Miracle-dz/UniFAR-FTUs huggingface dataset:
Credit default prediction based on genetic algorithm improved neural network

The article delves into the fundamental objective of credit risk management, which entails evaluating the likelihood of borrowers defaulting on their obligations. This statement underscores the significance of precise risk quantification to mitigate losses, as well as the necessity of a robust risk assessment framework that is bolstered by model algorithms and data. The utilization of extensive datasets and the progression of machine learning algorithms have facilitated the examination of borrowers' data, enabling the prediction of default probabilities and the subsequent evaluation of loan risk. This article provides an overview of the progression of credit assessment models, starting from statistical models and advancing to machine learning algorithms and composite algorithms. It emphasizes the use of ensemble learning algorithms and underscores the benefits of composite algorithms in enhancing the accuracy of these models. The paper introduces the ant colony-BP (back propagation) neural network model as a potential remedy for the limitations observed in conventional BP neural network models. Furthermore, the article delineates the sequential phases entailed in the ant colony approach. The paper examines various data processing strategies, including feature reduction, handling imbalance, and resampling methods. Among these techniques, the SMOTE-Tomek algorithm is found to be particularly effective in mitigating data imbalance. The present paper conducts a comparative analysis of the ant colony-BP neural network model and the XGBoost model, highlighting the superior performance metrics exhibited by the former. The essay finishes by highlighting the practical consequences of the research findings and their significance in the context of data preprocessing and model selection within the financial industry.
Human Metrics Explorer System for Multi-Device Physiological Measurements in Emotion Estimation

Synchronously measuring and analyzing multiple physiological signals is useful for estimating human emotion in more detail; however, it is difficult in practical environments because few systems can integrate various types of devices and be used easily. We developed a human metrics explorer (HuME) ™ system for realizing easy synchronous measurement and analysis of data from several devices. The purposes of this study are to confirm the accuracy of the trigger signal used for synchronization of the HuME system in a basic experiment and to demonstrate the feasibility of the use of the system through synchronous measurement and analysis of signals from multiple device types through a practical experiment of emotion estimation. In a basic experiment, we examined the time lag between the transmission of trigger signals from the transmitter and the recording of these signals on sensing devices placed at various positions. In a practical experiment concerning emotion estimation applications, we measured the physiological data of two participants during a board game using the developed wearable facial electromyogram and electrocardiogram devices, commercial electrodermal activity devices, and cameras. Consequently, the absolute maximum variation in the time lag was 2 ms. Additionally, we could measure and analyze data synchronously from the multiple device types using the HuME system and capture the physiological responses of the participants during the game. In conclusion, we confirmed that the trigger signal was accurate for the HuME system synchronization and demonstrated the feasibility of this system in the synchronization of multiple device types.
Assembly of Ultra-Thin MEMS Device on Driver Chip Using Anisotropic Conductive Film

Assembly solution for large-area, ultra-thin, MEMS-based transducer array dies on driver chips has been investigated using dummy Si dies and substrates. The dummy dies have a thickness of 50 $\mu \mathrm{m}$, a bonding area on the order of 100 mm2populated with more than a thousand through-silicon vias connecting corresponding metal pads and redistribution layer (RDL) on both sides. The dummy substrates have conventional thickness as well as necessary RDL and pads for bonding and monitoring electrical resistance and stray capacitance of interconnects. Flip-chip interconnection technology based on anisotropic conductive film (ACF) was selected for bonding and characterization. Handling and ACF bonding of ultra-thin dies to rigid substrates were eased by means of glass carriers attached to the inactive side of the dies. Proper bonding parameters were identified, providing sufficiently low interconnect resistance with satisfactory yield. Stray capacitance of interconnects was found in acceptable range, though lower stray capacitance should be further pursued. The results from this work have demonstrated the feasibility of using ACF for assembling large-area, ultra-thin dies on rigid substrates.
Basal shear strength inversions for ice sheets with an application to Jakobshavn Isbrae, Greenland

Satellite and in situ observations of ice sheet outlet glaciers around the turn of the 21st century showed that rapid changes in ice dynamics are possible and important for the evolution of ice sheets. When attempting to model these dynamic changes the conditions at the ice-bed interface are crucial. Inverse methods can be used to infer basal properties, such as the basal yield stress, from abundant surface velocity observations by using a physical model of ice flow. Inverse methods are very powerful, but they need to be applied with care, otherwise errors can dominate the solution. In this study we investigate the potentials and caveats of inverse methods. Synthetic experiments can be designed where basal conditions are assumed and an ice flow model is used to produce a set of ‘synthetic’ surface velocities. These can then be used to examine and evaluate inverse methods. We find that in iterative inverse methods it is essential to use a stopping criterion that will prevent overfitting the data. We introduce a new and rapidly-converging iterative inverse method called Incomplete Gauss Newton method, where the linearized problem is partly minimized in each step. In a practical application of inverse methods to the terminus region of Jakobshavn Isbræ, Greenland we investigate changes in basal conditions over time by performing inversions for different years of available surface velocity data. We find a decrease in basal yield stress in the lower areas of the glacier that agrees with effective pressure changes due to the changes in ice geometry. This supports an ocean and terminus driven system. The difference between the modeled and observed velocity fields, called residual, contains information about the ability to reproduce the velocities when only adjustment of the basal condition is allowed. With a properly regularized inversion the residual patterns can be used to investigate sources of error in the system. We find that the ice geometry and the model simplifications influence the ability to reproduce observed velocity fields more than the error in observed velocity does. This indicates that further progress must come from model improvements and improved capabilities to measure bedrock geometry.
An Investigation of Applications for Sheep Search in the Field

In the context of the livestock industry, sheep hold significant economic value. The economic losses incurred from the loss of a few sheep pose an unacceptable burden for small and medium-sized ranches. And manpower and time must be invested in locating lost sheep. This research focuses on the development of an application designed to aid in finding lost sheep. To save manpower and time, the use of drones is considered. The economy of small drone and their mobility offer an effective solution to this challenge. Machine learning techniques are employed for sheep recognition. Also, the research discusses the system, environmental conditions, and several approaches that contribute to a system's execution speed and accurate execution to the recognition target.
Deep Learning for Skin Disease Diagnosis with End-to-End Data Security

Skin diseases, particularly skin cancer, pose significant health risks, necessitating accurate and timely diagnosis. Traditional manual analysis methods, however, are subject to errors and can be time-consuming. To address these challenges, we propose an automated classification system for skin disease detection that leverages deep learning algorithms to accurately diagnose skin lesions. This system facilitates secure sharing of diagnostic information among healthcare professionals, promoting collaborative treatment planning. Recognizing the critical importance of data security and confidentiality in the healthcare industry, our system employs state-of-the-art security measures to protect sensitive patient data whilst medical data transfer. By maintaining the integrity of patient information and adhering to regulatory requirements, our skin disease classification system provides healthcare professionals with fast and accurate diagnosis, and the confidence that patient privacy is upheld at all times.
First record of dinosaur eggshells and teeth from the north-west African maastrichtian (Morocco)

We report the discovery of amniotic eggshells and theropod teeth from the Late Cretaceous period in Morocco. The megaloolithid family represents the only known dinosaur egg remains in the north part of Africa and attests indirectly to the occurrence of sauropod dinosaurs (titanosaurids) in the Maastrichtian of Africa.
Unexpected high 35S concentration revealing strong downward transport of stratospheric air during the monsoon transitional period in East Asia

October is the monsoon transitional period in East Asia (EA) involving a series of synoptic activities that may enhance the downward transport of stratospheric air to the planetary boundary layer (PBL). Here we use cosmogenic 35S in sulfate aerosols (35SO42−) as a tracer for air masses originating from the stratosphere and transported downward to quantify these mixing processes. From 1 year 35SO42− measurements (March 2014 to February 2015) at a background station in EA we find remarkably enhanced 35SO42− concentration (3150 atoms m−3) in October, the highest value ever reported for natural sulfate aerosols. A four‐box 1‐D model and meteorological analysis reveal that strong downward transport from the free troposphere is a vital process entraining aged stratospheric air masses to the PBL. The aged stratospheric masses are accumulated in the PBL, accelerating the SO2 transformation to SO42−. Implications for the tropospheric O3 budget and the CO2 biogeochemical cycle are discussed.
A study on the Motivation of Computer Science Students to Learn Programming

This work focuses on the motivation levels of introductory programming students and their relationship with their learning performance. The study involved students enrolled in the Introduction to Programming (IP) course included in two slightly different Informatics Engineering degrees at the same institution. The motivation section of the Motivated Strategies for Learning Questionnaire (MSLQ) instrument includes several scales and subscales used to analyse different motivational factors.Four research questions guided the study. The first is comparing the results of the two groups of students. The second considered the student’s previous programming experience and tried correlating it with motivational factors. The third is similar but separates students following IP for the first time and those who had failed it in previous years. Finally, the fourth research question examined the influence of motivational factors on students’ learning performance measured by their final grades.This paper provides a detailed study description and presents and discusses its results.
Information Presentation Strategies to Promote Pedestrian Behavior Change in Mixed Spaces with Automated Vehicle

This paper introduces an information presentation strategy for pedestrians, aiming to enhance traffic efficiency in a mixed pedestrian-automated vehicle environment, such as a public road. While automated driving technology has made remarkable progress, interactions with pedestrians on regular roads have mostly been studied in virtual environments using virtual reality goggles. According to these studies, potential traffic efficiency and safety issues arise from pedestrians' limited understanding of automated vehicle behavior. To address this, we propose a human-machine interface employing a head-mounted display (HMD) to mitigate traffic efficiency degradation caused by pedestrians. The proposed system draws upon behavioral economics principles to encourage pedestrians to modify their behavior and develop better interactions with automated vehicles. Simulations were conducted to identify an information presentation strategy that strongly supports learning, and its effectiveness was further validated through experiments involving a real vehicle. Notably, the experimental results confirmed that the information presentation strategies proven effective in simulations also facilitated pedestrian learning during real-world interactions.
360° Sound Localization Support System for Deaf and Hard-of-Hearing People Using Smartglasses Equipped with Two Microphone

Deaf and hard-of-hearing (DHH) people have difficulty getting information. As a result, they are unable to notice emergency warning sounds such as ambulance sirens or car horns, and it therefore takes them longer to avoid danger. In this paper, we propose a system that can calculate the direction of a 360° sound source using two microphones. The effectiveness of this system is demonstrated by configuring a system that obtains the direction of the sound source using the sound size relationship and the time difference between the two microphones installed on the smartglasses, and displaying the direction calculation results on the lens. We discuss the proposed method and our validation of the calculations of the direction of a sound source.
Applying Stochastic Fractal Search Algorithm for Solving Non-Convex Economic Dispatch Problems

Today, with the development of science and technology, electricity production is done from many different energy sources. The purpose of this is to help utilize and promote the benefits of energy sources to the power production process of the plant in order to obtain the highest profit. The following article proposes an algorithm to serve the goal of economic moderation of energy sources. Using the Stochastic fractal search algorithm (SFSA), the article will provide a solution and analysis for the problem of economic moderation for a system of 10 elements and 40 elements. Comparing the results with other algorithms, the proposed SFS algorithm gives better results and better solutions.
Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Predicting the geometry of channels and alluvial rivers is of primary importance in river engineering science. Appropriately designing channels and predicting stable river cross‐sections can decrease costs and prevent the destruction of installations and agricultural land by rivers. Consequently, researchers have applied different empirical and regression methods to achieve relations for predicting stable channel and river geometry. In this study, Group Method of Data Handling ]GMDH) models are used to predict three geometric variables of stable channels, namely width (w), depth (h) and slope (s). The effect of different input parameters, such discharge (Q), median grain size (d50) and the Shields parameter (τ*) on the GMDH models is assessed with regard to predicting stable channel geometry. The results indicate that the GMDH model with mean absolute percentage error (MAPE) of 5.53%, 4.05% and 4.89% for channel width, depth and slope prediction respectively, exhibits good accuracy. Moreover, a comparison of the GMDH models with previous theoretical equations (based on regression analysis) indicates the superiority of GMDH model performance, with error reductions of one‐fifth, one‐eighth and one‐sixth compared with the regression equations for channel width, depth and slope prediction, respectively. Copyright © 2016 John Wiley & Sons, Ltd.
INTEREST RATES AND THE ROLE OF EXCHANGE RATE REGIMES IN MAJOR SOUTHEAST ASIAN COUNTRIES

Peper ini meneliti apakah perbedaan sistim nilai tukar yang dianut oleh negara-negara Asia Tenggara mempunyai implikasi yang signifikan terhadap bagaimana gejolak pasar keuangan internasional ditransmisikan kedalam suku bunga domestik di negara-negara tersebut. Dengan menggunakan data dari 5 negara utama di Asia TenggaraƒIndonesia, Malaysia, Filipina, Singapura, dan Thailand kami menguji hipotesis bahwa tingkat suku bunga domestik dalam negara dengan sistim nilai tukar yang lebih fleksibel adalah lebih tidak terpengaruh oleh pasar keuangan internasional. Data yang digunakan adalah data harian dari Januari 1995 sampai dengan Desember 2003. Hasil estimasi menunjukkan bahwa sistem nilai tukar tidak mempunyai implikasi yang konklusif tentang bagaimana gejolak pasar keuangan internasional ditransmisikan ke dalam tingkat suku bunga domestik di negara-negara utama Asia Tenggara. Apapun sistem nilai tukar yang mereka anut, faktor domestik adalah merupakan faktor utama dalam pergerakan suku bunga domestik di negara-negara tersebut.
A highly susceptible hACE2-transgenic mouse model for SARS-CoV-2 research

Several animal models have been used to assist the development of vaccines and therapeutics since the COVID-19 outbreak. Due to the lack of binding affinity of mouse angiotensin-converting enzyme II (ACE2) to the S protein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), increasing the susceptibility of mice to SARS-CoV-2 infection was considered in several ways. Here, we generated a COVID-19 mouse model expressing human ACE2 (hACE2) under the control of the CAG promoter. Overexpression of hACE2 did not pose a significant effect on weight growth. After SARS-CoV-2 inoculation, mice showed obvious viral replication and production of inflammation within 7 days, with a gradual decrease in body weight until death. Virological testing found that the virus can replicate in the respiratory system, small intestine, and brain. Additionally, this mouse model was applied to compare two antibody drug candidates, the anti-RBD antibody (MW06) and the mouse CD24-conjugated anti-RBD antibody (mCD24-MW06). Differences in antiviral effects between these two antibodies can be demonstrated in this mouse model when a challenge dose that invalidates the anti-RBD antibody treatment was used. This study provided a new mouse model for studying SARS-CoV-2 pathogenesis and evaluating potential interventions.
Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data

Time‐lapse applications of electrical methods have grown significantly over the last decade. However, the quantitative interpretation of tomograms in terms of physical properties, such as salinity, temperature or saturation, remains difficult. In many applications, geophysical models are transformed into hydrological models, but this transformation suffers from spatially and temporally varying resolution resulting from the regularization used by the deterministic inversion. In this study, we investigate a prediction‐focused approach (PFA) to directly estimate subsurface physical properties with electrical resistance data, circumventing the need for classic tomographic inversions. First, we generate a prior set of resistance data and physical property forecast through hydrogeological and geophysical simulations mimicking the field experiment. We reduce the dimension of both the data and the forecast through principal component analysis in order to keep the most informative part of both sets in a reduced dimension space. Then, we apply canonical correlation analysis to explore the relationship between the data and the forecast in their reduced dimension space. If a linear relationship can be established, the posterior distribution of the forecast can be directly sampled using a Gaussian process regression where the field data scores are the conditioning data. In this paper, we demonstrate PFA for various physical property distributions. We also develop a framework to propagate the estimated noise level in the reduced dimension space. We validate the results by a Monte Carlo study on the posterior distribution and demonstrate that PFA yields accurate uncertainty for the cases studied.
Disutility of Cognitive Processing Speed (CPS) Impairment in the Context of Multiple Sclerosis: A Time Trade-Off (TTO) Elicitation Study

Introduction Cognitive impairment, especially relating to cognitive processing speed, is a major cause of disability in people with multiple sclerosis (MS). Utility values are quantitative estimates of the quality of life experienced in specific health states and are a key component of cost-effectiveness modelling. However, existing health state utility values in MS typically focus on physical ability and are generally derived using generic (not disease-specific) measures of quality of life. The objective of the current study was to generate health state utility values for levels of cognitive impairment. We used a direct utility elicitation approach called the time trade-off (TTO) methodology. Materials and Methods Health state descriptions were created following interviews with healthcare professionals, patients, and caregivers in the United States (n=35), and with healthcare professionals in the UK (n=5). Three health states (mild, moderate, and severe impairment) were defined based upon a well-established and validated test for cognitive dysfunction called the Symbol Digit Modalities Test (SDMT) and described using qualitative interview findings. Next, interviews with members of the general public in the UK were conducted to estimate utility values for each health state using the TTO methodology. The procedure was based on the established Measurement and Valuation of Health (MVH) protocol, which generates values on a scale from 0.0 to 1.0. Results Mean health state utility values were 0.77 ± 0.24 in “mild impairment” (SDMT 43-40), 0.57 ± 0.26 in “moderate impairment” (SDMT 39-32), and 0.34 ± 0.28 in “severe impairment” (SDMT ≤ 31). Discussion Results indicate that the public perceives that health states of cognitive slowing (as observed in MS) are associated with a substantial reduction in affected individuals’ health-related quality of life, quantified using the TTO methodology. Future economic modeling should consider how utility impacts of both cognitive and physical disability can be appropriately incorporated.
The satellite-measured sea surface temperature change in the Gulf of Finland

ABSTRACT Climate change in Baltic region and in the Gulf of Finland is an accomplished fact in human brains and in science. The purpose of this research is to retrieve quantitative level of changes for sea surface temperature (SST) of the Gulf of Finland. Two space systems National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) provided satellite data about temperature of the sea surface. SST data covers period 1981–2014 and includes 444 monthly data scenes with spatial resolution about 10 km. Data quality analysis displays high reliability of NOAA/AVHRR and Aqua/MODIS satellite information. The Gulf of Finland’s average annual SST has changed from 6.8°C in 1982 up to 8.2°C in 2014. Its mean speed of warming is about 0.04°C year–1. The growth of the temperature was irregular, in the middle of 80th year, the temperature dropped down to 5.0°C, and then sharply increased up to 7.3°C in 1989. SST growth in the Gulf of Finland coincides with air temperature and sea temperature growth. The climate change in the Gulf of Finland has special significance due to the fragility of the northern ecosystems and high anthropogenic load.