanism at the initial stage. The results indicate that abrasion primarily occurs at the airfoil’s leading edge and progresses through three stages: initiation, development, and stabilization. The calculated results closely matched the latest wear outcomes: In the initial stage, the wear rate density was influenced by the particle impact velocity, angle, volume fraction, and y-direction shear stress. A low-velocity zone near the impact point, combined with rebounding particles causing secondary impacts, increases the particle volume fraction and wear rate density. These secondary impacts are the primary causes of erosion on both the upstream and downstream surfaces. Furthermore, flow separation downstream from the leading edge makes this region highly susceptible to wear. This study provides valuable insights for addressing wear in hydraulic machinery for practical engineering applications.
Insight and Violence: An Overview of the Possible Link and Treatment Options in Forensic Psychiatric Settings
Authors: Horgoș Bianca-Mălina , Ungureanu Daniel , Crișan Cătălina-Angela
Published: December 2024

The belief that people suffering from psychiatric disorders are more violent, in particular psychotic patients that do not have insight into their illness, is very common in the general population. Therefore, this review aimed to present a more accurate depiction of the link between lack of insight and violent behavior, by evaluating the existing scientific literature on the topic. For this purpose, a literature search on PubMed, Embase, and Google Scholar was conducted, selecting the relevant papers published during a 20-year period (2004–2024). The paper defined insight as a multi-dimensional concept and discussed its classification, explanatory models, and clinical implications, followed by a presentation of several insight-measuring scales. The meaning of violent behavior, its prevalence, underlying mechanisms, and different measuring scales were discussed, followed by the confounding factors that influence the relationship between insight and violent behavior, treatment options for violence in forensic psychiatry settings, and methods to improve medication adherence. Contrasting results were observed regarding the impact of each factor on leading to violent acts, which suggested that the relationship between insight and violence is more complex than previously thought. In conclusion, increased attention must be paid to the investigated dimensions of both the concepts and the confounding factors, with further research required on this topic.
Fluoroscopy Dose and Time During Vertebral Augmentation for Spine Pain Due to Malignant Fractures
Authors: Roldan Carlos J. , Chai Thomas , Feng Lei , Huh Ian , Huh Billy
Published: December 2024

Background: Vertebral augmentation (VA) procedures are used to treat painful vertebral fractures caused by malignancies, but there are few data on the radiation exposure for patients and proceduralists during these VA procedures. We retrospectively examined the radiation dose exposure during VA procedures and defined the characteristics of patients who underwent such procedures. Methods: We conducted a retrospective observational cohort study including patients with cancer who experienced axial back pain from compression fractures caused by malignancies. Participants were identified using an electronic medical records database and must have had evidence of stable vertebral compression fractures upon imaging and documentation of a clinical evaluation. We collected data on patient demographics, fluoroscopy time (FT) and dose (FD) during the procedure, the volume of polymethylmethacrylate injected, and reported complications. Results: Overall, 140 patients were included. Their median age was 69, and they were mostly men (n = 79). The most common diagnosis was multiple myeloma (41.4%). Most patients had a single-level compression fracture of the thoracolumbar spine. The mean FT was 233.80 s, with higher FTs for patients with an elevated body mass index and patients younger than 60 years. The average FD was 157.98 mGy, with higher FDs for patients with an elevated BMI and for male patients. Pain relief was not associated with FT or FD. Conclusions: Patients with cancer who underwent VA experienced longer FT and higher FD compared to their non-cancer counterparts in the literature. However, we found multiple confounders for this relationship.
American and European Muslim Female Bloggers Increase Their Preaching Efforts in Social Media
Authors: Kurmanaliyev Maxat , Kerim Shamshadin , Almukhametov Aliy , Amankul Temur
Published: December 2024

This article investigates the evolving landscape of religious agitation in the digital age, focusing specifically on the religious propaganda activities of female bloggers in the USA and Europe. In today’s society, religious discourse is increasingly being held on various social media channels, shaping a new trend in religious agitation. Muslim female bloggers are emerging as influential voices on these platforms, employing diverse preaching methods and discussing certain feminine topics aimed at Muslim women. This research employs a mix of quantitative, qualitative, and empirical methodologies to discover the peculiarities of the sermons delivered by them. An extensive analysis was performed to evaluate their rising influence, audience interaction, and how they differ from their male counterparts. Additionally, this study examines how Muslim female bloggers are transforming the face of the Islamic call in the USA and Europe by adopting a feminist approach. Importantly, our research work highlights the activities of non-hijabi Muslim bloggers who are influencing Western Muslim women in social media. We examine the religious discourse of Western female and male Muslim bloggers by juxtaposing their preaching activities. Besides, a comparison is made between the female Muslim bloggers in Western regions and the Islamic world. This article underscores the profound influence of social media on religious discourse and highlights their contributions to religious discourse in the digital age.
Death and Life in the Hands of the Translators
Authors: Eskenazi Tamara Cohn
Published: December 2024

Taking its cue from Proverbs 18:21, which places death and life in the hands or authority of the tongue, i.e., language, this paper explores how translators throughout the millennia recomposed, through their translation choices, the story of the first couple in the garden, introducing some deadly consequences with their choices.
Modern Typologies as Spaces of Inter-Religious Engagement in British-Mandate Jerusalem, 1917–1938
Authors: Ben-Asher Gitler Inbal
Published: December 2024

The architecture of Jerusalem has for centuries been defined by its being a space sacred to Judaism, Christianity, and Islam. The end of World War I marked the beginning of British Mandatory rule, which lasted until 1948. During this period, Jerusalem witnessed a proliferation of architectural projects that repositioned religion within modern typologies representing the city’s communities. This research investigates four such buildings: the British Rockefeller Museum, the Palestinian Palace Hotel, the American YMCA Building, which functioned as a community center and hostel, and the new Zionist Executive Building. The integration of religious elements into these edifices is examined using the concept of inter-religious engagement and by applying the theory of purification and hybridization. The research demonstrates that British and American Christians, Zionist Jews, and Muslim Palestinians, used different strategies to produce inter-religious engagement—either intentionally or because of British-dictated political constructs. British and American Christians embedded religious elements within modern typologies to reflect peaceful co-existence, while Zionist Jews and Muslim Palestinians used them to construct national identity. Although conceived as “purely” secular, these modern typologies were hybridized by the integration of religious spaces or emblems, revealing further dimensions to our understanding and assessment of 20th-century urban secular architecture and its intersection with religions.
Analysis of Economic Vitality and Development Equilibrium of China’s Three Major Urban Agglomerations Based on Nighttime Light Data
Authors: Liu Saimiao , Liu Wenliang , Zhou Yi , Wang Shixin , Wang Zhenqing , Wang Zhuochen , Wang Yanchao , Wang Xinran , Hao Luoyao , Wang Futao
Published: December 2024

Eliminating poverty, reducing inequality, and achieving balanced development are one of the United Nations Sustainable Development Goals. Objectively and accurately measuring regional economic vitality and development equilibrium is a pressing scientific issue that needs to be addressed in order to achieve common prosperity. Nighttime light (NTL) remote sensing data have been proven to be a good proxy variable for socio-economic development, and are widely used due to their advantages of convenient access and wide spatial coverage. Based on multi-source data, this study constructs an Economic Development Index (EDI) that comprehensively reflects regional economic vitality from two aspects, economic quality and development potential, combines the Nighttime Light Development Index (NLDI) as the evaluation indicators to measure the economic vitality and development equilibrium, analyzes the economic vitality and development equilibrium of 300 district and county units in China’s three major urban agglomerations from 2000 to 2020 and their temporal and spatial variation characteristics, and discusses the connotation of EDI and its availability. The results show the following: (1) From 2000 to 2020, the average growth rate of EDI in China’s three major urban agglomerations reached 36.32%, while the average decrease rate of NLDI reached 38.75%; both economic vitality and the development equilibrium have been continuously enhanced. Among them, the Yangtze River Delta (YRD) urban agglomeration experienced the fastest economic growth, while the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) exhibited the strongest economic strength. (2) Both economic vitality and the development equilibrium in these three urban agglomerations exhibited distinct spatial agglomeration characteristics, namely center-surrounding distribution, coastal–inland distribution, and radial belt–pole distribution, respectively. (3) Over the past two decades, the economic development of these three urban agglomerations has progressed towards the pattern of regional coordinated development, pole-driven development and urban–rural integrated development. The research results can provide new research perspectives and scientific support for promoting regional balanced development, achieving sustainable development goals, and reducing inequality.
Superpixel Classification with the Aid of Neighborhood for Water Mapping in SAR Imagery
Authors: Miyamoto Tomokazu
Published: December 2024

Water mapping for satellite imagery has been an active research field for many applications, in particular natural disasters such as floods. Synthetic Aperture Radar (SAR) provides high-resolution imagery without constraints on weather conditions. The single-date SAR approach is less accurate than the multi-temporal approach but can produce results more promptly. This paper proposes novel segmentation schemes that are designed to process both a target superpixel and its surrounding ones for the input for machine learning. Mixture-based Superpixel-Shallow Deit-Ti/XGBoost (MISP-SDT/XGB) schemes are devised to generate, annotate, and classify superpixels, and perform the land/water segmentation of SAR imagery. These schemes are applied to Sentinel-1 SAR data to examine segmentation performances. Single/mask/neighborhood models and single/neighborhood models are introduced in the MISP-SDT scheme and the MISP-XGB scheme, respectively. The effects of the contextual information about the target and its neighbor superpixels are assessed on its segmentation performances. Regarding polarization, it is shown that the VH mode produces more encouraging results than the VV, which is consistent with previous studies. Also, under our MISP-SDT/XGP schemes, the neighborhood models show better performances than FCNN models. Overall, the neighborhood model gives better performances than the single model. Results from attention maps and feature importance scores show that neighbor regions are looked at or used by the algorithms in the neighborhood models. Our findings suggest that under our schemes, the contextual information has positive effects on land/water segmentation.
Sea Surface Height Measurements Using UAV Altimeters with Nadir LiDAR or Low-Cost GNSS Reflectometry
Authors: Ichikawa Kaoru , Noda Jyoushiro , Sakemi Ryosuke , Yufu Kei , Morimoto Akihiko , Onishi Hidejiro , Pokavanich Tanuspong
Published: December 2024

Although UAV height is precisely determined using GNSS, the vertical distance between the UAV and the sea surface should be subtracted to obtain the sea surface height (SSH). This distance can be measured using nadir-looking LiDAR or GNSS reflectometry (GNSS-R); thus, these two methods are examined in this study through three two-minute UAV experimental flights. The measurements of the flight-averaged SSHs made with both approaches were in good agreement with the reference SSH determined from a GNSS buoy, with differences of 0.03 m (LiDAR) and 0.05 m (GNSS-R), although the standard deviation (SD) for GNSS-R (1.72 m) was significantly larger than that for LiDAR (0.20 m). Each 4 Hz GNSS-R measurement was subject to errors caused by surface waves, though over 16 GNSS reflection points within a 70 m diameter footprint were used; these errors were, however, removed in the temporal mean. Extending the footprint diameter to 230 m with stronger data quality controls resulted in a smaller error (0.02 m) and SD (0.79 m). Meanwhile, LiDAR measured the flat-surface SSH at the nadir only, which inherently filtered out slant reflections, resulting in a lower SD. However, this filter reduces data acquisition rates, especially when the UAV attitude tilts.
Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery
Authors: Lagahit Miguel Luis Rivera , Liu Xin , Xiu Haoyi , Kim Taehoon , Kim Kyoung-Sook , Matsuoka Masashi
Published: December 2024

High-definition (HD) maps for autonomous driving rely on data from mobile mapping systems (MMS), but the high cost of MMS sensors has led researchers to explore cheaper alternatives like low-cost LiDAR sensors. While cost effective, these sensors produce sparser point clouds, leading to poor feature representation and degraded performance in deep learning techniques, such as convolutional neural networks (CNN), for tasks like road marking extraction and classification, which are essential for HD map generation. Examining common image segmentation workflows and the structure of U-Net, a CNN, reveals a source of performance loss in the succession of resizing operations, which further diminishes the already poorly represented features. Addressing this, we propose improving U-Net’s ability to extract and classify road markings from sparse-point-cloud-derived images by introducing a learnable resizer (LR) at the input stage and learnable resizer blocks (LRBs) throughout the network, thereby mitigating feature and localization degradation from resizing operations in the deep learning framework. Additionally, we incorporate Laplacian filters (LFs) to better manage activations along feature boundaries. Our analysis demonstrates significant improvements, with F1-scores increasing from below 20% to above 75%, showing the effectiveness of our approach in improving road marking extraction and classification from sparse-point-cloud-derived imagery.
Benefits of Using Design Patterns on Microcontrollers in Implemented IoT Applications
Authors: Babiuch Marek , Foltynek Petr
Published: December 2024

As part of our research for microcontroller software support, we have developed a modular framework that utilizes previously unimplemented architectural principles for developing applications on microcontrollers. These principles are still a privilege of enterprise and server applications. The paper describes the benefits of a new architectural approach to developing applications on microcontrollers and describes the most common application scenarios along with examples of IoT application development using a framework with design pattern architecture and SOLID principles. As a result, our framework supports developers in creating robust, adaptive, and scalable applications. It emphasizes a modular and clean design that increases development efficiency and enables easy deployment of new features or integration of new technologies, such as new types of sensors, upgraded development boards, or improved development tools and frameworks. The architectural concepts offered useful guidance for creating applications ready for future challenges and changing technology environments, especially in the IoT area.
Multi-Antenna GNSS–Accelerometer Fusion Attitude Correction Algorithm for Offshore Floating Platform Displacement Monitoring
Authors: Gao Xingguo , Jiang Junyi , Xu Guoyu , Chang Zengliang , Yang Jichao
Published: December 2024

In order to solve the problem of fixed ambiguity and decreased accuracy in GNSS displacement monitoring of the offshore floating platforms, an attitude correction algorithm based on the fusion of a multi-antenna GNSS and an accelerometer was proposed using the Kalman filtering method. The algorithm was validated on a physical simulation platform and a real offshore floating platform. The results indicate that this fusion method effectively compensates for the loss of high-frequency displacement information caused by low GNSS sampling rates, improves situations in which the fusion effect deteriorates due to attitude changes, and enhances the accuracy of GNSS and accelerometer fusion monitoring through offshore buoy testing.
Near-Field Passive Wireless Sensor for High-Temperature Metal Corrosion Monitoring
Authors: Strader Noah , Jordan Brian R. , Bilac Oguzhan , Tennant Kevin M. , Reynolds Daryl S. , Sabolsky Edward M. , Daniszewski Ashley C.
Published: December 2024

This work focuses on the fabrication and evaluation of a passive wireless sensor for the monitoring of the temperature and corrosion of a metal material at high temperatures. An inductor–capacitor (LC) resonator sensor was fabricated through the screen printing of Ag-based inks on dense polycrystalline Al2O3 substrates. The LC design was modeled using the ANSYS HFSS modeling package, with the LC passive wireless sensors operating at frequencies from 70 to 100 MHz. The wireless response of the LC was interrogated and received by a radio frequency signal generator and spectrum analyzer at temperatures from 50 to 800 °C in real time. The corrosion kinetics of the Cu 110 was characterized through thermogravimetric (TGA) analysis and microscopy images, and the oxide thickness growth was then correlated to the wireless sensor signal under isothermal conditions at 800 °C. The results showed that the wireless signal was consistent with the corrosion kinetics and temperature, indicating that these two characteristics can be further deconvoluted in the future. In addition, the sensor also showed a magnitude- and frequency-dependent response to crack/spallation events in the oxide corrosion layer, permitting the in situ wireless identification of these catastrophic events on the metal surface at high temperatures.
Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?
Authors: Duda-Goławska Joanna , Rogowski Aleksander , Laudańska Zuzanna , Żygierewicz Jarosław , Tomalski Przemysław
Published: December 2024

The efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. The manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling the automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4–12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of the Supine (97.7%), Sitting (93.5%), and Prone (89.9%) positions. Moreover, using data ablation experiments and analysing the SHAP (SHapley Additive exPlanations) values, the study assessed the importance of various groups of features from both the time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification.
SHM System for Composite Material Based on Lamb Waves and Using Machine Learning on Hardware
Authors: Batista Gracieth Cavalcanti , Zetterling Carl-Mikael , Öberg Johnny , Saotome Osamu
Published: December 2024

There is extensive use of nondestructive test (NDT) inspections on aircraft, and many techniques nowadays exist to inspect failures and cracks in their structures. Moreover, NDT inspections are part of a more general structural health monitoring (SHM) system, where cutting-edge technologies are needed as powerful resources to achieve high performance. The high-performance aspects of SHM systems are response time, power consumption, and usability, which are difficult to achieve because of the system’s complexity. Then, it is even more challenging to develop a real-time low-power SHM system. Today, the ideal process is for structural health information extraction to be completed on the flight; however, the defects and damage are quantitatively made offline and on the ground, and sometimes, the respective procedure test is applied later on the ground, after the flight. For this reason, the present paper introduces an FPGA-based intelligent SHM system that processes Lamb wave signals using piezoelectric sensors to detect, classify, and locate damage in composite structures. The system employs machine learning (ML), specifically support vector machines (SVM), to classify damage while addressing outlier challenges with the Mahalanobis distance during the classification phase. To process the complex Lamb wave signals, the system incorporates well-known signal processing (DSP) techniques, including power spectrum density (PSD), wavelet transform, and Principal Component Analysis (PCA), for noise reduction, feature extraction, and data compression. These techniques enable the system to handle material anisotropy and mitigate the effects of edge reflections and mode conversions. Damage is quantitatively evaluated with classification accuracies of 96.25% for internal defects and 97.5% for external defects, with localization achieved by associating receiver positions with damage occurrence. This robust system is validated through experiments and demonstrates its potential for real-time applications in aerospace composite structures, addressing challenges related to material complexity, outliers, and scalable hardware implementation for larger sensor networks.
MOMFNet: A Deep Learning Approach for InSAR Phase Filtering Based on Multi-Objective Multi-Kernel Feature Extraction
Authors: Zhang Xuedong , Peng Cheng , Li Ziqi , Zhang Yaqi , Liu Yongxuan , Wang Yong
Published: December 2024

Interferometric Synthetic Aperture Radar (InSAR) is a widely used remote sensing technology for Earth observation, enabling the detection and measurement of ground deformation through the generation of interferograms. However, phase noise remains a critical factor that degrades interferogram quality. To address this issue, this study proposes MOMFNet, a deep learning approach for InSAR phase filtering based on multi-objective multi-kernel feature extraction that leverages multi-objective multi-kernel feature extraction. MOMFNet incorporates a multi-objective loss function that accounts for both the spatial and statistical characteristics of the denoising results, while its multi-kernel convolutional feature extraction module captures multi-scale information comprehensively. Furthermore, the introduction of weighted residual blocks allows the model to adaptively adjust the importance of features, improving its ability to accurately identify and suppress noise. To train the MOMFNet network, we developed an interferogram simulation strategy that uses randomly distorted 2D Gaussian surfaces to simulate terrain variations, Perlin noise to model atmospheric turbulence phases, and negative Gaussian noise to generate random training samples at different noise levels. Comparative experiments with traditional denoising methods and other deep learning approaches, through both qualitative and quantitative analyses, demonstrated that MOMFNet excels in noise suppression and phase recovery, particularly in scenarios involving large gradients and random noise. Empirical studies using Sentinel-1 satellite data from the Yanzhou coal mine validated the practical value of MOMFNet, showing that it effectively removes irrelevant noise while preserving critical phase details, significantly improving interferogram quality. This research provides important insights into the application of deep learning for InSAR denoising.
Assessing Housing Preferences and Living Conditions of Migrant Workers in the Fringe Areas of Bengaluru City, India
Authors: Chadchan Jayprakash , Hossiney Nabil , Tamil Selvan Praveen Kumar , Vijayan Anagha
Published: December 2024

This study investigates the housing preferences and its impact on living conditions of migrant industrial workers in the fringe areas of Bengaluru, India, where rapid urbanization and economic expansion have led to a significant demand for affordable housing. Using the Analytic Hierarchy Process (AHP), a multi-criteria decision-making framework, this study analyzed key factors influencing housing choices, such as proximity to the workplace, affordability, and access to essential amenities. Data were collected from 400 respondents through a combination of surveys, complimented by field observations and expert consultations. The findings indicate a strong preference for affordable housing close to employment hubs, with “proximity to workplace” emerging as the most critical criterion with a priority weight of 43.36%, followed by “affordability” with a priority weight of 12.47%. However, field insights reveal a trade-off with housing quality and living conditions. Many migrant workers are confined to overcrowded and poorly ventilated rental units, often provided by employers, which compromises their health and well-being. Our results contribute to the understanding of urban housing challenges in rapidly growing economies and emphasize the importance of sustainable, health-oriented housing policies that can mitigate environmental impacts and improve the quality of life for low-income migrant populations.
Photoproduction of Loop Currents in Coronene Isomers Without Any Applied Magnetic Field
Authors: Ohara Jun , Yamamoto Shoji
Published: December 2024

Applying an extended Peierls–Hubbard model to π electrons in a coronene isomer, we investigate their ground-state properties and photoinduced dynamics with particular interest in possible loop current states. Once we switch on a static magnetic field perpendicular to the coronene disk, diamagnetic (diatropic) and paramagnetic (paratropic) loop currents appear on the rim circuit and inner hub, respectively. Besides this well-known homocentric two-loop current state, heterocentric multiloop current states can be stabilized by virtue of possible electron–lattice coupling. These multiloop current states generally have a larger diamagnetic moment than the conventional two-loop one, and hence it follows that coronene, or possibly polycyclic conjugated hydrocarbons in general, may become more aromatic than otherwise with their π electrons being coupled to phonons. When we photoirradiate a ground-state coronene isomer without applying a static magnetic field, loop currents are induced in keeping with the incident light polarization. Linearly and circularly polarized lights induce heterocentric two-loop and multiloop currents, respectively, without and together with two homocentric loop currents of the conventional type, respectively. The heterocentric two-loop currents occur in a mirror-symmetric manner, which reads as the emergence of a pair of antiparallel magnetic moments, whereas the heterocentric multiloop ones appear at random in both space and time, which reads as the emergence of disordered local magnetic moments.
Comparison of Vertical Jump Force–Time Metrics Between ACL-Injured and Healthy Semi-Professional Male and Female Soccer Players
Authors: Cabarkapa Dimitrije , Cabarkapa Damjana V. , Fry Andrew C. , Song Yu , Gisladottir Thordis , Petrovic Milos
Published: December 2024

Given the increasing use of innovative force plate systems in applied sports settings and the impact that anterior cruciate ligament (ACL) injuries have on team success, the purpose of the present study was to compare the lower-body neuromuscular performance characteristics of athletes who underwent ACL reconstruction (ACLR) and their non-injured counterparts (i.e., healthy controls). Forty-five male (thirteen injured) and twenty-six female (ten injured) semi-professional soccer players volunteered to participate in the present study. Each athlete performed three countermovement vertical jumps (CMJs) while standing on a uniaxial force plate system sampling at 1000 Hz. The injured athletes completed a nine-month recovery protocol and were screened 11–13 months post-ACLR. The dependent variables included the force–time metrics within both the eccentric and concentric phases of the CMJ. Independent t-tests or Mann–Whitney U-test were used to examine statistically significant (p < 0.05) differences in each variable (i.e., ACL-injured vs. healthy controls). The results revealed no significant between-group differences in any CMJ force–time metrics of interest (e.g., concentric peak force, eccentric mean power, countermovement depth) between ACL-injured and non-injured athletes, including inter-limb asymmetry measures (i.e., peak takeoff and landing force). Besides implying the effectiveness of the implemented rehabilitation protocol, these findings suggest that the CMJ may not present a sufficient neuromuscular performance stimulus needed to expose lower-limb asymmetries and strength and power deficiencies 11–13 months post-ACLR.
An Evolutionary Modular Product Development Under Circular Economy Approach
Authors: Córdoba-Roldán Antonio , Martín-Gómez Alejandro M. , Rodríguez-Núñez Marina , Lama-Ruiz Juan Ramón
Published: December 2024

The incorporation of new technologies and materials, as well as the rapid evolution of products, makes it necessary to guide the product design and development process from bio-inspired perspectives that promote a circular economy. Among the proposals based on a bio-inspired solution is the framework called the Genomic Model of Eco-innovation and Eco-design, which integrates the Cradle-to-Cradle paradigm, material and substance flow analysis, along with all aspects present in the life cycle analysis of products. In this work, innovation is carried out through the integration of genetic algorithms as a methodological tool for optimizing the modular product design in the definition of the genotype within the aforementioned framework. Through a systematic approach, it analyzes how the modular reconfiguration of products, based on circular design principles and genetic optimization, can contribute to a significant reduction in resource use and waste generation. The proposed methodology is applied to a practical case based on the redesign of a small household appliance, demonstrating its potential to facilitate the implementation of circular economy strategies in the early stages of the product design and development process for studying product modularity, with the aims of improving its design and reducing its impact.
Correction: Gao et al. Applications of the Separation of Variables Method and Duhamel’s Principle to Instantaneously Released Point-Source Solute Model in Water Environmental Flow. Sustainability 2024, 16, 6912
Authors: Gao Ran , Gao Juncai , Chu Linlin
Published: December 2024

The authors would like to make the following corrections to the published paper [...]
Determinant Factors of Entrepreneurial Culture in University Students: An Analysis from the Theory of Planned Behavior at a Peruvian University
Authors: Vera Kerwin Jose Chavez , Velita Joel Joab Alderete , Martinez Tomas Alcala , Ferrer Raul Marcelo
Published: December 2024

This study analyzed the influence of attitude toward innovation and creativity, entrepreneurial self-efficacy, and institutional support in the consolidation of an entrepreneurial culture among Peruvian university students, using the Theory of Planned Behavior (TPB) as the theoretical framework. Data were collected from 948 students through a validated 29-item questionnaire, and the data analysis was conducted using the PLS-SEM method. The results showed that both attitude toward innovation and entrepreneurial self-efficacy have a significant positive impact on entrepreneurial culture, and entrepreneurial self-efficacy acted as a partial mediator in this relationship. However, external resources and institutional support were not significant mediators between entrepreneurial intention and entrepreneurial culture. This study expands the understanding of TPB in the Peruvian context, highlighting the importance of fostering self-efficacy and an innovative attitude in students to strengthen entrepreneurial culture in universities. Educational institutions should consider these key factors when designing programs that promote entrepreneurship.
Sustainable Native Orchid Tourism Demand in Central Veracruz, Mexico
Authors: Hernández-García Arturo , Baltazar-Bernal Obdulia , Ramírez-Valverde Benito
Published: December 2024

Orchids are highly prized in the flower market; their extravagant colors and shapes make them an ideal target for rural tourism. Mexico has a diverse range of native orchids; this diversity can be exploited as a tourist resource since there are many enthusiasts who want to learn about them. However, information about orchid tourists’ demographics and consumption habits is scarce. The aim of this research was to evaluate the demand regarding native orchid tourism, as well as the consumption habits and tourism spending of orchid enthusiasts. We hypothesized that there is a high demand for native orchid tourism in central Veracruz and that tourists’ spending is mostly driven by age and educational level variables. To explore this hypothesis, we conducted a survey among the attendees of the “International Orchid Festival 2022” held in Coatepec, Veracruz. A total of 100 interviews were conducted among orchid tourists. The results show that most of them (92%) were interested in visiting and learning about orchids in their natural environment. Age and educational level variables were found to have a significant effect on daily and total spending during vacations. The orchid species preferred by tourists were Laelia anceps and Myrmecophila grandiflora. These results are useful for the creation of sustainable tourism services focused on the observation of native orchids and developing alternatives to improve the living conditions of families involved in cultivating native orchids.
Fostering Sustainable Workplace Through Leaders’ Compassionate Behaviors: Understanding the Role of Employee Well-Being and Work Engagement
Authors: Pansini Martina , Buonomo Ilaria , Benevene Paula
Published: December 2024

Social sustainability in organizations refers to fostering well-being, inclusion, and the development of supportive relationships that enhance individual well-being and collective resilience. This study explores the relationship between compassionate leadership and work engagement, focusing on the mediating role of employee well-being. Compassionate leaders actively engage with their followers’ needs, offering emotional support and practical help and aligning with social sustainability principles by promoting positive workplace outcomes and fostering a supportive environment. Despite compassionate leadership having generated growing interest, very few studies have adopted a quantitative approach to examining its specific effects on employee outcomes, creating a significant gap in the literature specifically concerning how compassionate leadership affects employee engagement or additional variables, such as employee well-being. Framed in the Job Demands-Resources (JD-R) Model, our study addresses this gap by examining whether compassionate leadership can serve as a job resource by increasing work engagement through employee well-being as a personal resource. The study was carried out on a sample of 225 Spanish workers, and the data were examined through a structural equation model (SEM) to test the proposed model. The results showed a good model fit to the data, showing that well-being acts as a key mediator in the relationship between compassionate leadership and job engagement. Although we did not find a direct association between compassionate leadership and engagement, we found that compassionate behaviors significantly improve employee well-being, which in turn promotes higher engagement. These results extend the JD-R model by demonstrating that leaders’ compassionate leadership can serve as a job resource, contributing to a supportive and socially sustainable workplace.