Search Results (83)

On 22 May 2012, a magnitude Mw 5.6 earthquake struck the Pernik region of western Bulgaria, causing structural damage in nearby cities, including Sofia. This study assesses the seismic response of a six-story reinforced concrete building in central Sofia, utilizing real accelerogram data recorded at the basement (SGL1) and sixth floor (SGL2) levels during the earthquake. Using the Kanai–Yoshizawa (KY) model, the study estimates inter-story motion and assesses amplification effects across the structure. Analysis of peak ground acceleration (PGA), velocity (PGV), displacement (PGD), and spectral ratios reveals significant dynamic amplification of peak ground acceleration and displacement on the sixth floor, indicating flexible and dynamic behavior, as well as potential resonance effects. The analysis combines three spectral techniques—Horizontal-to-Vertical Spectral Ratio (H/V), Floor Spectral Ratio (FSR), and the Random Decrement Method (RDM)—to determine the building’s dynamic characteristics, including natural frequency and damping ratio. The results indicate a dominant vibration frequency of approximately 2.2 Hz and damping ratios ranging from 3.6% to 6.5%, which is consistent with the typical damping ratios of mid-rise concrete buildings. The findings underscore the significance of soil–structure interaction (SSI), particularly in sedimentary basins like the Sofia Graben, where localized geological effects influence seismic amplification. By integrating accelerometric data with advanced spectral techniques, this research can enhance ongoing site-specific monitoring and seismic design practices, contributing to the refinement of earthquake engineering methodologies for mitigating seismic risk in earthquake-prone urban areas. Full article

This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. Simulations demonstrate that the B3I signal achieves a significantly enhanced range resolution (tens of meters) compared to the B1I signal (hundreds of meters), attributable to its wider bandwidth. Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. Experimental results show that four-satellite configurations outperform three-satellite setups, achieving <10 m position error for uniform motion and <18 m for maneuvering targets, with velocity errors within ±2 m/s using four satellites. The joint detection framework for multi-satellite, multi-target scenarios demonstrates an improved detection accuracy and robust localization performance. Full article

Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R² < 0.1), likely due to complete areal coverage with dense grass. The LiDAR canopy height models performed better for all the samples (R² = 0.37), with much stronger performance (R² = 0.81) when using a stratified model to predict the RDM in plots with predominantly standing (as opposed to laying) vegetation. This study demonstrates the potential of UAV LiDAR for direct RDM quantification where vegetation is standing upright, which could help improve RDM mapping and management for rangelands in California and beyond. Full article

Hyperspectral technology can non-destructively identify and analyze minerals. However, the quantitative inversion of different components in mixed minerals remains difficult in mineral spectral analysis. A set of mineral samples was prepared from dolomite and gypsum, varying in their components. Three improved spectral decomposition models were proposed: the Continuum Removal-Fully Constrained Linear Spectral Model (CR-FCLSM), the Natural Logarithm-Fully Constrained Linear Spectral Model (NL-FCLSM), and the Ratio Derivative Model (RDM). The unmixing Abundance Error (AE) was 0.161, 0.051, and 0.082 for CR-FCLSM, NL-FCLSM, and RDM. The results of the three improved linearized unmixing models are better than those of the traditional linear spectral unmixing model. The NL-FCLSM effectively enhanced the linear characteristics of the spectrum, making it more suitable for two mineral mixing scenarios. The systematic bias of CR-FCLSM may be due to its insufficient sensitivity to low-abundance signals. The stability of RDM depends on the selection of a strong linear band. The unmixing experiments of the measured spectra and the data from the USGS spectral library demonstrate that the improved linear unmixing model is more accurate than the traditional linear spectral model and simpler to calculate than the nonlinear spectral model, providing a new approach for demodulating hyperspectral images. Full article

Accurate behavioral monitoring of silkworms (Bombyx mori) during the fourth instar development is crucial for enhancing productivity and welfare in sericulture operations. Current manual observation paradigms face critical limitations in temporal resolution, inter-observer variability, and scalability. This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. Methodologically, Res2Net blocks are first integrated into the backbone network to enable hierarchical residual connections, expanding receptive fields and improving multi-scale feature representation. Second, standard convolutional layers are replaced with distribution shifting convolution (DSConv), leveraging dynamic sparsity and quantization mechanisms to reduce computational complexity. Additionally, the minimum point distance intersection over union (MPDIoU) loss function is proposed to enhance bounding box regression efficiency, mitigating challenges posed by overlapping targets and positional deviations. Experimental results demonstrate that RDM-YOLO achieves 99% mAP@0.5 accuracy and 150 FPS inference speed on the datasets, significantly outperforming baseline YOLOv5s while reducing the model parameters by 24%. Specifically designed for deployment on resource-constrained devices, the model ensures real-time monitoring capabilities in practical sericulture environments. Full article

Effective research data management (RDM) is essential for ensuring research integrity, reproducibility, and compliance with FAIR principles. Despite the development of comprehensive RDM frameworks, many institutions still struggle to ensure widespread engagement and compliance among researchers and staff. Adoption of RDM practices remains slow due to limited awareness, unclear benefits, and perceived administrative burdens. Using Mendelow’s Matrix, this study draws on survey data to map key stakeholders, such as researchers, RDM professionals, institutional leadership, funding bodies, and infrastructure providers, based on their power and interest to ensure developing tailored communication strategies. This paper presents a communication strategy to enhance RDM adoption by improving visibility, fostering engagement, and encouraging the integration of RDM into research workflows and curricula. It outlines key approaches, including awareness campaigns, targeted publishing, strategic partnerships, and knowledge-driven promotion to embed RDM into research workflows. Full article

This study proposes a simplified approach for the experimental dynamic characterization of the historic Messina Bell Tower (northeastern Sicily) using passive seismic single-station surveys. The Horizontal-to-Vertical Spectral Ratio (HVSR) analysis identified a site resonance frequency of approximately 1.06 Hz, while the Multichannel Analysis of Surface Waves (MASW) survey contributed to the characterization of the shear wave velocity profile, providing a coherent geophysical framework useful for structural dynamic analysis. Spectral ratios analysis revealed four distinct vibration modes, including a fundamental rocking mode (~1.4 Hz), a torsional mode (3.5 Hz), and two higher-frequencies flexural modes. The structure’s dynamic behavior, notably its sensitivity to torsion and rocking, is attributed to the deformable subsoil. Damping ratios estimated via the Random Decrement Method (RDM) were below 1%, consistent with the expected linear elastic response under ambient vibrations. The results show strong agreement with previous long-term monitoring, validating the effectiveness of passive seismic techniques for rapid, non-invasive assessment. This study demonstrates that streamlined, time-efficient methodologies are capable of delivering modal parameters consistent with those obtained from more extensive and resource-intensive monitoring campaigns, thereby providing a reliable and practical approach for the seismic vulnerability assessment of heritage structures. Full article

With an avalanche of market manipulations and unethical tactics in the Australian financial industry, the empowerment levels of female Australian consumers when making financial investment decisions are highly questionable. Through the theoretical lens of a utilitarian perspective, financial investment decisions are often built on the pillars of trust, security, and assurance, which allow consumers to make decisions rationally and gain empowerment when making these decisions. However, due to the widespread manipulations prevailing in Australian financial markets, the role of rationality and its influence on consumer empowerment remain understudied. Based on this context, this paper uncovers the association between how each stage of rational decision-making (RDM) (i.e., demand identification, information search, and the evaluation of alternatives) influences the consumer power (i.e., consumer resistance and consumer influence) of female Australian consumers when making financial investment decisions. In doing so, this study employs a quantitative approach, whereby the proposed conceptual framework is tested among 357 female Australian consumers to understand their decision-making power in the presence of heightened situations of market manipulation in the financial industry. The results show that information search has a significant positive relationship with consumer influence and consumer resistance when making financial investment decisions. Additionally, the findings suggest that female Australian consumers should not only rely on individual-based sources of power but also have exposure to network-based sources of power to gain empowerment when making financial investment decisions. Lastly, it is suggested that government bodies, financial institutions, and regulatory authorities should not only implement financial literacy programs but also promote gender diversity across organisations to encourage women’s empowerment (i.e., Goal 5 (SDGs)—Achieve Gender Equality and Empower all Women and Girls). Full article

Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory—a critical area for assessing logging impacts—remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rondônia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts. Full article

This study aimed to examine the age at first calving (AFC) in Hungarian Angus herds. This study was conducted on the basis of data from 2955 registered cows, classified into five groups (based on different Angus types), and 200 breeding bulls, which were the sires of the cows. The data were made available by the Hungarian Hereford, Angus, and Galloway Breeders’ Association. The variance and covariance components, heritability, breeding value (BV), and genetic trends of AFC between 1998 and 2021 were evaluated. A general linear model (univariate analysis of variance) was used to examine the various effects, while best linear unbiased prediction was used to estimate the population genetic parameters and BV, and linear regression analysis was used for the trend analysis. The average AFC obtained was 28.1 ± 0.1 months (SD = 5.3 months), showing a relatively large variance (CV = 18.9%). The environmental factors that influenced the development of the phenotype were the cow’s birth season (28.99%, p < 0.01), cow’s birth year (28.7%, p < 0.01), the cow’s sire (18.32%, p < 0.01), and the herd (11.77, p < 0.05). The cow’s color variant (8.10%, p > 0.05) was not significant and did not influence the AFC in this study. The direct heritability of AFC (h² = 0.51 ± 0.06) was higher than data in the literature (0.38 ± 0.05); however, the maternal heritability was low (h²_m = 0.00 ± 0.03). The correlation between direct and maternal genetic effects was zero (r_dm = −0.97 ± 1.00). The phenotypic trend of AFC increased by +0.03 months per year, which was not statistically significant. The genetic trend calculation showed no significant changes. Based on the h² and BV results, it seems that selecting a suitable sire can effectively reduce the AFC of daughters. Since AFC is also an important trait in economic terms, it would be appropriate to include AFC BV in the bull catalog. Full article

The aim of this study was to identify patterns of morphological adjustments associated with the Dickson Quality Index (DQI) in Theobroma cacao L. genotypes subjected to water deficit (WD), as a criterion for the pre-selection of drought-tolerant genotypes. Rooted cuttings from seven genotypes were subjected to water deficit (WD). The data from the growth analysis and DQI were subjected to analysis of variance, tests of means, and multivariate analysis. A high correlation was identified between IQD and the variables root dry mass (RDM), leaf dry mass (LDM), stem diameter (SD), and total dry mass (TDM) independently for each genotype; these correlations are more evident in genotypes CP-49, PS-1319, and Cepec-2002. The multivariate analysis divided the genotypes into two major groups: one consisting of the Ipiranga-01, CCN-51, SJ-02, and PH-16 genotypes, and the other comprising the CP-49, Cepec-2002, and PS-1319 genotypes. By correlating the results of the growth analysis with DQI, we were able to identify genotypes CP-49, PS-1319, and Cepec-2002 as tolerant; Ipiranga-01 and CCN-51 as moderately tolerant; and SJ-02 and PH-16 as poorly tolerant to WD. However, it is important that other fields of science are considered to provide greater insights into adaptation to drought. Full article

This paper presents exploratory findings suggesting that mathematics teachers can implement Rough Draft Math (RDM) by making small, incremental changes that align with their current practices and local contexts, including curriculum materials, with minimal support. Following a conference presentation and/or reading a book about pedagogy, teachers reported shifts in their thinking that facilitated their interest in enacting RDM and small changes they made to their teaching. The flexibility of RDM, as a general concept rather than a set of prescribed practices, allowed teachers to incorporate RDM to meet their own teaching goals. We propose that this adaptability enables teachers to incorporate RDM into their classrooms incrementally, reflecting their existing objectives for their students. Full article

In order to increase the suitability of coarse recycled concrete aggregates and improve the frost resistance of recycled coarse aggregate concrete, this study aims to investigate the effects of an antifreeze-type water-reducing admixture, air-entraining admixture, and antifreeze admixture on the frost resistance of recycled coarse aggregate concrete. The effectiveness of these admixtures is gauged by the mass loss rate and the relative dynamic modulus of elasticity (RDM). Mercury-impressed porosimetry (MIP), super depth of field microscopy, and scanning electron microscopy (SEM) were employed to characterize the hydration products, microstructure, and pore structure of recycled coarse aggregate concrete, with a view to establishing a connection between the microstructural characteristics and the macro properties and analyzing the micro-mechanism of the improvement effect of frost resistance. The test results demonstrate that the admixtures have a significant impact on the frost resistance of recycled coarse aggregate concrete. In particular, the recycled coarse aggregate concrete with an antifreeze admixture (dosage of 1%) and a water–cement ratio of 0.41 exhibited a mass loss of only 1.23% after 200 freezing and thawing cycles, a relative dynamic modulus of elasticity of up to 93.97%; however, the control group had reached the stopping condition at 150 freeze–thaw cycles with more than 10% mass loss. The recycled coarse aggregate concrete with added antifreeze admixture had a tight connection between the aggregate and the paste and a more pronounced improvement in the pore structure, indicating excellent resistance to frost damage. Full article

The rapid growth in the use of AI techniques, mainly machine learning (ML), is revolutionizing different industries by significantly enhancing decision-making processes through data-driven insights. This study investigates the influence of using ML, particularly supervised and unsupervised learning, on rational decision-making (RDM) within Jordanian e-government, focusing on the mediating role of trust. By analyzing the experiences of middle-level management within e-government in Jordan, the findings underscore that ML positively impacts the rational decision-making process in e-government. It enables more efficient and effective data gathering, improves the accuracy of data analysis, enhances the speed and accuracy of evaluating decision alternatives, and improves the assessment of potential risks. Additionally, this study reveals that trust plays a critical role in determining the effectiveness of ML adoption for decision-making, acting as a pivotal mediator that can either facilitate or impede the integration of these technologies. This study provides empirical evidence of how trust not only enhances the utilization of ML but also amplifies its positive impact on governance. The findings highlight the necessity of cultivating trust to ensure the successful deployment of ML in public administration, thereby enabling a more effective and sustainable digital transformation. Despite certain limitations, the outcomes of this study offer substantial insights for researchers and government policymakers alike, contributing to the advancement of sustainable practices in the e-government domain. Full article

Compared to other identity verification systems applications, vein patterns have the lowest potential for being used fraudulently. The present research examines the practicability of gathering vascular data from NIR images of veins. In this study, we propose a self-supervision learning algorithm that envisions an automated process to retrieve vascular patterns computationally using unsupervised approaches. This new self-learning algorithm sorts the vascular patterns into clusters and then uses 2D image data to recuperate the extracted vascular patterns linked to NIR templates. Our work incorporates multi-scale filtering followed by multi-scale feature extraction, recognition, identification, and matching. We design the ORC, GPO, and RDM algorithms with these inclusions and finally develop the vascular pattern mining model to visualize the computational retrieval of vascular patterns from NIR imageries. As a result, the developed self-supervised learning algorithm shows a 96.7% accuracy rate utilizing appropriate image quality assessment parameters. In our work, we also contend that we provide strategies that are both theoretically sound and practically efficient for concerns such as how many clusters should be used for specific tasks, which clustering technique should be used, how to set the threshold for single linkage algorithms, and how much data should be excluded as outliers. Consequently, we aim to circumvent Kleinberg’s impossibility while attaining significant clustering to develop a self-supervised learning algorithm using unsupervised methodologies. Full article