Miracle-dz/UniFAR-FTUs huggingface dataset:
POPULATION,HOUSING STATISTICS DATA SPATIALIZATION RESEARCH IN THE APPLICATION OF RAPID EARTHQUAKE LOSS ASSESSMENT —A CASE OF YILIANG EARTHQUAKE

The paper introduces the method of population and housing data spatialization. Field investigation data are used for the first time to test and verify the spatialized grid data. Based on Yiliang earthquake,the grid data and earthquake intensity map are overlaid to obtain the number of earthquake-affected population and housing in the support of GIS software. Compared with the data released by Earthquake Administration of Yunnan Province,the error of estimating earthquake direct economic loss of Yiliang earthquake in Yunnan area is minus six percent. The research shows that the map of population density and housing density can reasonably reflect the actual distribution of people and building. Using the grid data,the data of affected population and housing and the earthquake direct loss can be got directly and precisely,which can provide reliable basis for earthquake emergency rescue and decision-making.
Super-Resolution Neural Radiance Field via Learning High Frequency Details for High-Fidelity Novel View Synthesis

While neural rendering approaches facilitate photo-realistic rendering in novel view synthesis tasks, the challenge of high-resolution rendering persists due to the substantial costs associated with acquiring and training data. Recently, several studies have been proposed that render high-resolution scenes by either super-sampling points or using reference images, aiming to restore details in low-resolution (LR) images. However, super-sampling is computationally expensive, and methods with reference images require high-resolution (HR) images for inference. In this letter, we propose a novel super-resolution (SR) neural radiance field (NeRF) framework for high-fidelity novel view synthesis. To address the representation of high-fidelity HR images from the captured LR images, we learn a mapping function that maps LR rendering images to the Fourier space to restore insufficient high frequency details and render HR images at higher resolution. Experiments demonstrate that our results are quantitatively and qualitatively better than those of the existing SR methods in novel view synthesis. By visualizing the estimated dominant frequency components, we provide visual interpretations of the performance improvement.
A Skin-Inspired PDMS Optical Tactile Sensor Driven by a Convolutional Neural Network

Tactile sensors play a crucial role in enhancing the integration of automation, robotics, and biomedical equipment, particularly in perceptual functions. Optical fiber-based tactile sensors have gained significance due to their robustness and immunity to electromagnetic interference. However, existing optical fiber-based tactile sensors face limitations related to bio-imitation, scalability, and precise data processing algorithms. This study introduces a novel skin-inspired polydimethylsiloxane (PDMS)-manufactured tactile sensor utilizing a structured light source with low-cost light-emitting diodes and a multimode optical fiber, coupled with tactile information processing through a trained convolutional neural network (CNN). Specklegram images captured from the optical fiber are analyzed for force amplitude and tactile location. The CNN is trained, validated, and tested, achieving accuracies of 99.6%, 99.5%, and 99%, respectively. The tactile sensor demonstrates a spatial resolution of 2 mm and a force-sensing range up to 3 N. The confusion matrix, based on classification results, reveals only three misclassifications out of 315 tests, indicating a mean absolute error (MAE) of 0.95%. The spatial resolution and force-sensing capabilities, coupled with the machine learning approach of the proposed tactile sensor, showcase promising potential for future applications in tactile embodiment.
Multi-Agent Reinforcement Learning for Energy Harvesting Two-Hop Communications with a Partially Observable State

We consider an energy harvesting (EH) transmitter communicating with a receiver through an EH relay. The harvested energy is used for data transmission, including the circuit energy consumption. As in practical scenarios, the system state, comprised by the harvested energy, battery levels, data buffer levels, and channel gains, is only partially observable by the EH nodes. Moreover, the EH nodes have only outdated knowledge regarding the channel gains for their own transmit channels. Our goal is to find distributed transmission policies aiming at maximizing the throughput. A channel predictor based on a Kalman filter is implemented in each EH node to estimate the current channel gain for its own channel. Furthermore, to overcome the partial observability of the system state, the EH nodes cooperate with each other to obtain information about their parameters during a signaling phase. We model the problem as a Markov game and propose a multi-agent reinforcement learning algorithm to find the transmission policies. We show the trade-off between the achievable throughput and the signaling required, and provide convergence guarantees for the proposed algorithm. Results show that even when the signaling overhead is taken into account, the proposed algorithm outperforms other approaches that do not consider cooperation.
Tailoring the Techno-Functional Properties of Fava Bean Protein Isolates: A Comparative Evaluation of Ultrasonication and Pulsed Electric Field Treatments

The fava bean protein isolate (FBPI) holds promise as a sustainable plant-based protein ingredient. However, native FBPIs exhibit limited functionality, including unsuitable emulsifying activities and a low solubility at a neutral pH, restricting their applications. This study is focused on the effect of ultrasonication (US) and pulsed electric fields (PEF) on modulating the techno-functional properties of FBPIs. Native FBPIs were treated with US at amplitudes of 60–90% for 30 min in 0.5 s on-and-off cycles and with PEF at an electric field intensity of 1.5 kV/cm with 1000–4000 pulses of 20 μs pulse widths. US caused a reduction in the size and charge of the FBPIs more prominently than the PEF. Protein characterization by means of SDS-PAGE illustrated that US and PEF caused severe-to-moderate changes in the molecular weight of the FBPIs. In addition, a spectroscopic analysis using Fourier-transform infrared (FTIR) and circular dichroism (CD) revealed that US and the PEF induced conformational changes through partial unfolding and secondary structure remodeling from an α-helix to a β-sheet. Crystallographic and calorimetric determinations indicated decreased crystallinity and lowered thermal transition temperatures of the US- and PEF-modified FBPIs. Overall, non-thermal processing provided an effective strategy for upgrading FBPIs’ functionality, with implications for developing competitive plant-based protein alternatives.
A Novel GNSS Positioning Technique for Improved Accuracy in Urban Canyon Scenarios Using 3D City Model

Reliable positioning in urban canyon, especially in dense urban areas, is difficult to achieve in a cost-effective manner using standalone Global Navigation Satellite System (GNSS), due to multipath problems and NonLine-of-Sight (NLOS) signals. In this regard, several researches have focused towards identifying and rejecting NLOS measurements. However, very few researches have used NLOS signals, although generating final position using only one of the signals. In this regard, this research utilizes all the available signals, including all NLOS signals, from all the satellites, in order to estimate position, using an algorithm based on concept of constructive use of NLOS signals. The NLOS signals are used constructively by incorporating the information related to nearby reflectors, with the help of a 3D city model. The feasibility and performance of the algorithm was done using a real data collected in Downtown Calgary. In a way, this research attempts towards building a low cost GNSS based technology for improved navigation performance, in scenarios where fewer satellites are available and rejecting measurements due to blunders might cost a crucial stake of availability.
The role of speech style, frequency, and density in recognition memory for spoken words

What determines whether listeners remember a spoken word? The Effortfulness Hypothesis claims that memory is modulated by a word’s intelligibility during real-time processing, while the Distinctiveness Hypothesis claims that it is modulated by a word’s distinguishing characteristics. We tested these differing predictions using American English words that varied along three dimensions known to affect both intelligibility and distinctiveness: speech style (clear versus casual), frequency (high versus low), and neighborhood density (high versus low). In a recognition memory experiment, participants (n = 66) listened to a set of study words, and then gave yes/no judgments to indicate whether or not they had heard the word earlier. Results showed that those words which exhibited distinctive characteristics – whether due to clear speech style, low frequency, or low density – were remembered better. The finding supports the Distinctiveness Hypothesis, suggesting that our capacity for remembering words relies on their distinctiveness, rather than on our capacity for recognizing them in real time.
Role of Church Affiliation and the Sexual Behaviour Choices of Students in Selected Universities in Nairobi County, Kenya

Purpose: The current study sheds light on the role of church affiliation in shaping sexual attitudes and behaviour. Not enough was known about how Christianity can influence the construction of values and beliefs that discourage risky sexual behaviour among adolescents at universities in Kenya. Little was understood on the role of church affiliation explaining variation in adolescent sexuality among Christian communities at universities. Spirituality is an understudied subtype of religiosity yet it may explain patterns of sexual behaviour. Most studies that have examined the relationship between sexual behaviours and religiosity have focused on sexual intercourse. These studies have not examined other sexual behaviour choices, like internet sex. Methodology: Using a mixed-methods design, the relationship between church affiliation and sexual behaviour was examined. In quantitative analysis, a stratified probability sample of (n=176 students) who attend regular prayer sessions in three universities: the Kenyatta University (KU) Main Campus, the Jomo Kenyatta University of Agriculture and Technology (JKUAT) Karen Campus, and the Daystar University was used. Qualitative interviews were conducted with university administrators, chaplains, and other church workers. Undergirding the study was the social identity theory, which articulates the idea of people seeking to gain membership and acceptance in any social group. Findings: The findings suggest church affiliation has a significant effect on sexual attitudes, values, and behaviour. There seems to be an interaction between extrinsic and intrinsic religiosity, with frequent church attendance appearing to induce more conservative sexual beliefs, attitudes and behaviour. Church affiliation had a significant effect on sexual behaviour choices, which includes internet sex and cohabitation and extreme sexual activities.
The Utilization of Google Earth Images as Reference Data for The Multitemporal Land Cover Classification with MODIS Data of North Korea

One of the major obstacles to classify and validate Land Cover maps is the high cost of acquiring reference data. In case of inaccessible areas such as North Korea, the high resolution satellite imagery may be used for reference data. The objective of this paper is to investigate the possibility of utilizing QuickBird high resolution imagery of North Korea that can be obtained from Google Earth data via internet for reference data of land cover classification. Monthly MODIS NDVI data of nine months from the summer of 2004 were classified into L=54 cluster using ISODATA algorithm, and these L clusters were assigned to 7 classes - coniferous forest, deciduous forest, mixed forest, paddy field, dry field, water, and built-up areas - by careful use of reference data obtained through visual interpretation of the high resolution imagery. The overall accuracy and Kappa index were 85.98% and 0.82, respectively, which represents about 10% point increase of classification accuracy than our previous study based on GCP point data around North Korea. Thus we can conclude that Google Earth may be used to substitute the traditional reference data collection on the site where the accessibility is severely limited.
Endometrial DNA methylation signatures during the time of breeding in relation to the pregnancy outcome in postpartum dairy cows fed a control diet or supplemented with rumen-protected methionine

Post calving metabolic stress reduces the fertility of high producing dairy cows possibly by altering the expression of genes in the maternal environment via epigenetic modifications. Therefore, this study was conducted to identify endometrial DNA methylation marks that can be associated with pregnancy outcomes in postpartum cows at the time of breeding. For this, twelve days post-calving, cows were either offered a control diet or supplemented daily with rumen-protected methionine. Cows showing heat 50–64 days postpartum were artificially inseminated. Endometrial cytobrush samples were collected 4–8 h after artificial insemination and classified based on the pregnancy out comes as those derived from cows that resulted in pregnancy or resulted in no pregnancy. The DNAs isolated from endometrial samples were then subject to reduced representative bisulfite sequencing for DNA methylation analysis. Results showed that in the control diet group, 1,958 differentially methylated CpG sites (DMCGs) were identified between cows that resulted in pregnancy and those that resulted in no pregnancy of which 890 DMCGs were located on chr 27: 6217254–6225600 bp. A total of 537 DMCGs were overlapped with 313 annotated genes that were involved in various pathways including signal transduction, signalling by GPCR, aldosterone synthesis and secretion. Likewise, in methionine supplemented group, 3,430 CpG sites were differentially methylated between the two cow groups of which 18.7% were located on Chr27: 6217254–6225600 bp. A total of 1,781 DMCGS were overlapped with 890 genes which involved in developmental and signalling related pathways including WNT-signalling, focal adhesion and ECM receptor interaction. Interestingly, 149 genes involved in signal transduction, axon guidance and non-integrin membrane-ECM interactions were differentially methylated between the two cow groups irrespective of their feeding regime, while 453 genes involved in axon guidance, notch signalling and collagen formation were differentially methylated between cows that received rumen protected methionine and control diet irrespective of their fertility status. Overall, this study indicated that postpartum cows that could potentially become pregnant could be distinguishable based on their endometrial DNA methylation patterns at the time of breeding.
Differences in the Incidence of Hypotension and Hypertension between Sexes during Non-Cardiac Surgery: A Systematic Review and Meta-Analysis

Background: Major determinants of blood pressure (BP) include sex and age. In youth, females have lower BP than males, yet in advanced age, more pronounced BP increases result in higher average BPs in females over 65. This hypothesis-generating study explored whether age-related BP divergence impacts the incidence of sex-specific intraoperative hypotension (IOH) or hypertension. Methods: We systematically searched PubMed and Embase databases for studies reporting intraoperative BP in males and females in non-cardiac surgery. We analyzed between-sex differences in the incidence of IOH and intraoperative hypertension (primary endpoint). Results: Among 793 identified studies, 14 were included in this meta-analysis, comprising 1,110,636 patients (56% female). While sex was not associated with IOH overall (females: OR 1.10, 95%CI [0.98–1.23], I2 = 99%), a subset of studies with an average age ≥65 years showed increased exposure to IOH in females (OR 1.17, 95%CI [1.01–1.35], I2 = 94%). One study reported sex-specific differences in intraoperative hypertension, with a higher incidence in females (31% vs. 28%). Conclusions: While sex-specific reporting on intraoperative BP was limited, IOH did not differ between sexes. However, an exploratory subgroup analysis offers the hypothesis that females of advanced age may face an increased risk of IOH, warranting further investigation.
Spatiotemporal dynamics of the molluscan community associated with seagrass on the western equatorial Atlantic

Abstract This study aimed to qualitatively and quantitatively analyse the molluscan assemblages associated with a Halodule wrightii seagrass bed in a rarely studied area within a conservation unit in north-eastern Brazil. Seasonal and spatial changes in several seagrass meadow characteristics, including sediment, were evaluated to explain temporal and spatial variations in the molluscs found there. The molluscan community differed in its structure among periods and meadows, as well as in the composition of its infaunal and epifaunal assemblages. The results of this study indicated that molluscs are affected by the particular characteristics of a seagrass meadow, especially by its location in the intertidal zone, more than by the area of the meadow. Molluscs were also affected by other characteristics of the seagrass meadow, such as above-ground biomass and shoot density. Changes in all molluscan assemblages were also mediated by differences among months and seasons in this region of the western equatorial Atlantic, but not by seasonal changes of the meadow. The studied meadow was found to be one of the densest in Brazil, which has considerable importance to its associated fauna.
FNN for Diabetic Prediction Using Oppositional Whale Optimization Algorithm

The medical field is witnessing rapid adoption of artificial intelligence (AI) and machine learning (ML), revolutionizing disease diagnosis and treatment management. Researchers explore how AI and ML can optimize medical decision-making, promising to transform healthcare. Feed Forward Neural Networks (FNN) are widely used to create predictive disease models, cross-validated by medical experts. However, complex medical data like diabetes leads to multi-modal search spaces prone to local minima, affecting optimal solutions. In this study, we focus on optimizing a diabetes dataset from the Pima Indian community, evaluating decision-making performance in diabetes management. Employing multimodal datasets, we compare various optimization algorithms, including the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). The test results encompass essential metrics like best-fit value, mean, median, and standard deviation to assess the impact of different optimization techniques. The findings highlight the superiority of the Oppositional Whale Optimization Algorithm (OWOA) over other methods employed in our research setup. This study demonstrates the immense potential of AI and metaheuristic algorithms to revolutionize medical diagnosis and treatment approaches, paving the way for future advancements in the healthcare landscape. Results reveal the superiority of OWOA over other methods. AI and metaheuristics show tremendous potential in transforming medical diagnosis and treatment, driving future healthcare advancements.
A Radio Wave Propagation Modeling Method Based on High-Precision 3-D Mapping in Urban Scenarios

Knowledge of the radio channel in urban environments is of key importance in many critical applications, such as wireless communication and autonomous driving. This study proposes a promising radio wave propagation model for complex urban scenarios. First, electrical parameters of different object classes are estimated by utilizing deep-learning-based semantic segmentation applied to a point cloud. Second, a highly accurate 3-D geometrical and electromagnetic (EM) model of the environment is obtained by surface reconstruction. Finally, ray tracing, based on geometrical optics and the uniform theory of diffraction (UTD), is applied to the environmental model to generate path-loss predictions. A comparison with measured data shows that the ray tracing based on accurate 3-D building reconstruction is more accurate than when applied to simpler geometric models.
RF Characterization of a Photocurable PEDOT:PSS:PEGDA Conductive Biomaterial for 3-D-Printing Implantable Antennas

In this work, we demonstrate photocurable poly-3,4-ethylene di-oxy thiophene (PEDOT):polystyrene sulfonate (PSS):poly-ethylene glycol di-acrylate (PEGDA) biomaterial as a promising candidate for 3-D-printing implantable antennas intracorporeally. Previous work has demonstrated the feasibility of a robotic probe to 3-D-print biological tissues via a minor incision. This same probe could also 3-D-print implantable antennas as long as a suitable conductive material is identified in terms of conductivity, printability, biocompatibility, and ability to cure at room/body temperature for safety purposes. We assess the frequency-dependent conductivity of this biomaterial and explore the radio frequency (RF) performance of resulting antennas operating in free space and inside tissue-emulating phantoms. Results show that PEDOT:PSS biomaterials with 21% and 30% PEGDA concentration exhibit a conductivity of <inline-formula> <tex-math notation="LaTeX">$\sim 10^{4}$ </tex-math></inline-formula> S/m up to 5 GHz, suitable for wireless implants. Comparing the two, 21% PEGDA content exhibits poorer curing abilities, while 30% PEGDA exhibits slightly lower conductivity. Measurements for 2.4 GHz free-space dipoles conducted in an anechoic chamber reveal only <inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>0.8 and <inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>1 dB lower gain for PEDOT:PSS:21% PEGDA and PEDOT:PSS:30% PEGDA biomaterial, respectively, as compared to their copper counterpart. For a 5 mm-deep implanted patch antenna, these two biomaterials exhibit 3.05 and 3.84 dB higher transmission loss than copper, respectively. If deemed necessary, this performance degradation can be overcome by increasing the overall antenna size since the printing process is now minimally invasive and miniaturization requirements can be relaxed.
Application of Polarization and Depolarization Current in High Voltage Insulator - A Review

Deterioration of cable insulator can be caused by many factors such as aging, fault and breakdown. These factors are caused by the presence of moisture, impurities and heat due to high temperatures in an insulator. The maintenance and replacement for insulation system in power cables, power transformers and electric machine stator are generally expensive and time consuming. Thus, the performance and ability of an insulator can be monitored through polarization and depolarization current (PDC). Polarization and depolarization current (PDC) is a non- destructive test method that is used to analyse many factors that affects the behaviour of an insulator in time domain. This paper presents the review on application of polarization and depolarization current test method on different type of insulator. The factors that affects the performance of different type of insulator will be discussed in this paper. Moreover, the theory of polarization and depolarization current will be explained in order to understand the behaviour of polarization and depolarization current curve when fault occurs. Last but not least, this paper will provide a review on the different type of factors that affects the performance of different types of insulation.