Miracle-dz/UniFAR-FTUs huggingface dataset:
Repeated liquefaction and the effect on properties of silt

The in-situ vibrating test on silt was carried out in the tidal flat of the Yellow River estuary.The pore water pressure measurement,cone penetration tests and vane shear tests were conducted.The contrast tests of undisturbed silt samples were done in laboratory to study the changes of silt properties.The variation law of pore pressure and strength in the crust layer in the process of liquefaction were studied.The results indicate that liquefaction is always taking place at the crust layer exhibiting the increase of pore pressure and reduction of strength.The strength of silt both above and beneath the crust layer increases.The pore pressure changes abruptly during the action of cyclic loading and tends to dissipate when loading is stopped.Following the increase of cyclic number the depth of liquefaction layer tends to increase.The physical and mechanical indices of silt changed after the action of cyclic load and vary remarkably along the depth.
Luminosity–Luminosity Correlations in Flux-limited Multiwavelength Data

We explore the general question of correlations among different waveband luminosities in a flux-limited multiband observational data set. Such correlations, often observed for astronomical sources, may be either intrinsic or induced by the redshift evolution of the luminosities and the data truncation due to the flux limits. We first address this question analytically. We then use simulated flux-limited data with three different known intrinsic luminosity correlations and prescribed luminosity functions and evolution similar to the ones expected for quasars. We explore how the intrinsic nature of luminosity correlations can be deduced, including exploring the efficacy of partial correlation analysis with redshift binning in determining whether luminosity correlations are intrinsic and finding the form of the intrinsic correlation. By applying methods that we have developed in recent works, we show that we can recover the true cosmological evolution of the luminosity functions and the intrinsic correlations between the luminosities. Finally, we demonstrate the methods for determining intrinsic luminosity correlations on actual observed samples of quasars with mid-infrared, radio, and optical fluxes and redshifts, finding that the luminosity–luminosity correlation is significantly stronger between mid-infrared and optical than that between radio and optical luminosities, supporting the canonical jet-launching and heating model of active galaxies.
Evaluation of a quantitative phosphorus transport model for potential improvement of southern phosphorus indices

Management of agricultural nonpoint source phosphorus (P) requires identification of fields susceptible to P loss. P-Indices are the most common tools used to identify critical source areas of P loss. However, the success of the P-Index is impeded by insufficient testing against measured P loss data. Due to a shortage of available P loss data sets, simulated data from a quantitative P transport model may be used to test against a P-Index. The objective of this study was to compare predictions from the Texas Best Management Evaluation Tool (TBET) against measured P loss data to determine whether the model can improve P-Indices in the South. Measured P loss data, representing a range of conditions, were used to test TBET on an event-basis. Our results suggest that TBET can generate satisfactory quantitative predictions of runoff, sediment and P loss with site-specific calibration, but may be better suited for parameterization by physiographic region or state. INDEX WORDS: TBET, Texas Best Management Evaluation Tool, Phosphorus, Phosphorus Index, Modeling EVALUATION OF A QUANTITATIVE PHOSPHORUS TRANSPORT MODEL FOR POTENTIAL IMPROVEMENT OF SOUTHERN PHOSPHORUS INDICES
Infrared Small Target Detection Based on Saliency Guided Multi-Task Learning

Infrared (IR) small target detection is a challenging task due to the low contrast and low signal-to-noise ratio, generally yields high false alarm rates. To improve the performance of IR small target detection, we propose a saliency guided multi-task leaning model (SGMTLM). The model consists of two parts: feature fusion and saliency detection. The feature fusion module is to integrate shallow information and deep semantic information of small targets. The saliency detection module is used to guide the Feature Pyramid Networks (FPN) to focus on the small target area. It can effectively suppress the non-target information while enhancing the small target information. Finally, experimental results on two datasets Small-ExtIRShip and Small-SSDD demonstrated that, with the help of saliency detection, the proposed method can effectively improve the accuracy of IR small target detection, achieving 95.78% and 98.70% mAP on the two datasets, respectively.
An Integration of Pre-Trained Speech and Language Models for End-to-End Speech Recognition

Advances in machine learning have made it possible to perform various text and speech processing tasks, including automatic speech recognition (ASR), in an end-to-end (E2E) manner. Since typical E2E approaches require large amounts of training data and resources, leveraging pre-trained foundation models instead of training from scratch is gaining attention. Although there have been attempts to use pre-trained speech and language models in ASR, most of them are limited to using either. This paper explores the potential of integrating a pre-trained speech representation model with a large language model (LLM) for E2E ASR. The proposed model enables E2E ASR by generating text tokens in an autoregressive manner via speech representations as speech prompts, taking advantage of the vast knowledge provided by the LLM. Furthermore, the proposed model can incorporate remarkable developments for LLM utilization, such as inference optimization and parameter-efficient domain adaptation. Experimental results show that the proposed model achieves performance comparable to modern E2E ASR models.
Psicologia: Teoria e Pesquisa

The goal of this research was to investigate the psychometric properties of the Level of Personality Functioning Scale – Brief Form – 2.0 (LPFS-BF-2.0) in Brazilian samples. Therefore, two samples were used in this study, one with 415 and the other with 1,011 Brazilians. Participants completed the Brazilian version of the LPFS-BF-2.0 and other measures of common mental disorder symptoms, suicide risk, the severity of personality pathology, and pathological personality traits. The results indicated that the two-factor model (including self-functioning and interpersonal functioning domains) fits the Brazilian samples better than a one-factor model. The LPFS-BF-2.0 scales indicated adequate reliability coefficients and evidence of convergent validity.
A Multimodal Graph Fingerprinting Method for Indoor Positioning Systems

WiFi fingerprinting has been extensively studied for years to provide location estimation in indoor scenarios. Researchers have used various machine learning algorithms to match the online fingerprint to the pre-collected offline fingerprints, which have location labels, for location estimation. However, neither conventional machine learning algorithms nor modern deep neural networks explore the geometric relations between WiFi access points and the location where the fingerprint was taken. Therefore, they cannot capture the non-Euclidean nature of the WiFi fingerprint. Furthermore, prior research has indicated that fusing multiple modalities can improve positioning performance compared to using only WiFi. Therefore, this study proposes a novel Multimodal Graph Fingerprinting method for indoor positioning systems. The proposed method constructs a multimodal graph at the location of the user’s smart terminal by fusing radio frequency signals, electromagnetic field (EMF) strength, and inertial sensor measurements. A hierarchical deep graph neural network is developed to learn the relations between the multimodal graphs and their locations by capturing the features of the identities (such as MAC addresses, WiFi Received Signal Strength (RSS), and EMF data) and the topology information. Experiments on a real dataset built on a university campus demonstrate that the proposed model can achieve a median positioning error of 2.1m by fusing different modalities.
A method for detecting transient signals in GPS position time-series: smoothing and principal component analysis

SUMMARY We test a method for detecting anomalous transient signals from Global Positioning System (GPS) data. The method enhances signal-to-noise ratios of GPS position time-series through smoothing based on Kalman ﬁlter formulations in the time domain and principal component analysis (PCA) in the space domain. The smoother reduces measurement white noise, accounts for correlated noise and interpolates missing data. A ﬁrst-order Gauss–Markov (FOGM) process is used in the state vector to estimate transient signals and correlated noise. The parameters of the FOGM and white processes are determined by minimizing cost functions constructed with the innovations sequence from the forward Kalman ﬁlter. PCA decomposes the FOGM state estimates into principal components (PCs) for temporal variation and sample eigenvectors for spatial distribution. Uncertainties of the PCA estimates are approximated by propagating errors for PCs and by using asymptotic distributions with an effective sample size for sample eigenvectors. The uncertainties can help determine the signiﬁcance of the temporal variations of the PCs and the spatial distribution of sample eigenvectors. When the FOGM noise process has a long correlation time, the high order PCs show oscillatory behaviour and we develop a method to remove these effects. We show two examples of the detection capability of the algorithm with applications to transients in the Los Angeles basin, California, from the distant Hector Mine Earthquake in 1999 and ground water changes in 2005.
Crafting Realistic Virtual Humans: Unveiling Perspectives on Human Perception, Crowds, and Embodied Conversational Agents

Virtual Humans (VHs) were first developed more than 50 years ago and have undergone significant advancements since then. In the past, creating and animating VHs was a complex task. However, contemporary commercial and freely available technology now empowers users, programmers, and designers to create and animate VHs with relative ease. These technologies have even reached a point where they can replicate the authentic characteristics and behaviors of real actors, resulting in VHs that are visually convincing and behaviorally lifelike. This paper explores three closely related research areas in the context of virtual humans and discusses the far-reaching implications of highly realistic characters within these domains.
VRS-NeRF: Accelerating Neural Radiance Field Rendering with Variable Rate Shading

Recent advancements in Neural Radiance Fields (NeRF) provide enormous potential for a wide range of Mixed Reality (MR) applications. However, the applicability of NeRF to real-time MR systems is still largely limited by the rendering performance of NeRF. In this paper, we present a novel approach for Variable Rate Shading for Neural Radiance Fields (VRS-NeRF). In contrast to previous techniques, our approach does not require training multiple neural networks or re-training of already existing ones, but instead utilizes the raytracing properties of NeRF. This is achieved by merging rays depending on a variable shading rate, which reduces the overall number of queries to the neural network. We demonstrate the generalizability of our approach by implementing three alternative functions for the determination of the shading rate. The first method uses the gaze of users to effectively implement a foveated rendering technique in NeRF. For the other two techniques, we utilize shading rates based on edges and saliency. Based on a psychophysical experiment and multiple image-based metrics, we suggest a set of parameters for each technique, yielding an optimal tradeoff between rendering performance gain and perceived visual quality.
Concussion: Beyond the Cascade

Sport concussion affects millions of athletes each year at all levels of sport. Increasing evidence demonstrates clinical and physiological recovery are becoming more divergent definitions, as evidenced by several studies examining blood-based biomarkers of inflammation and imaging studies of the central nervous system (CNS). Recent studies have shown elevated microglial activation in the CNS in active and retired American football players, as well as in active collegiate athletes who were diagnosed with a concussion and returned to sport. These data are supportive of discordance in clinical symptomology and the inflammatory response in the CNS upon symptom resolution. In this review, we will summarize recent advances in the understanding of the inflammatory response associated with sport concussion and broader mild traumatic brain injury, as well as provide an outlook for important research questions to better align clinical and physiological recovery.
THE PROGRESS OF SEQUENCE STRATIGRAPHY

On the basis of previous research at home and abroad,this paper reviewed the origin and basic principle of sequence stratigraphy,and summarized its application and research progress in the field of continent, ocean, basin research, sequence filling dynamic facieology and pale geography mapping, the sequence surfacecategory, the sequence response to sea level changes in shallow platform and deep shelf environments.The future prospect of squence stratigraphy was also discussed.
Fracture Identification and Prediction Model of Ultra-Low Permeability Reservoir in Haituozi Oilfield,Southern of Songliao Basion

Development practice of Haituozi Oilfield which is an ultra-low permeability reservoir shows that the micro-fractures provide the main flow channel for reservoir fluid,so accuracy fracture description is directly related to reservoir development effect.In this paper,based on core data,imaging logging data and casting thin sections data,the fracture genetic type,species distribution,the authors reasearched occurrence characterization to get the detailed parameters of fracture characterization,and construct a system description and the authors reasearched occurrence characterization effective recognition method of ultra-permeability fractured reservoir.According to the research results,a fracture distribution model is built by using stochastic modeling method to predict distribution density,azimuth and joint opening of the reservoir fracture.This provides a basis of dual permeability model for reservoir numerical simulation.
Development and Clinical Evaluation of AlcoChange: a Digital Therapeutic for Patients with Alcohol-Related Liver Disease

Background and aims: Maintenance of abstinence in alcohol-related liver disease (ARLD) is a major unmet therapeutic need. Digital therapeutics can deliver ongoing behavioural therapy, in real-time, for chronic conditions. The aim of this project was to develop and clinically test AlcoChange, a novel digital therapeutic for ARLD. Methods: AlcoChange was developed using validated behaviour change techniques (BCTs) and a digital alcohol breathalyser. This was an open-label, single-centre study. Patients with ARLD, ongoing alcohol use (within 1 month) and possession of a suitable smartphone were eligible. Patients were recruited from inpatient and outpatient settings, and received AlcoChange therapy for 3-months. The primary outcome was reduction in alcohol use from baseline to 3-months, measured by timeline follow-back (TLFB). Secondary outcomes included: (i) compliance with the AlcoChange app, (ii) alcohol-related and all-cause hospital re-admissions up to 1-year, (iii) qualitative analysis to determine factors associated with compliance. Results: Sixty-five patients were recruited, of whom forty-one completed the study per-protocol. Patients compliant with the intervention (>60 logins over 3-months) had a significant reduction in alcohol use from baseline compared to non-compliant patients [median (IQR): -100% (100% to -55.1%) vs -57.1% (-95.3% to +32.13%), p=0.029]. The proportion attaining abstinence at 3-months was higher in the compliant group (57.1% vs 22.2%, p=0.025). The compliant group had a significantly decreased risk of subsequent alcohol-related re-admission up to 12-months (p=0.008). Qualitative analysis demonstrated receiving in-app feedback and presence of health-related sentinel event were predictors of compliance with the intervention. Conclusions: Use of the novel digital therapeutic, AlcoChange, was associated with a significant reduction in alcohol use and increase in proportion attaining abstinence in ARLD patients. Definitive, randomized trials are warranted for this intervention.
Sentiment Analysis: A Systematic Case Study with Yelp Scores

Sentiment Analysis is a classic and well-defined task for machine learning and natural language processing. Over the years, we have seen much progress in machine learning as a whole and in natural language processing. Given that in commercial applications, we are heavily constrained by cost, throughput and latency, we wonder how better accuracy can be brought about by using complex, high-latency models, than easy, low-latency models that can be deployed in embedded devices and in high throughput scenarios. In this article, we focus on the Yelp Review dataset as a test bench. By predicting Yelp overall ratings based on user review text and other related features, we experiment with various existing machine learning algorithms, from easy logistic regression to BERT embedding-based deep models. We also use ensemble to combine the aforementioned models into a single predictor, seeing if a combination of these models will achieve better performance. Among all the models, we can see that a simple TF-IDF baseline with MLP ensemble can reach an accuracy higher than pure MLP models, proving that in a production scenario, we may be able to emphasize
A method for Constructing an Urban Public Transport Network Based on the Assessment of Traffic Flows by ITS Detectors

The problem of adapting existing urban public transport networks for large cities remains relevant, as it requires quite expensive methods of collecting primary information about the movements of urban residents using tracking from cellular network signals, fare transactions or large-scale statistical surveys. The article compares the results of constructing a matrix of correspondence between residents of a large city based on three methods of collecting initial information: a statistical study of the transport demand of residents, the use of online surveys and modeling of potential demand based on the use of data from detectors of the city’s intelligent transport system (ITS). It is shown that, despite the significant difference between the methods and the resources spent, the last two methods give comparable results. In connection with the large-scale implementation of ITS in world practice, the latter method can be used to quickly and fairly accurately assess the uncovered transport needs of urban public transport networks. The article confirms the hypothesis about the formation of almost identical passenger flows by private car and public transport in travel areas, with little interest of citizens in using the latter for everyday trips.
Pothole Detection using YOLO v8 Algorithm

With advancing urbanization, roads serve as a most important way of communication. But the increasing potholes on the roads pose a bigger danger towards public safety. In this paper we tend to build a system so as to identify and detect potholes. Here we aim to identify the potholes present on the roads so as to beware of the accidents. In recent years vehicles do come with technologies to detect potholes, but we tend to build an interface to automatically identify and detect the same. As far as we know, most of the fatal accidents happen on roads in India, and majority of them occur due to uneven road terrain caused due to potholes. The most important goal that we hold is to maintain public safety. Conversely, we also wanted to check the performance of the YOLO v8 algorithm, on the pothole dataset, to see how efficiently the algorithm works. Now for the development of the system, we have built a model and used a dataset, containing pothole images and corresponding labels, so as to train the model. We have used the YOLO v8 algorithm to train the model. As a result, our proposed model can successfully identify and recognize potholes after the successful training of the system.
Model the Path: Impact of Propagation Models on the Scalability Analysis of a Wireless M-Bus Sensoring System for Smart Grids

This paper analyses the impact of the propagation model on the outcomes of a scalability analysis of a wireless M-Bus system simulated in OMNeT++. Four path loss models are compared: Free Space Path Loss (FSPL) model with obstacle-related losses, the ITU-R P.1238 model, and two configurations of the log-normal model used by previous studies in similar contexts. Performance is assessed using three main indicators: delivery ratio, message error ratio, and gross delivery ratio.Results show that, although all the propagation models considered validate the baseline implementation of the system, performance differences emerge when scaling it up. The two configurations of the log-normal models may provide a too-optimistic scalability potential, while the ITU-R model may provide a pessimistic one. The high influence of the propagation model in the scalability results of the system is demonstrated, as other factors, such as message collision, prove to have no impact on scalability for the scenarios analysed.Future research may compare other standardised propagation models or consider the inclusion of more than one data collector.
Experimental and Numerical Evaluation of the Heat Transfer Coefficient in Press Hardening

When producing thin ultra high strength steel components with the press hardening process, it is essential that the final component achieves desirable material properties. This applies in particular to passive automotive safety components where it is of great importance to accurately predict the final component properties early in the product development process. The transfer of heat is a key process that affects the evolution of the mechanical properties in the product and it is essential that the thermal contact conditions between the blank and tool are properly described in the forming simulations. In this study an experimental setup is developed combined with an elementary inverse simulation approach to predict the interfacial heat transfer coefficient (IHTC) when the hot blank and cold tool are in mechanical contact. Different process conditions such as contact pressure and blank material (22MnB5 and Usibor 1500P) are investigated. In the inverse simulation, a thermo‐mechanical coupled simulation model is used with a thermo‐elastic‐plastic constitutive model including effects from changes in the microstructure during quenching. The results from simulations give the variations of the heat transfer coefficient in time for best match to experimental results. It is found that the pressure dependence for the two materials is different and the heat transfer coefficient is varying during quenching. This information together with further testing will be used as a base in a future model of the heat transfer coefficient influence at different conditions in press hardening process.
Riba dan Gharar dalam Insurans: Satu Analisis Fiqh

This paper attempts to exemplify the elements of riba and gharar in insurance which have resulted the contact fasad as declared by the Jawatankuasa Fatwa Majlis Agama Kebangsaan in 1972. It is shown that, both of them pervade insurance conspicuously ang convincingly which may elucidate the stand of the fatwa. This paper also looks at their effect on the insurance contract and tries to show how takaful contract dismisses them.